diff --git a/.gitignore b/.gitignore
new file mode 100644
index 000000000..2a1270985
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+# Ignore these folders
+src/__pycache__/
+var/
diff --git a/doc/para.md b/doc/para.md
new file mode 100644
index 000000000..2628d786c
--- /dev/null
+++ b/doc/para.md
@@ -0,0 +1,41 @@
+## Paravision
+
+#### Creds
+ Username: 2a76e3b5-733a-4c93-98fb-339927b0f90c
+ Passwd: 453a6a2d-2935-430b-a22f-9b2880ca381
+
+#### Setup
+
+Like most things the paravision docs do not work in following setup. Below is how I was able to
+install models and setup my enviorment.
+
+##### To Install a Model
+
+To install a model we ran ~$ pip3 install --no-cache-dir --timeout 60 --no-deps --extra-index-url https://2a76e3b5-733a-4c93-98fb-339927b0f90c:453a6a2d-2935-430b-a22f-9b2880ca3818@paravision.mycloudrepo.io/repositories/python-recognition "paravision-recognition" "paravision-models-gen6-balanced-openvino-2022-3"
+by visting the URL you can view what models are avaliable.
+
+
+
+
+##### To Install the Liveness
+
+First we must install the Liveness SDK:
+
+pip3 install --no-cache-dir \
+ --extra-index-url https://2a76e3b5-733a-4c93-98fb-339927b0f90c:453a6a2d-2935-430b-a22f-9b2880ca3818@paravision.mycloudrepo.io/repositories/python-liveness2d \
+ "paravision-liveness2d-gen6==2.1.1"
+
+Then we must install the Liveness models:
+
+https://2a76e3b5-733a-4c93-98fb-339927b0f90c:453a6a2d-2935-430b-a22f-9b2880ca3818@paravision.mycloudrepo.io/repositories/python-liveness2d "paravision-models-gen6-liveness2d-openvino-2022-3"
+
+Then we must install the Validness models:
+
+pip3 install --no-cache-dir \
+ --extra-index-url https://2a76e3b5-733a-4c93-98fb-339927b0f90c:453a6a2d-2935-430b-a22f-9b2880ca3818@paravision.mycloudrepo.io/repositories/python-validness \
+ "paravision-models-gen6-validness-openvino-2022-3==1.0.0"
+
+pip3 install --no-cache-dir --timeout 60 \
+--extra-index-url https://2a76e3b5-733a-4c93-98fb-339927b0f90c:453a6a2d-2935-430b-a22f-9b2880ca3818@paravision.mycloudrepo.io/repositories/python-recognition \
+--extra-index-url https://2a76e3b5-733a-4c93-98fb-339927b0f90c:453a6a2d-2935-430b-a22f-9b2880ca3818@paravision.mycloudrepo.io/repositories/python-libs \
+"paravision-recognition[openvino]" "paravision-models-gen6-balanced-openvino-2022-3"
diff --git a/modules/paravision.libs/libmvec.so.1 b/modules/paravision.libs/libmvec.so.1
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diff --git a/modules/paravision.libs/libpugixml.so.1 b/modules/paravision.libs/libpugixml.so.1
new file mode 100644
index 000000000..c9813cb2d
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diff --git a/modules/paravision.libs/libtbb.so.2 b/modules/paravision.libs/libtbb.so.2
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index 000000000..63021fd9a
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diff --git a/modules/paravision.libs/plugins.xml b/modules/paravision.libs/plugins.xml
new file mode 100644
index 000000000..fc93b1c3e
--- /dev/null
+++ b/modules/paravision.libs/plugins.xml
@@ -0,0 +1 @@
+" % (self.x, self.y)
+
+ def __str__(self):
+ return "(%d, %d)" % (self.x, self.y)
+
+ def as_dict(self):
+ """Convert this object to a dictionary"""
+ return {"x": self.x, "y": self.y}
+
+ def todict(self):
+ return self.as_dict()
+
+ def tolist(self):
+ """Convert this object to a list"""
+ return [self.x, self.y]
+
+
+class Rectangle(object):
+ """
+ A rectangle, represented by top-left and bottom-right Points.
+
+ Attributes
+ ----------
+ top_left : Point
+ The top-left corner of the rectangle.
+ bottom_right : Point
+ The bottom-right corner of the rectangle.
+ """
+
+ def __init__(self, x1, y1, x2, y2):
+ self.top_left = Point(x1, y1)
+ self.bottom_right = Point(x2, y2)
+
+ def __repr__(self):
+ return "" % (
+ repr(self.top_left),
+ repr(self.bottom_right),
+ )
+
+ def __str__(self):
+ return "(%s, %s)" % (str(self.top_left), str(self.bottom_right))
+
+ def as_dict(self):
+ """Convert this object to a dictionary"""
+ return {
+ "top_left": self.top_left.as_dict(),
+ "bottom_right": self.bottom_right.as_dict(),
+ }
+
+ def todict(self):
+ return self.as_dict()
+
+ def tolist(self):
+ """Convert this object to a list"""
+ return [
+ self.top_left.x,
+ self.top_left.y,
+ self.bottom_right.x,
+ self.bottom_right.y,
+ ]
+
+ def width(self):
+ """Get the width of the Rectangle."""
+ return self.bottom_right.x - self.top_left.x
+
+ def height(self):
+ """Get the height of the Rectangle."""
+ return self.bottom_right.y - self.top_left.y
diff --git a/modules/paravision/liveness/utils.py b/modules/paravision/liveness/utils.py
new file mode 100644
index 000000000..daec95d1a
--- /dev/null
+++ b/modules/paravision/liveness/utils.py
@@ -0,0 +1,100 @@
+import numpy as np
+import json
+import pyrealsense2 as rs
+
+from .exceptions import InvalidSpecError
+
+DEPTH_MIN = 0.11
+DEPTH_MAX = 10.0
+EXPANSION_FACTOR = 0.7
+
+
+def _read_spec_value(model_loc, key):
+ try:
+ with open("{}/spec.json".format(model_loc), "r") as f:
+ spec = json.load(f)
+
+ return spec[key]
+ except (FileNotFoundError, KeyError):
+ raise InvalidSpecError("Invalid spec file. Try upgrading your model.")
+
+
+def model_location():
+ try:
+ import paravision_models.liveness
+
+ return paravision_models.liveness.location()
+ except ImportError:
+ raise ImportError("You need to install Paravision Liveness Models package")
+
+
+def expand_bbox_to_edge_and_crop(depth_frame, proj_depth_bb):
+ h, w = depth_frame.shape[:2]
+ exp_bbox = _expand_bbox_to_edges(h, w, proj_depth_bb)
+ cropped = _crop(depth_frame, exp_bbox)
+ return cropped
+
+
+def estimate_depth_bounding_box(bb, camera_params):
+ height = camera_params.depth_intr.height
+ width = camera_params.depth_intr.width
+
+ left_corner = [bb.top_left.x, bb.top_left.y]
+ right_corner = [bb.bottom_right.x, bb.bottom_right.y]
+
+ left_depth_corner = _compute_epipolar_midpoint(
+ height, width, left_corner, camera_params
+ )
+ right_depth_corner = _compute_epipolar_midpoint(
+ height, width, right_corner, camera_params
+ )
+ proj_bb = np.hstack([left_depth_corner, right_depth_corner])
+ return proj_bb
+
+
+def _get_depth_point(height, width, pt, scale, camera_params):
+ color_world_pt = rs.rs2_deproject_pixel_to_point(
+ camera_params.color_intr, pt, scale
+ )
+ depth_world_pt = rs.rs2_transform_point_to_point(
+ camera_params.color_to_depth_extr, color_world_pt
+ )
+ depth_pt = rs.rs2_project_point_to_pixel(camera_params.depth_intr, depth_world_pt)
+ depth_pt = rs.adjust_2D_point_to_boundary(depth_pt, width, height)
+ return depth_pt
+
+
+def _compute_epipolar_midpoint(height, width, pt, camera_params):
+ # define depth endpoints of epipolar line to search
+ start_depth_pt = _get_depth_point(height, width, pt, DEPTH_MIN, camera_params)
+ end_depth_pt = _get_depth_point(height, width, pt, DEPTH_MAX, camera_params)
+
+ mid_pt = (np.array(start_depth_pt) + np.array(end_depth_pt)) / 2
+ return mid_pt
+
+
+def _expand_bbox_to_edges(h, w, bbox):
+ x1, y1, x2, y2 = bbox
+ dx = (x2 - x1) * EXPANSION_FACTOR / 2
+ dy = (y2 - y1) * EXPANSION_FACTOR / 2
+ x1_ = max(0, x1 - dx)
+ y1_ = max(0, y1 - dy)
+ x2_ = min(w, x2 + dx)
+ y2_ = min(h, y2 + dy)
+ return _round(np.array([x1_, y1_, x2_, y2_]))
+
+
+def _crop(np_img, bbox):
+ bbox = _round(bbox)
+ x1, y1, x2, y2 = bbox
+ h, w = np_img.shape[:2]
+ x1 = max(x1, 0)
+ y1 = max(y1, 0)
+ x2 = min(x2, w)
+ y2 = min(y2, h)
+
+ return np_img[y1:y2, x1:x2]
+
+
+def _round(bbox):
+ return np.rint(bbox).astype(np.int32)
diff --git a/modules/paravision/liveness2d.cpython-310-x86_64-linux-gnu.so b/modules/paravision/liveness2d.cpython-310-x86_64-linux-gnu.so
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index 000000000..f88f20eb3
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diff --git a/modules/paravision/liveness2d/__init__.pyi b/modules/paravision/liveness2d/__init__.pyi
new file mode 100644
index 000000000..7bbcc2d09
--- /dev/null
+++ b/modules/paravision/liveness2d/__init__.pyi
@@ -0,0 +1,15 @@
+from __future__ import annotations
+import paravision.liveness2d
+import typing
+from .types import Engine
+from .sdk import SDK
+
+__all__ = [
+ "Engine",
+ "SDK",
+ "sdk",
+ "types"
+]
+
+
+__version__ = 'dev'
diff --git a/modules/paravision/recognition/tensorrt/__init__.py b/modules/paravision/liveness2d/py.typed
similarity index 100%
rename from modules/paravision/recognition/tensorrt/__init__.py
rename to modules/paravision/liveness2d/py.typed
diff --git a/modules/paravision/liveness2d/sdk/__init__.pyi b/modules/paravision/liveness2d/sdk/__init__.pyi
new file mode 100644
index 000000000..d30d21063
--- /dev/null
+++ b/modules/paravision/liveness2d/sdk/__init__.pyi
@@ -0,0 +1,113 @@
+from __future__ import annotations
+import paravision.liveness2d.sdk
+import typing
+import paravision.liveness2d.types
+import paravision.recognition.sdk
+import paravision.recognition.types
+
+__all__ = [
+ "Metadata",
+ "SDK"
+]
+
+
+class Metadata():
+ def __init__(self) -> None: ...
+
+ @property
+ def engine(self) -> str:
+ """
+ The engine or accelerator of the Liveness2D SDK instance being used.
+
+ :type: str
+ """
+ @engine.setter
+ def engine(self, arg0: str) -> None:
+ """
+ The engine or accelerator of the Liveness2D SDK instance being used.
+ """
+ @property
+ def engine_version(self) -> str:
+ """
+ The version of the engine or accelerator being used.
+
+ :type: str
+ """
+ @engine_version.setter
+ def engine_version(self, arg0: str) -> None:
+ """
+ The version of the engine or accelerator being used.
+ """
+ @property
+ def generation(self) -> int:
+ """
+ The generation of the Liveness2D SDK.
+
+ :type: int
+ """
+ @generation.setter
+ def generation(self, arg0: int) -> None:
+ """
+ The generation of the Liveness2D SDK.
+ """
+ @property
+ def model_version(self) -> str:
+ """
+ The version of the Liveness2D models.
+
+ :type: str
+ """
+ @model_version.setter
+ def model_version(self, arg0: str) -> None:
+ """
+ The version of the Liveness2D models.
+ """
+ @property
+ def sdk_version(self) -> str:
+ """
+ The version of the Liveness2D SDK.
+
+ :type: str
+ """
+ @sdk_version.setter
+ def sdk_version(self, arg0: str) -> None:
+ """
+ The version of the Liveness2D SDK.
+ """
+ pass
+
+
+class SDK():
+ """
+ SDK()
+
+ A sdk object contains an instance of the Paravision model and its
+ associated resources.
+
+ SDK objects are long-living and do not need to be re-instantiated between
+ method calls.
+ """
+ def __init__(self, models_dir: typing.Optional[str] = None, settings: typing.Optional[paravision.liveness2d.types.Settings] = None) -> None:
+ """
+ Create a new SDK instance.
+ """
+
+ def check_validness(self, face: paravision.recognition.types.Face, validness_settings: paravision.liveness2d.types.ValidnessSettings, reco_sdk: paravision.recognition.sdk.SDK) -> paravision.recognition.types.ValidnessResult:
+ """
+ Check if a face is valid for liveness inference.
+ """
+
+ def get_liveness(self, face: paravision.recognition.types.Face) -> paravision.liveness2d.types.LivenessResult:
+ """
+ Get the liveness of a face.
+ """
+
+ @staticmethod
+ def get_metadata(models_dir: typing.Optional[str] = None) -> Metadata:
+ """
+ Returns metadata for SDK and model info.
+ """
+
+ pass
+
+
diff --git a/modules/paravision/liveness2d/types/__init__.pyi b/modules/paravision/liveness2d/types/__init__.pyi
new file mode 100644
index 000000000..7249f9995
--- /dev/null
+++ b/modules/paravision/liveness2d/types/__init__.pyi
@@ -0,0 +1,383 @@
+from __future__ import annotations
+import paravision.liveness2d.types
+import typing
+import paravision.recognition.types
+
+__all__ = [
+ "Engine",
+ "GPUConfig",
+ "LivenessResult",
+ "Settings",
+ "ValidnessSettings"
+]
+
+
+class Engine():
+ """
+ Members:
+
+ AUTO : Automatically select the engine
+
+ OPENVINO : Use the OpenVINO engine
+
+ TENSORRT : Use the TensorRT engine
+ """
+ def __eq__(self, other: object) -> bool: ...
+
+ def __getstate__(self) -> int: ...
+
+ def __hash__(self) -> int: ...
+
+ def __index__(self) -> int: ...
+
+ def __init__(self, value: int) -> None: ...
+
+ def __int__(self) -> int: ...
+
+ def __ne__(self, other: object) -> bool: ...
+
+ def __repr__(self) -> str: ...
+
+ def __setstate__(self, state: int) -> None: ...
+
+ def __str__(self) -> str: ...
+
+ @property
+ def name(self) -> str:
+ """
+ :type: str
+ """
+ @property
+ def value(self) -> int:
+ """
+ :type: int
+ """
+ AUTO: paravision.liveness2d.types.Engine # value =
+ OPENVINO: paravision.liveness2d.types.Engine # value =
+ TENSORRT: paravision.liveness2d.types.Engine # value =
+ __members__: dict # value = {'AUTO': , 'OPENVINO': , 'TENSORRT': }
+ pass
+
+
+class GPUConfig():
+ def __init__(self) -> None: ...
+
+ @property
+ def gpu_id(self) -> int:
+ """
+ The index of the GPU device to use.
+
+ :type: int
+ """
+ @gpu_id.setter
+ def gpu_id(self, arg0: int) -> None:
+ """
+ The index of the GPU device to use.
+ """
+ @property
+ def worker_count(self) -> int:
+ """
+ The number of workers to allocate.
+
+ :type: int
+ """
+ @worker_count.setter
+ def worker_count(self, arg0: int) -> None:
+ """
+ The number of workers to allocate.
+ """
+ pass
+
+
+class LivenessResult():
+ """
+ LivenessResult()
+
+ A LivenessResult object contains the result of a liveness2d inference run on a face.
+
+ Attributes
+ ----------
+ live_probability : float
+ The probability that the face is real.
+ spoof_probability : float
+ The probability that the face is a spoof.
+ """
+ @typing.overload
+ def __init__(self) -> None:
+ """
+ Construct an empty LivenessResult
+ """
+
+ @typing.overload
+ def __init__(self, live_prob: float, spoof_prob: float) -> None:
+ """
+ Constructor a LiveFaceResult with the given probabilities
+ """
+
+ def __repr__(self) -> str: ...
+
+ def asdict(self) -> dict:
+ """
+ Convert this object to a dictionary
+ """
+
+ @property
+ def live_prob(self) -> float:
+ """
+ The probability that the face is real
+
+ :type: float
+ """
+ @property
+ def spoof_prob(self) -> float:
+ """
+ The probability that the face is a spoof
+
+ :type: float
+ """
+ pass
+
+
+class Settings():
+ def __init__(self) -> None: ...
+
+ @property
+ def cache_generated_engine(self) -> bool:
+ """
+ Cache generated engine for TensorRT only
+
+ :type: bool
+ """
+ @cache_generated_engine.setter
+ def cache_generated_engine(self, arg0: bool) -> None:
+ """
+ Cache generated engine for TensorRT only
+ """
+ @property
+ def engine(self) -> Engine:
+ """
+ Engine to use
+
+ :type: Engine
+ """
+ @engine.setter
+ def engine(self, arg0: Engine) -> None:
+ """
+ Engine to use
+ """
+ @property
+ def gpu_configs(self) -> list[GPUConfig]:
+ """
+ List of GPU configs for worker allocation in multiple GPUs
+
+ :type: list[GPUConfig]
+ """
+ @gpu_configs.setter
+ def gpu_configs(self, arg0: list[GPUConfig]) -> None:
+ """
+ List of GPU configs for worker allocation in multiple GPUs
+ """
+ @property
+ def openvino_threads_limit(self) -> int:
+ """
+ Thread limit for OpenVINO
+
+ :type: int
+ """
+ @openvino_threads_limit.setter
+ def openvino_threads_limit(self, arg0: int) -> None:
+ """
+ Thread limit for OpenVINO
+ """
+ @property
+ def tensorrt_engine_cache_path(self) -> str:
+ """
+ Directory of where serialized model files are stored
+
+ :type: str
+ """
+ @tensorrt_engine_cache_path.setter
+ def tensorrt_engine_cache_path(self, arg0: str) -> None:
+ """
+ Directory of where serialized model files are stored
+ """
+ @property
+ def use_cached_engine(self) -> bool:
+ """
+ Use cached engine for TensorRT only
+
+ :type: bool
+ """
+ @use_cached_engine.setter
+ def use_cached_engine(self, arg0: bool) -> None:
+ """
+ Use cached engine for TensorRT only
+ """
+ @property
+ def worker_count(self) -> int:
+ """
+ The number of workers for inference
+
+ :type: int
+ """
+ @worker_count.setter
+ def worker_count(self, arg0: int) -> None:
+ """
+ The number of workers for inference
+ """
+ pass
+
+
+class ValidnessSettings():
+ def __init__(self, face: paravision.recognition.types.Face) -> None:
+ """
+ Construct ValidnessSettings instance.
+ """
+
+ @property
+ def fail_fast(self) -> bool:
+ """
+ Option to early return if any check fails
+
+ :type: bool
+ """
+ @fail_fast.setter
+ def fail_fast(self, arg0: bool) -> None:
+ """
+ Option to early return if any check fails
+ """
+ @property
+ def image_boundary_height_pct(self) -> float:
+ """
+ The percentage of image height the face is permitted
+
+ :type: float
+ """
+ @image_boundary_height_pct.setter
+ def image_boundary_height_pct(self, arg0: float) -> None:
+ """
+ The percentage of image height the face is permitted
+ """
+ @property
+ def image_boundary_width_pct(self) -> float:
+ """
+ The percentage of image width the face is permitted
+
+ :type: float
+ """
+ @image_boundary_width_pct.setter
+ def image_boundary_width_pct(self, arg0: float) -> None:
+ """
+ The percentage of image width the face is permitted
+ """
+ @property
+ def image_illumination_control(self) -> int:
+ """
+ The image illumination control
+
+ :type: int
+ """
+ @image_illumination_control.setter
+ def image_illumination_control(self, arg0: int) -> None:
+ """
+ The image illumination control
+ """
+ @property
+ def max_face_mask_prob(self) -> float:
+ """
+ The max probability the face has a mask
+
+ :type: float
+ """
+ @max_face_mask_prob.setter
+ def max_face_mask_prob(self, arg0: float) -> None:
+ """
+ The max probability the face has a mask
+ """
+ @property
+ def max_face_roll_angle(self) -> int:
+ """
+ The maximum roll angle allowed of the face.
+
+ :type: int
+ """
+ @max_face_roll_angle.setter
+ def max_face_roll_angle(self, arg0: int) -> None:
+ """
+ The maximum roll angle allowed of the face.
+ """
+ @property
+ def max_face_size_pct(self) -> float:
+ """
+ The max percentage of the face size relative to the image boundaries
+
+ :type: float
+ """
+ @max_face_size_pct.setter
+ def max_face_size_pct(self, arg0: float) -> None:
+ """
+ The max percentage of the face size relative to the image boundaries
+ """
+ @property
+ def min_face_acceptability(self) -> float:
+ """
+ The minimum face acceptability threshold
+
+ :type: float
+ """
+ @min_face_acceptability.setter
+ def min_face_acceptability(self, arg0: float) -> None:
+ """
+ The minimum face acceptability threshold
+ """
+ @property
+ def min_face_frontality(self) -> int:
+ """
+ The minimum face frontality threshold
+
+ :type: int
+ """
+ @min_face_frontality.setter
+ def min_face_frontality(self, arg0: int) -> None:
+ """
+ The minimum face frontality threshold
+ """
+ @property
+ def min_face_quality(self) -> float:
+ """
+ The minimum image quality threshold
+
+ :type: float
+ """
+ @min_face_quality.setter
+ def min_face_quality(self, arg0: float) -> None:
+ """
+ The minimum image quality threshold
+ """
+ @property
+ def min_face_sharpness(self) -> float:
+ """
+ The minimum image sharpness threshold
+
+ :type: float
+ """
+ @min_face_sharpness.setter
+ def min_face_sharpness(self, arg0: float) -> None:
+ """
+ The minimum image sharpness threshold
+ """
+ @property
+ def min_face_size(self) -> int:
+ """
+ The minimum size of the face
+
+ :type: int
+ """
+ @min_face_size.setter
+ def min_face_size(self, arg0: int) -> None:
+ """
+ The minimum size of the face
+ """
+ pass
+
+
diff --git a/modules/paravision/recognition.cpython-310-x86_64-linux-gnu.so b/modules/paravision/recognition.cpython-310-x86_64-linux-gnu.so
new file mode 100755
index 000000000..ce040b283
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diff --git a/modules/paravision/recognition/__init__.py b/modules/paravision/recognition/__init__.py
deleted file mode 100644
index d962558b4..000000000
--- a/modules/paravision/recognition/__init__.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""
-Paravision
-======
-
-Provides an interface to the Paravision models.
-
-How to use the documentation
-----------------------------
-Documentation is available in two forms: docstrings provided within the code,
-and a reference guide, available
-`here `.
-
-Code snippets are indicated by three greater-than signs::
-
- >>> sdk = paravision.SDK()
-
-Use the built-in ``help`` function to view a function or object's docstring::
-
- >>> help(paravision.SDK)
- ...
-
-Example
--------
-This simple example illustrates how to detect the bounding boxes of faces in an image:
-
- >>> import paravision
- >>> from paravision.utils import load_image
- >>> img = load_image('/tmp/face.jpg')
- >>> sdk = paravision.SDK()
- >>> sdk.get_faces(img)
- ([], 0)
-"""
-
-from .sdk import SDK # noqa
-from .engine import Engine # noqa
-
-__version__ = "8.2.0"
diff --git a/modules/paravision/recognition/__init__.pyi b/modules/paravision/recognition/__init__.pyi
new file mode 100644
index 000000000..7c0a0d8ec
--- /dev/null
+++ b/modules/paravision/recognition/__init__.pyi
@@ -0,0 +1,19 @@
+from __future__ import annotations
+import paravision.recognition
+import typing
+from .types import Engine
+from .types import ImageManipulator
+from .sdk import SDK
+
+__all__ = [
+ "Engine",
+ "ImageManipulator",
+ "SDK",
+ "exceptions",
+ "sdk",
+ "types",
+ "utils"
+]
+
+
+__version__ = 'dev'
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diff --git a/modules/paravision/recognition/_internal.py b/modules/paravision/recognition/_internal.py
deleted file mode 100644
index 2cec95568..000000000
--- a/modules/paravision/recognition/_internal.py
+++ /dev/null
@@ -1,298 +0,0 @@
-import cv2
-import numpy as np
-
-from . import _utils as utils
-from .engine import Engine
-from .types import BoundingBox, Landmarks, Embedding
-from .exceptions import ModelLoadingException
-
-LANDMARKS_EXPAND_FACTOR = 0.3
-ALIGNMENT_EXPAND_FACTOR = 1.0
-MASK_EXPAND_FACTOR = 0.3
-
-"""The way the pipeline is run needs to be refactored.
- Making temporary fixes for now. """
-AVAILABLE_OPTIONS = ["find_landmarks", "compute_embeddings"]
-
-
-class SplitGraph:
- def __init__(self, models_dirpath, settings=None, engine=Engine.OPENVINO):
- if settings is None:
- settings = {}
-
- if isinstance(engine, Engine):
- self.engine_name = engine
- else:
- self.engine_name = engine.split("-")[0]
-
- if self.engine_name == Engine.OPENVINO:
- from .openvino.engine import Engine as E
- elif self.engine_name == Engine.TENSORRT:
- from .tensorrt.engine import Engine as E
- else:
- raise ModelLoadingException(
- f"This is not a valid engine choice: {engine}. Available choices are: {Engine.all()}."
- )
-
- self.engine = E(models_dirpath, settings)
-
- def prepare_for_detection(self, img):
- height, width = img.shape[:2]
- fd_input_height, fd_input_width = self.engine.fd_input_shape
- ratio = min(fd_input_height / height, fd_input_width / width)
- target_width = round(width * ratio)
- target_height = round(height * ratio)
- resized = utils.resize(img, target_height, target_width)
- offset_pad_height = fd_input_height - target_height
- offset_pad_width = fd_input_width - target_width
- padded = cv2.copyMakeBorder(
- resized,
- 0,
- offset_pad_height,
- 0,
- offset_pad_width,
- cv2.BORDER_CONSTANT,
- value=[0, 0, 0],
- )
-
- return padded, (target_height, target_width)
-
- def prepare_for_landmarks(self, np_img, bbox, original_size):
- exp_bbox, _, pre_pad_exp_img = utils.expand_and_crop(
- np_img, LANDMARKS_EXPAND_FACTOR, bbox, original_size
- )
-
- image_h, image_w = np_img.shape[:2]
- exp_img = utils.maybe_pad(pre_pad_exp_img, exp_bbox, image_h, image_w)
-
- target_h, target_w = self.engine.lm_input_shape
- resized_img = utils.resize(exp_img, target_h, target_w)
-
- return exp_bbox, resized_img
-
- def process_post_detection(
- self, imgs, relative_bboxes, detection_input_sizes, img_indexes
- ):
- absolute_bboxes = []
- alignment_images = []
- alignment_bounding_boxes = []
- landmarks_input_bounding_boxes = []
- landmarks_input_images = []
-
- for i, relative_bbox in enumerate(relative_bboxes):
- img = imgs[img_indexes[i]]
- detection_input_size = detection_input_sizes[img_indexes[i]]
- img_size = np.asarray(img.shape[:2])
- absolute_bbox = utils.convert_to_absolute_coordinates(
- relative_bbox,
- detection_input_size,
- img_size,
- self.engine.fd_input_shape,
- )
-
- if absolute_bbox[0] > img_size[1] or absolute_bbox[1] > img_size[0]:
- continue
-
- square_bb = utils.square(absolute_bbox)
- landmarks_input_bbox, landmarks_input_image = self.prepare_for_landmarks(
- img, square_bb, img_size
- )
- _, alignment_bbox, alignment_image = utils.expand_and_crop(
- img, ALIGNMENT_EXPAND_FACTOR, square_bb, img_size
- )
-
- absolute_bboxes.append(absolute_bbox)
- alignment_images.append(alignment_image)
- alignment_bounding_boxes.append(alignment_bbox)
- landmarks_input_images.append(landmarks_input_image)
- landmarks_input_bounding_boxes.append(landmarks_input_bbox)
-
- values = {
- "bounding_boxes": [BoundingBox(*_bb) for _bb in absolute_bboxes],
- "landmarks_input_bounding_boxes": landmarks_input_bounding_boxes,
- "landmarks_input_images": landmarks_input_images,
- "alignment_bounding_boxes": alignment_bounding_boxes,
- "alignment_images": alignment_images,
- }
-
- return values
-
- def process_detection_options(self, detect_resp, scoring_mode, options):
- values = {}
-
- if "get_qualities" in options:
- qualities, acceptabilities = self.get_qualities(
- detect_resp["landmarks_input_images"]
- )
- values["qualities"] = qualities
- values["acceptabilities"] = acceptabilities
-
- if any(option in AVAILABLE_OPTIONS for option in options):
- (landmarks, recognition_input_images) = self.find_landmarks(
- detect_resp["landmarks_input_bounding_boxes"],
- detect_resp["landmarks_input_images"],
- detect_resp["alignment_bounding_boxes"],
- detect_resp["alignment_images"],
- )
-
- values["landmarks"] = [Landmarks(*x) for x in landmarks]
- values["recognition_input_images"] = recognition_input_images
-
- if "compute_embeddings" in options:
- values["embeddings"] = [
- Embedding(data, scoring_mode)
- for data in self.compute_embeddings(recognition_input_images)
- ]
-
- return values
-
- def run(self, imgs, scoring_mode, options=None):
- if options is None:
- options = []
-
- detection_inputs = []
- detection_input_sizes = []
-
- for img in imgs:
- img_for_fd, resized_size = self.prepare_for_detection(img)
-
- detection_inputs.append(img_for_fd)
- detection_input_sizes.append(resized_size)
-
- relative_bboxes, confidences, img_indexes = self.engine.predict_bounding_boxes(
- detection_inputs
- )
-
- values = {"confidences": confidences}
-
- # post-process detection
- detect_resp = self.process_post_detection(
- imgs, relative_bboxes, detection_input_sizes, img_indexes
- )
- values.update(detect_resp)
-
- # process options
- options_resp = self.process_detection_options(
- detect_resp, scoring_mode, options
- )
- values.update(options_resp)
-
- return values, img_indexes
-
- def run_from_landmarks(self, img, bboxes):
- absolute_bboxes = []
- alignment_images = []
- alignment_bounding_boxes = []
- landmarks_input_bounding_boxes = []
- landmarks_input_images = []
-
- for absolute_bbox in bboxes:
- img_size = np.asarray(img.shape[:2])
- bounding_box = np.array(
- [
- absolute_bbox.origin.x,
- absolute_bbox.origin.y,
- absolute_bbox.origin.x + absolute_bbox.width,
- absolute_bbox.origin.y + absolute_bbox.height,
- ]
- )
-
- if bounding_box[0] > img_size[1] or bounding_box[1] > img_size[0]:
- continue
-
- square_bb = utils.square(bounding_box)
- landmarks_input_bbox, landmarks_input_image = self.prepare_for_landmarks(
- img, square_bb, img_size
- )
- _, alignment_bbox, alignment_image = utils.expand_and_crop(
- img, ALIGNMENT_EXPAND_FACTOR, square_bb, img_size
- )
-
- absolute_bboxes.append(bounding_box)
- alignment_images.append(alignment_image)
- alignment_bounding_boxes.append(alignment_bbox)
- landmarks_input_images.append(landmarks_input_image)
- landmarks_input_bounding_boxes.append(landmarks_input_bbox)
-
- (landmarks, recognition_input_images) = self.find_landmarks(
- landmarks_input_bounding_boxes,
- landmarks_input_images,
- alignment_bounding_boxes,
- alignment_images,
- )
-
- values = {
- "bounding_boxes": [BoundingBox(*_bb) for _bb in absolute_bboxes],
- "landmarks_input_bounding_boxes": landmarks_input_bounding_boxes,
- "landmarks_input_images": landmarks_input_images,
- "alignment_bounding_boxes": alignment_bounding_boxes,
- "alignment_images": alignment_images,
- "landmarks": [Landmarks(*x) for x in landmarks],
- "recognition_input_images": recognition_input_images,
- }
-
- return values
-
- def find_landmarks(
- self,
- landmarks_input_bounding_boxes,
- landmarks_input_images,
- alignment_bounding_boxes,
- alignment_images,
- ):
- if len(landmarks_input_bounding_boxes) == 0:
- return [], []
-
- relative_landmarks = self.engine.predict_landmarks(landmarks_input_images)
- relative_landmarks = relative_landmarks.reshape(-1, 5, 2)
- absolute_landmarks = []
- recognition_input_images = []
-
- for i, landmarks in enumerate(relative_landmarks):
- landmarks_input_bounding_box = landmarks_input_bounding_boxes[i]
- alignment_bounding_box = alignment_bounding_boxes[i]
- alignment_image = alignment_images[i]
- landmarks = utils.normalize(landmarks_input_bounding_box, landmarks)
- recognition_input_image = utils.crop_and_align(
- alignment_image,
- landmarks - alignment_bounding_box[:2],
- self.engine.fr_input_shape,
- )
-
- absolute_landmarks.append(landmarks)
- recognition_input_images.append(recognition_input_image)
-
- return absolute_landmarks, recognition_input_images
-
- def compute_embeddings(self, recognition_input_images):
- if len(recognition_input_images) == 0:
- return []
-
- return self.engine.predict_embeddings(recognition_input_images)
-
- def get_attributes(self, recognition_input_images):
- if len(recognition_input_images) == 0:
- return [], []
-
- return self.engine.predict_attributes(recognition_input_images)
-
- def get_fr_input_shape(self):
- return self.engine.fr_input_shape
-
- def get_fr_output_shape(self):
- return self.engine.fr_output_shape
-
- def check_for_mask(self, landmarks_input_images):
- if len(landmarks_input_images) == 0:
- return []
-
- return self.engine.check_for_masks(landmarks_input_images)
-
- def get_qualities(self, landmarks_input_images):
- if len(landmarks_input_images) == 0:
- return [], []
-
- qualities, acceptabilities = self.engine.get_qualities(landmarks_input_images)
- qualities = np.clip(qualities, 0, 1).tolist()
- acceptabilities = np.clip(acceptabilities, 0, 1).tolist()
- return qualities, acceptabilities
diff --git a/modules/paravision/recognition/_utils.py b/modules/paravision/recognition/_utils.py
deleted file mode 100644
index cd165c7fb..000000000
--- a/modules/paravision/recognition/_utils.py
+++ /dev/null
@@ -1,310 +0,0 @@
-import json
-import cv2
-import importlib
-import numpy as np
-
-from os import walk, path
-
-from .engine import Engine
-from .exceptions import ModelLoadingException, InternalErrorException
-from .types import Face
-
-OPENVINO_EXT = "xml"
-TENSORRT_EXT = "onnx"
-MODELS_DIRECTORY = "recognition"
-
-KEYS = {
- "acceptabilities": "acceptability",
- "bounding_boxes": "bounding_box",
- "confidences": "score",
- "recognition_input_images": "recognition_input_image",
- "embeddings": "embedding",
- "landmarks_input_images": "landmarks_input_image",
- "mask_input_images": "mask_input_image",
- "landmarks_input_bounding_boxes": "landmarks_input_bounding_box",
- "alignment_bounding_boxes": "alignment_bounding_box",
- "alignment_images": "alignment_image",
- "qualities": "quality",
-}
-
-_SQUARE_TO_POINTS = [
- [38.2946, 51.6963],
- [73.5318, 51.5014],
- [56.0252, 71.7366],
- [41.5493, 92.3655],
- [70.7299, 92.2041],
-]
-
-
-def model_location():
- try:
- paravision_models = importlib.import_module("paravision_models")
-
- return paravision_models.location()
- except ModuleNotFoundError as err:
- raise ModelLoadingException(
- "You need to install Paravision Models package"
- ) from err
-
-
-def match_engine():
- try:
- paravision_models = importlib.import_module("paravision_models")
-
- return paravision_models.engine()
- except ModuleNotFoundError as err:
- raise ModelLoadingException(
- "You need to install Paravision Models package"
- ) from err
-
-
-def match_engine_given_path(models_dir):
- (_, _, filenames) = next(walk(path.join(models_dir, MODELS_DIRECTORY)))
-
- if any(OPENVINO_EXT in f_name for f_name in filenames):
- return Engine.OPENVINO
-
- if any(TENSORRT_EXT in f_name for f_name in filenames):
- return Engine.TENSORRT
-
- raise ModelLoadingException(
- "No compatible models found. Please ensure that your model path is correct."
- )
-
-
-def mask_model_location():
- try:
- mask = importlib.import_module("paravision_models.mask")
-
- return mask.location()
- except ModuleNotFoundError as err:
- raise ModelLoadingException(
- "You need to install Paravision Mask Model package"
- ) from err
-
-
-def read_spec_value(model_loc, key):
- try:
- with open(path.join(model_loc, "spec.json"), "r", encoding="utf-8") as f:
- spec = json.load(f)
-
- return spec[key]
- except (FileNotFoundError, KeyError) as err:
- raise ModelLoadingException(
- "Invalid spec file. Please verify the models are installed correctly."
- ) from err
-
-
-def build_faces(graph_dict):
- faces = []
- for values in zip(*graph_dict.values()):
- face_dict = {KEYS.get(k, k): v for k, v in zip(graph_dict.keys(), values)}
- face_dict["bounding_box"].score = face_dict.get("score", None)
- face = Face(face_dict["bounding_box"])
- face_dict.pop("bounding_box")
- face_dict.pop("score", None)
- for k, v in face_dict.items():
- setattr(face, k, v)
- faces.append(face)
-
- return faces
-
-
-def read_fd_input_shape(model_loc, fd_model_type):
- if fd_model_type == "streaming":
- return read_spec_value(model_loc, "fd_streaming_input_shape")
- return read_spec_value(model_loc, "fd_input_shape")
-
-
-def read_lm_input_shape(model_loc):
- return read_spec_value(model_loc, "lm_input_shape")
-
-
-def read_fr_input_shape(model_loc):
- return read_spec_value(model_loc, "fr_input_shape")
-
-
-def read_fr_output_shape(model_loc):
- return read_spec_value(model_loc, "embedding_size")
-
-
-def read_at_input_shape(model_loc):
- return read_spec_value(model_loc, "at_input_shape")
-
-
-def read_em_input_shape(model_loc):
- return read_spec_value(model_loc, "em_input_shape")
-
-
-def read_md_input_shape(model_loc):
- return read_spec_value(model_loc, "md_input_shape")
-
-
-def resize(np_img, height, width):
- return cv2.resize(np_img, (width, height))
-
-
-def expand_bb(bbox, p=1.0):
- """Takes a bounding box and expand by a factor of 1 + p
-
- Args:
- bb: bounding box in the format of [x1, y1, x2, y2]
- p: additive factor
- """
- x1, y1, x2, y2 = bbox
-
- dx = (x2 - x1) * p / 2
- dy = (y2 - y1) * p / 2
-
- x1 -= dx
- y1 -= dy
- x2 += dx
- y2 += dy
-
- return x1, y1, x2, y2
-
-
-def restrict_bbox_to_edges(h, w, bbox):
- x1, y1, x2, y2 = bbox
-
- x1 = max(x1, 0)
- y1 = max(y1, 0)
- x2 = min(x2, w)
- y2 = min(y2, h)
-
- return x1, y1, x2, y2
-
-
-def maybe_pad(crop_img, exp_bbox, h, w):
- x1, y1, x2, y2 = exp_bbox
-
- pc1 = max(0 - x1, 0)
- pc2 = max(0, x2 - w)
- pr1 = max(0 - y1, 0)
- pr2 = max(0, y2 - h)
- pad = np.rint(np.array([(pr1, pr2), (pc1, pc2), (0, 0)])).astype(np.int32)
- crop_pad_img = np.pad(crop_img, pad, mode="constant")
-
- return crop_pad_img
-
-
-def square(bb):
- x1, y1, x2, y2 = bb
- padding = ((x2 - x1) - (y2 - y1)) / 2
-
- if padding < 0:
- x1 += padding
- x2 -= padding
- elif padding > 0:
- y1 -= padding
- y2 += padding
-
- return x1, y1, x2, y2
-
-
-def crop(np_img, bb, h, w):
- """Simple crop function in numpy
-
- Args:
- np_img: H x W x C image
- bb: list or tuple of format (x1, y1, x2, y2)
-
- Returns:
- cropped numpy image
- """
- x1, y1, x2, y2 = bb
-
- if x1 >= x2 or y1 >= y2:
- raise InternalErrorException("Invalid bounding box for image cropping.")
-
- x1 = max(x1, 0)
- y1 = max(y1, 0)
- x2 = min(x2, w)
- y2 = min(y2, h)
-
- x1, y1, x2, y2 = np.rint([x1, y1, x2, y2]).astype(np.int32)
-
- return np_img[y1:y2, x1:x2, :]
-
-
-def compute_transform(src_points, dst_points):
- """estimate the rigid transform needed to transform src_points into
- dst_points
- """
- points1 = np.asarray(src_points)
- points2 = np.asarray(dst_points)
-
- # zero-mean
- center1 = np.expand_dims(np.mean(points1, axis=0), axis=0)
- center2 = np.expand_dims(np.mean(points2, axis=0), axis=0)
- points1 -= center1
- points2 -= center2
-
- std1 = np.std(points1)
- std2 = np.std(points2)
- points1 /= std1
- points2 /= std2
-
- U, _, V = np.linalg.svd(points1.T.dot(points2))
- R = (U.dot(V)).T
- trans = np.hstack(
- ((std2 / std1) * R, center2.T - ((std2 / std1) * R).dot(center1.T))
- )
-
- return trans
-
-
-def crop_and_align(np_img, from_points, img_shape):
- h, w = img_shape
- trans = compute_transform(from_points, _SQUARE_TO_POINTS)
-
- return cv2.warpAffine(np_img, trans, (w, h))
-
-
-def normalize(exp_bbox, lmks):
- x1, y1, x2, y2 = exp_bbox
- return lmks * [x2 - x1, y2 - y1] + [x1, y1]
-
-
-def expand_and_crop(np_img, p, bbox, original_size):
- h, w = original_size
-
- exp_bbox = expand_bb(bbox, p)
- exp_edge_restricted_bbox = restrict_bbox_to_edges(h, w, exp_bbox)
-
- crop_img = crop(np_img, exp_edge_restricted_bbox, h, w)
-
- return exp_bbox, exp_edge_restricted_bbox, crop_img
-
-
-def convert_to_absolute_coordinates(bbox, resized_size, original_size, fd_input_shape):
- h, w = original_size
- ratio = fd_input_shape / np.asarray(resized_size)
-
- return (
- bbox
- * np.asarray([w, h, w, h])
- * np.asarray([ratio[1], ratio[0], ratio[1], ratio[0]])
- )
-
-
-def sigmoid_transform(value, weight, bias):
- return 1 / (1 + np.exp(-(weight * value + bias)))
-
-
-def get_model_types(settings):
- fd_model_type = settings.get("detection_model", "default")
- lm_model_type = "default"
- ql_model_type = "default"
- fr_model_type = "default"
- at_model_type = "default"
- md_model_type = "default"
-
- return (
- fd_model_type,
- lm_model_type,
- ql_model_type,
- fr_model_type,
- at_model_type,
- md_model_type,
- )
diff --git a/modules/paravision/recognition/engine.py b/modules/paravision/recognition/engine.py
deleted file mode 100644
index 3d0a597a7..000000000
--- a/modules/paravision/recognition/engine.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from enum import Enum
-
-
-class Engine(str, Enum):
- OPENVINO = "openvino"
- TENSORRT = "tensorrt"
- AUTO = "auto"
-
- @staticmethod
- def all():
- return [Engine.OPENVINO, Engine.TENSORRT, Engine.AUTO]
diff --git a/modules/paravision/recognition/exceptions.py b/modules/paravision/recognition/exceptions.py
deleted file mode 100644
index d6f7511ee..000000000
--- a/modules/paravision/recognition/exceptions.py
+++ /dev/null
@@ -1,15 +0,0 @@
-class ParavisionException(Exception):
- def __init__(self, message):
- self.message = message
-
-
-class ModelLoadingException(ParavisionException):
- pass
-
-
-class InvalidInputException(ParavisionException):
- pass
-
-
-class InternalErrorException(ParavisionException):
- pass
diff --git a/modules/paravision/recognition/exceptions/__init__.pyi b/modules/paravision/recognition/exceptions/__init__.pyi
new file mode 100644
index 000000000..bf8bdc384
--- /dev/null
+++ b/modules/paravision/recognition/exceptions/__init__.pyi
@@ -0,0 +1,28 @@
+from __future__ import annotations
+import paravision.recognition.exceptions
+import typing
+
+__all__ = [
+ "InternalErrorException",
+ "InvalidInputException",
+ "ModelLoadingException",
+ "ParavisionException"
+]
+
+
+class ParavisionException(Exception, BaseException):
+ pass
+
+
+class InvalidInputException(ParavisionException, Exception, BaseException):
+ pass
+
+
+class ModelLoadingException(ParavisionException, Exception, BaseException):
+ pass
+
+
+class InternalErrorException(ParavisionException, Exception, BaseException):
+ pass
+
+
diff --git a/modules/paravision/recognition/openvino/__pycache__/__init__.cpython-310.pyc b/modules/paravision/recognition/openvino/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index 4a313b9c9..000000000
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deleted file mode 100644
index 00d040814..000000000
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diff --git a/modules/paravision/recognition/openvino/__pycache__/engine.cpython-310.pyc b/modules/paravision/recognition/openvino/__pycache__/engine.cpython-310.pyc
deleted file mode 100644
index a2f2d0a93..000000000
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diff --git a/modules/paravision/recognition/openvino/__pycache__/engine.cpython-36.pyc b/modules/paravision/recognition/openvino/__pycache__/engine.cpython-36.pyc
deleted file mode 100644
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diff --git a/modules/paravision/recognition/openvino/engine.py b/modules/paravision/recognition/openvino/engine.py
deleted file mode 100644
index c19cc9559..000000000
--- a/modules/paravision/recognition/openvino/engine.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import multiprocessing
-import numpy as np
-import os
-
-from openvino.inference_engine import IECore
-
-from .. import _utils as utils
-
-UNIT_LOWER_LIMIT = 0
-UNIT_UPPER_LIMIT = 1
-
-FD_NAME = "detection"
-LM_NAME = "landmarks"
-QL_NAME = "quality"
-FR_NAME = "recognition"
-AT_NAME = "attributes"
-MD_NAME = "mask"
-
-BIN_EXT = ".bin"
-XML_EXT = ".xml"
-
-
-class Engine:
- def __init__(self, models_dir, settings):
- ie_core = IECore()
- num_threads = multiprocessing.cpu_count()
- try:
- num_threads = min(
- num_threads, max(int(os.getenv("PV_OPENVINO_THREADS_LIMIT")), 1)
- )
- except (TypeError, ValueError):
- pass
- ie_core.set_config({"CPU_THREADS_NUM": str(num_threads)}, "CPU")
-
- (
- fd_model_type,
- lm_model_type,
- ql_model_type,
- fr_model_type,
- at_model_type,
- md_model_type,
- ) = utils.get_model_types(settings)
-
- fd_net = ie_core.read_network(
- model=os.path.join(models_dir, FD_NAME, fd_model_type, FD_NAME + XML_EXT),
- weights=os.path.join(models_dir, FD_NAME, fd_model_type, FD_NAME + BIN_EXT),
- )
-
- self.fd_input_name = next(iter(fd_net.input_info))
- self.fd_input_shape = utils.read_fd_input_shape(models_dir, fd_model_type)
- self.fd_bboxes_name = "bboxes"
- self.fd_scores_name = "scores"
- self.fd_select_idxs_name = "selected_indices"
- self.fd_net = ie_core.load_network(network=fd_net, device_name="CPU")
-
- lm_net = ie_core.read_network(
- model=os.path.join(models_dir, LM_NAME, lm_model_type, LM_NAME + XML_EXT),
- weights=os.path.join(models_dir, LM_NAME, lm_model_type, LM_NAME + BIN_EXT),
- )
-
- self.lm_input_name = next(iter(lm_net.input_info))
- self.lm_input_shape = utils.read_lm_input_shape(models_dir)
- self.lm_landmarks_name = "landmarks"
- self.lm_net = ie_core.load_network(network=lm_net, device_name="CPU")
-
- ql_net = ie_core.read_network(
- model=os.path.join(models_dir, QL_NAME, ql_model_type, QL_NAME + XML_EXT),
- weights=os.path.join(models_dir, QL_NAME, ql_model_type, QL_NAME + BIN_EXT),
- )
-
- self.ql_input_name = next(iter(ql_net.input_info))
- self.ql_input_shape = utils.read_lm_input_shape(models_dir)
- self.ql_qualities_name = "qualities"
- self.ql_acceptabilities_name = "acceptabilities"
- self.ql_net = ie_core.load_network(network=ql_net, device_name="CPU")
-
- fr_net = ie_core.read_network(
- model=os.path.join(models_dir, FR_NAME, fr_model_type, FR_NAME + XML_EXT),
- weights=os.path.join(models_dir, FR_NAME, fr_model_type, FR_NAME + BIN_EXT),
- )
- self.fr_input_name = next(iter(fr_net.input_info))
- self.fr_input_shape = utils.read_fr_input_shape(models_dir)
- self.fr_output_name = next(iter(fr_net.outputs))
- self.fr_output_shape = utils.read_fr_output_shape(models_dir)
- self.fr_net = ie_core.load_network(network=fr_net, device_name="CPU")
-
- at_net = ie_core.read_network(
- model=os.path.join(models_dir, AT_NAME, at_model_type, AT_NAME + XML_EXT),
- weights=os.path.join(models_dir, AT_NAME, at_model_type, AT_NAME + BIN_EXT),
- )
- self.at_input_name = next(iter(at_net.input_info))
- self.at_input_shape = utils.read_at_input_shape(models_dir)
- self.at_net = ie_core.load_network(network=at_net, device_name="CPU")
-
- if "mask" in settings:
- md_model_path = settings["mask"]["models_dir"]
- md_net = ie_core.read_network(
- model=os.path.join(md_model_path, md_model_type, MD_NAME + XML_EXT),
- weights=os.path.join(md_model_path, md_model_type, MD_NAME + BIN_EXT),
- )
- self.md_input_name = next(iter(md_net.input_info))
- self.md_input_shape = md_net.input_info[
- self.md_input_name
- ].input_data.shape[2:]
- self.md_net = ie_core.load_network(network=md_net, device_name="CPU")
- self.mask_enabled = True
- else:
- self.mask_enabled = False
-
- def predict_bounding_boxes(self, np_imgs):
- """
- Args:
- np_imgs: (list) list of images loaded in numpy, of format (1, H, W, C)
-
- Returns:
- bboxes: (list) list containing arrays of bboxes for each image
- in order [x1, y1, x2, y2], scaled between 0, 1
- confs: (list) list containing arrays of confidences scores
- of the faces for each image
- """
- all_bboxes, all_scores, all_face_counts = [], [], []
- np_imgs = np.transpose(np_imgs, (0, 3, 1, 2))
-
- for np_img in np_imgs:
- ie_out = self.fd_net.infer(inputs={self.fd_input_name: np_img})
-
- bboxes = ie_out[self.fd_bboxes_name]
- scores = ie_out[self.fd_scores_name]
- select_idxs = ie_out[self.fd_select_idxs_name]
-
- # keep select_idxs until we see -1
- i = 0
- for idx in select_idxs[:, 0]:
- if idx == -1:
- break
- i += 1
-
- select_idxs = select_idxs[:i]
-
- # filter bboxes and scores based on select_idxs
- for batch_idx, class_idx, idx in select_idxs:
- all_bboxes.append(bboxes[batch_idx][idx])
- all_scores.append(scores[batch_idx][class_idx][idx].item())
-
- all_face_counts.append(len(select_idxs))
-
- img_idxs = []
-
- for img, num in enumerate(all_face_counts):
- img_idxs += [img] * num
-
- return all_bboxes, all_scores, img_idxs
-
- def predict_landmarks(self, np_imgs):
- """
- Args:
- np_imgs: (list) list of imgages loaded in numpy of format (1, C, H, W)
- Returns:
- qualities: (numpy array) qualities value between 0 and 1
- lmks: (numpy array) landmarks in the shape of (N, 5, 2)
- """
- np_imgs = np.transpose(np_imgs, (0, 3, 1, 2))
- landmarks = []
-
- for np_img in np_imgs:
- ie_out = self.lm_net.infer(inputs={self.lm_input_name: np_img})
- lmks = np.squeeze(ie_out[self.lm_landmarks_name])
- landmarks.append(lmks)
-
- return np.asarray(landmarks)
-
- def get_qualities(self, np_imgs):
- """
- Args:
- np_imgs: (list) list of imgages loaded in numpy of format (1, C, H, W)
- Returns:
- qualities: (numpy array) qualities value between 0 and 1
- """
- np_imgs = np.transpose(np_imgs, (0, 3, 1, 2))
- qualities, acceptabilities = [], []
-
- for np_img in np_imgs:
- ie_out = self.ql_net.infer(inputs={self.ql_input_name: np_img})
-
- quality = np.squeeze(ie_out[self.ql_qualities_name])
- qualities.append(quality)
-
- acceptability = np.squeeze(ie_out[self.ql_acceptabilities_name])
- acceptabilities.append(acceptability)
-
- return (
- np.clip(qualities, UNIT_LOWER_LIMIT, UNIT_UPPER_LIMIT),
- np.clip(acceptabilities, UNIT_LOWER_LIMIT, UNIT_UPPER_LIMIT),
- )
-
- def predict_embeddings(self, np_imgs):
- """
- Args:
- np_imgs: (list) list of images loaded in numpy of format (1, C, H, W)
-
- Returns:
- embs: (numpy array) array of embedding arrays
- """
- np_imgs = np.transpose(np_imgs, (0, 3, 1, 2))
- embeddings = []
-
- for np_img in np_imgs:
- ie_out = self.fr_net.infer(inputs={self.fr_input_name: np_img})
- embeddings.append(np.squeeze(ie_out[self.fr_output_name]))
-
- return np.asarray(embeddings)
-
- def predict_attributes(self, np_imgs):
- """
- Args:
- np_img: (numpy array) img loaded in numpy of format (1, C, H, W)
-
- Returns:
- ages: (numpy array) age probabilities in the shape of (N, 1, 7)
- genders: (numpy array) gender probabilities in the shape of (N, 1, 2)
- """
- np_imgs = np.transpose(np_imgs, (0, 3, 1, 2))
- ages, genders = [], []
-
- for np_img in np_imgs:
- ie_out = self.at_net.infer(inputs={self.at_input_name: np_img})
- ages.append(ie_out["age_probs"][0])
- genders.append(ie_out["gender_probs"][0])
-
- return ages, genders
-
- def check_for_masks(self, np_imgs):
- """
- Args:
- np_img: (numpy array) img loaded in numpy of format (1, C, H, W)
-
- Returns:
- mask_probabilities: (numpy array) mask probabilities in the shape of (N, 1, 4)
- """
- np_imgs = np.transpose(np_imgs, (0, 3, 1, 2))
- mask_probabilities = []
- for np_img in np_imgs:
- ie_out = self.md_net.infer(inputs={self.md_input_name: np_img})
- mask_probabilities.append(list(ie_out.values())[0][0][0])
- return mask_probabilities
diff --git a/modules/paravision/recognition/tests/__init__.py b/modules/paravision/recognition/py.typed
similarity index 100%
rename from modules/paravision/recognition/tests/__init__.py
rename to modules/paravision/recognition/py.typed
diff --git a/modules/paravision/recognition/sdk.py b/modules/paravision/recognition/sdk.py
deleted file mode 100644
index eec08f89f..000000000
--- a/modules/paravision/recognition/sdk.py
+++ /dev/null
@@ -1,471 +0,0 @@
-"""sdk: Instantiate the Paravision model."""
-from typing import List, Optional, Sequence
-import numpy as np
-import warnings
-import os
-
-from ._internal import SplitGraph
-from . import _utils as utils
-
-from .types import (
- BoundingBox,
- Face,
- Embedding,
- InferenceResult,
- ImageInferenceData,
- Landmarks,
- ScoringMode,
-)
-from .exceptions import InvalidInputException, InternalErrorException
-from .engine import Engine
-
-ERR_INVALID_EMB_MODE = "Invalid embedding scoring mode"
-ERR_INVALID_EMB_PREPARED_IMAGE = "Invalid prepared image for embedding"
-
-MATCH_SCORE_SCALE = 1000
-ENHANCED_MATCH_SCORE_WEIGHT = 2.3
-ENHANCED_MATCH_SCORE_BIAS = -0.5
-STANDARD_MATCH_SCORE_WEIGHT = 2.1
-STANDARD_MATCH_SCORE_BIAS = -5.3
-
-
-class SDK:
- """
- SDK()
-
- A sdk object contains an instance of the Paravision model and its
- associated resources.
-
- SDK objects are long-living and do not need to be re-instantiated between
- method calls.
- """
-
- def __init__(
- self,
- models_dir: Optional[str] = None,
- settings: Optional[dict] = None,
- engine: Engine = Engine.AUTO,
- ):
- """Create a SDK instance."""
-
- if settings is None:
- settings = {}
-
- if models_dir is None:
- models_dir = str(utils.model_location())
- if engine == Engine.AUTO:
- engine = utils.match_engine()
- elif engine == Engine.AUTO:
- engine = utils.match_engine_given_path(models_dir)
-
- if "attributes" not in settings:
- settings["attributes"] = {"models_dir": models_dir}
-
- if "mask" not in settings:
- if os.path.isdir(os.path.join(models_dir, "mask")):
- settings["mask"] = {"models_dir": os.path.join(models_dir, "mask")}
- else:
- try:
- settings["mask"] = {"models_dir": utils.mask_model_location()}
- except Exception:
- # TODO: temp solution to silent SonarCloud, should update when logging is added.
- settings.pop("mask", None)
-
- self._graph = SplitGraph(models_dir, settings, engine=engine)
- self._weight = utils.read_spec_value(models_dir, "weight")
- self._bias = utils.read_spec_value(models_dir, "bias")
- self._scoring_mode = settings.get("scoring_mode", ScoringMode.StandardEmbedding)
-
- def get_faces(
- self,
- imgs: Sequence[np.ndarray],
- qualities: bool = False,
- landmarks: bool = False,
- embeddings: bool = False,
- ) -> InferenceResult:
- """
- Detect faces in the image.
-
- Includes bounding boxes, landmarks, and [optionally] image quality
- details.
-
- Accepts a list of NumPy arrays (images).
-
- Returns InferenceResult object.
- """
- options = []
-
- if landmarks is True:
- options.append("find_landmarks")
-
- if embeddings is True:
- options.append("compute_embeddings")
-
- if qualities is True:
- options.append("get_qualities")
-
- outputs, img_idxs = self._graph.run(imgs, self._scoring_mode, options)
-
- faces = utils.build_faces(outputs)
-
- image_inferences = []
- for img in imgs:
- height, width = img.shape[:2]
- image_inferences.append(ImageInferenceData(width, height))
-
- for img_idx, face in zip(img_idxs, faces):
- image_inferences[img_idx].faces.append(face)
-
- return InferenceResult(image_inferences)
-
- def get_qualities(self, faces: Sequence[Face]) -> None:
- """
- Get qualities for faces in the image.
-
- Accepts a list of Face objects.
-
- No return values. Updates the face objects in place with qualities.
- """
- if len(faces) == 0:
- return
-
- imgs = [face.landmarks_input_image for face in faces]
- qualities, acceptabilities = self._graph.get_qualities(imgs)
-
- for face, quality, acceptability in zip(faces, qualities, acceptabilities):
- face.quality = quality
- face.acceptability = acceptability
-
- def get_masks(self, faces: Sequence[Face]) -> None:
- """
- Deprecated: This will be removed in the next major release. An Attributes SDK
- will be provided in the future to replace functionality.
-
- Get the mask probabilities for faces.
-
- Accepts a list of faces.
-
- No return values. Updates the face objects in place with mask probabilities.
- """
- warnings.warn(
- """get_masks is deprecated and will be removed in the next major release.
- An Attributes SDK will be provided in the future to replace functionality.""",
- DeprecationWarning,
- )
-
- if len(faces) == 0:
- return
-
- mask_input_images = []
- for face in faces:
- if face.landmarks_input_image is None:
- raise InvalidInputException(
- "Face.landmarks_input_image is needed but is None"
- )
- mask_input_images.append(face.landmarks_input_image)
-
- probability = self._graph.check_for_mask(mask_input_images)
-
- for i, face in enumerate(faces):
- face.mask = float(probability[i])
-
- def get_bounding_boxes(self, imgs: Sequence[np.ndarray]) -> InferenceResult:
- """
- Detect bounding boxes of faces in the image, returning a list of Faces.
-
- Accepts a list of NumPy arrays (images).
-
- Returns InferenceResult object.
- """
- return self.get_faces(imgs)
-
- def get_landmarks_from_bounding_boxes(
- self, img: np.ndarray, bboxes: Sequence[BoundingBox]
- ) -> InferenceResult:
- outputs = self._graph.run_from_landmarks(img, bboxes)
-
- faces = utils.build_faces(outputs)
- height, width = img.shape[:2]
-
- image_inference = ImageInferenceData(width, height)
- image_inference.faces.extend(faces)
-
- return InferenceResult([image_inference])
-
- def get_landmarks(self, faces: Sequence[Face]):
- """
- Get the landmarks for faces.
-
- Accepts a list of faces.
-
- No return values. Updates the face objects in place with landmark values.
- """
- if len(faces) == 0:
- return
-
- landmarks_input_bounding_boxes = []
- landmarks_input_images = []
- alignment_images = []
- alignment_bounding_boxes = []
-
- for face in faces:
- if face.landmarks_input_image is None:
- raise InvalidInputException("Face.landmarks_input_image is None.")
- if face.landmarks_input_bounding_box is None:
- raise InvalidInputException(
- "Face.landmarks_input_bounding_box is None."
- )
- if face.alignment_image is None:
- raise InvalidInputException("Face.alignment_image is None.")
- if face.alignment_bounding_box is None:
- raise InvalidInputException("Face.alignment_bounding_box is None.")
-
- landmarks_input_images.append(face.landmarks_input_image)
- landmarks_input_bounding_boxes.append(face.landmarks_input_bounding_box)
- alignment_images.append(face.alignment_image)
- alignment_bounding_boxes.append(face.alignment_bounding_box)
-
- landmarks, recognition_input_images = self._graph.find_landmarks(
- landmarks_input_bounding_boxes,
- landmarks_input_images,
- alignment_bounding_boxes,
- alignment_images,
- )
-
- for i, face in enumerate(faces):
- face.landmarks = Landmarks(*landmarks[i])
- face.recognition_input_image = recognition_input_images[i]
-
- def get_embeddings(self, faces: Sequence[Face]):
- """
- Get embeddings for faces.
-
- Accepts a list of Face objects.
-
- No return values. Updates the face objects in place with embeddings.
- """
- if len(faces) == 0:
- return
-
- recognition_input_images = []
- for face in faces:
- if face.recognition_input_image is None:
- raise InvalidInputException("Face.recognition_input_image is None.")
- recognition_input_images.append(face.recognition_input_image)
-
- embeddings = self._graph.compute_embeddings(recognition_input_images)
-
- for i, face in enumerate(faces):
- face.embedding = Embedding(embeddings[i], self._scoring_mode)
-
- def get_embeddings_from_landmarks(
- self, image: np.ndarray, landmarks: Sequence[Landmarks]
- ) -> List[Embedding]:
- recognition_input_images = [
- utils.crop_and_align(
- image, landmark.astuple(), self._graph.engine.fr_input_shape
- )
- for landmark in landmarks
- ]
-
- return [
- Embedding(data, self._scoring_mode)
- for data in self._graph.compute_embeddings(recognition_input_images)
- ]
-
- def get_embedding_from_prepared_image(
- self, prepared_image: np.ndarray
- ) -> Embedding:
- """
- Compute embedding using the prepared image i.e. recognition_input_image.
-
- Accepts one prepared image.
-
- Returns embedding.
- """
-
- if prepared_image is None:
- raise InvalidInputException(ERR_INVALID_EMB_PREPARED_IMAGE)
-
- embeddings = self._graph.compute_embeddings([prepared_image])
-
- return Embedding(embeddings[0], self._scoring_mode)
-
- def get_attributes(self, faces: Sequence[Face]):
- """
- Deprecated: This will be removed in the next major release. An Attributes SDK
- will be provided in the future to replace functionality.
-
- Computes age and gender attributes for faces.
-
- Accepts a list of Face objects.
-
- No return values. Updates the face objects in place with age and gender values.
- """
- warnings.warn(
- """get_attributes is deprecated and will be removed in the next major release.
- An Attributes SDK will be provided in the future to replace functionality.""",
- DeprecationWarning,
- )
-
- if len(faces) == 0:
- return
-
- recognition_input_images = []
- for face in faces:
- if face.recognition_input_image is None:
- raise InvalidInputException("Face.recognition_input_image is None.")
- recognition_input_images.append(face.recognition_input_image)
-
- ages, genders = self._graph.get_attributes(recognition_input_images)
-
- for i, face in enumerate(faces):
- face.ages = ages[i]
- face.genders = genders[i]
-
- @staticmethod
- def _get_standard_score(emb1: Embedding, emb2: Embedding) -> float:
- """
- Compute the difference score of two faces embeddings based on the Euclidean
- distance between them. A larger number indicates a greater similarity between
- the two embeddings; a lower number indicates a greater difference between the two embeddings.
-
- Accepts 2 embedding objects. Assumes the scoring mode of the embeddings to be standard.
-
- Returns a float between [0, 4]. If both embeddings are not in standard scoring mode,
- an InvalidInputException is thrown.
- """
- if (
- emb1.scoring_mode != ScoringMode.StandardEmbedding
- or emb1.scoring_mode != emb2.scoring_mode
- ):
- raise InvalidInputException(ERR_INVALID_EMB_MODE)
-
- score = 4 - np.sum((emb1.data - emb2.data) ** 2)
- return float(np.clip(score, 0, 4))
-
- @staticmethod
- def _get_enhanced_score(emb1: Embedding, emb2: Embedding) -> float:
- """
- Compute quality-aware score between two face embeddings. A larger number indicates a
- greater similarity between the two embeddings; a lower number indicates a
- greater difference between the two embeddings.
-
- Accepts 2 embedding vectors.
-
- Returns a float between [0, 2]. If both embeddings are not in enhanced scoring mode,
- an InvalidInputException is thrown.
- """
- if (
- emb1.scoring_mode != ScoringMode.EnhancedEmbedding
- or emb1.scoring_mode != emb2.scoring_mode
- ):
- raise InvalidInputException(ERR_INVALID_EMB_MODE)
-
- base_emb1, uncertainty1 = emb1.data[:-1], emb1.data[-1]
- base_emb2, uncertainty2 = emb2.data[:-1], emb2.data[-1]
-
- total_uncertainty = uncertainty1 + uncertainty2
- if total_uncertainty < 0:
- raise InternalErrorException("Uncertainty values cannot be negative.")
-
- attention = 2 * (1 - base_emb1 @ base_emb2) / (1e-10 + total_uncertainty)
- dist = attention + np.log(1e-10 + total_uncertainty)
-
- score = np.exp(-dist)
- return float(np.clip(score, 0, 2))
-
- @staticmethod
- def get_similarity(emb1: Embedding, emb2: Embedding) -> float:
-
- """
- Compute the difference score of two faces embeddings. A larger number indicates a
- greater similarity between the two embeddings; a lower number indicates a
- greater difference between the two embeddings.
-
- Accepts 2 embedding objects.
-
- Returns a float between [0, 2] for enhanced mode or [0, 4] for standard mode.
- If either of the embeddings is None, or if the embeddings are of different
- sizes, or if the embeddings have different scoring_method, raises InvalidInputException
- """
- if not (
- isinstance(emb1, Embedding)
- and isinstance(emb2, Embedding)
- and len(emb1.data) == len(emb2.data)
- ):
- raise InvalidInputException("Invalid input embedding")
-
- if emb1.scoring_mode != emb2.scoring_mode:
- raise InvalidInputException("Scoring mode mismatch for input embeddings")
-
- if emb1.scoring_mode == ScoringMode.EnhancedEmbedding:
- score = SDK._get_enhanced_score(emb1, emb2)
- elif emb1.scoring_mode == ScoringMode.StandardEmbedding:
- score = SDK._get_standard_score(emb1, emb2)
- else:
- raise InvalidInputException(ERR_INVALID_EMB_MODE)
-
- return score
-
- @staticmethod
- def get_match_score(emb1: Embedding, emb2: Embedding) -> int:
- """
- Compute the difference score of two faces embeddings. A larger number indicates a
- greater similarity between the two embeddings; a lower number indicates a
- greater difference between the two embeddings.
-
- Accepts 2 embedding objects.
-
- Returns a int between [0, 1000]. If either of the embeddings is None,
- or if the embeddings are of different sizes, or if the embeddings
- have different scoring_method, raises InvalidInputException
- """
- similarity = SDK.get_similarity(emb1, emb2)
- match_score = -1
-
- if emb1.scoring_mode == ScoringMode.EnhancedEmbedding:
- match_score = round(
- utils.sigmoid_transform(
- similarity, ENHANCED_MATCH_SCORE_WEIGHT, ENHANCED_MATCH_SCORE_BIAS
- )
- * MATCH_SCORE_SCALE
- )
- elif emb1.scoring_mode == ScoringMode.StandardEmbedding:
- match_score = round(
- utils.sigmoid_transform(
- similarity, STANDARD_MATCH_SCORE_WEIGHT, STANDARD_MATCH_SCORE_BIAS
- )
- * MATCH_SCORE_SCALE
- )
- else:
- raise InvalidInputException(ERR_INVALID_EMB_MODE)
-
- return int(np.clip(match_score, 0, 1000))
-
- def get_confidence(self, emb1: Embedding, emb2: Embedding) -> float:
- """
- Deprecated: This will be removed in the next major release. Use the
- get_match_score or get_similarity functions instead.
-
- Compute the probability of two faces being the same using the standard mode.
-
- Accepts 2 embedding objects.
-
- Returns a float between [0, 1]. If either of the embeddings is None,
- or if the embeddings are of different sizes, or if the embeddings
- have different scoring_method, raises InvalidInputException
- """
- warnings.warn(
- """get_confidence is deprecated and will be removed in the next major release.
- Use the get_match_score or get_similarity functions instead.""",
- DeprecationWarning,
- )
-
- if emb1 is not None and emb1.scoring_mode == ScoringMode.EnhancedEmbedding:
- emb1 = Embedding(emb1.data, ScoringMode.StandardEmbedding)
- if emb2 is not None and emb2.scoring_mode == ScoringMode.EnhancedEmbedding:
- emb2 = Embedding(emb2.data, ScoringMode.StandardEmbedding)
-
- score = self.get_similarity(emb1, emb2)
- return float(utils.sigmoid_transform(score, self._weight, self._bias))
diff --git a/modules/paravision/recognition/sdk/__init__.pyi b/modules/paravision/recognition/sdk/__init__.pyi
new file mode 100644
index 000000000..d1da51a5a
--- /dev/null
+++ b/modules/paravision/recognition/sdk/__init__.pyi
@@ -0,0 +1,219 @@
+from __future__ import annotations
+import paravision.recognition.sdk
+import typing
+import numpy
+import paravision.recognition.types
+_Shape = typing.Tuple[int, ...]
+
+__all__ = [
+ "Metadata",
+ "SDK"
+]
+
+
+class Metadata():
+ def __init__(self) -> None: ...
+
+ @property
+ def embedding_size(self) -> int:
+ """
+ The embedding size of the Recognition models being used.
+
+ :type: int
+ """
+ @embedding_size.setter
+ def embedding_size(self, arg0: int) -> None:
+ """
+ The embedding size of the Recognition models being used.
+ """
+ @property
+ def engine(self) -> str:
+ """
+ The engine or accelerator of the Recognition SDK instance being used.
+
+ :type: str
+ """
+ @engine.setter
+ def engine(self, arg0: str) -> None:
+ """
+ The engine or accelerator of the Recognition SDK instance being used.
+ """
+ @property
+ def engine_version(self) -> str:
+ """
+ The version of the engine or accelerator being used.
+
+ :type: str
+ """
+ @engine_version.setter
+ def engine_version(self, arg0: str) -> None:
+ """
+ The version of the engine or accelerator being used.
+ """
+ @property
+ def generation(self) -> int:
+ """
+ The generation of the Recognition SDK.
+
+ :type: int
+ """
+ @generation.setter
+ def generation(self, arg0: int) -> None:
+ """
+ The generation of the Recognition SDK.
+ """
+ @property
+ def model(self) -> str:
+ """
+ The name of the Recognition models.
+
+ :type: str
+ """
+ @model.setter
+ def model(self, arg0: str) -> None:
+ """
+ The name of the Recognition models.
+ """
+ @property
+ def model_version(self) -> str:
+ """
+ The version of the Recognition models.
+
+ :type: str
+ """
+ @model_version.setter
+ def model_version(self, arg0: str) -> None:
+ """
+ The version of the Recognition models.
+ """
+ @property
+ def sdk_version(self) -> str:
+ """
+ The version of the Recognition SDK.
+
+ :type: str
+ """
+ @sdk_version.setter
+ def sdk_version(self, arg0: str) -> None:
+ """
+ The version of the Recognition SDK.
+ """
+ pass
+
+
+class SDK():
+ """
+ SDK()
+
+ A sdk object contains an instance of the Paravision model and its
+ associated resources.
+
+ SDK objects are long-living and do not need to be re-instantiated between
+ method calls.
+ """
+ def __init__(self, models_dir: typing.Optional[str] = None, settings: typing.Optional[paravision.recognition.types.Settings] = None) -> None:
+ """
+ Create a new SDK instance with settings as a struct
+ """
+
+ @typing.overload
+ def get_bounding_boxes(self, imgs: list[numpy.ndarray], image_source: paravision.recognition.types.ImageSource = ImageSource.UNKNOWN) -> paravision.recognition.types.InferenceResult:
+ """
+ Detect bounding boxes of faces in the image, returning a list of Faces.
+ """
+
+ @typing.overload
+ def get_bounding_boxes(self, imgs: list[paravision.recognition.types.Image], detection_model: paravision.recognition.types.ImageSource = '') -> paravision.recognition.types.InferenceResult:
+ """
+ Accepts a list of NumPy arrays (images).
+ """
+
+ @typing.overload
+ def get_embedding_from_prepared_image(self, prepared_image: numpy.ndarray) -> paravision.recognition.types.Embedding:
+ """
+ Get the embedding for a prepared image.
+ """
+
+ @typing.overload
+ def get_embedding_from_prepared_image(self, prepared_image: paravision.recognition.types.Image) -> paravision.recognition.types.Embedding:
+ """
+ Accepts one prepared image (numpy array).
+ """
+
+ def get_embeddings(self, faces: list[paravision.recognition.types.Face]) -> None:
+ """
+ Get the embeddings for faces.
+ """
+
+ @typing.overload
+ def get_embeddings_from_landmarks(self, image: numpy.ndarray, landmarks: list[paravision.recognition.types.Landmarks]) -> list[paravision.recognition.types.Embedding]:
+ """
+ Get the embeddings for faces.
+ """
+
+ @typing.overload
+ def get_embeddings_from_landmarks(self, image: paravision.recognition.types.Image, landmarks: list[paravision.recognition.types.Landmarks]) -> list[paravision.recognition.types.Embedding]:
+ """
+ Accepts a NumPy array (image) and a list of landmarks.
+ """
+
+ @typing.overload
+ def get_faces(self, imgs: list[numpy.ndarray], qualities: bool = False, landmarks: bool = False, embeddings: bool = False, image_source: paravision.recognition.types.ImageSource = ImageSource.UNKNOWN) -> paravision.recognition.types.InferenceResult:
+ """
+ Detect faces in the image.
+ """
+
+ @typing.overload
+ def get_faces(self, imgs: list[paravision.recognition.types.Image], qualities: bool = False, landmarks: bool = False, embeddings: bool = False, image_source: paravision.recognition.types.ImageSource = ImageSource.UNKNOWN) -> paravision.recognition.types.InferenceResult:
+ """
+ Includes bounding boxes, landmarks, and [optionally] image quality
+ details.
+ """
+
+ def get_landmarks(self, faces: list[paravision.recognition.types.Face]) -> None:
+ """
+ Get the landmarks for faces.
+ """
+
+ @typing.overload
+ def get_landmarks_from_bounding_boxes(self, img: numpy.ndarray, bboxes: list[paravision.recognition.types.BoundingBox]) -> paravision.recognition.types.InferenceResult:
+ """
+ Get the landmarks from a bounding box.
+ """
+
+ @typing.overload
+ def get_landmarks_from_bounding_boxes(self, img: paravision.recognition.types.Image, bboxes: list[paravision.recognition.types.BoundingBox]) -> paravision.recognition.types.InferenceResult:
+ """
+ Accepts a NumPy array (image) and a list of bounding boxes.
+ """
+
+ @staticmethod
+ def get_match_score(emb1: paravision.recognition.types.Embedding, emb2: paravision.recognition.types.Embedding, scoring_mode: paravision.recognition.types.ScoringMode = ScoringMode.EnhancedEmbedding) -> int:
+ """
+ Compute the difference score of two faces embeddings. A larger number indicates a
+ greater similarity between the two embeddings; a lower number indicates a
+ greater difference between the two embeddings.
+ """
+
+ @staticmethod
+ def get_metadata(models_dir: typing.Optional[str] = None) -> Metadata:
+ """
+ Returns metadata for SDK and model info.
+ """
+
+ def get_qualities(self, faces: list[paravision.recognition.types.Face]) -> None:
+ """
+ Get the quality of the faces in the image.
+ """
+
+ @staticmethod
+ def get_similarity(emb1: paravision.recognition.types.Embedding, emb2: paravision.recognition.types.Embedding, scoring_mode: paravision.recognition.types.ScoringMode = ScoringMode.EnhancedEmbedding) -> float:
+ """
+ Compute the difference score of two faces embeddings. A larger number indicates a
+ greater similarity between the two embeddings; a lower number indicates a
+ greater difference between the two embeddings.
+ """
+
+ pass
+
+
diff --git a/modules/paravision/recognition/tensorrt/__pycache__/__init__.cpython-36.pyc b/modules/paravision/recognition/tensorrt/__pycache__/__init__.cpython-36.pyc
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deleted file mode 100644
index 4bfa2d24c..000000000
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diff --git a/modules/paravision/recognition/tensorrt/__pycache__/utils.cpython-36.pyc b/modules/paravision/recognition/tensorrt/__pycache__/utils.cpython-36.pyc
deleted file mode 100644
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diff --git a/modules/paravision/recognition/tensorrt/builder.py b/modules/paravision/recognition/tensorrt/builder.py
deleted file mode 100644
index b473555fe..000000000
--- a/modules/paravision/recognition/tensorrt/builder.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import tensorrt as trt
-import os
-from pathlib import Path
-
-from ..exceptions import (
- InvalidInputException,
- ModelLoadingException,
-)
-from contextlib import ExitStack
-
-LOGGER = trt.Logger(trt.Logger.Severity.ERROR)
-
-DEFAULT_DETECTION_MAX_BATCH_SIZE = 1
-DEFAULT_QUALITY_MAX_BATCH_SIZE = 4
-DEFAULT_LANDMARKS_MAX_BATCH_SIZE = 4
-DEFAULT_RECOGNITION_MAX_BATCH_SIZE = 4
-DEFAULT_ATTRIBUTES_MAX_BATCH_SIZE = 4
-DEFAULT_MASK_MAX_BATCH_SIZE = 4
-NUM_CHANNELS_RGB = 3
-MAX_WORKSPACE_SIZE = 1 << 28
-
-trt.init_libnvinfer_plugins(LOGGER, "")
-
-PLUGIN_CREATORS = trt.get_plugin_registry().plugin_creator_list
-
-
-def _get_max_batch_size(name, settings):
- if name == "detection":
- # batching is not enabled for detection yet
- return DEFAULT_DETECTION_MAX_BATCH_SIZE
-
- if name == "landmarks":
- size = settings.get(
- "landmarks_max_batch_size", DEFAULT_LANDMARKS_MAX_BATCH_SIZE
- )
- elif name == "recognition":
- size = settings.get(
- "recognition_max_batch_size", DEFAULT_RECOGNITION_MAX_BATCH_SIZE
- )
- elif name == "attributes":
- size = settings.get(
- "attributes_max_batch_size", DEFAULT_ATTRIBUTES_MAX_BATCH_SIZE
- )
- elif name == "mask":
- size = settings.get("mask_max_batch_size", DEFAULT_MASK_MAX_BATCH_SIZE)
- elif name == "quality":
- size = settings.get("quality_max_batch_size", DEFAULT_QUALITY_MAX_BATCH_SIZE)
- else:
- raise InvalidInputException("Batch size is not specified")
-
- return size
-
-
-def build_engine(name, models_dir, models_type, engine_path, settings, shape):
- if name == "mask":
- model_file = os.path.join(models_dir, models_type, f"{name}.onnx")
- else:
- model_file = os.path.join(models_dir, name, models_type, f"{name}.onnx")
-
- batch_size = _get_max_batch_size(name, settings)
-
- trt_version = int(trt.__version__.split(".")[0])
- if trt_version >= 8:
- # -1 indicates dynamic batching. Does not work for detection model currently
- input_shape = [
- batch_size if name == "detection" else -1,
- NUM_CHANNELS_RGB,
- ] + list(shape)
- net_flags = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
- else:
- raise ModelLoadingException(
- "TensorRT version 8 or higher required to build engine"
- )
-
- if not os.path.isfile(model_file):
- return None
-
- with ExitStack() as stack:
- builder = stack.enter_context(trt.Builder(LOGGER))
- config = stack.enter_context(builder.create_builder_config())
- network = stack.enter_context(builder.create_network(net_flags))
- parser = stack.enter_context(trt.OnnxParser(network, LOGGER))
-
- success = parser.parse_from_file(model_file)
- if not success:
- raise ModelLoadingException(f"Cannot parse {name} model.")
-
- builder.max_batch_size = batch_size
- config.max_workspace_size = MAX_WORKSPACE_SIZE
-
- profile = _create_opt_profile(builder, network, batch_size)
- config.add_optimization_profile(profile)
-
- network.get_input(0).shape = input_shape
- serialized_engine = builder.build_serialized_network(network, config)
- if serialized_engine is None:
- raise ModelLoadingException(f"Cannot serialize {name} engine.")
-
- engine_dir = Path(engine_path).parent
- engine_dir.mkdir(parents=True, exist_ok=True)
- with open(engine_path, "wb") as f:
- f.write(serialized_engine)
-
- return serialized_engine
-
-
-def _create_opt_profile(builder, network, max_batch_size):
- profile = builder.create_optimization_profile()
-
- if network.num_inputs <= 0:
- return profile
-
- input_ = network.get_input(0)
-
- min_shape = trt.Dims(input_.shape)
- min_shape[0] = 1
-
- opt_shape = trt.Dims(input_.shape)
- opt_shape[0] = max_batch_size
-
- max_shape = trt.Dims(input_.shape)
- max_shape[0] = max_batch_size
-
- profile.set_shape(input_.name, min_shape, opt_shape, max_shape)
-
- return profile
-
-
-def load_engine(name, engine_path, models_dir, models_type, settings, input_shape):
- if not os.path.isfile(engine_path):
- serialized_engine = build_engine(
- name, models_dir, models_type, engine_path, settings, input_shape
- )
- else:
- with open(engine_path, "rb") as f:
- serialized_engine = f.read()
-
- if not serialized_engine:
- raise ModelLoadingException(f"Cannot build {name} engine.")
-
- runtime = trt.Runtime(LOGGER)
- return runtime.deserialize_cuda_engine(serialized_engine)
diff --git a/modules/paravision/recognition/tensorrt/engine.py b/modules/paravision/recognition/tensorrt/engine.py
deleted file mode 100644
index 1d9fe0e3d..000000000
--- a/modules/paravision/recognition/tensorrt/engine.py
+++ /dev/null
@@ -1,462 +0,0 @@
-import os
-import importlib
-
-import numpy as np
-import pycuda.driver as cuda
-import pycuda.autoinit # noqa
-
-from .. import _utils as utils
-from ..exceptions import (
- ModelLoadingException,
-)
-from . import utils as trt_utils
-from .builder import load_engine
-
-QUALITIES_QUALITIES_NAME = "qualities"
-QUALITIES_ACCEPTABILTIES_NAME = "acceptabilities"
-LANDMARKS_LANDMARKS_NAME = "landmarks"
-
-ATTRIBUTES_AGES_NAME = "age_probs"
-ATTRIBUTES_GENDERS_NAME = "gender_probs"
-
-UNIT_LOWER_LIMIT = 0
-UNIT_UPPER_LIMIT = 1
-
-ERR_ENGINE_UNINITIALIZED = "The engine is not initialized."
-ERR_MASK_MODEL_NOT_LOADED = "Mask model not loaded."
-
-FD_NAME = "detection"
-LM_NAME = "landmarks"
-QL_NAME = "quality"
-FR_NAME = "recognition"
-AT_NAME = "attributes"
-MD_NAME = "mask"
-
-ENGINE_EXT = ".engine"
-
-
-class Engine:
- def __init__(self, models_dir, settings):
- engine_dirpath = models_dir
-
- try:
- paravision_models = importlib.import_module("paravision_models")
-
- if paravision_models.location() == models_dir:
- engine_dirpath = paravision_models.TRT_ENGINE_PATH
- except (ModuleNotFoundError, AttributeError):
- pass
-
- self.stream = cuda.Stream()
-
- (
- fd_model_type,
- lm_model_type,
- ql_model_type,
- fr_model_type,
- at_model_type,
- md_model_type,
- ) = utils.get_model_types(settings)
-
- self.fd_input_shape = utils.read_fd_input_shape(models_dir, fd_model_type)
-
- fd_engine_path = os.path.join(
- engine_dirpath, FD_NAME, fd_model_type, FD_NAME + ENGINE_EXT
- )
- self.fd_engine = load_engine(
- FD_NAME,
- fd_engine_path,
- models_dir,
- fd_model_type,
- settings,
- self.fd_input_shape,
- )
- if self.fd_engine:
- self.fd_context = self.fd_engine.create_execution_context()
- (
- self.fd_inputs,
- self.fd_outputs,
- self.fd_data,
- self.fd_bindings,
- ) = trt_utils.allocate_buffers(self.fd_engine)
-
- self.lm_input_shape = utils.read_lm_input_shape(models_dir)
- lm_engine_path = os.path.join(
- engine_dirpath, LM_NAME, lm_model_type, LM_NAME + ENGINE_EXT
- )
- self.lm_engine = load_engine(
- LM_NAME,
- lm_engine_path,
- models_dir,
- lm_model_type,
- settings,
- self.lm_input_shape,
- )
- if self.lm_engine:
- self.lm_context = self.lm_engine.create_execution_context()
- (
- self.lm_inputs,
- self.lm_outputs,
- self.lm_data,
- self.lm_bindings,
- ) = trt_utils.allocate_buffers(self.lm_engine)
-
- self.ql_input_shape = utils.read_lm_input_shape(models_dir)
- ql_engine_path = os.path.join(
- engine_dirpath, QL_NAME, ql_model_type, QL_NAME + ENGINE_EXT
- )
- self.ql_engine = load_engine(
- QL_NAME,
- ql_engine_path,
- models_dir,
- ql_model_type,
- settings,
- self.ql_input_shape,
- )
- if self.ql_engine:
- self.ql_context = self.ql_engine.create_execution_context()
- (
- self.ql_inputs,
- self.ql_outputs,
- self.ql_data,
- self.ql_bindings,
- ) = trt_utils.allocate_buffers(self.ql_engine)
-
- self.fr_input_shape = utils.read_fr_input_shape(models_dir)
- fr_engine_path = os.path.join(
- engine_dirpath, FR_NAME, fr_model_type, FR_NAME + ENGINE_EXT
- )
- self.fr_engine = load_engine(
- FR_NAME,
- fr_engine_path,
- models_dir,
- fr_model_type,
- settings,
- self.fr_input_shape,
- )
- if self.fr_engine:
- self.fr_context = self.fr_engine.create_execution_context()
- (
- self.fr_inputs,
- self.fr_outputs,
- self.fr_data,
- self.fr_bindings,
- ) = trt_utils.allocate_buffers(self.fr_engine)
- self.fr_output_shape = utils.read_fr_output_shape(models_dir)
-
- self.at_input_shape = utils.read_at_input_shape(models_dir)
- at_engine_path = os.path.join(
- engine_dirpath, AT_NAME, at_model_type, AT_NAME + ENGINE_EXT
- )
- self.at_engine = load_engine(
- AT_NAME,
- at_engine_path,
- models_dir,
- at_model_type,
- settings,
- self.at_input_shape,
- )
- if self.at_engine:
- self.at_context = self.at_engine.create_execution_context()
- (
- self.at_inputs,
- self.at_outputs,
- self.at_data,
- self.at_bindings,
- ) = trt_utils.allocate_buffers(self.at_engine)
-
- # Mask input image is prepared separately as the shape can deviate from landmark input images.
- if "mask" in settings:
- md_model_path = settings["mask"]["models_dir"]
- md_engine_path = os.path.join(
- md_model_path, md_model_type, MD_NAME + ENGINE_EXT
- )
-
- self.md_input_shape = utils.read_md_input_shape(models_dir)
- self.md_engine = load_engine(
- MD_NAME,
- md_engine_path,
- md_model_path,
- md_model_type,
- settings,
- self.md_input_shape,
- )
- if self.md_engine:
- self.md_context = self.md_engine.create_execution_context()
- (
- self.md_inputs,
- self.md_outputs,
- self.md_data,
- self.md_bindings,
- ) = trt_utils.allocate_buffers(self.md_engine)
- self.mask_enabled = True
- else:
- self.mask_enabled = False
-
- def predict_bounding_boxes(self, np_imgs):
- """
- Args:
- np_imgs: (list) list of images loaded in numpy, of format (1, H, W, C)
-
- Returns:
- bboxes: (list) list containing arrays of bboxes for each image
- in order [x1, y1, x2, y2], scaled between 0, 1
- confs: (list) list containing arrays of confidences scores
- of the faces for each image
- """
- if not self.fd_engine:
- raise ModelLoadingException(ERR_ENGINE_UNINITIALIZED)
-
- max_batch_size = self.fd_engine.max_batch_size
- bboxes, confidences, img_idxs = [], [], []
-
- for i in range(0, len(np_imgs), max_batch_size):
- batch = np_imgs[i : min(len(np_imgs), i + max_batch_size)]
- (
- bboxes_batch,
- confidences_batch,
- img_idxs_batch,
- ) = self._batch_predict_bounding_boxes(batch)
-
- bboxes.extend(bboxes_batch)
- confidences.extend(confidences_batch)
- img_idxs.extend(img_idxs_batch + i)
-
- bboxes = np.asarray(bboxes).reshape(-1, 4)
- confidences = np.asarray(confidences).reshape(-1)
-
- return bboxes, confidences, img_idxs
-
- def _batch_predict_bounding_boxes(self, np_imgs):
- np_imgs = np.transpose(np.asarray(np_imgs), [0, 3, 1, 2]).astype(np.float32)
- batch_size = len(np_imgs)
-
- results = trt_utils.do_inference(
- self.fd_context,
- bindings=self.fd_bindings,
- inputs=self.fd_inputs,
- input_data=np_imgs,
- outputs=self.fd_outputs,
- output_data=self.fd_data,
- stream=self.stream,
- batch_size=batch_size,
- )
-
- num_detections = int(results[0])
- bboxes = results[1].reshape(-1, 4)[:num_detections]
- scores = results[2][:num_detections].tolist()
- indexes = results[3][:num_detections].astype(np.int32)
- return bboxes, scores, indexes
-
- def predict_landmarks(self, np_imgs):
- """
- Args:
- np_imgs: (list) imgs loaded in numpy of format (1, H, W, C)
- Returns:
- qualities: (numpy array) qualities values between 0 and 1
- lmks: (numpy array) landmarks in the shape of (N, 5, 2)
- acceptabilities: (numpy array) acceptabilities values between 0 and 1
- """
- if not self.lm_engine:
- raise ModelLoadingException(ERR_ENGINE_UNINITIALIZED)
-
- max_batch_size = self.lm_engine.max_batch_size
- lmks = []
-
- for i in range(0, len(np_imgs), max_batch_size):
- batch = np_imgs[i : min(len(np_imgs), i + max_batch_size)]
- lmks_batch = self._batch_predict_landmarks(batch)
-
- lmks.extend(lmks_batch)
-
- return np.asarray(lmks)
-
- def _batch_predict_landmarks(self, np_imgs):
- np_imgs = np.transpose(np_imgs, [0, 3, 1, 2]).astype(np.float32)
- batch_size = len(np_imgs)
- results = trt_utils.do_inference(
- self.lm_context,
- bindings=self.lm_bindings,
- inputs=self.lm_inputs,
- input_data=np_imgs,
- outputs=self.lm_outputs,
- output_data=self.lm_data,
- stream=self.stream,
- batch_size=batch_size,
- )
-
- # because we pre-allocating the buffer to accomodate the max batch size,
- # the last elements of the results will be 0 unless we're finding
- # landmarks for max_batch_size faces, so we need to explicitly grab
- # the elements we want
- landmarks = results[self.lm_engine[LANDMARKS_LANDMARKS_NAME] - 1].reshape(
- -1, 10
- )[:batch_size]
-
- return landmarks
-
- def predict_embeddings(self, np_imgs):
- """
- Args:
- np_imgs: (list) list of images loaded in numpy of format (1, H, W, C)
-
- Returns:
- embs: (numpy array) array of embedding arrays
- """
- if not self.fr_engine:
- raise ModelLoadingException(ERR_ENGINE_UNINITIALIZED)
-
- max_batch_size = self.fr_engine.max_batch_size
- batch_size = len(np_imgs)
-
- embeddings = []
-
- for i in range(0, batch_size, max_batch_size):
- batch = np_imgs[i : min(batch_size, i + max_batch_size)]
- embs = self._batch_predict_embeddings(batch)
-
- embeddings.extend(embs)
-
- return np.asarray(embeddings).reshape(batch_size, -1)
-
- def _batch_predict_embeddings(self, np_imgs):
- np_imgs = np.transpose(np_imgs, [0, 3, 1, 2]).astype(np.float32)
- batch_size = len(np_imgs)
- results = trt_utils.do_inference(
- self.fr_context,
- bindings=self.fr_bindings,
- inputs=self.fr_inputs,
- input_data=np_imgs,
- outputs=self.fr_outputs,
- output_data=self.fr_data,
- stream=self.stream,
- batch_size=batch_size,
- )
-
- return results[0]
-
- def predict_attributes(self, np_imgs):
- if not self.at_engine:
- raise ModelLoadingException(ERR_ENGINE_UNINITIALIZED)
-
- max_batch_size = self.at_engine.max_batch_size
- batch_size = len(np_imgs)
- all_ages, all_genders = [], []
-
- for i in range(0, batch_size, max_batch_size):
- batch = np_imgs[i : min(batch_size, i + max_batch_size)]
- ages, genders = self._batch_predict_attributes(batch)
- all_ages.extend(ages)
- all_genders.extend(genders)
-
- return all_ages, all_genders
-
- def _batch_predict_attributes(self, np_imgs):
- """
- Args:
- np_img: (numpy array) img loaded in numpy of format (1, H, W, C)
-
- Returns:
- age_probs: (numpy array) age probabilities in the shape of (N, 1, 7)
- gender_probs: (numpy array) gender probabilities in the shape of (N, 1, 2)
- """
- np_imgs = np.transpose(np_imgs, [0, 3, 1, 2]).astype(np.float32)
- batch_size = len(np_imgs)
- results = trt_utils.do_inference(
- self.at_context,
- bindings=self.at_bindings,
- inputs=self.at_inputs,
- input_data=np_imgs,
- outputs=self.at_outputs,
- output_data=self.at_data,
- batch_size=batch_size,
- stream=self.stream,
- )
-
- ages = results[self.at_engine[ATTRIBUTES_AGES_NAME] - 1].reshape(-1, 7)[
- :batch_size
- ]
- genders = results[self.at_engine[ATTRIBUTES_GENDERS_NAME] - 1].reshape(-1, 2)[
- :batch_size
- ]
-
- return [ages, genders]
-
- def get_qualities(self, np_imgs):
- """
- Args:
- np_imgs: (list) imgs loaded in numpy of format (1, H, W, C)
- Returns:
- qualities: (numpy array) qualities values between 0 and 1
- """
- if not self.ql_engine:
- raise ModelLoadingException(ERR_ENGINE_UNINITIALIZED)
-
- max_batch_size = self.ql_engine.max_batch_size
- qualities, acceptabilities = [], []
-
- for i in range(0, len(np_imgs), max_batch_size):
- batch = np_imgs[i : min(len(np_imgs), i + max_batch_size)]
- qualities_batch, acceptabilities_batch = self._batch_get_qualities(batch)
- qualities.extend(qualities_batch)
- acceptabilities.extend(acceptabilities_batch)
-
- return (
- np.clip(qualities, UNIT_LOWER_LIMIT, UNIT_UPPER_LIMIT),
- np.clip(acceptabilities, UNIT_LOWER_LIMIT, UNIT_UPPER_LIMIT),
- )
-
- def _batch_get_qualities(self, np_imgs):
- np_imgs = np.transpose(np_imgs, [0, 3, 1, 2]).astype(np.float32)
- batch_size = len(np_imgs)
- results = trt_utils.do_inference(
- self.ql_context,
- bindings=self.ql_bindings,
- inputs=self.ql_inputs,
- input_data=np_imgs,
- outputs=self.ql_outputs,
- output_data=self.ql_data,
- stream=self.stream,
- batch_size=batch_size,
- )
-
- qualities = results[self.ql_engine[QUALITIES_QUALITIES_NAME] - 1][:batch_size]
- acceptabilities = results[self.ql_engine[QUALITIES_ACCEPTABILTIES_NAME] - 1][
- :batch_size
- ]
-
- return qualities, acceptabilities
-
- def check_for_masks(self, np_imgs):
- if not self.md_engine:
- raise ModelLoadingException(ERR_MASK_MODEL_NOT_LOADED)
-
- max_batch_size = self.md_engine.max_batch_size
- batch_size = len(np_imgs)
- mask_probabilities = []
-
- for i in range(0, batch_size, max_batch_size):
- batch = np_imgs[i : min(batch_size, i + max_batch_size)]
- mask_probabilities.extend(self._batch_check_for_masks(batch))
-
- return np.asarray(mask_probabilities)
-
- def _batch_check_for_masks(self, np_imgs):
- """
- Args:
- np_imgs: (list) imgs loaded in numpy of format (1, H, W, C)
- Returns:
- mask_probs: (numpy array) mask probabilities in the shape of (N, 1, 1)
- """
- np_imgs = np.transpose(np_imgs, [0, 3, 1, 2]).astype(np.float32)
- results = trt_utils.do_inference(
- self.md_context,
- bindings=self.md_bindings,
- inputs=self.md_inputs,
- input_data=np_imgs,
- outputs=self.md_outputs,
- output_data=self.md_data,
- stream=self.stream,
- batch_size=len(np_imgs),
- )
- return results[0]
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diff --git a/modules/paravision/recognition/tests/constants.py b/modules/paravision/recognition/tests/constants.py
deleted file mode 100644
index 425a20315..000000000
--- a/modules/paravision/recognition/tests/constants.py
+++ /dev/null
@@ -1,36 +0,0 @@
-IMG_NOFACE = "noface.jpg"
-IMG_ONEFACE = "oneface.jpg"
-IMG_MANYFACES = "manyfaces.jpg"
-IMG_ONEFACE_RECO_INPUT_IMG = "recognition_input_image_openvino.png"
-IMG_IDENTITY1_FACE1 = "bhargav.jpg"
-IMG_IDENTITY1_FACE2 = "bhargav-3.jpg"
-
-ERR_MISSING_BBOX = "missing bounding box values"
-ERR_MISSING_SCORE = "missing score value"
-ERR_MISSING_LANDMARKS = "missing landmarks values"
-ERR_MISSING_EMBEDDING = "missing embedding value"
-ERR_MISSING_MASK_PROB = "missing mask probability value"
-ERR_MISSING_FACES = "missing faces"
-
-ERR_UNEXPECTED_LANDMARKS = "unexpected landmarks found"
-ERR_UNEXPECTED_QUALITY = "unexpected quality found"
-ERR_UNEXPECTED_NUM_FACES = "unexpected number of faces found"
-ERR_UNEXPECTED_NUM_INFERENCES = "unexpected number of image inferences found"
-ERR_UNEXPECTED_AGES = "unexpected ages found"
-ERR_UNEXPECTED_GENDERS = "unexpected genders found"
-ERR_UNEXPECTED_AGE = "unexpected age found"
-ERR_UNEXPECTED_GENDER = "unexpected gender found"
-
-ERR_INVALID_MASK_PROB = "invalid mask probability value"
-ERR_INVALID_MPF = "invalid most prominent face"
-ERR_INVALID_SCORING_MODE = "invalid scoring mode"
-ERR_INVALID_EMBEDDING_SIZE = "invalid embedding size"
-ERR_INVALID_AGES = "invalid ages"
-
-ERR_JSON_FACE = "face is not JSON serializable"
-
-MAX_NO_MASK_SCORE = 0.5
-MASK_SCORE = 0.95
-
-EXPECTED_ENHANCED_EMBED_LEN = 257
-EXPECTED_STANDARD_EMBED_LEN = 256
diff --git a/modules/paravision/recognition/tests/test_sdk_enhanced.py b/modules/paravision/recognition/tests/test_sdk_enhanced.py
deleted file mode 100644
index bebcadee2..000000000
--- a/modules/paravision/recognition/tests/test_sdk_enhanced.py
+++ /dev/null
@@ -1,495 +0,0 @@
-import os
-import numpy as np
-from unittest import TestCase
-
-from ..sdk import SDK
-from ..types import ImageInferenceData
-from ..engine import Engine
-from ..utils import load_image
-from ..types import BoundingBox, ScoringMode, Embedding
-from ..exceptions import InvalidInputException
-
-from .utils import is_json_serializable
-from .constants import (
- IMG_NOFACE,
- IMG_ONEFACE,
- IMG_MANYFACES,
- IMG_IDENTITY1_FACE1,
- IMG_IDENTITY1_FACE2,
- IMG_ONEFACE_RECO_INPUT_IMG,
- ERR_MISSING_BBOX,
- ERR_MISSING_SCORE,
- ERR_MISSING_LANDMARKS,
- ERR_MISSING_EMBEDDING,
- ERR_MISSING_MASK_PROB,
- ERR_MISSING_FACES,
- ERR_JSON_FACE,
- ERR_UNEXPECTED_LANDMARKS,
- ERR_UNEXPECTED_QUALITY,
- ERR_UNEXPECTED_NUM_FACES,
- ERR_UNEXPECTED_NUM_INFERENCES,
- ERR_UNEXPECTED_AGES,
- ERR_UNEXPECTED_GENDERS,
- ERR_UNEXPECTED_AGE,
- ERR_UNEXPECTED_GENDER,
- ERR_INVALID_MASK_PROB,
- ERR_INVALID_MPF,
- MAX_NO_MASK_SCORE,
- MASK_SCORE,
- ERR_INVALID_SCORING_MODE,
- ERR_INVALID_EMBEDDING_SIZE,
- ERR_INVALID_AGES,
- EXPECTED_ENHANCED_EMBED_LEN,
-)
-
-ASSETS_PATH = os.path.join(os.path.dirname(__file__), "assets")
-engine_default = None
-scoring_mode = None
-sdk = None
-
-
-class TestSDK(TestCase):
- @classmethod
- def setUpClass(cls):
- global sdk
- global engine_default
- global scoring_mode
-
- engine_default = Engine.OPENVINO
- scoring_mode = ScoringMode.EnhancedEmbedding
-
- sdk = SDK(engine=engine_default, settings={"scoring_mode": scoring_mode})
-
- def setUp(self):
- self.sdk = sdk
-
- def test_load_image_invalid_input(self):
- with self.assertRaises(InvalidInputException):
- load_image("invalid-img.jpg")
-
- def test_empty_case(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_NOFACE))]
- detection_result = self.sdk.get_faces(
- imgs, qualities=True, landmarks=True, embeddings=True
- )
-
- faces = detection_result.faces
- self.assertEqual(len(faces), 0, msg=ERR_UNEXPECTED_NUM_FACES)
-
- image_inferences = detection_result.image_inferences
- self.assertEqual(len(image_inferences), 1, msg=ERR_UNEXPECTED_NUM_INFERENCES)
-
- detection_result = self.sdk.get_bounding_boxes(imgs)
- self.assertEqual(len(detection_result.faces), 0, msg=ERR_UNEXPECTED_NUM_FACES)
-
- def test_get_faces(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- detection_result = self.sdk.get_faces(
- imgs, qualities=True, landmarks=True, embeddings=True
- )
-
- faces = detection_result.faces
- self.assertEqual(len(faces), 1, msg=ERR_UNEXPECTED_NUM_FACES)
-
- image_inferences = detection_result.image_inferences
- self.assertEqual(len(image_inferences), 1, msg=ERR_UNEXPECTED_NUM_INFERENCES)
-
- self.assert_faces(faces)
-
- def test_get_faces_multiple(self):
- oneface_img = load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))
- noface_img = load_image(os.path.join(ASSETS_PATH, IMG_NOFACE))
- manyface_img = load_image(os.path.join(ASSETS_PATH, IMG_MANYFACES))
-
- imgs = [oneface_img, noface_img, manyface_img]
- detection_result = self.sdk.get_faces(
- imgs, qualities=True, landmarks=True, embeddings=True
- )
-
- faces = detection_result.faces
- self.assertEqual(len(faces), 9, msg=ERR_UNEXPECTED_NUM_FACES)
-
- self.assert_faces(faces)
-
- image_inferences = detection_result.image_inferences
- self.assertEqual(len(image_inferences), 3, msg=ERR_UNEXPECTED_NUM_INFERENCES)
-
- expected_num_faces = [1, 0, 8]
- for i, faces in enumerate(expected_num_faces):
- self.assertEqual(
- len(image_inferences[i].faces),
- faces,
- msg=f"unexpected number of faces found in image inference {i}",
- )
-
- def test_get_attributes(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_IDENTITY1_FACE1))]
- detection_result = self.sdk.get_faces(imgs, qualities=True, landmarks=True)
- faces = detection_result.faces
- self.assertIsNotNone(faces, msg=ERR_MISSING_FACES)
- self.assertEqual(len(faces), 1, msg=ERR_UNEXPECTED_NUM_FACES)
-
- face = faces[0]
- self.assertIsNone(face.ages, msg=ERR_UNEXPECTED_AGES)
- self.assertIsNone(face.genders, msg=ERR_UNEXPECTED_GENDERS)
-
- self.assertIsNone(face.age, msg=ERR_UNEXPECTED_AGE)
- self.assertIsNone(face.gender, msg=ERR_UNEXPECTED_GENDER)
-
- self.sdk.get_attributes(faces)
- self.assertIsNotNone(face.ages, msg="missing ages")
- self.assertIsNotNone(face.genders, msg="missing genders")
-
- self.assertIsNotNone(face.age, msg="missing age")
- self.assertTrue(face.age == "20-30", msg="incorrect age")
- self.assertIsNotNone(face.gender, msg="missing gender")
- self.assertTrue(face.gender == "male", msg="incorrect gender")
-
- self.assertTrue(face.ages[2] > face.ages[0], msg=ERR_INVALID_AGES)
- self.assertTrue(face.ages[2] > face.ages[1], msg=ERR_INVALID_AGES)
- self.assertTrue(face.ages[2] > face.ages[3], msg=ERR_INVALID_AGES)
- self.assertTrue(face.ages[2] > face.ages[4], msg=ERR_INVALID_AGES)
- self.assertTrue(face.ages[2] > face.ages[5], msg=ERR_INVALID_AGES)
- self.assertTrue(face.ages[2] > face.ages[6], msg=ERR_INVALID_AGES)
- self.assertTrue(face.genders[0] > face.genders[1], msg="invalid genders")
-
- self.assertTrue(is_json_serializable(face.asdict()), msg=ERR_JSON_FACE)
-
- def test_get_qualities(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_IDENTITY1_FACE1))]
- faces = self.sdk.get_faces(imgs).faces
- self.sdk.get_qualities(faces)
- self.assertAlmostEqual(faces[0].quality, 0.925, delta=0.001)
- self.assertAlmostEqual(faces[0].acceptability, 0.999, delta=0.001)
- self.assertTrue(is_json_serializable(faces[0].asdict()), msg=ERR_JSON_FACE)
-
- def test_get_faces_qualties(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_IDENTITY1_FACE1))]
- faces = self.sdk.get_faces(imgs, qualities=True).faces
-
- self.assertAlmostEqual(faces[0].quality, 0.925, delta=0.001)
- self.assertTrue(is_json_serializable(faces[0].asdict()), msg=ERR_JSON_FACE)
-
- def test_get_bounding_boxes(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- detection_result = self.sdk.get_bounding_boxes(imgs)
- faces = detection_result.faces
-
- self.assertEqual(len(faces), 1, msg=ERR_UNEXPECTED_NUM_FACES)
-
- f = faces[0]
- self.assertIsNotNone(f.bounding_box, msg=ERR_MISSING_BBOX)
- self.assertIsNotNone(f.bounding_box.score, msg=ERR_MISSING_SCORE)
- self.assertIsNone(f.landmarks, msg=ERR_UNEXPECTED_LANDMARKS)
- self.assertIsNone(f.quality, msg=ERR_UNEXPECTED_QUALITY)
- self.assertIsNone(f.acceptability, msg="unexpected acceptability")
- self.assertIsNone(f.embedding, msg="unexpected embedding")
- self.assertTrue(is_json_serializable(f.asdict()), msg=ERR_JSON_FACE)
-
- def test_get_landmarks(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- detection_result = self.sdk.get_faces(imgs)
- faces = detection_result.faces
- self.assertEqual(len(faces), 1, msg=ERR_UNEXPECTED_NUM_FACES)
- self.assertIsNone(faces[0].landmarks, msg=ERR_UNEXPECTED_LANDMARKS)
-
- self.sdk.get_landmarks(faces)
- self.assertIsNotNone(faces[0].landmarks, msg=ERR_MISSING_LANDMARKS)
- self.assertTrue(is_json_serializable(faces[0].asdict()), msg=ERR_JSON_FACE)
-
- def test_get_landmarks_from_bounding_box(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- detection_result = self.sdk.get_bounding_boxes(imgs)
- faces = detection_result.faces
- self.assertEqual(len(faces), 1, msg=ERR_UNEXPECTED_NUM_FACES)
- self.assertIsNotNone(faces[0].bounding_box, msg=ERR_MISSING_BBOX)
- self.assertIsNone(faces[0].landmarks, msg=ERR_UNEXPECTED_LANDMARKS)
-
- bbox = faces[0].bounding_box
- bounding_box = BoundingBox(
- bbox.origin.x,
- bbox.origin.y,
- bbox.origin.x + bbox.width,
- bbox.origin.y + bbox.height,
- )
- result = self.sdk.get_landmarks_from_bounding_boxes(imgs[0], [bounding_box])
-
- self.assertIsNotNone(result.faces[0].landmarks, msg=ERR_MISSING_LANDMARKS)
- self.assertTrue(
- is_json_serializable(result.faces[0].asdict()), msg=ERR_JSON_FACE
- )
-
- def test_get_embeddings(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- detection_result = self.sdk.get_faces(imgs, qualities=True, landmarks=True)
- faces = detection_result.faces
-
- self.sdk.get_embeddings(faces)
- self.assertEqual(len(faces), 1, msg=ERR_UNEXPECTED_NUM_FACES)
-
- f = faces[0]
- self.assertIsNotNone(f.bounding_box, msg=ERR_MISSING_BBOX)
- self.assertIsNotNone(f.landmarks, msg=ERR_MISSING_LANDMARKS)
- self.assertIsNotNone(f.embedding, msg=ERR_MISSING_EMBEDDING)
- self.assertEqual(
- f.embedding.scoring_mode,
- ScoringMode.EnhancedEmbedding,
- msg=ERR_INVALID_SCORING_MODE,
- )
- self.assertTrue(is_json_serializable(f.asdict()), msg=ERR_JSON_FACE)
-
- def test_get_embedding_from_landmarks(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- detection_result = self.sdk.get_faces(imgs, embeddings=True)
-
- faces = detection_result.faces
- f = faces[0]
- landmarks = f.landmarks
-
- embeddings = self.sdk.get_embeddings_from_landmarks(
- imgs[0], [landmarks, landmarks]
- )
- self.assertEqual(len(embeddings), 2)
- embedding = embeddings[0]
-
- self.assertTrue(embedding.scoring_mode == ScoringMode.EnhancedEmbedding)
- similarity = SDK.get_similarity(f.embedding, embedding)
- self.assertAlmostEqual(similarity, 1.51, delta=0.01)
-
- def test_check_for_mask(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, "woman-wearing-mask.jpg"))]
- detection_result = self.sdk.get_bounding_boxes(imgs)
- faces = detection_result.faces
-
- self.assertEqual(len(faces), 1, msg=ERR_UNEXPECTED_NUM_FACES)
-
- self.sdk.get_masks(faces)
- f = faces[0]
- self.assertIsNotNone(f.mask, msg=ERR_MISSING_MASK_PROB)
- self.assertTrue(f.mask >= MASK_SCORE, msg=ERR_INVALID_MASK_PROB)
- self.assertTrue(is_json_serializable(f.asdict()), msg=ERR_JSON_FACE)
-
- def test_check_for_no_mask(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- detection_result = self.sdk.get_bounding_boxes(imgs)
- faces = detection_result.faces
-
- self.assertEqual(len(faces), 1, msg=ERR_UNEXPECTED_NUM_FACES)
-
- self.sdk.get_masks(faces)
- f = faces[0]
- self.assertIsNotNone(f.mask, msg=ERR_MISSING_MASK_PROB)
- self.assertTrue(f.mask < MAX_NO_MASK_SCORE, msg=ERR_INVALID_MASK_PROB)
- self.assertTrue(is_json_serializable(f.asdict()), msg=ERR_JSON_FACE)
-
- def test_check_for_no_mask_in_many_faces(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_MANYFACES))]
- detection_result = self.sdk.get_bounding_boxes(imgs)
- faces = detection_result.faces
-
- self.assertTrue(len(faces) > 1, msg=ERR_UNEXPECTED_NUM_FACES)
-
- self.sdk.get_masks(faces)
-
- for f in faces:
- self.assertIsNotNone(f.mask, msg=ERR_MISSING_MASK_PROB)
- self.assertTrue(f.mask < MAX_NO_MASK_SCORE, msg=ERR_INVALID_MASK_PROB)
-
- def test_get_most_prominent_face_index_oneface(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- infer_result = self.sdk.get_bounding_boxes(imgs)
-
- self.assertTrue(
- len(infer_result.image_inferences) == 1, msg=ERR_UNEXPECTED_NUM_INFERENCES
- )
- self.assertNotEqual(len(infer_result.faces), 0, msg=ERR_UNEXPECTED_NUM_FACES)
-
- infer_image = infer_result.image_inferences[0]
- index = infer_image.most_prominent_face_index()
- self.assertTrue(index == 0, msg=ERR_INVALID_MPF)
-
- def test_get_most_prominent_face_index_manyfaces(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_MANYFACES))]
- infer_result = self.sdk.get_bounding_boxes(imgs)
-
- self.assertTrue(
- len(infer_result.image_inferences) == 1, msg=ERR_UNEXPECTED_NUM_INFERENCES
- )
- self.assertTrue(len(infer_result.faces) > 0, msg=ERR_UNEXPECTED_NUM_FACES)
-
- infer_image = infer_result.image_inferences[0]
- index = infer_image.most_prominent_face_index()
- self.assertTrue(index == 3, msg=ERR_INVALID_MPF)
-
- def test_get_most_prominent_face_index_noface(self):
- infer_image = ImageInferenceData(128, 128)
- index = infer_image.most_prominent_face_index()
- self.assertTrue(index == -1, msg=ERR_INVALID_MPF)
-
- def test_get_most_prominent_face_index_invalid_image_dims(self):
- infer_image = ImageInferenceData(0, 0)
- index = infer_image.most_prominent_face_index()
- self.assertTrue(index == -1, msg=ERR_INVALID_MPF)
-
- def test_scoring_same_image(self):
- img = load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))
- faces = self.sdk.get_faces([img, img], embeddings=True).faces
-
- similarity = SDK.get_similarity(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(similarity, 1.51, delta=0.01)
-
- confidence = self.sdk.get_confidence(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(confidence, 1.0, delta=0.01)
-
- match_score = SDK.get_match_score(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(match_score, 951, delta=2)
-
- def test_scoring_same_identity(self):
- img1 = load_image(os.path.join(ASSETS_PATH, IMG_IDENTITY1_FACE1))
- img2 = load_image(os.path.join(ASSETS_PATH, IMG_IDENTITY1_FACE2))
- faces = self.sdk.get_faces([img1, img2], embeddings=True).faces
-
- similarity = SDK.get_similarity(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(similarity, 0.788, delta=0.001)
-
- confidence = self.sdk.get_confidence(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(confidence, 1.0, delta=0.01)
-
- match_score = SDK.get_match_score(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(match_score, 788, delta=2)
-
- def test_scoring_diff_identity(self):
- img1 = load_image(os.path.join(ASSETS_PATH, IMG_IDENTITY1_FACE1))
- img2 = load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))
- faces = self.sdk.get_faces([img1, img2], embeddings=True).faces
-
- similarity = SDK.get_similarity(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(similarity, 0.05, delta=0.01)
-
- confidence = self.sdk.get_confidence(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(confidence, 0, delta=0.01)
-
- match_score = SDK.get_match_score(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(match_score, 403, delta=2)
-
- def test_get_confidence_invalid_faces(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_MANYFACES))]
- faces = self.sdk.get_faces(imgs).faces
- with self.assertRaises(InvalidInputException):
- self.sdk.get_confidence(faces[0].embedding, faces[1].embedding)
-
- def test_get_similarity_no_embedding(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_MANYFACES))]
- faces = self.sdk.get_faces(imgs).faces
- with self.assertRaises(InvalidInputException):
- SDK.get_similarity(faces[0].embedding, faces[1].embedding)
-
- def test_multi_inference_images(self):
- imgs = [
- load_image(os.path.join(ASSETS_PATH, IMG_MANYFACES)),
- load_image(os.path.join(ASSETS_PATH, IMG_MANYFACES)),
- load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE)),
- ]
- infer_result = self.sdk.get_bounding_boxes(imgs)
-
- self.assertTrue(
- len(infer_result.image_inferences) == 3, msg=ERR_UNEXPECTED_NUM_INFERENCES
- )
- self.assertTrue(
- len(infer_result.image_inferences[0].faces)
- + len(infer_result.image_inferences[1].faces)
- + len(infer_result.image_inferences[2].faces)
- == len(infer_result.faces),
- msg="inference image data mismatches faces len",
- )
-
- def test_inference_image_data(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- infer_result = self.sdk.get_bounding_boxes(imgs)
- faces = infer_result.faces
-
- self.assertEqual(len(faces), 1, msg=ERR_UNEXPECTED_NUM_FACES)
-
- self.sdk.get_qualities(faces)
- self.assertAlmostEqual(faces[0].quality, 0.895, delta=0.001)
- self.assertTrue(
- infer_result.image_inferences[0].faces[0].quality == faces[0].quality,
- msg="image inference data and face mismatch",
- )
-
- def test_check_embedding(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- ground_truth = np.load(
- os.path.join(ASSETS_PATH, "oneface_gen5_fast_enhanced_embedding.npy")
- )
-
- detection_result = self.sdk.get_faces(imgs, qualities=True, landmarks=True)
- faces = detection_result.faces
-
- self.sdk.get_embeddings(faces)
- self.assertEqual(len(faces), 1, msg=ERR_UNEXPECTED_NUM_FACES)
-
- f = faces[0]
- self.assertEqual(
- len(f.embedding.data), len(ground_truth), msg="Mismatched embedding size"
- )
- self.assertTrue(
- np.allclose(f.embedding.data, ground_truth, rtol=0, atol=35e-4),
- msg="Invalid embedding value",
- )
- self.assertTrue(is_json_serializable(f.asdict()), msg=ERR_JSON_FACE)
-
- def test_get_embedding_from_prepared_image(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- detection_result = self.sdk.get_faces(imgs, embeddings=True)
- faces = detection_result.faces
- f = faces[0]
-
- reco_img = load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE_RECO_INPUT_IMG))
- embedding = self.sdk.get_embedding_from_prepared_image(reco_img)
- self.assertTrue(len(embedding.data) == EXPECTED_ENHANCED_EMBED_LEN)
- self.assertTrue(embedding.scoring_mode == scoring_mode)
- self.assertTrue(
- np.allclose(f.embedding.data, embedding.data, rtol=0, atol=0.001),
- msg="Invalid embedding value",
- )
-
- def test_get_embedding_from_prepared_image_none(self):
- with self.assertRaises(InvalidInputException):
- self.sdk.get_embedding_from_prepared_image(None)
-
- def assert_faces(self, faces):
- for f in faces:
- self.assertIsNotNone(f.bounding_box, msg=ERR_MISSING_BBOX)
- self.assertIsNotNone(f.landmarks, msg=ERR_MISSING_LANDMARKS)
- self.assertIsNotNone(f.quality, msg="missing quality")
- self.assertIsNotNone(f.acceptability, msg="missing acceptability")
- self.assertIsNotNone(
- f.recognition_input_image, msg="missing recognition input image"
- )
- self.assertIsNotNone(
- f.landmarks_input_image, msg="missing landmarks input image"
- )
- self.assertIsNotNone(
- f.landmarks_input_bounding_box,
- msg="missing landmarks input bounding box",
- )
- self.assertIsNotNone(f.alignment_image, msg="missing alignment image")
- self.assertIsNotNone(
- f.alignment_bounding_box, msg="missing alignment bounding box"
- )
- self.assertIsNotNone(f.embedding, msg=ERR_MISSING_EMBEDDING)
- self.assertEqual(
- f.embedding.scoring_mode,
- ScoringMode.EnhancedEmbedding,
- msg=ERR_INVALID_SCORING_MODE,
- )
- self.assertTrue(
- len(f.embedding.data) in Embedding.ENHANCED_SIZES,
- msg=ERR_INVALID_EMBEDDING_SIZE,
- )
- self.assertIsNone(f.ages, msg=ERR_UNEXPECTED_AGES)
- self.assertIsNone(f.genders, msg=ERR_UNEXPECTED_GENDERS)
- self.assertIsNone(f.age, msg=ERR_UNEXPECTED_AGE)
- self.assertIsNone(f.gender, msg=ERR_UNEXPECTED_GENDER)
- self.assertTrue(is_json_serializable(f.asdict()), msg=ERR_JSON_FACE)
diff --git a/modules/paravision/recognition/tests/test_sdk_standard.py b/modules/paravision/recognition/tests/test_sdk_standard.py
deleted file mode 100644
index 72881ec56..000000000
--- a/modules/paravision/recognition/tests/test_sdk_standard.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import os
-import numpy as np
-from unittest import TestCase
-
-from ..sdk import SDK
-from ..engine import Engine
-from ..utils import load_image
-from ..types import ScoringMode, Embedding
-
-from .constants import (
- IMG_ONEFACE,
- IMG_IDENTITY1_FACE1,
- IMG_IDENTITY1_FACE2,
- IMG_ONEFACE_RECO_INPUT_IMG,
- ERR_UNEXPECTED_NUM_FACES,
- ERR_UNEXPECTED_NUM_INFERENCES,
- ERR_MISSING_EMBEDDING,
- ERR_INVALID_SCORING_MODE,
- ERR_INVALID_EMBEDDING_SIZE,
- EXPECTED_STANDARD_EMBED_LEN,
-)
-
-ASSETS_PATH = os.path.join(os.path.dirname(__file__), "assets")
-engine_default = None
-scoring_mode = None
-sdk = None
-
-
-class TestSDK(TestCase):
- @classmethod
- def setUpClass(cls):
- global sdk
- global engine_default
- global scoring_mode
-
- engine_default = Engine.OPENVINO
- scoring_mode = ScoringMode.StandardEmbedding
-
- sdk = SDK(engine=engine_default, settings={"scoring_mode": scoring_mode})
-
- def setUp(self):
- self.sdk = sdk
-
- def test_get_faces_with_embeddings(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- detection_result = self.sdk.get_faces(imgs, embeddings=True)
-
- faces = detection_result.faces
- self.assertEqual(len(faces), 1, msg=ERR_UNEXPECTED_NUM_FACES)
-
- image_inferences = detection_result.image_inferences
- self.assertEqual(len(image_inferences), 1, msg=ERR_UNEXPECTED_NUM_INFERENCES)
-
- self.assert_embeddings(faces)
-
- def test_get_embedding_from_landmarks(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- detection_result = self.sdk.get_faces(imgs, embeddings=True)
-
- faces = detection_result.faces
- f = faces[0]
- landmarks = f.landmarks
-
- embeddings = self.sdk.get_embeddings_from_landmarks(imgs[0], [landmarks, landmarks])
- self.assertEqual(len(embeddings), 2)
- embedding = embeddings[0]
-
- self.assertTrue(embedding.scoring_mode == ScoringMode.StandardEmbedding)
- similarity = SDK.get_similarity(f.embedding, embedding)
- self.assertAlmostEqual(similarity, 4.0, delta=0.01)
-
- def test_scoring_same_image(self):
- img = load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))
- faces = self.sdk.get_faces([img, img], embeddings=True).faces
-
- similarity = SDK.get_similarity(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(similarity, 4.0, delta=0.01)
-
- confidence = self.sdk.get_confidence(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(confidence, 1.0, delta=0.01)
-
- match_score = SDK.get_match_score(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(match_score, 957, delta=1)
-
- def test_scoring_same_identity(self):
- img1 = load_image(os.path.join(ASSETS_PATH, IMG_IDENTITY1_FACE1))
- img2 = load_image(os.path.join(ASSETS_PATH, IMG_IDENTITY1_FACE2))
- faces = self.sdk.get_faces([img1, img2], embeddings=True).faces
-
- similarity = SDK.get_similarity(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(similarity, 3.58, delta=0.01)
-
- confidence = self.sdk.get_confidence(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(confidence, 1.0, delta=0.01)
-
- match_score = SDK.get_match_score(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(match_score, 903, delta=2)
-
- def test_scoring_diff_identity(self):
- img1 = load_image(os.path.join(ASSETS_PATH, IMG_IDENTITY1_FACE1))
- img2 = load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))
- faces = self.sdk.get_faces([img1, img2], embeddings=True).faces
-
- similarity = SDK.get_similarity(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(similarity, 1.85, delta=0.01)
-
- confidence = self.sdk.get_confidence(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(confidence, 0, delta=0.01)
-
- match_score = SDK.get_match_score(faces[0].embedding, faces[1].embedding)
- self.assertAlmostEqual(match_score, 198, delta=2)
-
- def test_get_embedding_from_prepared_image(self):
- imgs = [load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE))]
- detection_result = self.sdk.get_faces(
- imgs, qualities=True, landmarks=True, embeddings=True
- )
- faces = detection_result.faces
- f = faces[0]
-
- reco_img = load_image(os.path.join(ASSETS_PATH, IMG_ONEFACE_RECO_INPUT_IMG))
- embedding = self.sdk.get_embedding_from_prepared_image(reco_img)
- self.assertTrue(len(embedding.data) == EXPECTED_STANDARD_EMBED_LEN)
- self.assertTrue(embedding.scoring_mode == scoring_mode)
- self.assertTrue(
- np.allclose(f.embedding.data, embedding.data, rtol=0, atol=0.001),
- msg="Invalid embedding value",
- )
-
- def assert_embeddings(self, faces):
- for f in faces:
- self.assertIsNotNone(f.embedding, msg=ERR_MISSING_EMBEDDING)
- self.assertEqual(
- f.embedding.scoring_mode,
- ScoringMode.StandardEmbedding,
- msg=ERR_INVALID_SCORING_MODE,
- )
- self.assertTrue(
- len(f.embedding.data) in Embedding.STANDARD_SIZES,
- msg=ERR_INVALID_EMBEDDING_SIZE,
- )
diff --git a/modules/paravision/recognition/tests/test_transform.py b/modules/paravision/recognition/tests/test_transform.py
deleted file mode 100644
index d8e07e460..000000000
--- a/modules/paravision/recognition/tests/test_transform.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import numpy as np
-
-from unittest import TestCase
-
-from .._utils import compute_transform
-
-
-class TestTransform(TestCase):
- def test_transform(self):
- src_points = [
- [146.08132502, 155.9912228],
- [218.04209101, 153.17409003],
- [176.5086686, 207.03067255],
- [153.90101734, 240.53104055],
- [214.96274501, 237.63263655],
- ]
-
- dst_points = [
- [38.2946, 51.6963],
- [73.5318, 51.5014],
- [56.0252, 71.7366],
- [41.5493, 92.3655],
- [70.7299, 92.2041],
- ]
-
- trans = compute_transform(src_points, dst_points)
-
- out = np.asarray(
- [
- [4.79823508e-01, -1.35817363e-02, -2.85523114e01],
- [1.35817363e-02, 4.79823508e-01, -2.59931548e01],
- ]
- )
-
- self.assertTrue(
- (np.isclose(trans.flatten(), out.flatten()).all()),
- msg="The transform wasn't computed sucessfully",
- )
diff --git a/modules/paravision/recognition/tests/test_types.py b/modules/paravision/recognition/tests/test_types.py
deleted file mode 100644
index 09d3a280b..000000000
--- a/modules/paravision/recognition/tests/test_types.py
+++ /dev/null
@@ -1,95 +0,0 @@
-from unittest import TestCase
-
-import numpy as np
-
-from .utils import is_json_serializable
-from ..types import BoundingBox, Face, Embedding, Landmarks, Point, ScoringMode
-
-
-class TestBoundingBox(TestCase):
- def setUp(self):
- self.bb = BoundingBox(1.1, 2.2, 3.3, 4.4)
-
- def test_as_dict(self):
- d = self.bb.asdict()
- props = ["origin", "width", "height"]
-
- for p in props:
- self.assertIn(p, d)
-
- self.assertTrue(is_json_serializable(d))
-
-
-class TestFaceWithStandardScoringMode(TestCase):
- def setUp(self):
- self.face = Face(
- bounding_box=BoundingBox(*np.random.rand(4)),
- )
- self.face.quality = 0.5
- self.face.embedding = Embedding(
- np.random.rand(512), ScoringMode.StandardEmbedding
- )
- landmarks = np.random.rand(5, 2)
- self.face.landmarks = Landmarks(*landmarks)
-
- def test_as_dict(self):
- d = self.face.asdict()
-
- self.assertIn("quality", d)
- self.assertEqual(d["quality"], 0.5)
-
- props = ["bounding_box", "landmarks", "embedding"]
- for p in props:
- self.assertIn(p, d)
- self.assertIsInstance(d[p], dict)
-
- self.assertTrue(is_json_serializable(d))
-
-
-class TestFaceWithEnhancedScoringMode(TestCase):
- def setUp(self):
- self.face = Face(
- bounding_box=BoundingBox(*np.random.rand(4)),
- )
- self.face.embedding = Embedding(
- np.random.rand(513), ScoringMode.EnhancedEmbedding
- )
-
- def test_as_dict(self):
- d = self.face.asdict()
-
- props = ["bounding_box", "embedding"]
- for p in props:
- self.assertIn(p, d)
- self.assertIsInstance(d[p], dict)
-
- self.assertTrue(is_json_serializable(d))
-
-
-class TestLandmarks(TestCase):
- def setUp(self):
- self.landmarks = Landmarks((0, 1), (2, 3), (4, 5), (6, 7), (8, 9))
-
- def test_as_dict(self):
- d = self.landmarks.asdict()
-
- props = ["left_eye", "right_eye", "nose", "left_mouth", "right_mouth"]
- for p in props:
- self.assertIn(p, d)
- self.assertIsInstance(d[p], dict)
-
- self.assertTrue(is_json_serializable(d))
-
-
-class TestPoint(TestCase):
- def setUp(self):
- self.p = Point(1, 2)
-
- def test_as_dict(self):
- d = self.p.asdict()
-
- self.assertIn("x", d)
- self.assertIn("y", d)
- self.assertEqual(d["x"], 1)
- self.assertEqual(d["y"], 2)
- self.assertTrue(is_json_serializable(d))
diff --git a/modules/paravision/recognition/tests/utils.py b/modules/paravision/recognition/tests/utils.py
deleted file mode 100644
index 19a059a17..000000000
--- a/modules/paravision/recognition/tests/utils.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import json
-
-
-def is_json_serializable(x):
- try:
- json.dumps(x)
- return True
- except TypeError:
- return False
diff --git a/modules/paravision/recognition/types.py b/modules/paravision/recognition/types.py
deleted file mode 100644
index 6b32c3c3c..000000000
--- a/modules/paravision/recognition/types.py
+++ /dev/null
@@ -1,624 +0,0 @@
-from typing import Any, Dict, List, Optional, Tuple, Union
-import numpy as np
-from enum import IntEnum
-
-from .exceptions import InvalidInputException
-
-AGE_LABELS = ["2-12", "13-19", "20-30", "31-40", "41-50", "51-60", "60+"]
-GENDER_LABELS = ["male", "female"]
-
-
-class Point:
- """
- A point within an image, represented by x- and y-coordinates.
-
- Attributes
- ----------
- x : float
- The x-coordinate.
- y : float
- The y-coordinate.
- """
-
- def __init__(self, x: float, y: float):
- self._x = x
- self._y = y
-
- @property
- def x(self):
- return self._x
-
- @x.setter
- def x(self, x: float):
- self._x = x
-
- @property
- def y(self):
- return self._y
-
- @y.setter
- def y(self, y: float):
- self._y = y
-
- def __iter__(self):
- yield self.x
- yield self.y
-
- def __repr__(self):
- return f""
-
- def asdict(self):
- """Convert this object to a dictionary"""
- return {"x": self._x, "y": self._y}
-
- def astuple(self):
- """Convert this object to a tuple"""
- return self._x, self._y
-
-
-PointLike = Union[Point, np.ndarray, List[float], Tuple[float, float]]
-
-
-class ScoringMode(IntEnum):
- StandardEmbedding = 1
- EnhancedEmbedding = 2
-
-
-class BoundingBox:
- """
- A bounding box, represented by origin(top-left point), width, and height.
-
- Attributes
- ----------
- origin : Point
- Point object including coordinates of the top-left corner of the rectangle.
- width : float
- The width of the rectangle.
- height : float
- The height of the rectangle.
- _score : float
- The score for confidence for a face in the bounding box
- """
-
- def __init__(self, x1: float, y1: float, x2: float, y2: float):
- self._origin = Point(x1, y1)
- self._width = x2 - x1
- self._height = y2 - y1
- self._score = None
-
- @property
- def origin(self):
- return self._origin
-
- @origin.setter
- def origin(self, origin: Point):
- self._origin = origin
-
- @property
- def width(self):
- return self._width
-
- @width.setter
- def width(self, width: float):
- self._width = width
-
- @property
- def height(self):
- return self._height
-
- @height.setter
- def height(self, height: float):
- self._height = height
-
- @property
- def score(self):
- return self._score
-
- @score.setter
- def score(self, score):
- self._score = score
-
- def __repr__(self):
- return f""
-
- def asdict(self):
- """Convert this object to a dictionary"""
- return {
- "origin": self._origin.asdict(),
- "width": self._width,
- "height": self._height,
- }
-
- def astuple(self) -> Tuple[float, float, float, float]:
- """Convert this object to a tuple"""
- x, y = self._origin.astuple()
- return x, y, self._width, self._height
-
-
-class Landmarks:
- """
- A set of facial landmarks, represented by Points.
-
- Attributes
- ----------
- left_eye : Point
- The center of the left eye.
- right_eye : Point
- The center of the right eye.
- nose : Point
- The tip of the nose.
- left_mouth : Point
- The left corner of the mouth.
- right_mouth : Point
- The right corner of the mouth.
- """
-
- def __init__(
- self,
- left_eye: PointLike,
- right_eye: PointLike,
- nose: PointLike,
- left_mouth: PointLike,
- right_mouth: PointLike,
- ):
- self._left_eye = Point(*left_eye)
- self._right_eye = Point(*right_eye)
- self._nose = Point(*nose)
- self._left_mouth = Point(*left_mouth)
- self._right_mouth = Point(*right_mouth)
-
- @property
- def left_eye(self):
- return self._left_eye
-
- @left_eye.setter
- def left_eye(self, left_eye: PointLike):
- self._left_eye = Point(*left_eye)
-
- @property
- def right_eye(self):
- return self._right_eye
-
- @right_eye.setter
- def right_eye(self, right_eye: PointLike):
- self._right_eye = Point(*right_eye)
-
- @property
- def nose(self):
- return self._nose
-
- @nose.setter
- def nose(self, nose: PointLike):
- self._nose = Point(*nose)
-
- @property
- def left_mouth(self):
- return self._left_mouth
-
- @left_mouth.setter
- def left_mouth(self, left_mouth: PointLike):
- self._left_mouth = Point(*left_mouth)
-
- @property
- def right_mouth(self):
- return self._right_mouth
-
- @right_mouth.setter
- def right_mouth(self, right_mouth: PointLike):
- self._right_mouth = Point(*right_mouth)
-
- def __repr__(self):
- return (
- ""
- )
-
- def asdict(self):
- """Convert this object to a dictionary"""
- return {
- "left_eye": self._left_eye.asdict(),
- "right_eye": self._right_eye.asdict(),
- "nose": self._nose.asdict(),
- "left_mouth": self._left_mouth.asdict(),
- "right_mouth": self._right_mouth.asdict(),
- }
-
- def astuple(self):
- """Convert this object to a tuple"""
- return (
- self._left_eye.astuple(),
- self._right_eye.astuple(),
- self._nose.astuple(),
- self._left_mouth.astuple(),
- self._right_mouth.astuple(),
- )
-
-
-class Embedding:
- """
- A numerical representation of a face found in an image.
-
- Attributes
- ----------
- data : numpy.ndarray
- The embedding data representing a face.
- """
-
- STANDARD_SIZES = np.array([256, 512, 1024])
- ENHANCED_SIZES = STANDARD_SIZES + 1
-
- def __init__(
- self,
- data: np.ndarray,
- scoring_mode: ScoringMode = ScoringMode.EnhancedEmbedding,
- ):
- self._validate_data(data, scoring_mode)
- self._data = data
- self._scoring_mode = scoring_mode
-
- @property
- def data(self) -> np.ndarray:
- if (
- self._scoring_mode == ScoringMode.StandardEmbedding
- and len(self._data) in self.ENHANCED_SIZES
- ):
- return self._data[:-1]
- return self._data
-
- @property
- def scoring_mode(self):
- return self._scoring_mode
-
- def asdict(self):
- return {
- "data": self._data.tolist(),
- "scoring_mode": self._scoring_mode.name,
- }
-
- @classmethod
- def _validate_data(cls, data: np.ndarray, scoring_mode: ScoringMode):
- if scoring_mode == ScoringMode.EnhancedEmbedding:
- if len(data) not in cls.ENHANCED_SIZES:
- raise InvalidInputException(
- f"Invalid embedding size, enhanced embedding size must be one of {cls.ENHANCED_SIZES}"
- )
- elif scoring_mode == ScoringMode.StandardEmbedding:
- if (
- len(data) not in cls.ENHANCED_SIZES
- and len(data) not in cls.STANDARD_SIZES
- ):
- raise InvalidInputException(
- f"Invalid embedding size, standard embedding size must be one of "
- f"{cls.ENHANCED_SIZES + cls.STANDARD_SIZES}"
- )
- else:
- raise InvalidInputException("Invalid scoring mode")
-
-
-class BaseAttributes:
- """
- This is an empty class inherited by the AttributesSDK.
- For internal use only.
- Customers should use the implementation from Attributes SDK:
-
- from paravision.attributes.types import get_attributes, Attributes
- attr: Attributes = get_attributes(face)
- """
-
-
-class Face:
- """
- A face, minimally represented by a bounding box.
-
- Attributes
- ----------
- bounding_box : BoundingBox
- The bounding box of the face.
-
- landmarks : Landmarks
- The locations of various parts of the face.
- embedding : Embedding
- The embedding representing the face.
- ages: numpy.ndarray
- The probability distribution over the given age groups.
- genders: numpy.ndarry
- The probability distribution over the given genders.
- quality : float
- An overall assessment of how acceptable the face is for facial
- recognition. Overall range of [0, 1], "acceptable" quality is >= 0.15.
- acceptability: float
- The assessment of the acceptability of the face. Overall range [0, 1]
- mask: float
- The probability of a face wearing mask. Overall range [0, 1]
- recognition_input_image, alignment_input_image, landmarks_input_image: numpy.ndarray
- Images used at different stages of the detection pipeline.
- alignment_bounding_box, landmarks_input_bounding_box: BoundingBox
- The bounding boxes that are associated to alignment_input_image and landmarks_input_image
- """
-
- def __init__(self, bounding_box: Optional[BoundingBox] = None):
- self._bounding_box = bounding_box
-
- self._landmarks = None
- self._embedding = None
-
- self._ages = None
- self._genders = None
-
- self._quality = None
- self._acceptability = None
- self._mask = None
-
- self._recognition_input_image = None
- self._landmarks_input_image = None
- self._landmarks_input_bounding_box = None
- self._alignment_image = None
- self._alignment_bounding_box = None
- self._attributes = None
-
- @property
- def bounding_box(self):
- return self._bounding_box
-
- @bounding_box.setter
- def bounding_box(self, bbox: BoundingBox):
- self._bounding_box = bbox
-
- @property
- def landmarks(self):
- return self._landmarks
-
- @landmarks.setter
- def landmarks(self, landmarks: Landmarks):
- self._landmarks = landmarks
-
- @property
- def embedding(self):
- return self._embedding
-
- @embedding.setter
- def embedding(self, embedding: Embedding):
- self._embedding = embedding
-
- @property
- def age(self):
- return None if self._ages is None else AGE_LABELS[np.argmax(self._ages)]
-
- @property
- def ages(self):
- return self._ages
-
- @ages.setter
- def ages(self, ages: Union[List[float], np.ndarray]):
- self._ages: Optional[List[float]] = [*ages]
-
- @property
- def gender(self):
- return (
- None if self._genders is None else GENDER_LABELS[np.argmax(self._genders)]
- )
-
- @property
- def genders(self):
- return self._genders
-
- @genders.setter
- def genders(self, genders: Union[List[float], np.ndarray]):
- self._genders: Optional[List[float]] = [*genders]
-
- @property
- def quality(self):
- return self._quality
-
- @quality.setter
- def quality(self, quality: float):
- self._quality = quality
-
- @property
- def acceptability(self):
- return self._acceptability
-
- @acceptability.setter
- def acceptability(self, acceptability: float):
- self._acceptability = acceptability
-
- @property
- def mask(self):
- return self._mask
-
- @mask.setter
- def mask(self, mask: float):
- self._mask = mask
-
- @property
- def recognition_input_image(self):
- return self._recognition_input_image
-
- @recognition_input_image.setter
- def recognition_input_image(self, recognition_input_image: np.ndarray):
- self._recognition_input_image = recognition_input_image
-
- @property
- def landmarks_input_image(self):
- return self._landmarks_input_image
-
- @landmarks_input_image.setter
- def landmarks_input_image(self, landmarks_input_image: np.ndarray):
- self._landmarks_input_image = landmarks_input_image
-
- @property
- def landmarks_input_bounding_box(self):
- return self._landmarks_input_bounding_box
-
- @landmarks_input_bounding_box.setter
- def landmarks_input_bounding_box(self, landmarks_input_bbox: BoundingBox):
- self._landmarks_input_bounding_box = landmarks_input_bbox
-
- @property
- def alignment_image(self):
- return self._alignment_image
-
- @alignment_image.setter
- def alignment_image(self, alignment_image: np.ndarray):
- self._alignment_image = alignment_image
-
- @property
- def alignment_bounding_box(self):
- return self._alignment_bounding_box
-
- @alignment_bounding_box.setter
- def alignment_bounding_box(self, alignment_bbox: BoundingBox):
- self._alignment_bounding_box = alignment_bbox
-
- @property
- def attributes(self):
- return self._attributes
-
- @attributes.setter
- def attributes(self, attributes: BaseAttributes):
- self._attributes = attributes
-
- def __repr__(self):
- return "" % (str(self._bounding_box),)
-
- def asdict(self):
- """Convert this object to a dictionary representation."""
- if self._bounding_box is None:
- return {}
-
- face: Dict[str, Any] = {"bounding_box": self._bounding_box.asdict()}
-
- if self._landmarks is not None:
- face["landmarks"] = self._landmarks.asdict()
-
- if self._embedding is not None:
- face["embedding"] = self._embedding.asdict()
-
- if self._quality is not None:
- face["quality"] = self._quality
-
- if self._acceptability is not None:
- face["acceptability"] = self._acceptability
-
- if self._mask is not None:
- face["mask"] = self._mask
-
- return face
-
-
-class ImageInferenceData:
- """
- The result of running get faces pipeline for a single image
-
- Attributes
- ----------
- faces : List
- A list of Face objects detected in the image
- width : Int
- width of the inference image
- height : Int
- height of the inference image
- """
-
- def __init__(self, width: int, height: int):
- self._width = width
- self._height = height
- self._faces: List[Face] = []
-
- @property
- def width(self):
- return self._width
-
- @width.setter
- def width(self, width):
- self._width = width
-
- @property
- def height(self):
- return self._height
-
- @height.setter
- def height(self, height):
- self._height = height
-
- @property
- def faces(self):
- return self._faces
-
- @faces.setter
- def faces(self, faces):
- self._faces = faces
-
- def __repr__(self):
- return f""
-
- def most_prominent_face_index(self) -> int:
- if self._height <= 0 or self._width <= 0 or len(self._faces) == 0:
- return -1
-
- face_areas = []
-
- for face in self._faces:
- bb = face.bounding_box
-
- if bb is None:
- continue
-
- x1 = max(min(bb.origin.x, self._width), 0)
- y1 = max(min(bb.origin.y, self._height), 0)
-
- x2 = max(min((bb.origin.x + bb.width), self._width), 0)
- y2 = max(min((bb.origin.y + bb.height), self._height), 0)
-
- area = (x2 - x1) * (y2 - y1)
- face_areas.append(area)
-
- return int(np.argmax(face_areas)) if face_areas else -1
-
- def asdict(self):
- """Convert this object to a dictionary"""
- return {"faces": self._faces}
-
-
-class InferenceResult:
- """
- The result of running the get faces pipeline on a group of images
-
- Attributes
- ----------
- faces : List
- A list of Face objects detected in all images
- image_inferences : List
- A list of detection results grouped by an image
- """
-
- def __init__(self, image_inferences: List[ImageInferenceData]):
- self._image_inferences = image_inferences
- self._faces: List[Face] = []
- for image_inference in image_inferences:
- self._faces.extend(image_inference.faces)
-
- @property
- def image_inferences(self):
- return self._image_inferences
-
- @image_inferences.setter
- def image_inferences(self, image_inferences: List[ImageInferenceData]):
- self._image_inferences = image_inferences
-
- @property
- def faces(self):
- return self._faces
-
- @faces.setter
- def faces(self, faces: List[Face]):
- self._faces = faces
-
- def __repr__(self):
- return f""
-
- def asdict(self):
- """Convert this object to a dictionary"""
- return {"image_inferences": self._image_inferences}
diff --git a/modules/paravision/recognition/types/__init__.pyi b/modules/paravision/recognition/types/__init__.pyi
new file mode 100644
index 000000000..dd850b216
--- /dev/null
+++ b/modules/paravision/recognition/types/__init__.pyi
@@ -0,0 +1,1373 @@
+from __future__ import annotations
+import paravision.recognition.types
+import typing
+import numpy
+_Shape = typing.Tuple[int, ...]
+
+__all__ = [
+ "BaseAttributes",
+ "BoundingBox",
+ "Embedding",
+ "Engine",
+ "Face",
+ "GPUConfig",
+ "Image",
+ "ImageInferenceData",
+ "ImageManipulator",
+ "ImageSource",
+ "InferenceResult",
+ "Landmarks",
+ "Point",
+ "ScoringMode",
+ "Settings",
+ "ValidnessCheck",
+ "ValidnessFeedback",
+ "ValidnessResult"
+]
+
+
+class BaseAttributes():
+ """
+ This is an empty class inherited by the AttributesSDK.
+ For internal use only.
+ Customers should use the implementation from Attributes SDK:
+
+ from paravision.attributes.types import get_attributes, Attributes
+ attr: Attributes = get_attributes(face)
+ """
+ pass
+
+
+class BoundingBox():
+ """
+ A bounding box, represented by origin(top-left point), width, and height.
+
+ Attributes
+ ----------
+ origin : Point
+ Point object including coordinates of the top-left corner of the rectangle.
+ width : float
+ The width of the rectangle.
+ height : float
+ The height of the rectangle.
+ score : float
+ The score for confidence for a face in the bounding box
+ """
+ def __bool__(self) -> bool: ...
+
+ def __eq__(self, arg0: BoundingBox) -> bool: ...
+
+ @typing.overload
+ def __init__(self, cords: Annotated[list[float], FixedSize(4)]) -> None:
+ """
+ Construct a BoundingBox from x1, y1, x2, and y2.
+ """
+
+ @typing.overload
+ def __init__(self, origin: Point, width: float, height: float) -> None:
+ """
+ Construct a BoundingBox from an origin Point, width, and height.
+ """
+
+ @typing.overload
+ def __init__(self, x1: float, y1: float, x2: float, y2: float) -> None:
+ """
+ Construct a BoundingBox from an array of four floats.
+ """
+
+ def __ne__(self, arg0: BoundingBox) -> bool: ...
+
+ def __repr__(self) -> str: ...
+
+ def asdict(self) -> dict:
+ """
+ Convert this object to a dictionary
+ """
+
+ def empty(self) -> bool:
+ """
+ Check if this object is empty
+ """
+
+ @property
+ def height(self) -> float:
+ """
+ The height of the bounding box.
+
+ :type: float
+ """
+ @height.setter
+ def height(self, arg1: float) -> None:
+ """
+ The height of the bounding box.
+ """
+ @property
+ def origin(self) -> Point:
+ """
+ The origin of the bounding box.
+
+ :type: Point
+ """
+ @origin.setter
+ def origin(self, arg1: float, arg2: float) -> None:
+ """
+ The origin of the bounding box.
+ """
+ @property
+ def score(self) -> float:
+ """
+ The score of the bounding box.
+
+ :type: float
+ """
+ @score.setter
+ def score(self, arg1: float) -> None:
+ """
+ The score of the bounding box.
+ """
+ @property
+ def width(self) -> float:
+ """
+ The width of the bounding box.
+
+ :type: float
+ """
+ @width.setter
+ def width(self, arg1: float) -> None:
+ """
+ The width of the bounding box.
+ """
+ __hash__ = None
+ pass
+
+
+class Embedding():
+ """
+ A numerical representation of a face found in an image.
+
+ Attributes
+ ----------
+ data : numpy.ndarray
+ The embedding data representing a face.
+ """
+ def __bool__(self) -> bool: ...
+
+ def __eq__(self, arg0: Embedding) -> bool: ...
+
+ def __init__(self, data: list[float]) -> None:
+ """
+ Construct an Embedding from a vector of floats.
+ """
+
+ def __ne__(self, arg0: Embedding) -> bool: ...
+
+ def asdict(self, scoring_mode: ScoringMode = ScoringMode.EnhancedEmbedding) -> dict:
+ """
+ Convert this object to a dictionary
+ """
+
+ def data(self, scoring_mode: ScoringMode = ScoringMode.EnhancedEmbedding) -> numpy.ndarray:
+ """
+ The data of the embedding.
+ """
+
+ def empty(self) -> bool:
+ """
+ Check if this object is empty
+ """
+
+ ENHANCED_SIZES = [257, 513, 1025]
+ STANDARD_SIZES = [256, 512, 1024]
+ __hash__ = None
+ pass
+
+
+class Engine():
+ """
+ Members:
+
+ AUTO : Automatically select the engine
+
+ OPENVINO : Use the OpenVINO engine
+
+ TENSORRT : Use the TensorRT engine
+ """
+ def __eq__(self, other: object) -> bool: ...
+
+ def __getstate__(self) -> int: ...
+
+ def __hash__(self) -> int: ...
+
+ def __index__(self) -> int: ...
+
+ def __init__(self, value: int) -> None: ...
+
+ def __int__(self) -> int: ...
+
+ def __ne__(self, other: object) -> bool: ...
+
+ def __repr__(self) -> str: ...
+
+ def __setstate__(self, state: int) -> None: ...
+
+ def __str__(self) -> str: ...
+
+ @property
+ def name(self) -> str:
+ """
+ :type: str
+ """
+ @property
+ def value(self) -> int:
+ """
+ :type: int
+ """
+ AUTO: paravision.recognition.types.Engine # value =
+ OPENVINO: paravision.recognition.types.Engine # value =
+ TENSORRT: paravision.recognition.types.Engine # value =
+ __members__: dict # value = {'AUTO': , 'OPENVINO': , 'TENSORRT': }
+ pass
+
+
+class Face():
+ """
+ A face, minimally represented by a bounding box.
+
+ Attributes
+ ----------
+ bounding_box : BoundingBox
+ The bounding box of the face.
+ landmarks : Landmarks
+ The locations of various parts of the face.
+ embedding : Embedding
+ The embedding representing the face.
+ quality : float
+ An overall assessment of how acceptable the face is for facial
+ recognition. Overall range of [0, 1], "acceptable" quality is >= 0.15.
+ acceptability: float
+ The assessment of the acceptability of the face. Overall range [0, 1]
+ recognition_input_image, alignment_input_image, landmarks_input_image: numpy.ndarray
+ Images used at different stages of the detection pipeline.
+ alignment_bounding_box, landmarks_input_bounding_box: BoundingBox
+ The bounding boxes that are associated to alignment_input_image and landmarks_input_image
+ """
+ def __bool__(self) -> bool: ...
+
+ def __init__(self, bounding_box: BoundingBox) -> None:
+ """
+ Create a Face object from a BoundingBox.
+ """
+
+ def __repr__(self) -> str: ...
+
+ def asdict(self) -> dict:
+ """
+ Returns the face as a dictionary.
+ """
+
+ @property
+ def acceptability(self) -> float:
+ """
+ The acceptability of the face.
+
+ :type: float
+ """
+ @acceptability.setter
+ def acceptability(self, arg1: float) -> None:
+ """
+ The acceptability of the face.
+ """
+ @property
+ def alignment_bounding_box(self) -> BoundingBox:
+ """
+ The alignment bounding box of the face.
+
+ :type: BoundingBox
+ """
+ @alignment_bounding_box.setter
+ def alignment_bounding_box(self, arg1: BoundingBox) -> None:
+ """
+ The alignment bounding box of the face.
+ """
+ @property
+ def alignment_image(self) -> Image:
+ """
+ The alignment image of the face.
+
+ :type: Image
+ """
+ @alignment_image.setter
+ def alignment_image(self, arg1: Image) -> None:
+ """
+ The alignment image of the face.
+ """
+ @property
+ def attributes(self) -> BaseAttributes:
+ """
+ The attributes of the face.
+
+ :type: BaseAttributes
+ """
+ @attributes.setter
+ def attributes(self, arg1: BaseAttributes) -> None:
+ """
+ The attributes of the face.
+ """
+ @property
+ def bounding_box(self) -> BoundingBox:
+ """
+ The bounding box of the face.
+
+ :type: BoundingBox
+ """
+ @bounding_box.setter
+ def bounding_box(self, arg1: BoundingBox) -> None:
+ """
+ The bounding box of the face.
+ """
+ @property
+ def embedding(self) -> Embedding:
+ """
+ The embedding of the face.
+
+ :type: Embedding
+ """
+ @embedding.setter
+ def embedding(self, arg1: Embedding) -> None:
+ """
+ The embedding of the face.
+ """
+ @property
+ def image_source(self) -> ImageSource:
+ """
+ The source of the image where the face was detected. Possible values are unknown, mobile and webcam
+
+ :type: ImageSource
+ """
+ @image_source.setter
+ def image_source(self, arg1: ImageSource) -> None:
+ """
+ The source of the image where the face was detected. Possible values are unknown, mobile and webcam
+ """
+ @property
+ def landmarks(self) -> Landmarks:
+ """
+ The landmarks of the face.
+
+ :type: Landmarks
+ """
+ @landmarks.setter
+ def landmarks(self, arg1: Landmarks) -> None:
+ """
+ The landmarks of the face.
+ """
+ @property
+ def landmarks_input_bounding_box(self) -> BoundingBox:
+ """
+ The landmarks input bounding box of the face.
+
+ :type: BoundingBox
+ """
+ @landmarks_input_bounding_box.setter
+ def landmarks_input_bounding_box(self, arg1: BoundingBox) -> None:
+ """
+ The landmarks input bounding box of the face.
+ """
+ @property
+ def landmarks_input_image(self) -> Image:
+ """
+ The landmarks input image of the face.
+
+ :type: Image
+ """
+ @landmarks_input_image.setter
+ def landmarks_input_image(self, arg1: Image) -> None:
+ """
+ The landmarks input image of the face.
+ """
+ @property
+ def quality(self) -> float:
+ """
+ The quality of the face.
+
+ :type: float
+ """
+ @quality.setter
+ def quality(self, arg1: float) -> None:
+ """
+ The quality of the face.
+ """
+ @property
+ def recognition_input_image(self) -> Image:
+ """
+ The recognition input image of the face.
+
+ :type: Image
+ """
+ @recognition_input_image.setter
+ def recognition_input_image(self, arg1: Image) -> None:
+ """
+ The recognition input image of the face.
+ """
+ pass
+
+
+class GPUConfig():
+ def __init__(self) -> None: ...
+
+ @property
+ def gpu_id(self) -> int:
+ """
+ The index of the GPU device to use.
+
+ :type: int
+ """
+ @gpu_id.setter
+ def gpu_id(self, arg0: int) -> None:
+ """
+ The index of the GPU device to use.
+ """
+ @property
+ def worker_count(self) -> int:
+ """
+ The number of workers to allocate.
+
+ :type: int
+ """
+ @worker_count.setter
+ def worker_count(self, arg0: int) -> None:
+ """
+ The number of workers to allocate.
+ """
+ pass
+
+
+class Image():
+ """
+ An image, could be either GPU backed or CPU backed.
+
+ Attributes
+ ----------
+ data : numpy.ndarray
+ The image data, if the image is GPU backed the data will be copied to CPU.
+ is_gpu : bool
+ Whether the image is GPU backed.
+ """
+ def __init__(self, img_data_or_path: object) -> None:
+ """
+ Construct an Image from either a path or a numpy array.
+ """
+
+ @property
+ def data(self) -> numpy.ndarray:
+ """
+ The image data, if the image is GPU backed the data will be copied to CPU.
+
+ :type: numpy.ndarray
+ """
+ @property
+ def is_gpu(self) -> bool:
+ """
+ Whether the image is GPU backed.
+
+ :type: bool
+ """
+ pass
+
+
+class ImageInferenceData():
+ """
+ The result of running get faces pipeline for a single image
+
+ Attributes
+ ----------
+ faces : List
+ A list of Face objects detected in the image
+ width : Int
+ width of the inference image
+ height : Int
+ height of the inference image
+ """
+ def most_prominent_face_index(self) -> int:
+ """
+ The index of the most prominent face in the image.
+ """
+
+ @property
+ def faces(self) -> list[Face]:
+ """
+ The faces detected in the image.
+
+ :type: list[Face]
+ """
+ @faces.setter
+ def faces(self, arg1: list[Face]) -> None:
+ """
+ The faces detected in the image.
+ """
+ @property
+ def height(self) -> int:
+ """
+ The height of the image.
+
+ :type: int
+ """
+ @property
+ def width(self) -> int:
+ """
+ The width of the image.
+
+ :type: int
+ """
+ pass
+
+
+class ImageManipulator():
+ """
+ Members:
+
+ AUTO : Automatically select the image manipulation device
+
+ CPU : Use the CPU for image manipulation
+
+ GPU : Use the GPU for image manipulation
+ """
+ def __eq__(self, other: object) -> bool: ...
+
+ def __getstate__(self) -> int: ...
+
+ def __hash__(self) -> int: ...
+
+ def __index__(self) -> int: ...
+
+ def __init__(self, value: int) -> None: ...
+
+ def __int__(self) -> int: ...
+
+ def __ne__(self, other: object) -> bool: ...
+
+ def __repr__(self) -> str: ...
+
+ def __setstate__(self, state: int) -> None: ...
+
+ def __str__(self) -> str: ...
+
+ @property
+ def name(self) -> str:
+ """
+ :type: str
+ """
+ @property
+ def value(self) -> int:
+ """
+ :type: int
+ """
+ AUTO: paravision.recognition.types.ImageManipulator # value =
+ CPU: paravision.recognition.types.ImageManipulator # value =
+ GPU: paravision.recognition.types.ImageManipulator # value =
+ __members__: dict # value = {'AUTO': , 'CPU': , 'GPU': }
+ pass
+
+
+class ImageSource():
+ """
+ Members:
+
+ UNKNOWN : Unknown image source
+
+ MOBILE : Mobile image source
+
+ WEBCAM : Webcam image source
+ """
+ def __eq__(self, other: object) -> bool: ...
+
+ def __getstate__(self) -> int: ...
+
+ def __hash__(self) -> int: ...
+
+ def __index__(self) -> int: ...
+
+ def __init__(self, value: int) -> None: ...
+
+ def __int__(self) -> int: ...
+
+ def __ne__(self, other: object) -> bool: ...
+
+ def __repr__(self) -> str: ...
+
+ def __setstate__(self, state: int) -> None: ...
+
+ def __str__(self) -> str: ...
+
+ @property
+ def name(self) -> str:
+ """
+ :type: str
+ """
+ @property
+ def value(self) -> int:
+ """
+ :type: int
+ """
+ MOBILE: paravision.recognition.types.ImageSource # value =
+ UNKNOWN: paravision.recognition.types.ImageSource # value =
+ WEBCAM: paravision.recognition.types.ImageSource # value =
+ __members__: dict # value = {'UNKNOWN': , 'MOBILE': , 'WEBCAM': }
+ pass
+
+
+class InferenceResult():
+ """
+ The result of running the get faces pipeline on a group of images
+
+ Attributes
+ ----------
+ faces : List
+ A list of Face objects detected in all images
+ inferences : List
+ A list of detection results grouped by an image
+ """
+ @property
+ def faces(self) -> list[Face]:
+ """
+ List of Face objects
+
+ :type: list[Face]
+ """
+ @property
+ def image_inferences(self) -> list[ImageInferenceData]:
+ """
+ "Deprecated: This property will be removed in the next major release. Use the inferences property instead.
+
+ :type: list[ImageInferenceData]
+ """
+ @property
+ def inferences(self) -> list[ImageInferenceData]:
+ """
+ List of ImageInferenceData objects
+
+ :type: list[ImageInferenceData]
+ """
+ pass
+
+
+class Landmarks():
+ """
+ A set of facial landmarks, represented by Points.
+
+ Attributes
+ ----------
+ left_eye : Point
+ The center of the left eye.
+ right_eye : Point
+ The center of the right eye.
+ nose : Point
+ The tip of the nose.
+ left_mouth : Point
+ The left corner of the mouth.
+ right_mouth : Point
+ The right corner of the mouth.
+ """
+ def __bool__(self) -> bool: ...
+
+ def __eq__(self, arg0: Landmarks) -> bool: ...
+
+ @typing.overload
+ def __init__(self, left_eye: Point, right_eye: Point, nose: Point, left_mouth: Point, right_mouth: Point) -> None:
+ """
+ Construct a Landmarks from five Points.
+ """
+
+ @typing.overload
+ def __init__(self, left_eye: typing.Iterable, right_eye: typing.Iterable, nose: typing.Iterable, left_mouth: typing.Iterable, right_mouth: typing.Iterable) -> None:
+ """
+ Construct a Landmarks from five iterables of two floats.
+ """
+
+ def __ne__(self, arg0: Landmarks) -> bool: ...
+
+ def __repr__(self) -> str: ...
+
+ def asdict(self) -> dict:
+ """
+ Convert this object to a dictionary
+ """
+
+ def empty(self) -> bool:
+ """
+ Check if this object is empty
+ """
+
+ @property
+ def left_eye(self) -> Point:
+ """
+ The left eye.
+
+ :type: Point
+ """
+ @left_eye.setter
+ def left_eye(self, arg1: Point) -> None:
+ """
+ The left eye.
+ """
+ @property
+ def left_mouth(self) -> Point:
+ """
+ The left mouth.
+
+ :type: Point
+ """
+ @left_mouth.setter
+ def left_mouth(self, arg1: Point) -> None:
+ """
+ The left mouth.
+ """
+ @property
+ def nose(self) -> Point:
+ """
+ The nose.
+
+ :type: Point
+ """
+ @nose.setter
+ def nose(self, arg1: Point) -> None:
+ """
+ The nose.
+ """
+ @property
+ def right_eye(self) -> Point:
+ """
+ The right eye.
+
+ :type: Point
+ """
+ @right_eye.setter
+ def right_eye(self, arg1: Point) -> None:
+ """
+ The right eye.
+ """
+ @property
+ def right_mouth(self) -> Point:
+ """
+ The right mouth.
+
+ :type: Point
+ """
+ @right_mouth.setter
+ def right_mouth(self, arg1: Point) -> None:
+ """
+ The right mouth.
+ """
+ __hash__ = None
+ pass
+
+
+class Point():
+ """
+ A point within an image, represented by x- and y-coordinates.
+
+ Attributes
+ ----------
+ x : float
+ The x-coordinate.
+ y : float
+ The y-coordinate.
+ """
+ def __bool__(self) -> bool: ...
+
+ def __eq__(self, arg0: Point) -> bool: ...
+
+ @typing.overload
+ def __init__(self) -> None:
+ """
+ Construct a point from x- and y-coordinates.
+ """
+
+ @typing.overload
+ def __init__(self, point: typing.Iterable) -> None:
+ """
+ Construct a Point from an iterable of two floats
+ """
+
+ @typing.overload
+ def __init__(self, x: float, y: float) -> None: ...
+
+ def __iter__(self) -> typing.Iterator: ...
+
+ def __ne__(self, arg0: Point) -> bool: ...
+
+ def __repr__(self) -> str: ...
+
+ def asdict(self) -> dict:
+ """
+ Convert this object to a dictionary
+ """
+
+ def empty(self) -> bool:
+ """
+ Check if this object is empty
+ """
+
+ @property
+ def x(self) -> float:
+ """
+ The x-coordinate.
+
+ :type: float
+ """
+ @x.setter
+ def x(self, arg1: float) -> None:
+ """
+ The x-coordinate.
+ """
+ @property
+ def y(self) -> float:
+ """
+ The y-coordinate.
+
+ :type: float
+ """
+ @y.setter
+ def y(self, arg1: float) -> None:
+ """
+ The y-coordinate.
+ """
+ __hash__ = None
+ pass
+
+
+class ScoringMode():
+ """
+ Members:
+
+ StandardEmbedding
+
+ EnhancedEmbedding
+ """
+ def __eq__(self, other: object) -> bool: ...
+
+ def __getstate__(self) -> int: ...
+
+ def __hash__(self) -> int: ...
+
+ def __index__(self) -> int: ...
+
+ def __init__(self, value: int) -> None: ...
+
+ def __int__(self) -> int: ...
+
+ def __ne__(self, other: object) -> bool: ...
+
+ def __repr__(self) -> str: ...
+
+ def __setstate__(self, state: int) -> None: ...
+
+ def __str__(self) -> str: ...
+
+ @property
+ def name(self) -> str:
+ """
+ :type: str
+ """
+ @property
+ def value(self) -> int:
+ """
+ :type: int
+ """
+ EnhancedEmbedding: paravision.recognition.types.ScoringMode # value =
+ StandardEmbedding: paravision.recognition.types.ScoringMode # value =
+ __members__: dict # value = {'StandardEmbedding': , 'EnhancedEmbedding': }
+ pass
+
+
+class Settings():
+ def __init__(self) -> None: ...
+
+ @property
+ def additional_detection_models(self) -> list[str]:
+ """
+ additional detection models to load
+
+ :type: list[str]
+ """
+ @additional_detection_models.setter
+ def additional_detection_models(self, arg0: list[str]) -> None:
+ """
+ additional detection models to load
+ """
+ @property
+ def cache_generated_engine(self) -> bool:
+ """
+ TensorRT cache generated engine file
+
+ :type: bool
+ """
+ @cache_generated_engine.setter
+ def cache_generated_engine(self, arg0: bool) -> None:
+ """
+ TensorRT cache generated engine file
+ """
+ @property
+ def detection_model(self) -> str:
+ """
+ detection model, default, streaming, mobile
+
+ :type: str
+ """
+ @detection_model.setter
+ def detection_model(self, arg0: str) -> None:
+ """
+ detection model, default, streaming, mobile
+ """
+ @property
+ def engine(self) -> Engine:
+ """
+ Engine to use
+
+ :type: Engine
+ """
+ @engine.setter
+ def engine(self, arg0: Engine) -> None:
+ """
+ Engine to use
+ """
+ @property
+ def gpu_configs(self) -> list[GPUConfig]:
+ """
+ List of GPU configs for worker allocation in multiple GPUs
+
+ :type: list[GPUConfig]
+ """
+ @gpu_configs.setter
+ def gpu_configs(self, arg0: list[GPUConfig]) -> None:
+ """
+ List of GPU configs for worker allocation in multiple GPUs
+ """
+ @property
+ def image_manipulator(self) -> ImageManipulator:
+ """
+ Image manipulator
+
+ :type: ImageManipulator
+ """
+ @image_manipulator.setter
+ def image_manipulator(self, arg0: ImageManipulator) -> None:
+ """
+ Image manipulator
+ """
+ @property
+ def load_landmarks_model(self) -> bool:
+ """
+ Whether to load the landmarks model. Default is true.
+
+ :type: bool
+ """
+ @load_landmarks_model.setter
+ def load_landmarks_model(self, arg0: bool) -> None:
+ """
+ Whether to load the landmarks model. Default is true.
+ """
+ @property
+ def load_quality_model(self) -> bool:
+ """
+ Whether to load the quality model. Default is true.
+
+ :type: bool
+ """
+ @load_quality_model.setter
+ def load_quality_model(self, arg0: bool) -> None:
+ """
+ Whether to load the quality model. Default is true.
+ """
+ @property
+ def load_recognition_model(self) -> bool:
+ """
+ Whether to load the recognition model. Default is true.
+
+ :type: bool
+ """
+ @load_recognition_model.setter
+ def load_recognition_model(self, arg0: bool) -> None:
+ """
+ Whether to load the recognition model. Default is true.
+ """
+ @property
+ def openvino_threads_limit(self) -> int:
+ """
+ OpenVINO model threads
+
+ :type: int
+ """
+ @openvino_threads_limit.setter
+ def openvino_threads_limit(self, arg0: int) -> None:
+ """
+ OpenVINO model threads
+ """
+ @property
+ def tensorrt_engine_cache_path(self) -> str:
+ """
+ Location of where to store serialized model file
+
+ :type: str
+ """
+ @tensorrt_engine_cache_path.setter
+ def tensorrt_engine_cache_path(self, arg0: str) -> None:
+ """
+ Location of where to store serialized model file
+ """
+ @property
+ def tensorrt_fp16_layer_selection(self) -> bool:
+ """
+ TensorRT FP16 layer selection
+
+ :type: bool
+ """
+ @tensorrt_fp16_layer_selection.setter
+ def tensorrt_fp16_layer_selection(self, arg0: bool) -> None:
+ """
+ TensorRT FP16 layer selection
+ """
+ @property
+ def tensorrt_max_batch_size(self) -> int:
+ """
+ TensorRT batch size
+
+ :type: int
+ """
+ @tensorrt_max_batch_size.setter
+ def tensorrt_max_batch_size(self, arg0: int) -> None:
+ """
+ TensorRT batch size
+ """
+ @property
+ def tensorrt_optimization_level(self) -> int:
+ """
+ TensorRT optimization level
+
+ :type: int
+ """
+ @tensorrt_optimization_level.setter
+ def tensorrt_optimization_level(self, arg0: int) -> None:
+ """
+ TensorRT optimization level
+ """
+ @property
+ def use_cached_engine(self) -> bool:
+ """
+ TensorRT use cached engine file
+
+ :type: bool
+ """
+ @use_cached_engine.setter
+ def use_cached_engine(self, arg0: bool) -> None:
+ """
+ TensorRT use cached engine file
+ """
+ @property
+ def validness_checks(self) -> ValidnessCheck:
+ """
+ validness check flags
+
+ :type: ValidnessCheck
+ """
+ @validness_checks.setter
+ def validness_checks(self, arg0: ValidnessCheck) -> None:
+ """
+ validness check flags
+ """
+ @property
+ def validness_models_dir(self) -> str:
+ """
+ path to validness models directory
+
+ :type: str
+ """
+ @validness_models_dir.setter
+ def validness_models_dir(self, arg0: str) -> None:
+ """
+ path to validness models directory
+ """
+ @property
+ def worker_count(self) -> int:
+ """
+ The number of workers to allocate
+
+ :type: int
+ """
+ @worker_count.setter
+ def worker_count(self, arg0: int) -> None:
+ """
+ The number of workers to allocate
+ """
+ pass
+
+
+class ValidnessCheck():
+ """
+ Members:
+
+ NONE : No validness check flag
+
+ LIVENESS : Liveness validness check flag
+
+ AGE : Age validness check flag
+
+ DEEPFAKE : Deepfake validness check
+ """
+ def __eq__(self, other: object) -> bool: ...
+
+ def __getstate__(self) -> int: ...
+
+ def __hash__(self) -> int: ...
+
+ def __index__(self) -> int: ...
+
+ def __init__(self, value: int) -> None: ...
+
+ def __int__(self) -> int: ...
+
+ def __ne__(self, other: object) -> bool: ...
+
+ def __or__(self, arg0: ValidnessCheck) -> ValidnessCheck: ...
+
+ def __repr__(self) -> str: ...
+
+ def __setstate__(self, state: int) -> None: ...
+
+ def __str__(self) -> str: ...
+
+ @property
+ def name(self) -> str:
+ """
+ :type: str
+ """
+ @property
+ def value(self) -> int:
+ """
+ :type: int
+ """
+ AGE: paravision.recognition.types.ValidnessCheck # value =
+ DEEPFAKE: paravision.recognition.types.ValidnessCheck # value =
+ LIVENESS: paravision.recognition.types.ValidnessCheck # value =
+ NONE: paravision.recognition.types.ValidnessCheck # value =
+ __members__: dict # value = {'NONE': , 'LIVENESS': , 'AGE': , 'DEEPFAKE': }
+ pass
+
+
+class ValidnessFeedback():
+ """
+ Members:
+
+ UNKNOWN : The result is unknown
+
+ FACE_QUALITY_POOR : The image quality is poor
+
+ FACE_ACCEPTABILITY_POOR : The image quality is poor
+
+ IMG_LIGHTING_DARK : The image lighting is too dark
+
+ IMG_LIGHTING_BRIGHT : The image lighting is too bright
+
+ FACE_SIZE_SMALL : The face is too small
+
+ FACE_SIZE_LARGE : The face is too large
+
+ FACE_POS_LEFT : The face is too far left
+
+ FACE_POS_RIGHT : The face is too far right
+
+ FACE_POS_HIGH : The face is too far up
+
+ FACE_POS_LOW : The face is too far down
+
+ FACE_MASK_FOUND : The face contains a mask
+
+ FACE_FRONTALITY_POOR : The face is not facing forward
+
+ FACE_SHARPNESS_POOR : The face sharpness is poor
+
+ FACE_RES_LOW : The face width is too low
+
+ TOO_MANY_FACES : The face contains one or more faces nearby
+
+ FACE_ROLL_ANGLE_EXCEEDED : The maximum roll angle of the face has exceeded.
+ """
+ def __eq__(self, other: object) -> bool: ...
+
+ def __getstate__(self) -> int: ...
+
+ def __hash__(self) -> int: ...
+
+ def __index__(self) -> int: ...
+
+ def __init__(self, value: int) -> None: ...
+
+ def __int__(self) -> int: ...
+
+ def __ne__(self, other: object) -> bool: ...
+
+ def __repr__(self) -> str: ...
+
+ def __setstate__(self, state: int) -> None: ...
+
+ def __str__(self) -> str: ...
+
+ @property
+ def name(self) -> str:
+ """
+ :type: str
+ """
+ @property
+ def value(self) -> int:
+ """
+ :type: int
+ """
+ FACE_ACCEPTABILITY_POOR: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_FRONTALITY_POOR: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_MASK_FOUND: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_POS_HIGH: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_POS_LEFT: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_POS_LOW: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_POS_RIGHT: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_QUALITY_POOR: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_RES_LOW: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_ROLL_ANGLE_EXCEEDED: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_SHARPNESS_POOR: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_SIZE_LARGE: paravision.recognition.types.ValidnessFeedback # value =
+ FACE_SIZE_SMALL: paravision.recognition.types.ValidnessFeedback # value =
+ IMG_LIGHTING_BRIGHT: paravision.recognition.types.ValidnessFeedback # value =
+ IMG_LIGHTING_DARK: paravision.recognition.types.ValidnessFeedback # value =
+ TOO_MANY_FACES: paravision.recognition.types.ValidnessFeedback # value =
+ UNKNOWN: paravision.recognition.types.ValidnessFeedback # value =
+ __members__: dict # value = {'UNKNOWN': , 'FACE_QUALITY_POOR': , 'FACE_ACCEPTABILITY_POOR': , 'IMG_LIGHTING_DARK': , 'IMG_LIGHTING_BRIGHT': , 'FACE_SIZE_SMALL': , 'FACE_SIZE_LARGE': , 'FACE_POS_LEFT': , 'FACE_POS_RIGHT': , 'FACE_POS_HIGH': , 'FACE_POS_LOW': , 'FACE_MASK_FOUND': , 'FACE_FRONTALITY_POOR': , 'FACE_SHARPNESS_POOR': , 'FACE_RES_LOW': , 'TOO_MANY_FACES': , 'FACE_ROLL_ANGLE_EXCEEDED': }
+ pass
+
+
+class ValidnessResult():
+ def __init__(self) -> None:
+ """
+ Construct an empty ValidnessResult
+ """
+
+ def __repr__(self) -> str: ...
+
+ def asdict(self) -> dict:
+ """
+ Convert this object to a dictionary
+ """
+
+ @property
+ def face_acceptability(self) -> float:
+ """
+ The acceptability of the face
+
+ :type: float
+ """
+ @property
+ def face_frontality(self) -> float:
+ """
+ The frontality of the face
+
+ :type: float
+ """
+ @property
+ def face_height(self) -> float:
+ """
+ The absolute face height found in the image
+
+ :type: float
+ """
+ @property
+ def face_height_pct(self) -> float:
+ """
+ The percentage of the face height relative to the image
+
+ :type: float
+ """
+ @property
+ def face_mask_prob(self) -> float:
+ """
+ The probability that the face has a mask
+
+ :type: float
+ """
+ @property
+ def face_position_pcts(self) -> tuple[float, float, float, float]:
+ """
+ The percentage of the face position relative to the image
+
+ :type: tuple[float, float, float, float]
+ """
+ @property
+ def face_quality(self) -> float:
+ """
+ The quality of the face
+
+ :type: float
+ """
+ @property
+ def face_roll_angle(self) -> float:
+ """
+ The roll angle of the face
+
+ :type: float
+ """
+ @property
+ def face_sharpness(self) -> float:
+ """
+ The sharpness of the face
+
+ :type: float
+ """
+ @property
+ def face_width(self) -> float:
+ """
+ The absolute face width found in the image
+
+ :type: float
+ """
+ @property
+ def face_width_pct(self) -> float:
+ """
+ The percentage of the face width relative to the image
+
+ :type: float
+ """
+ @property
+ def feedbacks(self) -> list[ValidnessFeedback]:
+ """
+ The list of validness check feedbacks
+
+ :type: list[ValidnessFeedback]
+ """
+ @property
+ def image_average_pixel_value(self) -> float:
+ """
+ The average pixel value of the image
+
+ :type: float
+ """
+ @property
+ def image_bright_pixel_pct(self) -> float:
+ """
+ The percentage of bright pixels in the image
+
+ :type: float
+ """
+ @property
+ def image_dark_pixel_pct(self) -> float:
+ """
+ The percentage of dark pixels in the image
+
+ :type: float
+ """
+ @property
+ def is_valid(self) -> bool:
+ """
+ Whether all the validness checks passed
+
+ :type: bool
+ """
+ pass
+
+
diff --git a/modules/paravision/recognition/utils.py b/modules/paravision/recognition/utils.py
deleted file mode 100644
index 2160b4f0e..000000000
--- a/modules/paravision/recognition/utils.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import cv2
-import numpy as np
-from typing import Union
-
-from .exceptions import InvalidInputException
-
-
-def load_image(img_data_or_path: Union[str, bytes]) -> np.ndarray:
- if isinstance(img_data_or_path, str):
- img = cv2.imread(img_data_or_path, cv2.IMREAD_COLOR)
- else:
- img = cv2.imdecode(
- np.frombuffer(img_data_or_path.read(), dtype=np.uint8), cv2.IMREAD_COLOR
- )
-
- if img is None:
- raise InvalidInputException("Couldn't load the invalid input image")
-
- return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
diff --git a/modules/paravision/recognition/utils/__init__.pyi b/modules/paravision/recognition/utils/__init__.pyi
new file mode 100644
index 000000000..b63188560
--- /dev/null
+++ b/modules/paravision/recognition/utils/__init__.pyi
@@ -0,0 +1,16 @@
+from __future__ import annotations
+import paravision.recognition.utils
+import typing
+import numpy
+_Shape = typing.Tuple[int, ...]
+
+__all__ = [
+ "load_image"
+]
+
+
+def load_image(img_data_or_path: str) -> numpy.ndarray:
+ """
+ Loads an image from a given path
+ """
+
diff --git a/modules/paravision_models/__init__.py b/modules/paravision_models/__init__.py
deleted file mode 100644
index 3eb6673ae..000000000
--- a/modules/paravision_models/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-
-try:
- from .recognition import *
-except ImportError:
- pass
diff --git a/modules/paravision_models/__pycache__/__init__.cpython-310.pyc b/modules/paravision_models/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index 0d1456440..000000000
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diff --git a/modules/paravision_models/__pycache__/__init__.cpython-36.pyc b/modules/paravision_models/__pycache__/__init__.cpython-36.pyc
deleted file mode 100644
index a92d17d58..000000000
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diff --git a/modules/paravision_models/recognition/__init__.py b/modules/paravision_models/recognition/__init__.py
deleted file mode 100644
index 98efde90d..000000000
--- a/modules/paravision_models/recognition/__init__.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import os
-
-# Variables populated by release build. Do not edit.
-_MODEL_NAME = "gen5-balanced"
-_MODEL_VERSION = "v1.1.2"
-_MODEL_ENGINE = "openvino-2021.4"
-
-
-def name():
- return _MODEL_NAME
-
-
-def version():
- return _MODEL_VERSION
-
-
-def engine():
- return _MODEL_ENGINE
-
-
-def location():
- return os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
-
-
-if engine() == "tensorrt":
- import glob
-
- TRT_ENGINE_PATH = location()
-
- def clear_cached_trt_engine():
- engine_files = glob.glob("{}/**/*.engine".format(TRT_ENGINE_PATH))
-
- for f in engine_files:
- try:
- os.remove(f)
- except Exception:
- raise Exception("Error deleting engine file: ", f)
diff --git a/modules/paravision_models/recognition/__pycache__/__init__.cpython-310.pyc b/modules/paravision_models/recognition/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
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diff --git a/modules/paravision_models/recognition/models/attributes/default/attributes.xml b/modules/paravision_models/recognition/models/attributes/default/attributes.xml
deleted file mode 100644
index d136840b2..000000000
--- a/modules/paravision_models/recognition/models/attributes/default/attributes.xml
+++ /dev/null
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- 1
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- 28
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- 1
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- 28
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- 1
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- 1
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- 1
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- 3
- 3
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- 1
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- 1
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- 1
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- 1
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- 1
- 1
- 1
- 128
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- 128
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- 1
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- 128
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- 1
- 1
- 1
- 16
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- 1
- 1
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- 16
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- 1
- 1
- 1
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- 1
- 1
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- 16
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- 1
- 1
- 16
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- 128
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- 1
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- 1
- 1
- 1
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- 1
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- 1
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- 1
- 1
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- 1
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- 1
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- 1
- 1
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diff --git a/modules/paravision_models/recognition/models/detection/default/detection.bin b/modules/paravision_models/recognition/models/detection/default/detection.bin
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diff --git a/modules/paravision_models/recognition/models/landmarks/default/landmarks.bin b/modules/paravision_models/recognition/models/landmarks/default/landmarks.bin
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diff --git a/modules/paravision_models/recognition/models/landmarks/default/landmarks.xml b/modules/paravision_models/recognition/models/landmarks/default/landmarks.xml
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- 1
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- 1
- 1
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- 28
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- 3
- 3
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- 28
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- 3
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- 1
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- 1
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- 3
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- 3
- 3
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- 28
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- 1
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- 28
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- 1
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- 28
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- 1
- 1
- 1
- 128
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- 16
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- 128
- 28
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- 128
- 28
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- 1
- 1
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- 1
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- 1
- 1
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- 4
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- 1
- 1
- 1
- 128
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- 128
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- 1
- 1
- 1
- 128
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- 128
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- 1
- 1
- 1
- 16
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- 1
- 1
- 1
- 16
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- 1
- 1
- 1
- 16
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- 1
- 1
- 1
- 16
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- 1
- 1
- 1
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- 1
- 1
- 1
- 16
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- 1
- 1
- 1
- 16
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- 1
- 1
- 1
- 128
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- 1
- 1
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- 128
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- 1
- 1
- 1
- 128
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- 1
- 1
- 1
- 128
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- 1
- 1
- 1
- 128
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- 1
- 1
- 1
- 128
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- 4
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- 1
- 1
- 1
- 128
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- 4
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- 1
- 128
- 1
- 1
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- 1
- 128
- 28
- 28
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- 1
- 128
- 1
- 1
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- 1
- 128
- 28
- 28
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- 1
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- 28
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- 1
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- 28
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- 1
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- 28
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- 1
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- 1
- 1
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- 28
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- 1
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- 1
- 1
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- 1
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- 28
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- 1
- 128
- 1
- 1
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- 1
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- 28
- 28
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- 1
- 128
- 1
- 1
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- 1
- 128
- 28
- 28
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- 128
- 3
- 3
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- 1
- 128
- 28
- 28
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- 128
- 3
- 3
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- 1
- 128
- 28
- 28
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- 1
- 128
- 1
- 1
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- 1
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- 28
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- 1
- 128
- 1
- 1
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- 1
- 128
- 28
- 28
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- 1
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- 28
- 28
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- 1
- 128
- 28
- 28
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- 3
- 3
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- 1
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- 28
- 28
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- 128
- 3
- 3
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- 1
- 128
- 28
- 28
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- 1
- 128
- 1
- 1
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- 1
- 128
- 28
- 28
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- 1
- 128
- 1
- 1
-
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- 1
- 128
- 28
- 28
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- 1
- 1
- 1
- 128
-
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- 1
- 1
- 1
- 16
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- 1
- 128
- 28
- 28
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- 4
-
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- 4
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- 1
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- 1
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- 1
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- 1
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- 2
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- 1
- 128
- 28
- 28
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- 2
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- 1
- 128
- 1
- 1
-
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- 4
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- 1
- 128
- 1
- 1
-
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- 4
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- 1
- 1
- 1
- 128
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- 16
- 128
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-
-
-
- 1
- 1
- 1
- 128
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-
- 16
- 128
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-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
- 1
- 1
- 1
- 16
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
-
- 128
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
- 128
- 16
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 1
- 1
- 1
- 128
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 4
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 4
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 128
- 128
- 3
- 3
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 128
- 128
- 3
- 3
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 128
- 128
- 3
- 3
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 128
- 128
- 3
- 3
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 4
-
-
-
-
-
- 4
-
-
-
-
- 1
-
-
-
-
-
-
- 1
-
-
-
-
-
- 1
-
-
- 1
-
-
-
-
-
-
-
-
-
-
-
-
- 2
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 2
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
-
- 4
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
- 4
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 16
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 16
- 128
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
- 1
- 1
- 1
- 16
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
-
- 128
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
- 128
- 16
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 1
- 1
- 1
- 128
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 4
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 4
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 128
- 128
- 3
- 3
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 128
- 128
- 3
- 3
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 128
- 128
- 3
- 3
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 128
- 128
- 3
- 3
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 4
-
-
-
-
-
- 4
-
-
-
-
- 1
-
-
-
-
-
-
- 1
-
-
-
-
-
- 1
-
-
- 1
-
-
-
-
-
-
-
-
-
-
-
-
- 2
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 2
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
-
- 4
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
- 4
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 16
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 16
- 128
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
- 1
- 1
- 1
- 16
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
-
- 128
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
- 128
- 16
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 1
- 1
- 1
- 128
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 4
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 4
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 128
- 128
- 3
- 3
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 128
- 128
- 3
- 3
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 128
- 128
- 3
- 3
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 128
- 128
- 3
- 3
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 4
-
-
-
-
-
- 4
-
-
-
-
- 1
-
-
-
-
-
-
- 1
-
-
-
-
-
- 1
-
-
- 1
-
-
-
-
-
-
-
-
-
-
-
-
- 2
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 2
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
-
- 4
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
- 4
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 16
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 16
- 128
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
- 1
- 1
- 1
- 16
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
-
- 128
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
- 128
- 16
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 1
- 1
- 1
- 128
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 4
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 4
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 128
- 128
- 3
- 3
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 128
- 128
- 3
- 3
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 128
- 128
- 3
- 3
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 128
- 128
- 3
- 3
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
-
-
- 4
-
-
-
-
-
- 4
-
-
-
-
- 1
-
-
-
-
-
-
- 1
-
-
-
-
-
- 1
-
-
- 1
-
-
-
-
-
-
-
-
-
-
-
-
- 2
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 2
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
-
- 4
-
-
-
-
-
- 1
- 128
- 1
- 1
-
-
- 4
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 16
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 16
- 128
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
- 1
- 1
- 1
- 16
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
- 1
- 1
- 1
- 16
-
-
-
-
-
-
- 128
- 16
-
-
-
-
-
- 1
- 1
- 1
- 16
-
-
- 128
- 16
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 1
- 1
- 1
- 128
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
- 1
- 1
- 1
- 128
-
-
-
-
-
-
- 4
-
-
-
-
-
- 1
- 1
- 1
- 128
-
-
- 4
-
-
-
-
- 1
- 128
- 1
- 1
-
-
-
-
-
- 1
- 128
- 28
- 28
-
-
- 1
- 128
- 1
- 1
-
-
-
-
- 1
- 128
- 28
- 28
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- 1
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- 1
- 1
- 1
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- 16
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- 128
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- 1
- 1
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- 16
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- 1
- 1
- 1
- 128
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- 1
- 1
- 1
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- 1
- 1
- 1
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- 1
- 1
- 1
- 128
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- 1
- 1
- 1
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- 1
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- 1
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- 1
- 1
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- 1
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- 1
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- 1
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- 3
- 3
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- 3
- 3
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- 1
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- 1
- 1
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- 1
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- 1
- 1
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- 1
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- 1
- 1
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- 1
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- 1
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- 1
- 1
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- 1
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- 3
- 3
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- 3
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- 1
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- 1
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- 3
- 3
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- 3
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- 1
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- 3
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- 3
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- 1
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- 1
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- 512
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- 64
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- 7
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- 7
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- 1
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- 1
- 1
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- 1
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- 1
- 1
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- 1
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- 1
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- 1
- 64
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- 1
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- 1
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- 1
- 1
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- 64
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- 1
- 64
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- 1
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- 64
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- 64
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- 1
- 1
- 1
- 64
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- 1
- 1
- 1
- 512
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- 1
- 1
- 1
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- 1
- 1
- 1
- 512
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- 1
- 1
- 1
- 512
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- 1
- 1
- 1
- 512
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- 1
- 1
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- 512
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- 4
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- 1
- 1
- 1
- 512
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- 4
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- 1
- 512
- 1
- 1
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- 1
- 512
- 7
- 7
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- 1
- 512
- 1
- 1
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- 1
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- 7
- 7
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- 1
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- 7
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- 7
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- 1
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- 7
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- 1
- 1
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- 7
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- 1
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- 1
- 1
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- 7
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- 1
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- 1
- 1
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- 7
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- 1
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- 1
- 1
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- 7
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- 3
- 3
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- 1
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- 7
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- 3
- 3
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- 1
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- 7
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- 1
- 1
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- 7
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- 1
- 1
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- 7
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- 7
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- 3
- 3
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- 1
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- 7
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- 3
- 3
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- 7
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- 1
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- 7
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- 1
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- 1
- 1
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- 7
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- 1
- 1
- 1
- 512
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- 1
- 1
- 1
- 64
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- 1
- 512
- 7
- 7
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- 4
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- 1
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- 1
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- 2
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- 1
- 512
- 7
- 7
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- 2
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- 1
- 512
- 1
- 1
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- 4
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- 1
- 512
- 1
- 1
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- 4
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- 1
- 1
- 1
- 512
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- 64
- 512
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- 1
- 1
- 1
- 512
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- 512
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- 1
- 1
- 1
- 64
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- 1
- 1
- 1
- 64
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- 1
- 1
- 1
- 64
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- 1
- 1
- 1
- 64
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- 1
- 1
- 1
- 64
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- 1
- 1
- 1
- 64
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- 64
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- 1
- 1
- 1
- 64
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- 64
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- 1
- 1
- 1
- 512
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- 1
- 1
- 1
- 512
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- 1
- 1
- 1
- 512
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- 1
- 1
- 1
- 512
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- 1
- 1
- 1
- 512
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- 1
- 1
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- 512
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- 4
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- 1
- 1
- 1
- 512
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- 4
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- 1
- 512
- 1
- 1
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- 1
- 512
- 7
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- 1
- 512
- 1
- 1
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- 512
- 7
- 7
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- 1
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- 1
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- 1
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- 1
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- 7
- 7
-
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- 7
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-
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-
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-
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- 1
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- 1
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-
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- 1
- 1
-
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- 1
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- 7
-
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- 1
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- 1
- 1
-
-
-
-
- 1
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- 7
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-
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-
-
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diff --git a/modules/paravision_models/recognition/models/spec.json b/modules/paravision_models/recognition/models/spec.json
deleted file mode 100644
index 6ab6a0dbf..000000000
--- a/modules/paravision_models/recognition/models/spec.json
+++ /dev/null
@@ -1,34 +0,0 @@
-{
- "name": "gen5-balanced",
- "fd_input_shape": [
- 513,
- 513
- ],
- "fd_streaming_input_shape": [
- 257,
- 449
- ],
- "lm_input_shape": [
- 112,
- 112
- ],
- "md_input_shape": [
- 112,
- 112
- ],
- "fr_input_shape": [
- 112,
- 112
- ],
- "embedding_size": 513,
- "weight": 20.967800306995212,
- "bias": -58.33307640674702,
- "at_input_shape": [
- 112,
- 112
- ],
- "em_input_shape": [
- 112,
- 112
- ]
-}
\ No newline at end of file
diff --git a/sbin/ctl.sh b/sbin/ctl.sh
index 91f197d11..e3e8d2925 100755
--- a/sbin/ctl.sh
+++ b/sbin/ctl.sh
@@ -68,12 +68,12 @@ mkdir -p $UKDI_yolo_outdir
# Determina all the possible libs based on whats in UKDI_yolo_devices
-PYP_OPENVINO="modules"
+PYP_OPENVINO="modules/para_models/gen6-balanced/venv_balanced/lib/python3.10/site-packages"
LIB_OPENVINO="modules/openvino/inference_engine/lib/intel64/:\
modules/openvino/inference_engine:\
modules/openvino/python3.10/dist-packages/openvino/libs:\
modules/openvino/python3.10/dist-packages/openvino/inference_engine"
-PYP_PARAVISION="modules"
+PYP_PARAVISION="modules/para_models/gen6-balanced/venv_balanced/lib/python3.10/site-packages"
PYP_DEEPFACE="modules"
PYP_YOLOV5="modules/yolov5-face_Jan1"
LIB_SEEK="modules/seek/Seekware_SDK_3.6.0.0/lib/x86_64-linux-gnu/"
@@ -103,6 +103,7 @@ do
"regula") ;;
"traffic") ;;
"camera") ;;
+ "camera_stream") ;;
*) echo "yoloserv does not implement backend $i. Edit /etc/ukdi.json::yolo_devices and try again."
exit 1
esac
@@ -153,6 +154,16 @@ function f_apt(){
# --trusted-host paravision.mycloudrepo.io
+ # . /usr/local/lib/python3.10/dist-packages/
+ # ROOTURL="http://2a76e3b5-733a-4c93-98fb-339927b0f90c:453a6a2d-2935-430b-a22f-9b2880ca381@paravision.mycloudrepo.io"
+ # pip3 install cmake --upgrade
+ # pip3 install --no-cache-dir\
+ # --extra-index-url $ROOTURL/repositories/python-sdk\
+ # --extra-index-url $ROOTURL/repositories/python-recognition\
+ # "paravision-recognition" "paravision-models-gen6-balanced-openvino-2022-3" "openvino==2022.3"\
+ # --trusted-host paravision.mycloudrepo.io
+
+
wget -O downloads/facerec.zip https://github.com/ageitgey/face_recognition/archive/refs/heads/master.zip
cd downloads
unzip facerec.zip
diff --git a/src/camera_livefeed.py b/src/camera_livefeed.py
new file mode 100644
index 000000000..08496f003
--- /dev/null
+++ b/src/camera_livefeed.py
@@ -0,0 +1,55 @@
+# camera_livefeed.py
+import cherrypy
+import cv2
+import time
+from camera_stream import Camera_Stream # your class with __init__
+
+# init camera
+cam = Camera_Stream()
+cam.open(cam=0, width=640, height=480)
+cam.start(fps=10)
+print("camera started on /dev/video0")
+
+class Root:
+ @cherrypy.expose
+ def index(self):
+ # note: explicit full URL if your UI is on another port
+ return "MJPEG stream