doc/fm_project2026.md

@@ -1,113 +0,0 @@
-# Yoloserv 2026 Project
-
-## Project Objectives
-
-### Repo to large for GitLab / Streamline the repo for Deployment
-
-### Move camera_stream from core to YOLO
-
-#### Problems with camera_stream
-
-Camera stream is a proccess that provides a live video feed to the UI. It has led to better success with the face matching proccess and has allowed for a more seamless user experience.  Right now the camera_stream functionalty is in the CORE repo and runs and runs contiously in the background. There are a few issues with this:
-
-- It occupies and holds the camera would cause issues with other applications that use core and not the camera_stream
-- It uses a full CPU core to run which as our applications scale is not maintainable.
-
-#### Solution
-
-The solution was to move the camera_stream functionality to the YOLO repo and make it a UKDI variable that can be enabled or disabled. However this failed as it added to much delay and lagging when loading the stream and moving frames back and forth from yolo to core back to yolo. The decision was made to keep the camera_stream in core for now but init it from a UKDI var. 
-
-#### Flow Diagram
-
-The Flow has been broken into two parts to show the flow of the camera stream.
-
-```mermaid
-flowchart LR
-    UI[UI]
-    CORE[Core]
-    YOLO[YOLO]
-    CAM[Camera]
-
-    UI --> | Open Camera Stream | CORE
-
-    CORE --> | Open Camera Stream | YOLO
-    YOLO --> | Open Camera Stream | CAM
-    CAM --> | Frames | YOLO
-    YOLO --> | Frames | CORE
-```
-
-This flow below can be viewed within the method `cam_livefeed` in the file `yoloserv.py`.
-
-```mermaid
-flowchart LR
-    UI[UI]
-    CORE[CORE]
-    CAM[Camera]
-
-    UI --> | Open Camera Stream | CORE
-    CORE --> | Open Camera Stream | CAM
-    CAM --> | Frames | CORE
-    CORE --> | Camera Stream | UI
-```
-
-
-### Yoloserv pipeline
-
-```mermaid
-sequenceDiagram
-    participant UI
-    participant CORE
-    participant CAM
-    participant YOLO
-    participant para
-
-    rect rgba(200, 220, 255, 0.35)
-        UI->>CORE: pic_still
-        CORE->>CAM: capture still
-        CAM-->>CORE: still image
-        CORE-->>UI: still image
-    end
-
-    rect rgba(220, 255, 220, 0.35)
-        UI->>CORE: facematch (regula)
-        CORE->>YOLO: facematch
-        YOLO->>para: facematch
-        para-->>YOLO: result
-        YOLO-->>CORE: result
-        CORE-->>UI: result
-    end
-   
-```
-
-
-
-
-
-
-
-
-
-
-### Implement 2D detection
-
-Dispension faced the problem of needing a detection model in place for fully autonomous operation. To resolve this we implemented a 2D detection model that can be used to detect faces in a video stream. We implemented paravisions 2D model detection. Refer to para.md for more information on installing models. 
-
-Here are the results from a live user test:
-
-
-```bash
- <ValidnessResult (isValid=0, sharpness=-1.000000, quality=-1.000000, acceptability=-1.000000, frontality=-1.000000, mask=-1.000000, image_avg_pixel=115.375671, image_bright_pixel_pct=0.145549, image_dark_pixel_pct=0.030642, face_height_pct=0.318821, face_width_pct=0.208163, face_width=133.224304, face_height=153.033981, face_roll_angle=3.102657, face_positions=[0.377501,-0.001564,0.585664,0.317257])>
-HERE IS THE LIVENESS RESULT <LivenessResult (liveProbability=0.999998, spoofProbability=0.000002)>
-
-HERE IS THE VALIDNESS RESULT <ValidnessResult (isValid=0, sharpness=0.023801, quality=-1.000000, acceptability=-1.000000, frontality=97.000000, mask=0.008516, image_avg_pixel=58.403454, image_bright_pixel_pct=0.000000, image_dark_pixel_pct=0.000000, face_height_pct=0.551221, face_width_pct=0.579525, face_width=373.213989, face_height=446.488739, face_roll_angle=0.489146, face_positions=[0.182151,0.242310,0.761676,0.793531])>
-HERE IS THE LIVENESS RESULT <LivenessResult (liveProbability=0.057056, spoofProbability=0.830750)>
-
-
-```
-
-
-
-
-## Improvements
-
-Ideally we should be using the first frame to complete the facematch process and not for rendering. This would speed things up by a few seconds but is something for the future.

doc/gitea_setup.md

@@ -1,37 +0,0 @@
-# Gitea Setup
-
-## Table of Contents
-
-- [Gitea Setup](#gitea-setup)
-  - [Table of Contents](#table-of-contents)
-  - [Installation](#installation)
-  - [Configuration](#configuration)
-  - [Usage](#usage)
-  - [Troubleshooting](#troubleshooting)
-
-The Problem we faced is that yoloserv was to big to be hosted on Gitlab. Gitlab would also suspend the repo if bills were not paid and user seats were maxed. Obviously this was not a viable solution. Explored maybe self hosting without an intreface just to have repos but when I read Gitea did not need docker and could be run as a system service I tried it out.
-
-
-## Setting up the Server
-
-First step was setting up the server to run gitea. I scanned my local netowrk with ```bash arp-scan --localnet``` to find the IP address of the server. I could nto find my server running I had to reset the DHCP client with these commands. Use ```nmcli connection show``` to find the connection name and then ```nmcli connection down <connection name>``` and ```nmcli connection up <connection name>``` to reset the DHCP client. Was able to succesfully ```ping 8.8.8.8``` to confirm the server was running.
-
-## Installation
-
-I remoted onto the server and installed gitea with the following commands:
-
-Ran ```free -h``` to check memory and ```df -h``` to check disk space. Was recommended to have at least 8GB of RAM and 2.5 repo size of disk space. These seem a bit much. 
-
-Depending on what distro you run commands may vary but I:
-
-- Updated and upgraded the system
-- Installed git, openssh-server, ufw and fail2ban
-- sudo ufw enable, sudo ufw allow ssh, sudo ufw allow 3000
-
-#### SSH Hardening
-
-Can harden the SSH to be keys only should be done will be a future addition. Done in ```/etc/ssh/sshd_config```. Run ```sudo systemctl restart sshd``` to apply changes.
-```bash 
-PasswordAuthentication no
-PermitRootLogin no
-```

sbin/ctl.sh

@@ -101,7 +101,6 @@ do
                   PYP="$PYP:$PYP_DEEPFACE"
                    ;;
     "regula")     ;;
-    "traffic")     ;;
     "camera")     ;;
     *)            echo "yoloserv does not implement backend $i. Edit /etc/ukdi.json::yolo_devices and try again."
                   exit 1

src/deepfacex.py

@@ -80,9 +80,10 @@ class Deepfacex(FaceClass):
         return json.dumps(verification)
 
 
-    def analyze(self,name):
-        result = DeepFace.analyze(self.imgs["name"], actions=['age', 'gender', 'emotion', 'race'], enforce_detection=False)
-        return result
+    def metadata(self,name):
+        f1 = "/tmp/%s.png" % (name)
+        metadata = DeepFace.analyze(img_path = f1, actions = ["age", "gender", "emotion", "race"]) 
+        return json.dumps(metadata)
 
 
 #

src/face_recognitionx.py

@@ -38,11 +38,8 @@ import sys
 import os
 import face_recognition
 import json
-import numpy as np
 import urllib.request
 from faceclass import FaceClass
-from keras.models import load_model
-
 
 import time
 #
@@ -62,12 +59,8 @@ class FaceRecognition(FaceClass):
     #def init(): 
         #model = load_model("./emotion_detector_models/model.hdf5")
 
-    #def prep_detectors(self):
 
-
-    # @doc find all the faces in the named image.
-    #   The detectors tend to return propritery formats, so this "detect" method 
-    #    is going to be new for each implementation of FaceClass
+    # @doc find all the faces in the named image
     def detect(self, name):
         self.tree["detectms"] = time.time()
         boxes = []
@@ -89,25 +82,24 @@ class FaceRecognition(FaceClass):
         return '{ "status":0, "remark":"OK", "faces":%d, "boxes":%s, "time":%d }' % (len(self.boxes), json.dumps(self.boxes), self.tree["detectms"] )
 
 
-    # @doc find the landmarks of the given face (eyes, mouth, nose etc)
+    # @doc find the landmarks of the given face
     def landmarks(self, name):
         landmarks = face_recognition.face_landmarks(self.imgs[name])
         return '{ "status":0, "remark":"OK", "landmarks":%s }' %  json.dumps(landmarks)
 
 
-    # @doc find the metadata of the given face (emotion, age, gender, race)
-    def metadata(self, npimg):
-        model = load_model("models/emotion_model.hdf5")
-        print(time.time())
+    # @doc find the metadata of the given face
+    def metadata(self, name):
         emotion_dict= {'Angry': 0, 'Sad': 5, 'Neutral': 4, 'Disgust': 1, 'Surprise': 6, 'Fear': 2, 'Happy': 3}
-        im = cv2.resize(npimg, (64, 64))
-        im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
-        im = np.reshape(im, [1, im.shape[0], im.shape[1], 1])
-        predicted_class = np.argmax(model.predict(im))
+        face_image  = cv2.imread("..test_images/39.jpg")
+        face_image = cv2.resize(face_image, (48,48))
+        face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)
+        face_image = np.reshape(face_image, [1, face_image.shape[0], face_image.shape[1], 1])
+        model = load_model("./emotion_detector_models/model.hdf5")
+        predicted_class = np.argmax(model.predict(face_image))
         label_map = dict((v,k) for k,v in emotion_dict.items()) 
         predicted_label = label_map[predicted_class]
-        print(time.time())
-        return predicted_label
+        return '{ "status":88324, "remark":"override this", "landmarks":%s }' %  json.dumps(landmarks)
  
 
     # @doc compare two named images, previously loaded
@@ -158,8 +150,8 @@ if __name__ == '__main__':
     
     d = FaceRecognition()
 
-    if sys.argv[1]=="regula":
-        jsonstr = d.crowd_vs_govid("pic1", "/tmp/localcam.jpg", 0, "pic2", "/tmp/regula/Portrait_0.jpg", 0.25)
+    if sys.argv[1]=="kiosk":
+        jsonstr = d.crowd_vs_govid("pic1", "testimg/ox.jpg", 0, "pic2", "testimg/ox_govid.jpg", 0.25)
         print(jsonstr)
 
     if sys.argv[1]=="messi":
@@ -181,11 +173,8 @@ if __name__ == '__main__':
         d.stats("messi2_crop")
         d.compare("messi4_crop","messi2_rect")
 
-    if sys.argv[1]=="traffic":
-        jsonstr = d.traffic("traffic4", "testimg/messi4.jpg")
-        print(jsonstr)
 
-    if sys.argv[1]=="group":
+    if sys.argv[1]=="crowd":
         jsonstr = d.crowd_vs_govid("pic1", "testimg/messi4.jpg", 0, "pic2", "testimg/messi2.jpg", 0)
         print(jsonstr)
 
@@ -195,3 +184,17 @@ if __name__ == '__main__':
 
 
 
+    if sys.argv[1]=="match":
+        # lfw
+        n=0
+        print("LFW Matching")
+        for lfw in sorted(os.listdir("lfw/0001")):
+            d.load2("localcam","regula", "lfw/0001" + lfw, "lfw/0002/" + lfw)
+            d.get_faces()
+            d.compute_scores()
+            print(d.get_scores())
+            print(d.get_landmarks())
+            n+=1
+            if n > 1:
+                sys.exit(0)
+

src/faceclass.py

@@ -15,24 +15,13 @@ class FaceClass(object):
     model = None
     imgs = {}
     encs = {}
-    crops = {}
+    faces = {}
     visual = 0
     files = {}  
     boxes = []
-    jsonx = ""
     errstr = ""
-    vidcap = None
     tree = { "device1":"NA", "device2":"NA", "threshold":380, "device1_qual":0.5, "device2_qual":0.5, "score":0, "detectms":0, "comparems":0 }
  
-
-
-    def json2obj(self,jsonx):
-        print(jsonx)
-        self.jsonx = jsonx
-        return json.loads(jsonx)
-
-
- 
     #    #    #     #     #####
     #    ##   #     #       #
     #    # #  #     #       #
@@ -93,14 +82,11 @@ class FaceClass(object):
         return '{ "status":88241, "remark":"override this!" }' 
 
 
-
-
     # @doc find the biggest face in the named image
     # Imge has origin at top left and value are b = [ X1, Y1, X2, Y2 ]
     def ideal(self, name, rectname, cropname):
         found = -1
         biggest = -1
-        self.crops[name] = []
         print("** faceclass::ideal ... %s with %d boxes => %s + %s" % (name,  len(self.boxes), rectname, cropname ))
         # resize boxes
         for i in range(len(self.boxes)):
@@ -115,7 +101,6 @@ class FaceClass(object):
         b = self.boxes[found]
         # extract crops and highlights - colours are BGR
         self.imgs[cropname] = self.crop(name,b[0],b[1],b[2],b[3])
-        self.crops[name].append(self.imgs[cropname])
         self.imgs[rectname] = deepcopy(self.imgs[name])
         #print(self.imgs[name])
         #print(self.imgs[rectname])
@@ -146,36 +131,36 @@ class FaceClass(object):
         return '{ "status":88245, "remark":"override this!" }' 
 
 
+
     # @doc This does everything for you.
     #      If you are smartserv, "crowd" means cam and "govid" means regula pic
-    def crowd_vs_govid(self, name1,file1,scale1, name2,file2,scale2):
+    def crowd_vs_govid(self, name1,file1,scale1str, name2,file2,scale2str):
         print("##1##")
-        if self.json2obj(self.load1(name1, file1))["status"] != 0:
-            return self.jsonx
+        scale1 = float(scale1str)
+        scale2 = float(scale2str)
+        self.load1(name1, file1)
         if scale1 !=0:
             self.shrink(name1,scale1)
-        if self.json2obj(self.detect(name1))["status"] != 0:
-            return self.jsonx
+        jsonstr = self.detect(name1)
+        if json.loads(jsonstr)["status"]!=0:
+            return jsonstr
         self.boxscale(name1,0.3)
-
-        if self.json2obj(self.ideal(name1,name1+"_rect",name1+"_crop"))["status"] != 0:
-            return self.jsonx
+        self.ideal(name1,name1+"_rect",name1+"_crop")
         self.save(name1,"/tmp")
         self.save(name1+"_rect","/tmp")
         self.save(name1+"_crop","/tmp")
         print(self.imgs.keys())
 
         print("##2##")
-        if self.json2obj(self.load1(name2, file2))["status"] != 0:
-            return self.jsonx
+        self.load1(name2, file2)
         if scale2 !=0:
             self.shrink(name2,scale2)
         self.save(name2,"/tmp")
-        if self.json2obj(self.detect(name2))["status"]!=0:
-            return self.jsonx
+        jsonstr = self.detect(name2)
+        if json.loads(jsonstr)["status"]!=0:
+            return jsonstr
         self.boxscale(name2,0.3)
-        if self.json2obj(self.ideal(name2,name2+"_rect",name2+"_crop"))["status"] != 0:
-            return self.jsonx
+        self.ideal(name2,name2+"_rect",name2+"_crop")
         self.save(name2,"/tmp")
         self.save(name2+"_rect","/tmp")
         self.save(name2+"_crop","/tmp")
@@ -184,29 +169,6 @@ class FaceClass(object):
         print("##R##")        
         jsonstr = self.compare(name1+"_crop",name2+"_crop")
         print(jsonstr)
-        return jsonstr
-
-
-    # @doc This does deomgraphic examination on a pic.
-    #      If you are smartserv, "crowd" means cam and "govid" means regula pic
-    def traffic(self, name, file, scale=0):
-        print("##1##")
-        jsons = []
-        if self.json2obj(self.load1(name, file))["status"] != 0:
-            return self.jsonx
-        if scale !=0:
-            self.shrink(name,scale)
-        if self.json2obj(self.detect(name))["status"] != 0:
-            return self.jsonx
-        for i in range(len(self.boxes)):
-            b = self.boxes[i]
-            print(">>>>" , b)
-            analysis = self.metadata(self.imgs[name][b[0]:b[0]+b[1],b[2]:b[2]+b[3]])
-            jsons.append(analysis)
-        print(json.dumps(jsons))
-        return jsonx
-
- 
 
 
  ######  #####      #     #####
@@ -226,10 +188,10 @@ class FaceClass(object):
 
     def rebox(self,x1,y1,x2,y2,shape,scale=0.2):
         print("!!!!!!1 rebox with shape ",shape)
-        xx1 = x1 - int((x2-x1)*scale*0.8)
-        xx2 = x2 + int((x2-x1)*scale*0.8)
-        yy1 = y1 - int((y2-y1)*scale*1.3)
-        yy2 = y2 + int((y2-y1)*scale*1.3)
+        xx1 = x1 - int((x2-x1)*scale)
+        xx2 = x2 + int((x2-x1)*scale)
+        yy1 = y1 - int((y2-y1)*scale)
+        yy2 = y2 + int((y2-y1)*scale)
         if xx1 < 0:
             xx1 = 0
         if yy1 < 0:
@@ -252,14 +214,11 @@ class FaceClass(object):
 
     # @doc crop an image, allowing a gutter.
     def shrink(self, name, skale=0.5):
-        print ("shrinking ",name,skale)
-        if skale == 0:
-            return
+        print ("shrinking ",name)
         self.imgs[name] = cv2.resize(self.imgs[name],None,fx=skale,fy=skale)
 
 
 
-
  #####   ######   #####  #    #  #####   #    #
  #    #  #          #    #    #  #    #  ##   #
  #    #  #####      #    #    #  #    #  # #  #
@@ -267,7 +226,7 @@ class FaceClass(object):
  #   #   #          #    #    #  #   #   #   ##
  #    #  ######     #     ####   #    #  #    #
 
-    def scores(self):
+    def get_scores(self):
         return json.dumps(self.tree)
 
     # return a base64 version of the pic in memory

src/paravisionx.py

@@ -1,207 +1,199 @@
-from paravision.recognition.sdk import SDK
-from paravision.recognition.types import Settings, ValidnessCheck
-from paravision.recognition.exceptions import ParavisionException
-from paravision.liveness2d import SDK as Liveness2DSDK
-from paravision.liveness2d.types import (
-    Settings as Liveness2DSettings,
-    ValidnessSettings,
-)
-import paravision.recognition.utils as pru
+#
+# Paravision based face matcher
+#
 import json
 import sys
-import os
+
+### export PYTHONPATH=/wherever/yoloserv/modules ... as long as "paravision/.../" is in there
+from paravision.recognition.exceptions import ParavisionException
+from paravision.recognition.engine import Engine
+from paravision.recognition.sdk import SDK
+import paravision.recognition.utils as pru
+#from openvino.inference_engine import Engineq
+
+#from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
 from faceclass import FaceClass
 
 
+ 
 class Paravision(FaceClass):
-    models = {}
-    scores = {}
-    quality = {}
-    sdk = None
-    match_score = 0
 
-    def init(self):
+    def init(self,backend=None,model=None):
+        print("@@@ initialising paravision")
         try:
-            print("INIT FROM PARAVISION")
-            settings = Settings()
-            # 1 is the default
-            settings.worker_count = 4
-            settings.detection_model = "default"
-            settings.validness_checks = ValidnessCheck.LIVENESS
-            self.sdk = SDK(settings=settings)
-            liveness2d_settings = Liveness2DSettings()
-            self.liveness2d_sdk = Liveness2DSDK(settings=liveness2d_settings)
-        except ParavisionException as e:
-            # error handling logic
-            print("Exception:", e)
+            self.sdk = SDK(engine=Engine.AUTO)
+        except ParavisionException:
+            pass
+
 
-    # @doc Load a pic using the device label
-    def load1(self, name, fname):
-        print(" Loading image '%s' from file %s" % (name, fname))
-        if not os.path.isfile(fname):
-            print(" * file not found: %s" % (fname))
-            return (
-                '{ "status":442565, "remark":"file name not found", "guilty_param":"fname", "guilty_value":"%s" }'
-                % (fname)
-            )
-        self.files[name] = fname
-        self.imgs[name] = pru.load_image(fname)
-        print(" Loaded %s from file %s" % (name, fname))
-        return '{ "status":0, "remark":"OK", "name":"%s", "fname":"%s" }' % (
-            name,
-            fname,
-        )
 
     # @doc find all the faces in the named image
     def detect(self, name):
+        boxes = []
         self.boxes = []
         print("** face_recognition::detect ... %s" % name)     
         try:
             # Get all faces from images with qualities, landmarks, and embeddings
-            faces = self.sdk.get_faces(
-                [self.imgs[name]], qualities=True, landmarks=True, embeddings=True
-            )
-            print("HERE IS THE FACES %s" % faces)
-            inferences = faces.image_inferences
-            print("HERE IS THE INFERENCE %s" % inferences)
-            ix = inferences[0].most_prominent_face_index()
-            face = inferences[0].faces[ix]
-            print("HERE IS THE FACE %s" % face)
-            self.models[name] = inferences[0].faces[ix].embedding
-            self.quality[name] = round(1000 * inferences[0].faces[ix].quality)
-            self.boxes = [(0, 0, 0, 0)]
+            res = self.sdk.get_faces([self.imgs[name]], qualities=True, landmarks=True, embeddings=True)
+            boxes = res.faces
+            print(boxes)
+            for a in boxes:
+                print(box)
+            # box is somehow =  y1 / x2 / y2 / x1 for face_recognition .
+            #                =  
+            # Thats crazy lol. We need to fix that to x1, y1, x2, y2 with origin at top left
+            for b in boxes:
+                self.boxes.append((b[3],b[0],b[1],b[2]))
+            print("found %d boxes for %s" % (len(self.boxes), name) )
         except Exception as ex:
-            self.errstr = "image processing exception at get_faces: " + str(ex)
-            return (
-                '{ "status":222310, "remark":"image processing exception", "guilty_param":"error", "guilty_value":"%s" }'
-                % str(ex)
-            )
-        return '{ "status":0, "remark":"OK", "faces":%d, "boxes":%s }' % (
-            len(self.boxes),
-            json.dumps(self.boxes),
-        )
+            self.errstr = "image processing exception at get_faces: "+str(ex)
+            return '{ "status":222310, "remark":"image processing exception", "guilty_param":"error", "guilty_value":"%s" }' % str(ex)
+        return '{ "status":0, "remark":"OK", "faces":%d, "boxes":%s }' % (len(self.boxes), json.dumps(self.boxes))
 
-    def detect_liveness(self, name):
-        self.boxes = []
-        print("** face_recognition::detect ... %s" % name)
+
+
+    # Assess the face that was read in
+    def processiii(self):
+        # Get all faces metadata
+        print("Finding faces in %s" %(self.imgpath))
+        faces = self.sdk.get_faces([self.image], qualities=True, landmarks=True, embeddings=True)
+        print("Getting metadata")
+        inferences = faces.image_inferences
+        print("Getting best face")
+        ix = inferences[0].most_prominent_face_index()
+        print("Getting a mathematical mode of that best face")
+        self.model = inferences[0].faces[ix].embedding
+        print("Getting image quality scores..")
+        self.score = round(1000*inferences[0].faces[ix].quality)
+        print("Score was %d" %(self.score))
+        return self.score
+       
+    # Compare to a face in another Facematch instance   
+    def compare(self,other): 
+        # Get face match score
+        return self.sdk.get_match_score(self.model, other.model)
+
+
+    #mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm
+ 
+
+    # 
+    def load(self, dev1, dev2, id_image_filepath, photo_image_filepath):
+        print("## loading images", dev1, dev2)
+        self.dev1 = dev1
+        self.dev2 = dev2
+        try:
+            self.id_image = pru.load_image(id_image_filepath)
+        except Exception as e:
+            return "id image loading failed ", e
+        try:
+            self.photo_image = pru.load_image(photo_image_filepath)
+        except Exception as e:
+            return "client image loading failed ", e
+        return None
+
+
+
+    def get_faces(self):
         try:
             # Get all faces from images with qualities, landmarks, and embeddings
-            faces = self.sdk.get_faces(
-                [self.imgs[name]], qualities=True, landmarks=True, embeddings=True
-            )
-            print("HERE IS THE FACES %s" % faces)
-            inferences = faces.image_inferences
-            print("HERE IS THE INFERENCE %s" % inferences)
-            ix = inferences[0].most_prominent_face_index()
-            face = inferences[0].faces[ix]
-            print("HERE IS THE FACE %s" % face)
-            liveness_result = self.validness(face, self.sdk)
-            live_prob = getattr(liveness_result, "live_prob", None)
-            print("HERE IS THE LIVE PROB %s" % live_prob)
-            if live_prob < 0.5:
-                return (
-                    '{ "status":222310, "remark":"image processing exception", "guilty_param":"error", "guilty_value":"%s" }'
-                    % live_prob
-                )
-            self.models[name] = inferences[0].faces[ix].embedding
-            self.quality[name] = round(1000 * inferences[0].faces[ix].quality)
-            self.boxes = [(0, 0, 0, 0)]
+            print("Finding faces...")
+            self.inference_result = self.sdk.get_faces([self.id_image, self.photo_image], qualities=True, landmarks=True, embeddings=True)
+            print("Inferences...")
+            self.image_inference_result = self.inference_result.image_inferences
+            if len(self.image_inference_result)==0:
+                return "no inferences found"
+
+            # Get most prominent face
+            print("Most prominent...")
+            self.id_face = self.image_inference_result[0].most_prominent_face_index()
+            self.photo_face = self.image_inference_result[1].most_prominent_face_index()
+            if self.id_face<0:
+                return "no id face found"
+            if self.photo_face<0:
+                return "no live face found"
+
+            # Get numerical representation of faces (required for face match)
+            print("stats...")
+            if (len(self.image_inference_result)<2):
+                return "ID or human face could not be recognised"
+            self.id_emb = self.image_inference_result[0].faces[self.id_face].embedding
+            self.photo_emb = self.image_inference_result[1].faces[self.photo_face].embedding
+
         except Exception as ex:
-            self.errstr = "image processing exception at get_faces: " + str(ex)
-            return (
-                '{ "status":222310, "remark":"image processing exception", "guilty_param":"error", "guilty_value":"%s" }'
-                % str(ex)
-            )
-        return '{ "status":0, "remark":"OK", "faces":%d, "boxes":%s }' % (
-            len(self.boxes),
-            json.dumps(self.boxes),
-        )
+            return "image processing exception "+str(ex)
 
-    # @doc This does everything for you.
-    #      If you are smartserv, "crowd" means cam and "govid" means regula pic
-    def crowd_vs_govid(self, name1, file1, scale1, name2, file2, scale2):
-        print("##1## DETECTING FROM IMG")
-        if self.json2obj(self.load1(name1, file1))["status"] != 0:
-            return self.jsonx
-        if self.json2obj(self.detect_liveness(name1))["status"] != 0:
-            return self.jsonx
-        self.save(name1, "/tmp")
+        return None
 
-        print("##2## DETECTING FROM ID")
-        if self.json2obj(self.load1(name2, file2))["status"] != 0:
-            return self.jsonx
-        self.save(name2, "/tmp")
-        if self.json2obj(self.detect(name2))["status"] != 0:
-            return self.jsonx
-        self.save(name2, "/tmp")
 
-        print("##R##")
-        jsonstr = self.compare(name1, name2)
-        print(jsonstr)
-        return jsonstr
 
-    # @doc compare two named images, previously loaded
-    def compare(self, name1, name2):
-        print("** face_recognition::compare ... %s vs %s" % (name1, name2))
+    def compute_scores(self):
         try:
-            res = self.sdk.get_match_score(self.models[name1], self.models[name2])
-            print("Match is ", res)
-            self.match_score = res
+            # Get image quality scores (how 'good' a face is)
+            self.id_qual = self.image_inference_result[0].faces[self.id_face].quality
+            self.photo_qual = self.image_inference_result[1].faces[self.photo_face].quality
+
+            self.id_qual = round(self.id_qual, 3)
+            self.photo_qual = round(self.photo_qual, 3)
+
+            # Get face match score
+            self.match_score = self.sdk.get_match_score(self.id_emb, self.photo_emb)
+
+            # Create .json
+            self.face_match_json = {"device1":self.dev1,
+                                    "device2":self.dev2,
+                                  "passmark":500,
+                               "device1_qual":self.id_qual,
+                               "device2_qual":self.photo_qual,
+                               "match_score":self.match_score}
+
+            #return json.dumps(self.face_match_json)
+
+            #print(self.face_match_json)
+
+            # Send to core
+            #url = "%s/notify/%s/%s" % (self.conf["core"], self.conf["identity"], face_match_json)
+            #url = url.replace(" ", "%20") # Remove spaces
+            #buf = []
+            #req = urllib.request.Request( url )
+            #with urllib.request.urlopen(req) as response:
+                #print(response.read())
+
         except Exception as ex:
-            print("** paravision::compare exception ... " + str(ex))
-            self.errstr = "image comparison exception at compute_scores: " + str(ex)
-            return '{ "status":332410, "remark":"%s" }' % self.errstr
-        return (
-            '{ "status":0, "threshold": 500, "device1_qual": 0.5, "device2_qual": 0.5, "remark":"OK", "score":%d }'
-            % self.match_score
-        )
-
-    def validness(self, face, sdk):
-        validness_settings = ValidnessSettings(face)
-        validness_result = self.liveness2d_sdk.check_validness(
-            face, validness_settings, sdk
-        )
-        print("HERE IS THE VALIDNESS RESULT %s" % validness_result)
-        liveness_result = self.liveness2d_sdk.get_liveness(face)
-        print("HERE IS THE LIVENESS RESULT %s" % liveness_result)
-        return liveness_result
-
-    def scores(self):
-        return (
-            '{ "status":0, "threshold": 500, "device1_qual": 0.5, "device2_qual": 0.5, "remark":"OK", "score":%d }'
-            % self.match_score
-        )
+            return str(ex)
 
 
-if __name__ == "__main__":
+    def get_scores(self):
+        return json.dumps(self.face_match_json)
 
-    d = Paravisionox()
+
+
+
+
+
+if __name__ == '__main__':
+    
+    d = Paravision()
     d.init()
 
-    if sys.argv[1] == "messia":
+    if sys.argv[1]=="messia":
         jsonstr = d.load1("pic1", "testimg/messi4.jpg")
         print(jsonstr)
         jsonstr = d.detect("pic1")
         print(jsonstr)
 
-    if sys.argv[1] == "test":
+    if sys.argv[1]=="test":
         d.load1("pic1", "testimg/ox.jpg")
         d.detect("pic1")
 
-    if sys.argv[1] == "kiosk":
-        jsonstr = d.crowd_vs_govid(
-            "pic1", "testimg/ox.jpg", 0, "pic2", "testimg/ox_govid.jpg", 0.25
-        )
+    if sys.argv[1]=="kiosk":
+        jsonstr = d.crowd_vs_govid("pic1", "testimg/ox.jpg", 0, "pic2", "testimg/ox_govid.jpg", 0.25)
         print(jsonstr)
 
-    if sys.argv[1] == "messi":
-        jsonstr = d.crowd_vs_govid(
-            "pic1", "testimg/messi4.jpg", 0, "pic2", "testimg/messi2.jpg", 0
-        )
+    if sys.argv[1]=="messi":
+        jsonstr = d.crowd_vs_govid("pic1", "testimg/messi4.jpg", 0, "pic2", "testimg/messi2.jpg", 0)
         print(jsonstr)
 
-    if sys.argv[1] == "maiden":
-        jsonstr = d.crowd_vs_govid(
-            "pic1", "testimg/ironmaiden.jpg", 0, "pic2", "testimg/davemurray.jpg", 0
-        )
+    if sys.argv[1]=="maiden":
+        jsonstr = d.crowd_vs_govid("pic1", "testimg/ironmaiden.jpg", 0, "pic2", "testimg/davemurray.jpg", 0)
         print(jsonstr)

src/yoloserv.py

@@ -32,7 +32,6 @@ class yoloserv(object):
     palmdetector = None
     facematcher = None
     palmmatcher = None
-    traffic = None
     ir_camera = None 
     devices = []
     points = []
@@ -216,13 +215,6 @@ class yoloserv(object):
         return self.facematcher.compare(name1,name2)
 
         
-    # Traffic analysis
-    @cherrypy.expose
-    def svc_traffic(self,infile=None):
-        return self.facematcher.traffic(infile)
-
-
-        
 
     @cherrypy.expose
     def shutdown(self):
@@ -240,15 +232,15 @@ class yoloserv(object):
 
     # Match faces together
     @cherrypy.expose
-    def svc_match_faces(self,dev1,fil1,scl1s,dev2,fil2,scl2s):
-        scl1 = float(scl1s)
-        scl2 = float(scl2s)
-        jsonstr = self.facematcher.crowd_vs_govid(dev1,self.conf["yolo_indir"]+fil1,scl1, dev2,self.conf["yolo_indir"]+fil2,scl2)
+    def svc_match_faces(self,dev1,fil1,scl1,dev2,fil2,scl2):
+        jsonstr = self.facematcher.crowd_vs_govid(dev1,self.conf["yolo_indir"]+fil1,scl1, dev2,self.conf["yolo_outdir"]+fil2,scl2)
         obj = self.json2obj(jsonstr)
         return jsonstr
+        if obj.status > 0:
+            return jsonstr
  
     def json2obj(self,jsonx):
-        return json.loads(jsonx)
+        return json.laods(jsonx)
 
     # @doc put all the steps for a retail facematch into one convenient functions
     @cherrypy.expose
@@ -276,7 +268,7 @@ class yoloserv(object):
 
         jsonstr = self.facematcher.crowd_vs_govid(dev1,fil1,scl1, dev2,fil2,scl2)
         obj = self.json2obj(jsonstr)
-        if obj["status"] > 0:
+        if obj.status > 0:
             return jsonstr
         jsonstr = self.facematcher.scores()
         return '{ "status":0, "remark":"OK", "data": %s }' % (jsonstr)