172 lines
6.0 KiB
Python
172 lines
6.0 KiB
Python
from statistics import mode
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import imutils
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import cv2
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import numpy as np
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from imutils.video import VideoStream
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import time
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from preprocessor import preprocess_input
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from imutils.face_utils import FaceAligner
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from imutils.face_utils import rect_to_bb
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import dlib
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shape_predictor = "shape_predictor_68_face_landmarks.dat"
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predictor = dlib.shape_predictor(shape_predictor)
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from mtcnn.mtcnn import MTCNN
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detector = MTCNN()
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# Support functions
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def get_labels(dataset_name):
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if dataset_name == 'KDEF':
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return {0: 'AN', 1: 'DI', 2: 'AF', 3: 'HA', 4: 'SA', 5: 'SU', 6: 'NE'}
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else:
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raise Exception('Invalid dataset name')
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def detect_faces(detection_model, gray_image_array, conf):
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frame = gray_image_array
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# Grab frame dimention and convert to blob
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(h,w) = frame.shape[:2]
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# Preprocess input image: mean subtraction, normalization
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blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,
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(300, 300), (104.0, 177.0, 123.0))
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# Set read image as input to model
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detection_model.setInput(blob)
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# Run forward pass on model. Receive output of shape (1,1,no_of_predictions, 7)
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predictions = detection_model.forward()
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coord_list = []
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count = 0
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for i in range(0, predictions.shape[2]):
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confidence = predictions[0,0,i,2]
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if confidence > conf:
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# Find box coordinates rescaled to original image
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box_coord = predictions[0,0,i,3:7] * np.array([w,h,w,h])
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conf_text = '{:.2f}'.format(confidence)
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# Find output coordinates
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xmin, ymin, xmax, ymax = box_coord.astype('int')
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coord_list.append([xmin, ymin, (xmax-xmin), (ymax-ymin)])
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print('Coordinate list:', coord_list)
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return coord_list
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def draw_text(coordinates, image_array, text, color, x_offset=0, y_offset=0,
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font_scale=2, thickness=2):
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x, y = coordinates[:2]
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cv2.putText(image_array, text, (x + x_offset, y + y_offset),
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cv2.FONT_HERSHEY_SIMPLEX,
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font_scale, color, thickness, cv2.LINE_AA)
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def draw_bounding_box(face_coordinates, image_array, color, identity):
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x, y, w, h = face_coordinates
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cv2.rectangle(image_array, (x, y), (x + w, y + h), color, 2)
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cv2.putText(image_array, str(identity), (x+5,y-5), font, 1, (255,255,255), 2)
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def apply_offsets(face_coordinates, offsets):
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x, y, width, height = face_coordinates
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x_off, y_off = offsets
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return (x - x_off, x + width + x_off, y - y_off, y + height + y_off)
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def load_detection_model(prototxt, weights):
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detection_model = cv2.dnn.readNetFromCaffe(prototxt, weights)
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return detection_model
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# parameters for loading data and images
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prototxt = 'trained_models/deploy.prototxt.txt'
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weights = 'trained_models/res10_300x300_ssd_iter_140000.caffemodel'
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font = cv2.FONT_HERSHEY_SIMPLEX
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frame_window = 10
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face_offsets = (30, 40)
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emotion_offsets = (20, 40)
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confidence = 0.6
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# loading models
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face_detection = load_detection_model(prototxt, weights)
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# face_detection_size = (40, 40)
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counter = 0
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# frame_process_counter = 0
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def crop_face(file_name, face_detection, name_count):
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face_detection_size = (40, 40)
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counter = 0
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frame_process_counter = 0
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# starting video streaming
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cv2.namedWindow('Attendence_Tracker', cv2.WINDOW_NORMAL)
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# cv2.namedWindow('Attendence_Tracker')
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# file_name = '../top10/person1.mp4'
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video_capture = cv2.VideoCapture(file_name)
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time.sleep(1.0)
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while (video_capture.isOpened()):
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ret, bgr_image = video_capture.read()
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if ret == False:
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break
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counter += 1
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if counter % 1 == 0:
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frame_process_counter += 1
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gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
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rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
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faces = detect_faces(face_detection, bgr_image,confidence)
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count = 0
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for face_coordinates in faces:
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x1, x2, y1, y2 = apply_offsets(face_coordinates, face_offsets)
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rgb_face = rgb_image[y1:y2, x1:x2]
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print("len", len(rgb_face))
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# print(rgb_face)
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if len(rgb_face) != 0 and counter % 1 ==0:
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print(detector.detect_faces(rgb_face))
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dict_mtcnn = detector.detect_faces(rgb_face)
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if len(dict_mtcnn) != 0:
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bounding_box = dict_mtcnn[0]['box']
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new_image = rgb_image[bounding_box[2]:bounding_box[3], bounding_box[0]:bounding_box[1]]
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cv2.rectangle(new_image,
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(bounding_box[0], bounding_box[1]),
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(bounding_box[0]+bounding_box[2], bounding_box[1] + bounding_box[3]),
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(0,155,255), 2)
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# cv2.imwrite("align/align_{}/align_{}_{}".format(name_count, name_count,counter) + ".jpg", cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
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cv2.imwrite("align/emp_{}/emp_{}_{}".format(name_count, name_count,counter) + ".jpg", cv2.cvtColor(rgb_face, cv2.COLOR_RGB2BGR))
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print("image saved-------------------", counter)
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count += 1
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try:
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rgb_face = cv2.resize(rgb_face, (face_detection_size))
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except:
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continue
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rgb_face = np.expand_dims(rgb_face, 0)
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rgb_face = preprocess_input(rgb_face, False)
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# Bounding box color
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color = (255, 0, 0)
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identity = "this is me"
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draw_bounding_box(face_coordinates, rgb_image, color, identity)
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bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
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cv2.imshow('Attendence_Tracker', bgr_image)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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print('Total frames processed:', counter, frame_process_counter)
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break
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video_capture.release()
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# out.release()
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cv2.destroyAllWindows()
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return "successful"
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