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### General imports ###
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import numpy as np
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import pandas as pd
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import cv2
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from time import time
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from time import sleep
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import re
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import os
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import argparse
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from collections import OrderedDict
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import matplotlib.animation as animation
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### Image processing ###
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from scipy.ndimage import zoom
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from scipy.spatial import distance
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import imutils
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from scipy import ndimage
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import dlib
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from __future__ import division
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from tensorflow.keras.models import load_model
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from imutils import face_utils
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import requests
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class ModelPredict() :
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def __init__(self, path, model, image) :
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self.image = image
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self.path = path
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self.model = model
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def detect_face(frame):
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global shape_x
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global shape_y
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global input_shape
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global nClasses
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shape_x = 48
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shape_y = 48
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input_shape = (shape_x, shape_y, 1)
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nClasses = 7
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#Cascade classifier pre-trained model
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cascPath = 'Models/face_landmarks.dat'
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faceCascade = cv2.CascadeClassifier(cascPath)
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#BGR -> Gray conversion
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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#Cascade MultiScale classifier
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detected_faces = faceCascade.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=6,
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minSize=(shape_x, shape_y),
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flags=cv2.CASCADE_SCALE_IMAGE)
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coord = []
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for x, y, w, h in detected_faces :
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if w > 100 :
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sub_img=frame[y:y+h,x:x+w]
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cv2.rectangle(frame,(x,y),(x+w,y+h),(0, 255,255),1)
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coord.append([x,y,w,h])
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return gray, detected_faces, coord
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def extract_face_features(faces, offset_coefficients=(0.075, 0.05)):
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gray = faces[0]
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detected_face = faces[1]
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new_face = []
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for det in detected_face :
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#Region dans laquelle la face est détectée
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x, y, w, h = det
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#X et y correspondent à la conversion en gris par gray, et w, h correspondent à la hauteur/largeur
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#Offset coefficient, np.floor takes the lowest integer (delete border of the image)
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horizontal_offset = np.int(np.floor(offset_coefficients[0] * w))
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vertical_offset = np.int(np.floor(offset_coefficients[1] * h))
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#gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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#gray transforme l'image
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extracted_face = gray[y+vertical_offset:y+h, x+horizontal_offset:x-horizontal_offset+w]
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#Zoom sur la face extraite
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new_extracted_face = zoom(extracted_face, (shape_x / extracted_face.shape[0],shape_y / extracted_face.shape[1]))
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#cast type float
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new_extracted_face = new_extracted_face.astype(np.float32)
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#scale
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new_extracted_face /= float(new_extracted_face.max())
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#print(new_extracted_face)
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new_face.append(new_extracted_face)
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return new_face
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def start_live() :
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(nStart, nEnd) = face_utils.FACIAL_LANDMARKS_IDXS["nose"]
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(mStart, mEnd) = face_utils.FACIAL_LANDMARKS_IDXS["mouth"]
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(jStart, jEnd) = face_utils.FACIAL_LANDMARKS_IDXS["jaw"]
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(eblStart, eblEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eyebrow"]
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(ebrStart, ebrEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eyebrow"]
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model = load_model('Models/video.h5')
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#Lancer la capture video
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video_capture = cv2.VideoCapture(0)
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while True:
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# Capture frame-by-frame
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ret, frame = video_capture.read()
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face_index = 0
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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rects = face_detect(gray, 1)
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#gray, detected_faces, coord = detect_face(frame)
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for (i, rect) in enumerate(rects):
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shape = predictor_landmarks(gray, rect)
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shape = face_utils.shape_to_np(shape)
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# Identify face coordinates
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(x, y, w, h) = face_utils.rect_to_bb(rect)
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face = gray[y:y+h,x:x+w]
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#Zoom on extracted face
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face = zoom(face, (shape_x / face.shape[0],shape_y / face.shape[1]))
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#Cast type float
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face = face.astype(np.float32)
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#Scale
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face /= float(face.max())
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face = np.reshape(face.flatten(), (1, 48, 48, 1))
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#Make Prediction
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prediction = model.predict(face)
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prediction_result = np.argmax(prediction)
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# Rectangle around the face
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cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
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cv2.putText(frame, "Face #{}".format(i + 1), (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
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for (j, k) in shape:
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cv2.circle(frame, (j, k), 1, (0, 0, 255), -1)
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# 1. Add prediction probabilities
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cv2.putText(frame, "----------------",(40,100 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 0)
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cv2.putText(frame, "Emotional report : Face #" + str(i+1),(40,120 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 0)
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cv2.putText(frame, "Angry : " + str(round(prediction[0][0],3)),(40,140 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 0)
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cv2.putText(frame, "Disgust : " + str(round(prediction[0][1],3)),(40,160 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 0)
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cv2.putText(frame, "Fear : " + str(round(prediction[0][2],3)),(40,180 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 1)
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cv2.putText(frame, "Happy : " + str(round(prediction[0][3],3)),(40,200 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 1)
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cv2.putText(frame, "Sad : " + str(round(prediction[0][4],3)),(40,220 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 1)
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cv2.putText(frame, "Surprise : " + str(round(prediction[0][5],3)),(40,240 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 1)
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cv2.putText(frame, "Neutral : " + str(round(prediction[0][6],3)),(40,260 + 180*i), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 155, 1)
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# 2. Annotate main image with a label
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if prediction_result == 0 :
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cv2.putText(frame, "Angry",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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elif prediction_result == 1 :
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cv2.putText(frame, "Disgust",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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elif prediction_result == 2 :
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cv2.putText(frame, "Fear",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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elif prediction_result == 3 :
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cv2.putText(frame, "Happy",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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elif prediction_result == 4 :
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cv2.putText(frame, "Sad",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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elif prediction_result == 5 :
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cv2.putText(frame, "Surprise",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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else :
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cv2.putText(frame, "Neutral",(x+w-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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# 3. Eye Detection and Blink Count
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leftEye = shape[lStart:lEnd]
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rightEye = shape[rStart:rEnd]
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# Compute Eye Aspect Ratio
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leftEAR = eye_aspect_ratio(leftEye)
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rightEAR = eye_aspect_ratio(rightEye)
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ear = (leftEAR + rightEAR) / 2.0
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# And plot its contours
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leftEyeHull = cv2.convexHull(leftEye)
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rightEyeHull = cv2.convexHull(rightEye)
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cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
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cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
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# 4. Detect Nose
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nose = shape[nStart:nEnd]
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noseHull = cv2.convexHull(nose)
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cv2.drawContours(frame, [noseHull], -1, (0, 255, 0), 1)
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# 5. Detect Mouth
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mouth = shape[mStart:mEnd]
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mouthHull = cv2.convexHull(mouth)
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cv2.drawContours(frame, [mouthHull], -1, (0, 255, 0), 1)
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# 6. Detect Jaw
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jaw = shape[jStart:jEnd]
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jawHull = cv2.convexHull(jaw)
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cv2.drawContours(frame, [jawHull], -1, (0, 255, 0), 1)
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# 7. Detect Eyebrows
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ebr = shape[ebrStart:ebrEnd]
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ebrHull = cv2.convexHull(ebr)
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cv2.drawContours(frame, [ebrHull], -1, (0, 255, 0), 1)
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ebl = shape[eblStart:eblEnd]
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eblHull = cv2.convexHull(ebl)
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cv2.drawContours(frame, [eblHull], -1, (0, 255, 0), 1)
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cv2.putText(frame,'Number of Faces : ' + str(len(rects)),(40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, 155, 1)
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cv2.imshow('Video', frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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# When everything is done, release the capture
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video_capture.release()
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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import sys
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if len(sys.argv) is not 3:
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print("Usage: python google_drive.py drive_file_id destination_file_path")
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else:
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# TAKE ID FROM SHAREABLE LINK
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file_id = sys.argv[1]
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# DESTINATION FILE ON YOUR DISK
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destination = sys.argv[2]
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download_file_from_google_drive(file_id, destination)
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