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| import cv2 | |
| import dlib | |
| import numpy as np | |
| from math import hypot | |
| import time | |
| from scipy.spatial.distance import euclidean | |
| # face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') | |
| detector = dlib.get_frontal_face_detector() | |
| # wget -nd https://github.com/JeffTrain/selfie/raw/master/shape_predictor_68_face_landmarks.dat | |
| predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") | |
| blink_count = 0 | |
| time_limit = 7 | |
| def mid_point(p1,p2): | |
| return int((p1.x + p2.x)/2) , int((p1.y + p2.y)/2) | |
| def get_ratio(eye_points, facial_landmarks): | |
| # x-axis | |
| left_point = (facial_landmarks.part(eye_points[0]).x, facial_landmarks.part(eye_points[0]).y) | |
| right_point = (facial_landmarks.part(eye_points[3]).x, facial_landmarks.part(eye_points[3]).y) | |
| # y-axis | |
| center_top = mid_point(facial_landmarks.part(eye_points[1]), facial_landmarks.part(eye_points[2])) | |
| center_bottom = mid_point(facial_landmarks.part(eye_points[5]), facial_landmarks.part(eye_points[4])) | |
| hor_line = cv2.line(frame, left_point, right_point, (0, 255, 0), 1) | |
| ver_line = cv2.line(frame, center_top, center_bottom, (0, 255, 0), 1) | |
| # hor_line_length = hypot((left_point[0] - right_point[0]), (left_point[1] - right_point[1])) | |
| # ver_line_length = hypot((center_top[0] - center_bottom[0]), (center_top[1] - center_bottom[1])) | |
| hor_line_length = euclidean(left_point, right_point) | |
| ver_line_length = euclidean(center_top, center_bottom) | |
| if ver_line_length == 0: | |
| return 0 | |
| ratio = hor_line_length / ver_line_length | |
| return ratio | |
| cap = cv2.VideoCapture(0) | |
| start = time.time() | |
| while True: | |
| _ , frame = cap.read() | |
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| faces = detector(gray) | |
| # print(faces) | |
| for face in faces: | |
| x,y = face.left(), face.top() | |
| x1, y1 = face.right() , face.bottom() | |
| cv2.rectangle(frame, (x,y), (x1,y1), (0,0,255), 2) | |
| landmarks = predictor(gray, face) | |
| left_eye_ratio = get_ratio([36,37,38,39,40,41], landmarks) | |
| right_eye_ratio = get_ratio([42,43,44,45,46,47],landmarks) | |
| blink_ratio = left_eye_ratio + right_eye_ratio / 2 | |
| # print(blink_ratio) | |
| if blink_ratio > 8.2 and blink_ratio < 9.3 : | |
| cv2.putText(frame, "BLINKING..", (130,180), cv2.FONT_HERSHEY_PLAIN, 6, (255, 0, 0)) | |
| blink_count = blink_count + 1 | |
| blink_count | |
| cv2.putText(frame, str(blink_count), (40,130), cv2.FONT_HERSHEY_PLAIN, 6, (255, 0, 0), 2) | |
| if blink_count >= 2: | |
| cv2.putText(frame, "HUMANNNN!!!!", (70,200), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2) | |
| print("\n\n\n\n HUMAN !! \n\n\n\n\n") | |
| break | |
| if time.time() - start > time_limit: | |
| cv2.putText(frame, "BOT!!!!", (70,200), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2) | |
| print("\n\n\n\n BOT !!!!\n\n\n\n\n") | |
| break | |
| cv2.putText(frame, "BLINK 3-4 times", (40,150), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2) | |
| cv2.imshow("frame", frame) | |
| time_left = time.time() - start | |
| print(f'TIME LEFT : {time_left:.2f} sec / 7 sec') | |
| if cv2.waitKey(1) & 0xFF == ord('q'): | |
| break | |
| cap.release() | |
| cv2.destroyAllWindows() |