Create app.py

app.py

@@ -0,0 +1,90 @@
+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()