Added eyes and blink detector

Author: jay24rajput

blink_detection_demo.mp4

@@ -0,0 +1,152 @@
+# USAGE
+# python detect_blinks.py --shape-predictor shape_predictor_68_face_landmarks.dat --video blink_detection_demo.mp4
+# python detect_blinks.py --shape-predictor shape_predictor_68_face_landmarks.dat
+
+# import the necessary packages
+from scipy.spatial import distance as dist
+from imutils.video import FileVideoStream
+from imutils.video import VideoStream
+from imutils import face_utils
+import numpy as np
+import argparse
+import imutils
+import time
+import dlib
+import cv2
+
+def eye_aspect_ratio(eye):
+	# compute the euclidean distances between the two sets of
+	# vertical eye landmarks (x, y)-coordinates
+	A = dist.euclidean(eye[1], eye[5])
+	B = dist.euclidean(eye[2], eye[4])
+
+	# compute the euclidean distance between the horizontal
+	# eye landmark (x, y)-coordinates
+	C = dist.euclidean(eye[0], eye[3])
+
+	# compute the eye aspect ratio
+	ear = (A + B) / (2.0 * C)
+
+	# return the eye aspect ratio
+	return ear
+ 
+# construct the argument parse and parse the arguments
+# ap = argparse.ArgumentParser()
+# ap.add_argument("-p", "--shape-predictor", required=True,
+# 	help="path to facial landmark predictor")
+# ap.add_argument("-v", "--video", type=str, default="",
+# 	help="path to input video file")
+# args = vars(ap.parse_args())
+ 
+# define two constants, one for the eye aspect ratio to indicate
+# blink and then a second constant for the number of consecutive
+# frames the eye must be below the threshold
+EYE_AR_THRESH = 0.3
+EYE_AR_CONSEC_FRAMES = 3
+
+# initialize the frame counters and the total number of blinks
+COUNTER = 0
+TOTAL = 0
+
+# initialize dlib's face detector (HOG-based) and then create
+# the facial landmark predictor
+print("[INFO] loading facial landmark predictor...")
+detector = dlib.get_frontal_face_detector()
+# print(type(args["shape_predictor"]))
+# exit()
+# predictor = dlib.shape_predictor(args["shape_predictor"])
+predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
+# print(type(predictor))
+# exit()
+# predictor="shape_predictor_68_face_landmarks.dat"
+
+# grab the indexes of the facial landmarks for the left and
+# right eye, respectively
+(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
+(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
+
+# start the video stream thread
+print("[INFO] starting video stream thread...")
+vs = cv2.VideoCapture(0)
+fileStream = True
+# vs = VideoStream(src=0).start()
+# vs = VideoStream(usePiCamera=True).start()
+# fileStream = False
+# time.sleep(1.0)
+ret, frame = vs.read()
+
+# loop over frames from the video stream
+while ret:
+	# if this is a file video stream, then we need to check if
+	# there any more frames left in the buffer to process
+
+	# grab the frame from the threaded video file stream, resize
+	# it, and convert it to grayscale
+	# channels)
+	ret,frame = vs.read()
+	frame = imutils.resize(frame, width=450)
+	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+	cv2.imshow('frame',frame)
+
+	# detect faces in the grayscale frame
+	rects = detector(gray, 0)
+
+	# loop over the face detections
+	for rect in rects:
+		# determine the facial landmarks for the face region, then
+		# convert the facial landmark (x, y)-coordinates to a NumPy
+		# array
+		shape = predictor(gray, rect)
+		shape = face_utils.shape_to_np(shape)
+
+		# extract the left and right eye coordinates, then use the
+		# coordinates to compute the eye aspect ratio for both eyes
+		leftEye = shape[lStart:lEnd]
+		rightEye = shape[rStart:rEnd]
+		leftEAR = eye_aspect_ratio(leftEye)
+		rightEAR = eye_aspect_ratio(rightEye)
+
+		# average the eye aspect ratio together for both eyes
+		ear = (leftEAR + rightEAR) / 2.0
+
+		# compute the convex hull for the left and right eye, then
+		# visualize each of the eyes
+		leftEyeHull = cv2.convexHull(leftEye)
+		rightEyeHull = cv2.convexHull(rightEye)
+		cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
+		cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
+
+		# check to see if the eye aspect ratio is below the blink
+		# threshold, and if so, increment the blink frame counter
+		if ear < EYE_AR_THRESH:
+			COUNTER += 1
+
+		# otherwise, the eye aspect ratio is not below the blink
+		# threshold
+		else:
+			# if the eyes were closed for a sufficient number of
+			# then increment the total number of blinks
+			if COUNTER >= EYE_AR_CONSEC_FRAMES:
+				TOTAL += 1
+
+			# reset the eye frame counter
+			COUNTER = 0
+
+		# draw the total number of blinks on the frame along with
+		# the computed eye aspect ratio for the frame
+		cv2.putText(frame, "Blinks: {}".format(TOTAL), (10, 30),
+			cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
+		cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30),
+			cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
+ 
+	# show the frame
+	cv2.imshow("Frame", frame)
+	key = cv2.waitKey(1) & 0xFF
+ 
+	# if the `q` key was pressed, break from the loop
+	if key == ord("q"):
+		break
+
+# do a bit of cleanup
+vs.release()
+cv2.destroyAllWindows()

detect_blinks.py

@@ -2,4 +2,4 @@ def main():
 	print("Ganpati Bappa Morya!!!!!!!")
 
 if __name__ == '__main__':
-	main()
+	s