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feat: Capture images on left and right eye with resize pixel #6

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feat: Capture images on left and right eye with resize pixel #6

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72 changes: 29 additions & 43 deletions drowsiness-detector/detector/blink_detector.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,8 @@
import cv2
import dlib
import numpy as np
from keras.models import load_model
from scipy.spatial import distance as dist
from imutils import face_utils
import matplotlib.pyplot as plt
import os
import time

predictor = dlib.shape_predictor("./shape_predictor_68_face_landmarks.dat")
face_cascade = cv2.CascadeClassifier('./haarcascade_frontalface_alt.xml')
Expand All @@ -16,7 +14,7 @@ def detect(img, cascade = face_cascade , minimumFeatureSize=(20, 20)):
rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=1, minSize=minimumFeatureSize)

# if it doesn't return rectangle return array
# with zero lenght
# with zero length
if len(rects) == 0:
return []

Expand Down Expand Up @@ -68,7 +66,7 @@ def cropEyes(frame):
# compute the width of the eye
lw = (leftEye[3][0] - leftEye[0][0])

# we want the image for the cnn to be (26,34)
# we want the image for the CNN to be (26,34)
# so we add the half of the difference at x and y
# axis from the width at height respectively left-right
# and up-down
Expand Down Expand Up @@ -113,8 +111,21 @@ def cnnPreprocess(img):
img = np.expand_dims(img, axis=0)
return img

def save_images(left_eye_image, right_eye_image, folder):
if not os.path.exists(folder):
os.makedirs(folder)

# Create unique filenames
timestamp = str(int(time.time()))
left_filename = os.path.join(folder, f"left_eye_{timestamp}.jpg")
right_filename = os.path.join(folder, f"right_eye_{timestamp}.jpg")

# Save images
cv2.imwrite(left_filename, left_eye_image)
cv2.imwrite(right_filename, right_eye_image)

def main():
# open the camera,load the cnn model
# open the camera
camera = cv2.VideoCapture(0)
# model = load_model('/Users/phuc1403/projects/simple-blink-detector/detector/blinkModel.hdf5')

Expand All @@ -124,15 +135,14 @@ def main():
close_counter = blinks = mem_counter= 0
state = ''
while True:

ret, frame = camera.read()

# detect eyes
eyes = cropEyes(frame)
if eyes is None:
continue
else:
left_eye,right_eye, left_eye_rect, right_eye_rect = eyes
left_eye,right_eye, _, _ = eyes

# left_eye_rect[0] -= 20 # Giảm tọa độ x bên trái
# left_eye_rect[1] -= 20 # Giảm tọa độ y bên trên
Expand All @@ -149,45 +159,21 @@ def main():
# Vẽ hộp giới hạn xung quanh mắt phải trên hình ảnh gốc
cv2.rectangle(frame, (right_eye_rect[0], right_eye_rect[1]), (right_eye_rect[2], right_eye_rect[3]), (0, 255, 0), 2)


# average the predictions of the two eyes
# prediction = (model.predict(cnnPreprocess(left_eye)) + model.predict(cnnPreprocess(right_eye)))/2.0

# # blinks
# # if the eyes are open reset the counter for close eyes
# if prediction > 0.5 :
# state = 'open'
# close_counter = 0
# else:
# state = 'close'
# close_counter += 1

# # if the eyes are open and previousle were closed
# # for sufficient number of frames then increcement
# # the total blinks
# if state == 'open' and mem_counter > 1:
# blinks += 1
# # keep the counter for the next loop
# mem_counter = close_counter

# draw the total number of blinks on the frame along with
# the state for the frame
cv2.putText(frame, "Blinks: {}".format(blinks), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, "State: {}".format(state), (300, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

# show the frame
cv2.imshow('blinks counter', frame)
key = cv2.waitKey(1) & 0xFF
key = cv2.waitKey(1)

# if the `q` key was pressed, break from the loop
if key == ord('q'):
break
# do a little clean up

# Delay for 1 second
if close_counter == 10: # 10 frames = approximately 1 second at 10 fps
save_images(left_eye, right_eye, "eye_images")
close_counter = 0 # Reset the counter after saving images

close_counter += 1

cv2.destroyAllWindows()
del(camera)


if __name__ == '__main__':
main()
main()