57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
import cv2
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import numpy as np
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from mtcnn.mtcnn import MTCNN
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detector = MTCNN()
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filename = "video/video10.mp4"
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# cv2.namedWindow('Attendence_Tracker', cv2.WINDOW_NORMAL)
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# cap = cv2.VideoCapture(filename)
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# # cap = cv2.VideoCapture(0)
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# while True:
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# #Capture frame-by-frame
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# __, frame = cap.read()
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# #Use MTCNN to detect faces
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# result = detector.detect_faces(frame)
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# if result != []:
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# for person in result:
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# bounding_box = person['box']
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# keypoints = person['keypoints']
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# cv2.rectangle(frame,
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# (bounding_box[0], bounding_box[1]),
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# (bounding_box[0]+bounding_box[2], bounding_box[1] + bounding_box[3]),
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# (0,155,255),
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# 2)
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# cv2.circle(frame,(keypoints['left_eye']), 2, (0,155,255), 2)
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# cv2.circle(frame,(keypoints['right_eye']), 2, (0,155,255), 2)
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# cv2.circle(frame,(keypoints['nose']), 2, (0,155,255), 2)
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# cv2.circle(frame,(keypoints['mouth_left']), 2, (0,155,255), 2)
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# cv2.circle(frame,(keypoints['mouth_right']), 2, (0,155,255), 2)
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# #display resulting frame
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# cv2.imshow('Attendence_Tracker',frame)
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# if cv2.waitKey(1) &0xFF == ord('q'):
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# break
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# #When everything's done, release capture
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# cap.release()
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# cv2.destroyAllWindows()
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img = "align/3.jpg"
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def adjust_gamma(image, gamma=1.5):
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invGamma = 1.0 / gamma
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table = np.array([((i / 255.0) ** invGamma) * 255
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for i in np.arange(0, 256)]).astype("uint8")
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return cv2.LUT(image, table)
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image = cv2.imread(img)
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img = adjust_gamma(image)
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result = detector.detect_faces(image)
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print(result)
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