71 lines
2.8 KiB
Python
71 lines
2.8 KiB
Python
# import face_recognition
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import cv2
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from skimage.feature import hog
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from skimage import data, exposure
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import matplotlib.pyplot as plt
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import numpy as np
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import dlib
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# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
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# other example, but it includes some basic performance tweaks to make things run a lot faster:
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# 1. Process each video frame at 1/4 resolution (though still display it at full resolution)
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# 2. Only detect faces in every other frame of video.
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# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
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# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
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# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
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# Get a reference to webcam #0 (the default one)
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video_capture = cv2.VideoCapture(2)
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face_detector = dlib.get_frontal_face_detector()
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process_this_frame = True
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while True:
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if process_this_frame:
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# Grab a single frame of video
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ret, frame = video_capture.read()
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small_frame = cv2.resize(frame, (0, 0), fx=0.7, fy=0.7)
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fd, hog_image = hog(small_frame, orientations=8, pixels_per_cell=(16, 16),
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cells_per_block=(1, 1), visualize=True, multichannel=True)
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hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 15))
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# Resize frame of video to 1/4 size for faster face recognition processing
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# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
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rgb_small_frame = small_frame[:, :, ::-1]
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dets, scores, idxs = face_detector.run(rgb_small_frame, 1, -1)
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print(dets, scores, idxs)
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# Display the results
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for i, rectangle in enumerate(dets):
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probability = scores[i]
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print(rectangle)
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# Scale back up face locations since the frame we detected in was scaled to 1/4 size
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top = rectangle.top() #* 4
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right = rectangle.right() #* 4
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bottom = rectangle.bottom() #* 4
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left = rectangle.left() #* 4
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brightness = (probability + 1)/3
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# Draw a box around the face
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cv2.rectangle(hog_image_rescaled, (left, top), (right, bottom), brightness, 2)
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# # Draw a label with a name below the face
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# cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
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# Display the resulting image
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cv2.imshow('Video', hog_image_rescaled)
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process_this_frame = not process_this_frame
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# Hit 'q' on the keyboard to quit!
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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# Release handle to the webcam
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video_capture.release()
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cv2.destroyAllWindows() |