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55 lines
1.5 KiB
55 lines
1.5 KiB
import matplotlib.pyplot as plt
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import time
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from skimage.feature import hog
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from skimage import data, exposure
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import face_recognition
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import cv2
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image = data.astronaut()
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image = face_recognition.load_image_file("testimage.png")
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face_location = face_recognition.face_locations(image)
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# print(image)
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fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
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cells_per_block=(1, 1), visualize=True, multichannel=True)
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)
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ax1.axis('off')
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ax1.imshow(image, cmap=plt.cm.gray)
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ax1.set_title('Input image')
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# Rescale histogram for better display
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hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))
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ax2.axis('off')
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ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)
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ax2.set_title('Histogram of Oriented Gradients')
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plt.show()
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print('done')
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# # Display the results
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# (top, right, bottom, left) = face_location[0]
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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 *= 4
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# right *= 4
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# bottom *= 4
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# left *= 4
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# # Draw a box around the face
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# cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 255), 2)
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# # Draw a label with a name below the face
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# cv2.rectangle(image, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
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# font = cv2.FONT_HERSHEY_DUPLEX
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# cv2.putText(image, '...', (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
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# # Display the resulting image
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# cv2.imshow('Video', image)
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# time.sleep(10) |