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