Turning to grayscale before detecting edges/corners
Reducing noise and restoring images
Blurring faces detected
Approximation of objects' sizes
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# Import Cascade of classifiers and gaussian filter
from skimage.feature import Cascade
from skimage.filters import gaussian
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# Detect the faces
detected = detector.detect_multi_scale(img=image,
scale_factor=1.2, step_ratio=1,
min_size=(50, 50), max_size=(100, 100))
# For each detected face
for d in detected:
# Obtain the face cropped from detected coordinates
face = getFace(d)
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def getFace(d):
''' Extracts the face rectangle from the image using the
coordinates of the detected.'''
# X and Y starting points of the face rectangle
x, y = d['r'], d['c']
# The width and height of the face rectangle
width, height = d['r'] + d['width'], d['c'] + d['height']
# Extract the detected face
face= image[x:width, y:height]
return face
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# Detect the faces
detected = detector.detect_multi_scale(img=image,
scale_factor=1.2, step_ratio=1,
min_size=(50, 50), max_size=(100, 100))
# For each detected face
for d in detected:
# Obtain the face cropped from detected coordinates
face = getFace(d)
# Apply gaussian filter to extracted face
gaussian_face = gaussian(face, multichannel=True, sigma = 10)
# Merge this blurry face to our final image and show it
resulting_image = mergeBlurryFace(image, gaussian_face)
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def mergeBlurryFace(original, gaussian_image):
# X and Y starting points of the face rectangle
x, y = d['r'], d['c']
# The width and height of the face rectangle
width, height = d['r'] + d['width'], d['c'] + d['height']
original[ x:width, y:height] = gaussian_image
return original
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