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from skimage import io, color, img_as_float
from skimage.feature import corner_peaks, plot_matches
import matplotlib.pyplot as plt
import numpy as np
from skimage import io, img_as_float, color, exposure
img = img_as_float(io.imread('./ml/old-front.jpg'))
# Isolate paint marks
# Put image into LAB colour space
image_lab = color.rgb2lab(img)
img = exposure.rescale_intensity(img)
# Colours of interest
color_array = np.array([
[[[255, 255, 0.]]], # Yellow stuff
[[[255, 190, 200.]]], # Pink stuff
[[[255, 165, 0.]]], # Orange stuff
[[[255, 0, 0.]]], # Red stuff
])
idx = 2
# Loop through the color array and pick out the colored features
distance_color = color.deltaE_ciede2000(color_array[idx], image_lab, kL=2, kC=1, kH=0.5)
# Normalise distance
distance_color = exposure.rescale_intensity(distance_color)
# Mask image
image_filtered = img
image_filtered[distance_color > 0.5] = 1
# Plot it up
print("Filtered to: ", color_array[idx])
f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(20, 10))
ax0.imshow(img)
ax1.imshow(distance_color, cmap='gray')
ax2.imshow(image_filtered)
plt.show()
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