# Load Preprocessor
print("Preprocessing")
p = Preprocessor("../images/slum_image.jpg")
p.scale_image(scale_factor)
p.exposure_equalization(method="equal")
p.convert_color("RGB","HSV")
p.save_current_as("structure")
p.reset()
p.scale_image(mask_scale)
p.exposure_equalization(method="equal")
p.convert_color("RGB","HSV")
p.save_current_as("mask")
# Load images for mask and structure information
img2 = p.get_version("mask")[:,:,0]
img = p.get_version("structure")[:,:,2]
print("Masking")
med_img = median(img2, disk(50*mask_scale))
mask = np.zeros(img2.shape)
mask[med_img>np.mean(med_img)] = 1
mask_c = closing(mask, disk(100*mask_scale))
# plot_comparison(mask, mask_c, 'closing')
# plot_comparison(mask_c, mean(mask_c, disk(20)), 'mean')
masked = np.copy(img)
# masked = img * resize(mask_c, img.shape)
# plot_comparison(img, masked, "masked")
p.exposure_equalization(method="contrast")
p.convert_color("RGB","RGB CIE")
p.save_current_as("contrast_rgb_cie")
p.reset()
p.scale_image(scale_factor)
p.exposure_equalization(method="equal")
p.convert_color("RGB","HSV")
p.save_current_as("structure")
# ========== Plot img & patches =========
if plot:
plt.figure()
ax = plt.gca()
img = p.get_version('structure')[:,:,1]
plt.imshow(img)
# Plot patch centers
for name, coords in patches.items():
plt.scatter(coords.T[0], coords.T[1], c=name, s=100*scale_factor)
# Plot patch bounding boxes
for name, coords in patches.items():
for row in coords:
plot_bounding_box(ax, row[0], row[1], box_size, name)
plt.xlim((0,img.shape[1]))
plt.ylim((img.shape[0],0))
plt.show(block=False)
p.exposure_equalization(method="contrast")
p.convert_color("RGB", "RGB CIE")
p.save_current_as("contrast_rgb_cie")
p.reset()
p.scale_image(scale_factor)
p.exposure_equalization(method="equal")
p.convert_color("RGB", "HSV")
p.save_current_as("structure")
# ========== Plot img & patches =========
if plot:
plt.figure()
ax = plt.gca()
img = p.get_version('structure')[:, :, 1]
plt.imshow(img)
# Plot patch centers
for name, coords in patches.items():
plt.scatter(coords.T[0], coords.T[1], c=name, s=100 * scale_factor)
# Plot patch bounding boxes
for name, coords in patches.items():
for row in coords:
plot_bounding_box(ax, row[0], row[1], box_size, name)
plt.xlim((0, img.shape[1]))
plt.ylim((img.shape[0], 0))
plt.show(block=False)
# Load Preprocessor
print("Preprocessing")
p = Preprocessor("../images/slum_image.jpg")
p.scale_image(scale_factor)
p.exposure_equalization(method="equal")
p.convert_color("RGB", "HSV")
p.save_current_as("structure")
p.reset()
p.scale_image(mask_scale)
p.exposure_equalization(method="equal")
p.convert_color("RGB", "HSV")
p.save_current_as("mask")
# Load images for mask and structure information
img2 = p.get_version("mask")[:, :, 0]
img = p.get_version("structure")[:, :, 2]
print("Masking")
med_img = median(img2, disk(50 * mask_scale))
mask = np.zeros(img2.shape)
mask[med_img > np.mean(med_img)] = 1
mask_c = closing(mask, disk(100 * mask_scale))
# plot_comparison(mask, mask_c, 'closing')
# plot_comparison(mask_c, mean(mask_c, disk(20)), 'mean')
masked = np.copy(img)
# masked = img * resize(mask_c, img.shape)
# plot_comparison(img, masked, "masked")
