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50 lines
2.1 KiB
Python
50 lines
2.1 KiB
Python
import numpy as np
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from scipy import ndimage
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from ..util import img_as_float
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def km_segmentation(image, n_segments=100, ratio=10., max_iter=100, sigma=1.0):
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image = ndimage.gaussian_filter(img_as_float(image), sigma)
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# initialize on grid:
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height, width = image.shape[:2]
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# approximate grid size for desired n_segments
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step = np.sqrt(height * width / n_segments)
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grid_y, grid_x = np.mgrid[:height, :width]
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means_y = grid_y[::step, ::step]
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means_x = grid_x[::step, ::step]
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means_color = image[means_y, means_x, :]
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means = np.dstack([means_y, means_x, means_color]).reshape(-1, 5)
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# we do the scaling of ratio in the same way as in the SLIC paper
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# so the values have the same meaning
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ratio = (ratio / float(step)) ** 2
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print(ratio)
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image = np.dstack([grid_y, grid_x, image / ratio])
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nearest_mean = np.zeros((height, width), dtype=np.int)
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distance = np.ones((height, width), dtype=np.float) * np.inf
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for i in xrange(max_iter):
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print("iteration %d" % i)
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nearest_mean_old = nearest_mean.copy()
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# assign pixels to means
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for k, mean in enumerate(means):
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# compute windows:
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y_min = int(max(mean[0] - 2 * step, 0))
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y_max = int(min(mean[0] + 2 * step, height))
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x_min = int(max(mean[1] - 2 * step, 0))
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x_max = int(min(mean[1] + 2 * step, height))
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search_window = image[y_min:y_max + 1, x_min:x_max + 1]
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dist_mean = np.sum((search_window - mean) ** 2, axis=2)
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assign = distance[y_min:y_max + 1, x_min:x_max + 1] > dist_mean
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nearest_mean[y_min:y_max + 1, x_min:x_max + 1][assign] = k
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distance[y_min:y_max + 1, x_min:x_max + 1][assign] = \
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dist_mean[assign]
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if (nearest_mean == nearest_mean_old).all():
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break
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# recompute means:
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means = [np.bincount(nearest_mean.ravel(), image[:, :, j].ravel())
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for j in xrange(5)]
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in_mean = np.bincount(nearest_mean.ravel())
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means = (np.vstack(means) / in_mean).T
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return nearest_mean
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