74 lines
2.0 KiB
Python
74 lines
2.0 KiB
Python
from scipy import ndimage
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import math
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import matplotlib.pyplot as plt
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from skimage import filters
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import numpy as np
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from skimage import feature
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img = ndimage.imread("/home/tpolgrabia/Pobrane/CSFID/tracks_cropped/00003.jpg")
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otsu_lvl = filters.threshold_otsu(img)
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img_mask = img >= otsu_lvl
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img_mask_fil = True ^ ndimage.minimum_filter(img_mask, 3)
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img_label, nb_labels = ndimage.label(img_mask_fil)
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sizes = ndimage.sum(img_mask_fil, img_label, range(0, nb_labels+1))
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remove_labels = sizes < 100
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lbls = True & remove_labels
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lbls[0] = False
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print("Remove labels: {}".format(ndimage.sum(remove_labels)))
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remove_mask = remove_labels[img_label]
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img_label[remove_mask] = 0
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idx = 0
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n2 = nb_labels + 1 - np.sum(remove_labels)
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centers = np.zeros((nb_labels+1,2))
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(n,m) = img.shape
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corners = np.zeros((n,m), dtype=np.bool)
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for i in range(0, nb_labels+1):
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if remove_labels[i]:
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continue
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seg = img_label == i
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slice_y, slice_x = ndimage.find_objects(seg)[0]
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seg_window = seg[slice_y, slice_x]
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edge = feature.canny(seg_window)
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plt.imshow(edge, cmap="Greys_r")
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plt.show()
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(en,em) = edge.shape
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distances = np.zeros((en,em))
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centers[idx,:] = ndimage.center_of_mass(seg)
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for j in range(0, en):
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for k in range(0, em):
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if edge[j,k]:
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diff = centers[i,:] - np.array([slice_y.start, slice_x.start])
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dx = k - diff[1]
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dy = j - diff[0]
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d = math.sqrt(dx*dx+dy*dy)
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distances[j,k] = d
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# max_distances = ndimage.maximum_filter(distances, 20)
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# t_corners = np.abs(max_distances - distances) < 1e-6
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# print(t_corners)
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# plt.imshow(t_corners, cmap="Greys_r")
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# plt.show()
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# corners[slice_y,slice_x] = corners[slice_y,slice_x] + edge
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idx += 1
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(n,m) = img.shape
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mx = np.tile(np.arange(0,m),n).reshape((n,m))
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my = np.tile(np.arange(0,n),m).reshape((m,n)).T
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xs2 = mx[corners]
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ys2 = my[corners]
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xs = centers[:,1]
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ys = centers[:,0]
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plt.imshow(img_label, cmap="Greys_r")
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# plt.plot(xs, ys, "r+")
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plt.plot(xs2,ys2,"b+")
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plt.show()
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