115 lines
3.2 KiB
Python
115 lines
3.2 KiB
Python
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#!/usr/bin/python3
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import numpy as np
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import matplotlib.pyplot as plt
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import sys
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from scipy import ndimage
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from skimage import feature
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from skimage import filters
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from skimage.feature import corner_harris, corner_subpix, corner_peaks
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def detect_corner(img, windowx = 2, windowy = 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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nr_cuts = 0
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for i in range(0, n):
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print("Row: {}".format(i))
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for j in range(0, m):
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corner = False
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for k in range(-windowy, windowy):
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nr_cuts = 0
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for l in range(-windowx, windowx):
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y = (i+k)%n
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x1 = (j+l)%m
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x2 = (j+l+1)%m
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if img[y,x1] ^ img[y,x2]:
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nr_cuts += 1
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if nr_cuts > 1:
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corner = True
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# print("{}x{} is corner".format(j,i))
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break
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corners[i,j] = corner
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return corners
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def produce_poi_list(img):
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r = []
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(n,m) = img.shape
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for i in range(0, n):
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row = []
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for j in range(0, m):
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if img[i,j]:
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row.append((float(j)/m,float(i)/n))
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r.append(row)
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return r
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# not time-effective comparision
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# TODO compare only rows in the r window
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def compare_poi_list(pl1, pl2, r):
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matches = []
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for pr1 in pl1:
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for (x1,y1) in pr1:
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for pr2 in pl2:
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for (x2,y2) in pr2:
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if r * r > ((x1 - x2)*(x1 - x2) + (y1-y2)*(y1-y2)):
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matches.append(((x1,y1),(x2,y2)))
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return matches
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file_path = sys.argv[1]
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print("File {} to be analysed".format(file_path))
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img = ndimage.imread(file_path)
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img_gray = 0.21 * img[:,:,0] + 0.72 * img[:,:,1] + 0.07 * img[:,:,2]
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n,m = img_gray.shape
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print("Width {}, Height {}".format(m,n))
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otsu_lvl = filters.threshold_otsu(img_gray)
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seg_shoe = img_gray <= otsu_lvl
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img_shoe_selected = seg_shoe * img_gray
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data_selected = img_gray[seg_shoe]
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shoe_otsu_lvl = filters.threshold_otsu(data_selected)
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img_sole_segs1 = img_shoe_selected <= shoe_otsu_lvl
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img_sole_segs = ndimage.median_filter(img_sole_segs1, 5)
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img_sole_segs_nr = np.array(img_sole_segs, np.int)
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k = np.ones((11, 11))
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img_sole_seg_count = ndimage.convolve(img_sole_segs_nr, k)
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m1 = img_sole_seg_count >= 80
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m2 = True ^ img_sole_segs
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# m2 = img_sole_seg_count <= 1
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m3 = m1 * m2
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# m3i = np.array(m3, np.int)
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corners = detect_corner(img_sole_segs)
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poi_list = produce_poi_list(corners)
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print(poi_list)
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m3i = np.array(corners, np.int)
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# color_red = np.ones((n,m,3)) * np.array([255, 0, 0])
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color_red = 255
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img_annotated = img_gray * (1 - m3i) + m3i * color_red
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# img_annotated = img_gray
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# plt.imshow(m1 * m2, cmap="Greys_r")
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# plt.imshow(img_annotated, cmap="Greys_r")
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# coords = corner_peaks(corner_harris(img_gray), min_distance=5)
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# coords_subpix = corner_subpix(img_gray, coords, window_size=13)
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fig = plt.figure()
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ax1 = fig.add_subplot(2,1,1)
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ax1.imshow(img_sole_segs, cmap="Greys_r")
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ax2 = fig.add_subplot(2,1,2)
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ax2.imshow(img_annotated, cmap="Greys_r")
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# ax2.plot(coords[:, 1], coords[:, 0], '.b', markersize=3)
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# ax2.plot(coords_subpix[:, 1], coords_subpix[:, 0], '+r', markersize=15)
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plt.show()
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plt.imsave("sole_seg.png", img_sole_segs)
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