31 lines
1.2 KiB
Python
31 lines
1.2 KiB
Python
import cv2, numpy, matplotlib.pyplot as plt, scipy.interpolate, sys
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from collections import defaultdict
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img = cv2.imread(sys.argv[1])
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H, S, L = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
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img_blur = cv2.GaussianBlur(H, (15,15), 0)
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thresh = cv2.threshold(img_blur, 55, 255, cv2.THRESH_BINARY)[1]
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edges = cv2.Canny(image=thresh, threshold1=100, threshold2=200)
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c, hier = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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best = max(c, key=lambda x: x.shape[0])[:, 0, :]
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min_x, max_x = min(best[:, 0]), max(best[:, 0])
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min_y, max_y = min(best[:, 1]), max(best[:, 1])
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xrange = max_x - min_x
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yrange = max_y - min_y
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coords = [[((x - min_x) / xrange) * 2 - 1, ((y - min_y) / yrange) * 2 - 1] for x, y in best ]
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coords.sort(key=lambda x: x[0])
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coords2 = defaultdict(list)
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for x, y in coords:
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coords2[x].append(y)
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coords3x = []
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coords3y = []
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last = -1
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for x, ys in coords2.items():
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coords3y.append(min(ys, key=lambda x: abs(x - last)))
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coords3x.append(x)
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last = coords3y[-1]
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i = scipy.interpolate.CubicSpline(coords3x, coords3y, extrapolate=True)
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xs = numpy.arange(-1.0, 1.0, 0.02)
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plt.plot(coords3x, coords3y, label='data')
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plt.plot(xs, i(xs))
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plt.show() |