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3dcc8c38b4
Removed obsolete python samples #25268 Clean Samples #25006 This PR removes 36 obsolete python samples from the project, as part of an effort to keep the codebase clean and focused on current best practices. Some of these samples will be updated with latest algorithms or will be combined with other existing samples. Removed Samples: > browse.py camshift.py coherence.py color_histogram.py contours.py deconvolution.py dft.py dis_opt_flow.py distrans.py edge.py feature_homography.py find_obj.py fitline.py gabor_threads.py hist.py houghcircles.py houghlines.py inpaint.py kalman.py kmeans.py laplace.py lk_homography.py lk_track.py logpolar.py mosse.py mser.py opt_flow.py plane_ar.py squares.py stitching.py text_skewness_correction.py texture_flow.py turing.py video_threaded.py video_v4l2.py watershed.py These changes aim to improve the repository's clarity and usability by removing examples that are no longer relevant or have been superseded by more up-to-date techniques.
95 lines
2.3 KiB
Python
Executable File
95 lines
2.3 KiB
Python
Executable File
#!/usr/bin/env python
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'''
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Robust line fitting.
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==================
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Example of using cv.fitLine function for fitting line
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to points in presence of outliers.
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Usage
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-----
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fitline.py
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Switch through different M-estimator functions and see,
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how well the robust functions fit the line even
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in case of ~50% of outliers.
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Keys
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----
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SPACE - generate random points
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f - change distance function
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ESC - exit
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'''
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import numpy as np
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import cv2 as cv
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# built-in modules
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import itertools as it
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# local modules
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from common import draw_str
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w, h = 512, 256
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def toint(p):
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return tuple(map(int, p))
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def sample_line(p1, p2, n, noise=0.0):
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p1 = np.float32(p1)
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t = np.random.rand(n,1)
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return p1 + (p2-p1)*t + np.random.normal(size=(n, 2))*noise
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dist_func_names = it.cycle('DIST_L2 DIST_L1 DIST_L12 DIST_FAIR DIST_WELSCH DIST_HUBER'.split())
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cur_func_name = next(dist_func_names)
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def update(_=None):
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noise = cv.getTrackbarPos('noise', 'fit line')
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n = cv.getTrackbarPos('point n', 'fit line')
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r = cv.getTrackbarPos('outlier %', 'fit line') / 100.0
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outn = int(n*r)
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p0, p1 = (90, 80), (w-90, h-80)
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img = np.zeros((h, w, 3), np.uint8)
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cv.line(img, toint(p0), toint(p1), (0, 255, 0))
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if n > 0:
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line_points = sample_line(p0, p1, n-outn, noise)
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outliers = np.random.rand(outn, 2) * (w, h)
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points = np.vstack([line_points, outliers])
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for p in line_points:
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cv.circle(img, toint(p), 2, (255, 255, 255), -1)
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for p in outliers:
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cv.circle(img, toint(p), 2, (64, 64, 255), -1)
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func = getattr(cv, cur_func_name)
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vx, vy, cx, cy = cv.fitLine(np.float32(points), func, 0, 0.01, 0.01)
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cv.line(img, (int(cx-vx*w), int(cy-vy*w)), (int(cx+vx*w), int(cy+vy*w)), (0, 0, 255))
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draw_str(img, (20, 20), cur_func_name)
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cv.imshow('fit line', img)
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def main():
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cv.namedWindow('fit line')
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cv.createTrackbar('noise', 'fit line', 3, 50, update)
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cv.createTrackbar('point n', 'fit line', 100, 500, update)
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cv.createTrackbar('outlier %', 'fit line', 30, 100, update)
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while True:
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update()
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ch = cv.waitKey(0)
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if ch == ord('f'):
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global cur_func_name
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cur_func_name = next(dist_func_names)
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if ch == 27:
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break
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print('Done')
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if __name__ == '__main__':
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print(__doc__)
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main()
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cv.destroyAllWindows()
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