Merge pull request #14124 from alalek:fix_python_samples

samples/python/dis_opt_flow.py

@@ -18,6 +18,7 @@ from __future__ import print_function
 
 import numpy as np
 import cv2 as cv
+
 import video
 
 
@@ -56,7 +57,7 @@ def warp_flow(img, flow):
     return res
 
 
-if __name__ == '__main__':
+def main():
     import sys
     print(__doc__)
     try:
@@ -111,4 +112,11 @@ if __name__ == '__main__':
         if ch == ord('4'):
             use_temporal_propagation = not use_temporal_propagation
             print('temporal propagation is', ['off', 'on'][use_temporal_propagation])
+
+    print('Done')
+
+
+if __name__ == '__main__':
+    print(__doc__)
+    main()
     cv.destroyAllWindows()

samples/python/stitching.py

@@ -8,15 +8,18 @@ Show how to use Stitcher API from python in a simple way to stitch panoramas
 or scans.
 '''
 
+# Python 2/3 compatibility
 from __future__ import print_function
-import cv2 as cv
+
 import numpy as np
+import cv2 as cv
+
 import argparse
 import sys
 
 modes = (cv.Stitcher_PANORAMA, cv.Stitcher_SCANS)
 
-parser = argparse.ArgumentParser(description='Stitching sample.')
+parser = argparse.ArgumentParser(prog='stitching.py', description='Stitching sample.')
 parser.add_argument('--mode',
     type = int, choices = modes, default = cv.Stitcher_PANORAMA,
     help = 'Determines configuration of stitcher. The default is `PANORAMA` (%d), '
@@ -25,12 +28,16 @@ parser.add_argument('--mode',
 parser.add_argument('--output', default = 'result.jpg',
     help = 'Resulting image. The default is `result.jpg`.')
 parser.add_argument('img', nargs='+', help = 'input images')
+
+__doc__ += '\n' + parser.format_help()
+
+def main():
     args = parser.parse_args()
 
     # read input images
     imgs = []
     for img_name in args.img:
-    img = cv.imread(img_name)
+        img = cv.imread(cv.samples.findFile(img_name))
         if img is None:
             print("can't read image " + img_name)
             sys.exit(-1)
@@ -45,3 +52,11 @@ if status != cv.Stitcher_OK:
 
     cv.imwrite(args.output, pano);
     print("stitching completed successfully. %s saved!" % args.output)
+
+    print('Done')
+
+
+if __name__ == '__main__':
+    print(__doc__)
+    main()
+    cv.destroyAllWindows()

samples/python/stitching_detailed.py

@@ -1,71 +1,20 @@
-"""Rotation model images stitcher.
-stitching_detailed img1 img2 [...imgN] [flags]
-Flags:
-    --preview
-        Run stitching in the preview mode. Works faster than usual mode,
-        but output image will have lower resolution.
-    --try_cuda (yes|no)
-        Try to use CUDA. The default value is 'no'. All default values
-        are for CPU mode.
-\nMotion Estimation Flags:
-    --work_megapix <float>
-        Resolution for image registration step. The default is 0.6 Mpx.
-    --features (surf|orb|sift)
-        Type of features used for images matching. The default is surf.
-    --matcher (homography|affine)
-        Matcher used for pairwise image matching.
-    --estimator (homography|affine)
-        Type of estimator used for transformation estimation.
-    --match_conf <float>
-        Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb.
-    --conf_thresh <float>
-        Threshold for two images are from the same panorama confidence.
-        The default is 1.0.
-    --ba (no|reproj|ray|affine)
-        Bundle adjustment cost function. The default is ray.
-    --ba_refine_mask (mask)
-        Set refinement mask for bundle adjustment. It looks like 'x_xxx',
-        where 'x' means refine respective parameter and '_' means don't
-        refine one, and has the following format:
-        <fx><skew><ppx><aspect><ppy>. The default mask is 'xxxxx'. If bundle
-        adjustment doesn't support estimation of selected parameter then
-        the respective flag is ignored.
-    --wave_correct (no|horiz|vert)
-        Perform wave effect correction. The default is 'horiz'.
-    --save_graph <file_name>
-        Save matches graph represented in DOT language to <file_name> file.
-        Labels description: Nm is number of matches, Ni is number of inliers,
-        C is confidence.
-\nCompositing Flags:
-    --warp (affine|plane|cylindrical|spherical|fisheye|stereographic|compressedPlaneA2B1|compressedPlaneA1.5B1|compressedPlanePortraitA2B1|compressedPlanePortraitA1.5B1|paniniA2B1|paniniA1.5B1|paniniPortraitA2B1|paniniPortraitA1.5B1|mercator|transverseMercator)
-        Warp surface type. The default is 'spherical'.
-    --seam_megapix <float>
-        Resolution for seam estimation step. The default is 0.1 Mpx.
-    --seam (no|voronoi|gc_color|gc_colorgrad)
-        Seam estimation method. The default is 'gc_color'.
-    --compose_megapix <float>
-        Resolution for compositing step. Use -1 for original resolution.
-        The default is -1.
-    --expos_comp (no|gain|gain_blocks)
-        Exposure compensation method. The default is 'gain_blocks'.
-    --blend (no|feather|multiband)
-        Blending method. The default is 'multiband'.
-    --blend_strength <float>
-        Blending strength from [0,100] range. The default is 5.
-    --output <result_img>
-        The default is 'result.jpg'.
-    --timelapse (as_is|crop)
-        Output warped images separately as frames of a time lapse movie, with 'fixed_' prepended to input file names.
-    --rangewidth <int>
-        uses range_width to limit number of images to match with.\n
 """
+Stitching sample (advanced)
+===========================
+
+Show how to use Stitcher API from python.
+"""
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
 import numpy as np
 import cv2 as cv
+
 import sys
 import argparse
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='stitching_detailed')
+parser = argparse.ArgumentParser(prog='stitching_detailed.py', description='Rotation model images stitcher')
 parser.add_argument('img_names', nargs='+',help='files to stitch',type=str)
 parser.add_argument('--preview',help='Run stitching in the preview mode. Works faster than usual mode but output image will have lower resolution.',type=bool,dest = 'preview' )
 parser.add_argument('--try_cuda',action = 'store', default = False,help='Try to use CUDA. The default value is no. All default values are for CPU mode.',type=bool,dest = 'try_cuda' )
@@ -92,6 +41,10 @@ if __name__ == '__main__':
 parser.add_argument('--output',action = 'store', default = 'result.jpg',help='The default is "result.jpg"',type=str,dest = 'output' )
 parser.add_argument('--timelapse',action = 'store', default = None,help='Output warped images separately as frames of a time lapse movie, with "fixed_" prepended to input file names.',type=str,dest = 'timelapse' )
 parser.add_argument('--rangewidth',action = 'store', default = -1,help='uses range_width to limit number of images to match with.',type=int,dest = 'rangewidth' )
+
+__doc__ += '\n' + parser.format_help()
+
+def main():
     args = parser.parse_args()
     img_names=args.img_names
     print(img_names)
@@ -167,7 +120,7 @@ if __name__ == '__main__':
     is_seam_scale_set = False
     is_compose_scale_set = False;
     for name in img_names:
-        full_img = cv.imread(name)
+        full_img = cv.imread(cv.samples.findFile(name))
         if full_img is None:
             print("Cannot read image ", name)
             exit()
@@ -401,8 +354,16 @@ if __name__ == '__main__':
         result_mask=None
         result,result_mask = blender.blend(result,result_mask)
         cv.imwrite(result_name,result)
-        zoomx =600/result.shape[1]
+        zoomx = 600.0 / result.shape[1]
         dst=cv.normalize(src=result,dst=None,alpha=255.,norm_type=cv.NORM_MINMAX,dtype=cv.CV_8U)
         dst=cv.resize(dst,dsize=None,fx=zoomx,fy=zoomx)
         cv.imshow(result_name,dst)
         cv.waitKey()
+
+    print('Done')
+
+
+if __name__ == '__main__':
+    print(__doc__)
+    main()
+    cv.destroyAllWindows()