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Merge pull request #14124 from alalek:fix_python_samples

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Alexander Alekhin 2019-03-22 15:10:46 +00:00
commit f147ba16e2
3 changed files with 94 additions and 110 deletions
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10
samples/python/dis_opt_flow.py
View File

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

39
samples/python/stitching.py
View File

@ -8,15 +8,18 @@ Show how to use Stitcher API from python in a simple way to stitch panoramas
or scans. or scans.
''' '''
# Python 2/3 compatibility
from __future__ import print_function from __future__ import print_function
import cv2 as cv
import numpy as np import numpy as np
import cv2 as cv
import argparse import argparse
import sys import sys
modes = (cv.Stitcher_PANORAMA, cv.Stitcher_SCANS) 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', parser.add_argument('--mode',
type = int, choices = modes, default = cv.Stitcher_PANORAMA, type = int, choices = modes, default = cv.Stitcher_PANORAMA,
help = 'Determines configuration of stitcher. The default is `PANORAMA` (%d), ' help = 'Determines configuration of stitcher. The default is `PANORAMA` (%d), '
@ -25,23 +28,35 @@ parser.add_argument('--mode',
parser.add_argument('--output', default = 'result.jpg', parser.add_argument('--output', default = 'result.jpg',
help = 'Resulting image. The default is `result.jpg`.') help = 'Resulting image. The default is `result.jpg`.')
parser.add_argument('img', nargs='+', help = 'input images') parser.add_argument('img', nargs='+', help = 'input images')
args = parser.parse_args()
# read input images __doc__ += '\n' + parser.format_help()
imgs = []
for img_name in args.img: def main():
img = cv.imread(img_name) args = parser.parse_args()
# read input images
imgs = []
for img_name in args.img:
img = cv.imread(cv.samples.findFile(img_name))
if img is None: if img is None:
print("can't read image " + img_name) print("can't read image " + img_name)
sys.exit(-1) sys.exit(-1)
imgs.append(img) imgs.append(img)
stitcher = cv.Stitcher.create(args.mode) stitcher = cv.Stitcher.create(args.mode)
status, pano = stitcher.stitch(imgs) status, pano = stitcher.stitch(imgs)
if status != cv.Stitcher_OK: if status != cv.Stitcher_OK:
print("Can't stitch images, error code = %d" % status) print("Can't stitch images, error code = %d" % status)
sys.exit(-1) sys.exit(-1)
cv.imwrite(args.output, pano); cv.imwrite(args.output, pano);
print("stitching completed successfully. %s saved!" % args.output) print("stitching completed successfully. %s saved!" % args.output)
print('Done')
if __name__ == '__main__':
print(__doc__)
main()
cv.destroyAllWindows()

145
samples/python/stitching_detailed.py
View File

@ -1,97 +1,50 @@
"""Rotation model images stitcher. """
stitching_detailed img1 img2 [...imgN] [flags] Stitching sample (advanced)
Flags: ===========================
--preview
Run stitching in the preview mode. Works faster than usual mode, Show how to use Stitcher API from python.
but output image will have lower resolution. """
--try_cuda (yes|no)
Try to use CUDA. The default value is 'no'. All default values # Python 2/3 compatibility
are for CPU mode. from __future__ import print_function
\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
"""
import numpy as np import numpy as np
import cv2 as cv import cv2 as cv
import sys import sys
import argparse import argparse
if __name__ == '__main__': parser = argparse.ArgumentParser(prog='stitching_detailed.py', description='Rotation model images stitcher')
parser = argparse.ArgumentParser(description='stitching_detailed') parser.add_argument('img_names', nargs='+',help='files to stitch',type=str)
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('--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' )
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' ) parser.add_argument('--work_megapix',action = 'store', default = 0.6,help=' Resolution for image registration step. The default is 0.6 Mpx',type=float,dest = 'work_megapix' )
parser.add_argument('--work_megapix',action = 'store', default = 0.6,help=' Resolution for image registration step. The default is 0.6 Mpx',type=float,dest = 'work_megapix' ) parser.add_argument('--features',action = 'store', default = 'orb',help='Type of features used for images matching. The default is orb.',type=str,dest = 'features' )
parser.add_argument('--features',action = 'store', default = 'orb',help='Type of features used for images matching. The default is orb.',type=str,dest = 'features' ) parser.add_argument('--matcher',action = 'store', default = 'homography',help='Matcher used for pairwise image matching.',type=str,dest = 'matcher' )
parser.add_argument('--matcher',action = 'store', default = 'homography',help='Matcher used for pairwise image matching.',type=str,dest = 'matcher' ) parser.add_argument('--estimator',action = 'store', default = 'homography',help='Type of estimator used for transformation estimation.',type=str,dest = 'estimator' )
parser.add_argument('--estimator',action = 'store', default = 'homography',help='Type of estimator used for transformation estimation.',type=str,dest = 'estimator' ) parser.add_argument('--match_conf',action = 'store', default = 0.3,help='Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb.',type=float,dest = 'match_conf' )
parser.add_argument('--match_conf',action = 'store', default = 0.3,help='Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb.',type=float,dest = 'match_conf' ) parser.add_argument('--conf_thresh',action = 'store', default = 1.0,help='Threshold for two images are from the same panorama confidence.The default is 1.0.',type=float,dest = 'conf_thresh' )
parser.add_argument('--conf_thresh',action = 'store', default = 1.0,help='Threshold for two images are from the same panorama confidence.The default is 1.0.',type=float,dest = 'conf_thresh' ) parser.add_argument('--ba',action = 'store', default = 'ray',help='Bundle adjustment cost function. The default is ray.',type=str,dest = 'ba' )
parser.add_argument('--ba',action = 'store', default = 'ray',help='Bundle adjustment cost function. The default is ray.',type=str,dest = 'ba' ) parser.add_argument('--ba_refine_mask',action = 'store', default = 'xxxxx',help='Set refinement mask for bundle adjustment. mask is "xxxxx"',type=str,dest = 'ba_refine_mask' )
parser.add_argument('--ba_refine_mask',action = 'store', default = 'xxxxx',help='Set refinement mask for bundle adjustment. mask is "xxxxx"',type=str,dest = 'ba_refine_mask' ) parser.add_argument('--wave_correct',action = 'store', default = 'horiz',help='Perform wave effect correction. The default is "horiz"',type=str,dest = 'wave_correct' )
parser.add_argument('--wave_correct',action = 'store', default = 'horiz',help='Perform wave effect correction. The default is "horiz"',type=str,dest = 'wave_correct' ) parser.add_argument('--save_graph',action = 'store', default = None,help='Save matches graph represented in DOT language to <file_name> file.',type=str,dest = 'save_graph' )
parser.add_argument('--save_graph',action = 'store', default = None,help='Save matches graph represented in DOT language to <file_name> file.',type=str,dest = 'save_graph' ) parser.add_argument('--warp',action = 'store', default = 'plane',help='Warp surface type. The default is "spherical".',type=str,dest = 'warp' )
parser.add_argument('--warp',action = 'store', default = 'plane',help='Warp surface type. The default is "spherical".',type=str,dest = 'warp' ) parser.add_argument('--seam_megapix',action = 'store', default = 0.1,help=' Resolution for seam estimation step. The default is 0.1 Mpx.',type=float,dest = 'seam_megapix' )
parser.add_argument('--seam_megapix',action = 'store', default = 0.1,help=' Resolution for seam estimation step. The default is 0.1 Mpx.',type=float,dest = 'seam_megapix' ) parser.add_argument('--seam',action = 'store', default = 'no',help='Seam estimation method. The default is "gc_color".',type=str,dest = 'seam' )
parser.add_argument('--seam',action = 'store', default = 'no',help='Seam estimation method. The default is "gc_color".',type=str,dest = 'seam' ) parser.add_argument('--compose_megapix',action = 'store', default = -1,help='Resolution for compositing step. Use -1 for original resolution.',type=float,dest = 'compose_megapix' )
parser.add_argument('--compose_megapix',action = 'store', default = -1,help='Resolution for compositing step. Use -1 for original resolution.',type=float,dest = 'compose_megapix' ) parser.add_argument('--expos_comp',action = 'store', default = 'no',help='Exposure compensation method. The default is "gain_blocks".',type=str,dest = 'expos_comp' )
parser.add_argument('--expos_comp',action = 'store', default = 'no',help='Exposure compensation method. The default is "gain_blocks".',type=str,dest = 'expos_comp' ) parser.add_argument('--expos_comp_nr_feeds',action = 'store', default = 1,help='Number of exposure compensation feed.',type=np.int32,dest = 'expos_comp_nr_feeds' )
parser.add_argument('--expos_comp_nr_feeds',action = 'store', default = 1,help='Number of exposure compensation feed.',type=np.int32,dest = 'expos_comp_nr_feeds' ) parser.add_argument('--expos_comp_nr_filtering',action = 'store', default = 2,help='Number of filtering iterations of the exposure compensation gains',type=float,dest = 'expos_comp_nr_filtering' )
parser.add_argument('--expos_comp_nr_filtering',action = 'store', default = 2,help='Number of filtering iterations of the exposure compensation gains',type=float,dest = 'expos_comp_nr_filtering' ) parser.add_argument('--expos_comp_block_size',action = 'store', default = 32,help='BLock size in pixels used by the exposure compensator.',type=np.int32,dest = 'expos_comp_block_size' )
parser.add_argument('--expos_comp_block_size',action = 'store', default = 32,help='BLock size in pixels used by the exposure compensator.',type=np.int32,dest = 'expos_comp_block_size' ) parser.add_argument('--blend',action = 'store', default = 'multiband',help='Blending method. The default is "multiband".',type=str,dest = 'blend' )
parser.add_argument('--blend',action = 'store', default = 'multiband',help='Blending method. The default is "multiband".',type=str,dest = 'blend' ) parser.add_argument('--blend_strength',action = 'store', default = 5,help='Blending strength from [0,100] range.',type=np.int32,dest = 'blend_strength' )
parser.add_argument('--blend_strength',action = 'store', default = 5,help='Blending strength from [0,100] range.',type=np.int32,dest = 'blend_strength' ) parser.add_argument('--output',action = 'store', default = 'result.jpg',help='The default is "result.jpg"',type=str,dest = 'output' )
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('--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' )
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() args = parser.parse_args()
img_names=args.img_names img_names=args.img_names
print(img_names) print(img_names)
@ -167,9 +120,9 @@ if __name__ == '__main__':
is_seam_scale_set = False is_seam_scale_set = False
is_compose_scale_set = False; is_compose_scale_set = False;
for name in img_names: for name in img_names:
full_img = cv.imread(name) full_img = cv.imread(cv.samples.findFile(name))
if full_img is None: if full_img is None:
print("Cannot read image ",name) print("Cannot read image ", name)
exit() exit()
full_img_sizes.append((full_img.shape[1],full_img.shape[0])) full_img_sizes.append((full_img.shape[1],full_img.shape[0]))
if work_megapix < 0: if work_megapix < 0:
@ -401,8 +354,16 @@ if __name__ == '__main__':
result_mask=None result_mask=None
result,result_mask = blender.blend(result,result_mask) result,result_mask = blender.blend(result,result_mask)
cv.imwrite(result_name,result) 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.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) dst=cv.resize(dst,dsize=None,fx=zoomx,fy=zoomx)
cv.imshow(result_name,dst) cv.imshow(result_name,dst)
cv.waitKey() cv.waitKey()
print('Done')
if __name__ == '__main__':
print(__doc__)
main()
cv.destroyAllWindows()