import sys
import argparse
-parser = argparse.ArgumentParser(description='stitching_detailed')
-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' )
-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('--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('--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('--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('--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('--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',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('--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_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('--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('--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' )
-args = parser.parse_args()
-img_names=args.img_names
-print(img_names)
-preview = args.preview
-try_cuda = args.try_cuda
-work_megapix = args.work_megapix
-seam_megapix = args.seam_megapix
-compose_megapix = args.compose_megapix
-conf_thresh = args.conf_thresh
-features_type = args.features
-matcher_type = args.matcher
-estimator_type = args.estimator
-ba_cost_func = args.ba
-ba_refine_mask = args.ba_refine_mask
-wave_correct = args.wave_correct
-if wave_correct=='no':
- do_wave_correct= False
-else:
- do_wave_correct=True
-if args.save_graph is None:
- save_graph = False
-else:
- save_graph =True
- save_graph_to = args.save_graph
-warp_type = args.warp
-if args.expos_comp=='no':
- expos_comp_type = cv.detail.ExposureCompensator_NO
-elif args.expos_comp=='gain':
- expos_comp_type = cv.detail.ExposureCompensator_GAIN
-elif args.expos_comp=='gain_blocks':
- expos_comp_type = cv.detail.ExposureCompensator_GAIN_BLOCKS
-elif args.expos_comp=='channel':
- expos_comp_type = cv.detail.ExposureCompensator_CHANNELS
-elif args.expos_comp=='channel_blocks':
- expos_comp_type = cv.detail.ExposureCompensator_CHANNELS_BLOCKS
-else:
- print("Bad exposure compensation method")
- exit()
-expos_comp_nr_feeds = args.expos_comp_nr_feeds
-expos_comp_nr_filtering = args.expos_comp_nr_filtering
-expos_comp_block_size = args.expos_comp_block_size
-match_conf = args.match_conf
-seam_find_type = args.seam
-blend_type = args.blend
-blend_strength = args.blend_strength
-result_name = args.output
-if args.timelapse is not None:
- timelapse = True
- if args.timelapse=="as_is":
- timelapse_type = cv.detail.Timelapser_AS_IS
- elif args.timelapse=="crop":
- timelapse_type = cv.detail.Timelapser_CROP
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(description='stitching_detailed')
+ 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' )
+ 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('--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('--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('--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('--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('--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',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('--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_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('--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('--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' )
+ args = parser.parse_args()
+ img_names=args.img_names
+ print(img_names)
+ preview = args.preview
+ try_cuda = args.try_cuda
+ work_megapix = args.work_megapix
+ seam_megapix = args.seam_megapix
+ compose_megapix = args.compose_megapix
+ conf_thresh = args.conf_thresh
+ features_type = args.features
+ matcher_type = args.matcher
+ estimator_type = args.estimator
+ ba_cost_func = args.ba
+ ba_refine_mask = args.ba_refine_mask
+ wave_correct = args.wave_correct
+ if wave_correct=='no':
+ do_wave_correct= False
else:
- print("Bad timelapse method")
- exit()
-else:
- timelapse= False
-range_width = args.rangewidth
-if features_type=='orb':
- finder= cv.ORB.create()
-elif features_type=='surf':
- finder= cv.xfeatures2d_SURF.create()
-elif features_type=='sift':
- finder= cv.xfeatures2d_SIFT.create()
-else:
- print ("Unknown descriptor type")
- exit()
-seam_work_aspect = 1
-full_img_sizes=[]
-features=[]
-images=[]
-is_work_scale_set = False
-is_seam_scale_set = False
-is_compose_scale_set = False;
-for name in img_names:
- full_img = cv.imread(name)
- if full_img is None:
- print("Cannot read image ",name)
+ do_wave_correct=True
+ if args.save_graph is None:
+ save_graph = False
+ else:
+ save_graph =True
+ save_graph_to = args.save_graph
+ warp_type = args.warp
+ if args.expos_comp=='no':
+ expos_comp_type = cv.detail.ExposureCompensator_NO
+ elif args.expos_comp=='gain':
+ expos_comp_type = cv.detail.ExposureCompensator_GAIN
+ elif args.expos_comp=='gain_blocks':
+ expos_comp_type = cv.detail.ExposureCompensator_GAIN_BLOCKS
+ elif args.expos_comp=='channel':
+ expos_comp_type = cv.detail.ExposureCompensator_CHANNELS
+ elif args.expos_comp=='channel_blocks':
+ expos_comp_type = cv.detail.ExposureCompensator_CHANNELS_BLOCKS
+ else:
+ print("Bad exposure compensation method")
exit()
- full_img_sizes.append((full_img.shape[1],full_img.shape[0]))
- if work_megapix < 0:
- img = full_img
- work_scale = 1
- is_work_scale_set = True
+ expos_comp_nr_feeds = args.expos_comp_nr_feeds
+ expos_comp_nr_filtering = args.expos_comp_nr_filtering
+ expos_comp_block_size = args.expos_comp_block_size
+ match_conf = args.match_conf
+ seam_find_type = args.seam
+ blend_type = args.blend
+ blend_strength = args.blend_strength
+ result_name = args.output
+ if args.timelapse is not None:
+ timelapse = True
+ if args.timelapse=="as_is":
+ timelapse_type = cv.detail.Timelapser_AS_IS
+ elif args.timelapse=="crop":
+ timelapse_type = cv.detail.Timelapser_CROP
+ else:
+ print("Bad timelapse method")
+ exit()
+ else:
+ timelapse= False
+ range_width = args.rangewidth
+ if features_type=='orb':
+ finder= cv.ORB.create()
+ elif features_type=='surf':
+ finder= cv.xfeatures2d_SURF.create()
+ elif features_type=='sift':
+ finder= cv.xfeatures2d_SIFT.create()
else:
- if is_work_scale_set is False:
- work_scale = min(1.0, np.sqrt(work_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1])))
+ print ("Unknown descriptor type")
+ exit()
+ seam_work_aspect = 1
+ full_img_sizes=[]
+ features=[]
+ images=[]
+ is_work_scale_set = False
+ is_seam_scale_set = False
+ is_compose_scale_set = False;
+ for name in img_names:
+ full_img = cv.imread(name)
+ if full_img is None:
+ print("Cannot read image ",name)
+ exit()
+ full_img_sizes.append((full_img.shape[1],full_img.shape[0]))
+ if work_megapix < 0:
+ img = full_img
+ work_scale = 1
is_work_scale_set = True
- img = cv.resize(src=full_img, dsize=None, fx=work_scale, fy=work_scale, interpolation=cv.INTER_LINEAR_EXACT)
- if is_seam_scale_set is False:
- seam_scale = min(1.0, np.sqrt(seam_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1])))
- seam_work_aspect = seam_scale / work_scale
- is_seam_scale_set = True
- imgFea= cv.detail.computeImageFeatures2(finder,img)
- features.append(imgFea)
- img = cv.resize(src=full_img, dsize=None, fx=seam_scale, fy=seam_scale, interpolation=cv.INTER_LINEAR_EXACT)
- images.append(img)
-if matcher_type== "affine":
- matcher = cv.detail_AffineBestOf2NearestMatcher(False, try_cuda, match_conf)
-elif range_width==-1:
- matcher = cv.detail.BestOf2NearestMatcher_create(try_cuda, match_conf)
-else:
- matcher = cv.detail.BestOf2NearestRangeMatcher_create(range_width, try_cuda, match_conf)
-p=matcher.apply2(features)
-matcher.collectGarbage()
-if save_graph:
- f = open(save_graph_to,"w")
- f.write(cv.detail.matchesGraphAsString(img_names, p, conf_thresh))
- f.close()
-indices=cv.detail.leaveBiggestComponent(features,p,0.3)
-img_subset =[]
-img_names_subset=[]
-full_img_sizes_subset=[]
-num_images=len(indices)
-for i in range(len(indices)):
- img_names_subset.append(img_names[indices[i,0]])
- img_subset.append(images[indices[i,0]])
- full_img_sizes_subset.append(full_img_sizes[indices[i,0]])
-images = img_subset;
-img_names = img_names_subset;
-full_img_sizes = full_img_sizes_subset;
-num_images = len(img_names)
-if num_images < 2:
- print("Need more images")
- exit()
+ else:
+ if is_work_scale_set is False:
+ work_scale = min(1.0, np.sqrt(work_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1])))
+ is_work_scale_set = True
+ img = cv.resize(src=full_img, dsize=None, fx=work_scale, fy=work_scale, interpolation=cv.INTER_LINEAR_EXACT)
+ if is_seam_scale_set is False:
+ seam_scale = min(1.0, np.sqrt(seam_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1])))
+ seam_work_aspect = seam_scale / work_scale
+ is_seam_scale_set = True
+ imgFea= cv.detail.computeImageFeatures2(finder,img)
+ features.append(imgFea)
+ img = cv.resize(src=full_img, dsize=None, fx=seam_scale, fy=seam_scale, interpolation=cv.INTER_LINEAR_EXACT)
+ images.append(img)
+ if matcher_type== "affine":
+ matcher = cv.detail_AffineBestOf2NearestMatcher(False, try_cuda, match_conf)
+ elif range_width==-1:
+ matcher = cv.detail.BestOf2NearestMatcher_create(try_cuda, match_conf)
+ else:
+ matcher = cv.detail.BestOf2NearestRangeMatcher_create(range_width, try_cuda, match_conf)
+ p=matcher.apply2(features)
+ matcher.collectGarbage()
+ if save_graph:
+ f = open(save_graph_to,"w")
+ f.write(cv.detail.matchesGraphAsString(img_names, p, conf_thresh))
+ f.close()
+ indices=cv.detail.leaveBiggestComponent(features,p,0.3)
+ img_subset =[]
+ img_names_subset=[]
+ full_img_sizes_subset=[]
+ num_images=len(indices)
+ for i in range(len(indices)):
+ img_names_subset.append(img_names[indices[i,0]])
+ img_subset.append(images[indices[i,0]])
+ full_img_sizes_subset.append(full_img_sizes[indices[i,0]])
+ images = img_subset;
+ img_names = img_names_subset;
+ full_img_sizes = full_img_sizes_subset;
+ num_images = len(img_names)
+ if num_images < 2:
+ print("Need more images")
+ exit()
-if estimator_type == "affine":
- estimator = cv.detail_AffineBasedEstimator()
-else:
- estimator = cv.detail_HomographyBasedEstimator()
-b, cameras =estimator.apply(features,p,None)
-if not b:
- print("Homography estimation failed.")
- exit()
-for cam in cameras:
- cam.R=cam.R.astype(np.float32)
+ if estimator_type == "affine":
+ estimator = cv.detail_AffineBasedEstimator()
+ else:
+ estimator = cv.detail_HomographyBasedEstimator()
+ b, cameras =estimator.apply(features,p,None)
+ if not b:
+ print("Homography estimation failed.")
+ exit()
+ for cam in cameras:
+ cam.R=cam.R.astype(np.float32)
-if ba_cost_func == "reproj":
- adjuster = cv.detail_BundleAdjusterReproj()
-elif ba_cost_func == "ray":
- adjuster = cv.detail_BundleAdjusterRay()
-elif ba_cost_func == "affine":
- adjuster = cv.detail_BundleAdjusterAffinePartial()
-elif ba_cost_func == "no":
- adjuster = cv.detail_NoBundleAdjuster()
-else:
- print( "Unknown bundle adjustment cost function: ", ba_cost_func )
- exit()
-adjuster.setConfThresh(1)
-refine_mask=np.zeros((3,3),np.uint8)
-if ba_refine_mask[0] == 'x':
- refine_mask[0,0] = 1
-if ba_refine_mask[1] == 'x':
- refine_mask[0,1] = 1
-if ba_refine_mask[2] == 'x':
- refine_mask[0,2] = 1
-if ba_refine_mask[3] == 'x':
- refine_mask[1,1] = 1
-if ba_refine_mask[4] == 'x':
- refine_mask[1,2] = 1
-adjuster.setRefinementMask(refine_mask)
-b,cameras = adjuster.apply(features,p,cameras)
-if not b:
- print("Camera parameters adjusting failed.")
- exit()
-focals=[]
-for cam in cameras:
- focals.append(cam.focal)
-sorted(focals)
-if len(focals)%2==1:
- warped_image_scale = focals[len(focals) // 2]
-else:
- warped_image_scale = (focals[len(focals) // 2]+focals[len(focals) // 2-1])/2
-if do_wave_correct:
- rmats=[]
+ if ba_cost_func == "reproj":
+ adjuster = cv.detail_BundleAdjusterReproj()
+ elif ba_cost_func == "ray":
+ adjuster = cv.detail_BundleAdjusterRay()
+ elif ba_cost_func == "affine":
+ adjuster = cv.detail_BundleAdjusterAffinePartial()
+ elif ba_cost_func == "no":
+ adjuster = cv.detail_NoBundleAdjuster()
+ else:
+ print( "Unknown bundle adjustment cost function: ", ba_cost_func )
+ exit()
+ adjuster.setConfThresh(1)
+ refine_mask=np.zeros((3,3),np.uint8)
+ if ba_refine_mask[0] == 'x':
+ refine_mask[0,0] = 1
+ if ba_refine_mask[1] == 'x':
+ refine_mask[0,1] = 1
+ if ba_refine_mask[2] == 'x':
+ refine_mask[0,2] = 1
+ if ba_refine_mask[3] == 'x':
+ refine_mask[1,1] = 1
+ if ba_refine_mask[4] == 'x':
+ refine_mask[1,2] = 1
+ adjuster.setRefinementMask(refine_mask)
+ b,cameras = adjuster.apply(features,p,cameras)
+ if not b:
+ print("Camera parameters adjusting failed.")
+ exit()
+ focals=[]
for cam in cameras:
- rmats.append(np.copy(cam.R))
- rmats = cv.detail.waveCorrect( rmats, cv.detail.WAVE_CORRECT_HORIZ)
- for idx,cam in enumerate(cameras):
- cam.R = rmats[idx]
-corners=[]
-mask=[]
-masks_warped=[]
-images_warped=[]
-sizes=[]
-masks=[]
-for i in range(0,num_images):
- um=cv.UMat(255*np.ones((images[i].shape[0],images[i].shape[1]),np.uint8))
- masks.append(um)
+ focals.append(cam.focal)
+ sorted(focals)
+ if len(focals)%2==1:
+ warped_image_scale = focals[len(focals) // 2]
+ else:
+ warped_image_scale = (focals[len(focals) // 2]+focals[len(focals) // 2-1])/2
+ if do_wave_correct:
+ rmats=[]
+ for cam in cameras:
+ rmats.append(np.copy(cam.R))
+ rmats = cv.detail.waveCorrect( rmats, cv.detail.WAVE_CORRECT_HORIZ)
+ for idx,cam in enumerate(cameras):
+ cam.R = rmats[idx]
+ corners=[]
+ mask=[]
+ masks_warped=[]
+ images_warped=[]
+ sizes=[]
+ masks=[]
+ for i in range(0,num_images):
+ um=cv.UMat(255*np.ones((images[i].shape[0],images[i].shape[1]),np.uint8))
+ masks.append(um)
-warper = cv.PyRotationWarper(warp_type,warped_image_scale*seam_work_aspect) # warper peut etre nullptr?
-for idx in range(0,num_images):
- K = cameras[idx].K().astype(np.float32)
- swa = seam_work_aspect
- K[0,0] *= swa
- K[0,2] *= swa
- K[1,1] *= swa
- K[1,2] *= swa
- corner,image_wp =warper.warp(images[idx],K,cameras[idx].R,cv.INTER_LINEAR, cv.BORDER_REFLECT)
- corners.append(corner)
- sizes.append((image_wp.shape[1],image_wp.shape[0]))
- images_warped.append(image_wp)
+ warper = cv.PyRotationWarper(warp_type,warped_image_scale*seam_work_aspect) # warper peut etre nullptr?
+ for idx in range(0,num_images):
+ K = cameras[idx].K().astype(np.float32)
+ swa = seam_work_aspect
+ K[0,0] *= swa
+ K[0,2] *= swa
+ K[1,1] *= swa
+ K[1,2] *= swa
+ corner,image_wp =warper.warp(images[idx],K,cameras[idx].R,cv.INTER_LINEAR, cv.BORDER_REFLECT)
+ corners.append(corner)
+ sizes.append((image_wp.shape[1],image_wp.shape[0]))
+ images_warped.append(image_wp)
- p,mask_wp =warper.warp(masks[idx],K,cameras[idx].R,cv.INTER_NEAREST, cv.BORDER_CONSTANT)
- masks_warped.append(mask_wp.get())
-images_warped_f=[]
-for img in images_warped:
- imgf=img.astype(np.float32)
- images_warped_f.append(imgf)
-if cv.detail.ExposureCompensator_CHANNELS == expos_comp_type:
- compensator = cv.detail_ChannelsCompensator(expos_comp_nr_feeds)
-# compensator.setNrGainsFilteringIterations(expos_comp_nr_filtering)
-elif cv.detail.ExposureCompensator_CHANNELS_BLOCKS == expos_comp_type:
- compensator=cv.detail_BlocksChannelsCompensator(expos_comp_block_size, expos_comp_block_size,expos_comp_nr_feeds)
-# compensator.setNrGainsFilteringIterations(expos_comp_nr_filtering)
-else:
- compensator=cv.detail.ExposureCompensator_createDefault(expos_comp_type)
-compensator.feed(corners=corners, images=images_warped, masks=masks_warped)
-if seam_find_type == "no":
- seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
-elif seam_find_type == "voronoi":
- seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM);
-elif seam_find_type == "gc_color":
- seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR")
-elif seam_find_type == "gc_colorgrad":
- seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR_GRAD")
-elif seam_find_type == "dp_color":
- seam_finder = cv.detail_DpSeamFinder("COLOR")
-elif seam_find_type == "dp_colorgrad":
- seam_finder = cv.detail_DpSeamFinder("COLOR_GRAD")
-if seam_finder is None:
- print("Can't create the following seam finder ",seam_find_type)
- exit()
-seam_finder.find(images_warped_f, corners,masks_warped )
-imgListe=[]
-compose_scale=1
-corners=[]
-sizes=[]
-images_warped=[]
-images_warped_f=[]
-masks=[]
-blender= None
-timelapser=None
-compose_work_aspect=1
-for idx,name in enumerate(img_names): # https://github.com/opencv/opencv/blob/master/samples/cpp/stitching_detailed.cpp#L725 ?
- full_img = cv.imread(name)
- if not is_compose_scale_set:
- if compose_megapix > 0:
- compose_scale = min(1.0, np.sqrt(compose_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1])))
- is_compose_scale_set = True;
- compose_work_aspect = compose_scale / work_scale;
- warped_image_scale *= compose_work_aspect
- warper = cv.PyRotationWarper(warp_type,warped_image_scale)
- for i in range(0,len(img_names)):
- cameras[i].focal *= compose_work_aspect
- cameras[i].ppx *= compose_work_aspect
- cameras[i].ppy *= compose_work_aspect
- sz = (full_img_sizes[i][0] * compose_scale,full_img_sizes[i][1]* compose_scale)
- K = cameras[i].K().astype(np.float32)
- roi = warper.warpRoi(sz, K, cameras[i].R);
- corners.append(roi[0:2])
- sizes.append(roi[2:4])
- if abs(compose_scale - 1) > 1e-1:
- img =cv.resize(src=full_img, dsize=None, fx=compose_scale, fy=compose_scale, interpolation=cv.INTER_LINEAR_EXACT)
+ p,mask_wp =warper.warp(masks[idx],K,cameras[idx].R,cv.INTER_NEAREST, cv.BORDER_CONSTANT)
+ masks_warped.append(mask_wp.get())
+ images_warped_f=[]
+ for img in images_warped:
+ imgf=img.astype(np.float32)
+ images_warped_f.append(imgf)
+ if cv.detail.ExposureCompensator_CHANNELS == expos_comp_type:
+ compensator = cv.detail_ChannelsCompensator(expos_comp_nr_feeds)
+ # compensator.setNrGainsFilteringIterations(expos_comp_nr_filtering)
+ elif cv.detail.ExposureCompensator_CHANNELS_BLOCKS == expos_comp_type:
+ compensator=cv.detail_BlocksChannelsCompensator(expos_comp_block_size, expos_comp_block_size,expos_comp_nr_feeds)
+ # compensator.setNrGainsFilteringIterations(expos_comp_nr_filtering)
else:
- img = full_img;
- img_size = (img.shape[1],img.shape[0]);
- K=cameras[idx].K().astype(np.float32)
- corner,image_warped =warper.warp(img,K,cameras[idx].R,cv.INTER_LINEAR, cv.BORDER_REFLECT)
- mask =255*np.ones((img.shape[0],img.shape[1]),np.uint8)
- p,mask_warped =warper.warp(mask,K,cameras[idx].R,cv.INTER_NEAREST, cv.BORDER_CONSTANT)
- compensator.apply(idx,corners[idx],image_warped,mask_warped)
- image_warped_s = image_warped.astype(np.int16)
- image_warped=[]
- dilated_mask = cv.dilate(masks_warped[idx],None)
- seam_mask = cv.resize(dilated_mask,(mask_warped.shape[1],mask_warped.shape[0]),0,0,cv.INTER_LINEAR_EXACT)
- mask_warped = cv.bitwise_and(seam_mask,mask_warped)
- if blender==None and not timelapse:
- blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO)
- dst_sz = cv.detail.resultRoi(corners=corners,sizes=sizes)
- blend_width = np.sqrt(dst_sz[2]*dst_sz[3]) * blend_strength / 100
- if blend_width < 1:
+ compensator=cv.detail.ExposureCompensator_createDefault(expos_comp_type)
+ compensator.feed(corners=corners, images=images_warped, masks=masks_warped)
+ if seam_find_type == "no":
+ seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
+ elif seam_find_type == "voronoi":
+ seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM);
+ elif seam_find_type == "gc_color":
+ seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR")
+ elif seam_find_type == "gc_colorgrad":
+ seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR_GRAD")
+ elif seam_find_type == "dp_color":
+ seam_finder = cv.detail_DpSeamFinder("COLOR")
+ elif seam_find_type == "dp_colorgrad":
+ seam_finder = cv.detail_DpSeamFinder("COLOR_GRAD")
+ if seam_finder is None:
+ print("Can't create the following seam finder ",seam_find_type)
+ exit()
+ seam_finder.find(images_warped_f, corners,masks_warped )
+ imgListe=[]
+ compose_scale=1
+ corners=[]
+ sizes=[]
+ images_warped=[]
+ images_warped_f=[]
+ masks=[]
+ blender= None
+ timelapser=None
+ compose_work_aspect=1
+ for idx,name in enumerate(img_names): # https://github.com/opencv/opencv/blob/master/samples/cpp/stitching_detailed.cpp#L725 ?
+ full_img = cv.imread(name)
+ if not is_compose_scale_set:
+ if compose_megapix > 0:
+ compose_scale = min(1.0, np.sqrt(compose_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1])))
+ is_compose_scale_set = True;
+ compose_work_aspect = compose_scale / work_scale;
+ warped_image_scale *= compose_work_aspect
+ warper = cv.PyRotationWarper(warp_type,warped_image_scale)
+ for i in range(0,len(img_names)):
+ cameras[i].focal *= compose_work_aspect
+ cameras[i].ppx *= compose_work_aspect
+ cameras[i].ppy *= compose_work_aspect
+ sz = (full_img_sizes[i][0] * compose_scale,full_img_sizes[i][1]* compose_scale)
+ K = cameras[i].K().astype(np.float32)
+ roi = warper.warpRoi(sz, K, cameras[i].R);
+ corners.append(roi[0:2])
+ sizes.append(roi[2:4])
+ if abs(compose_scale - 1) > 1e-1:
+ img =cv.resize(src=full_img, dsize=None, fx=compose_scale, fy=compose_scale, interpolation=cv.INTER_LINEAR_EXACT)
+ else:
+ img = full_img;
+ img_size = (img.shape[1],img.shape[0]);
+ K=cameras[idx].K().astype(np.float32)
+ corner,image_warped =warper.warp(img,K,cameras[idx].R,cv.INTER_LINEAR, cv.BORDER_REFLECT)
+ mask =255*np.ones((img.shape[0],img.shape[1]),np.uint8)
+ p,mask_warped =warper.warp(mask,K,cameras[idx].R,cv.INTER_NEAREST, cv.BORDER_CONSTANT)
+ compensator.apply(idx,corners[idx],image_warped,mask_warped)
+ image_warped_s = image_warped.astype(np.int16)
+ image_warped=[]
+ dilated_mask = cv.dilate(masks_warped[idx],None)
+ seam_mask = cv.resize(dilated_mask,(mask_warped.shape[1],mask_warped.shape[0]),0,0,cv.INTER_LINEAR_EXACT)
+ mask_warped = cv.bitwise_and(seam_mask,mask_warped)
+ if blender==None and not timelapse:
blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO)
- elif blend_type == "multiband":
- blender = cv.detail_MultiBandBlender()
- blender.setNumBands((np.log(blend_width)/np.log(2.) - 1.).astype(np.int))
- elif blend_type == "feather":
- blender = cv.detail_FeatherBlender()
- blender.setSharpness(1./blend_width)
- blender.prepare(dst_sz)
- elif timelapser==None and timelapse:
- timelapser = cv.detail.Timelapser_createDefault(timelapse_type)
- timelapser.initialize(corners, sizes)
- if timelapse:
- matones=np.ones((image_warped_s.shape[0],image_warped_s.shape[1]), np.uint8)
- timelapser.process(image_warped_s, matones, corners[idx])
- pos_s = img_names[idx].rfind("/");
- if pos_s == -1:
- fixedFileName = "fixed_" + img_names[idx];
+ dst_sz = cv.detail.resultRoi(corners=corners,sizes=sizes)
+ blend_width = np.sqrt(dst_sz[2]*dst_sz[3]) * blend_strength / 100
+ if blend_width < 1:
+ blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO)
+ elif blend_type == "multiband":
+ blender = cv.detail_MultiBandBlender()
+ blender.setNumBands((np.log(blend_width)/np.log(2.) - 1.).astype(np.int))
+ elif blend_type == "feather":
+ blender = cv.detail_FeatherBlender()
+ blender.setSharpness(1./blend_width)
+ blender.prepare(dst_sz)
+ elif timelapser==None and timelapse:
+ timelapser = cv.detail.Timelapser_createDefault(timelapse_type)
+ timelapser.initialize(corners, sizes)
+ if timelapse:
+ matones=np.ones((image_warped_s.shape[0],image_warped_s.shape[1]), np.uint8)
+ timelapser.process(image_warped_s, matones, corners[idx])
+ pos_s = img_names[idx].rfind("/");
+ if pos_s == -1:
+ fixedFileName = "fixed_" + img_names[idx];
+ else:
+ fixedFileName = img_names[idx][:pos_s + 1 ]+"fixed_" + img_names[idx][pos_s + 1: ]
+ cv.imwrite(fixedFileName, timelapser.getDst())
else:
- fixedFileName = img_names[idx][:pos_s + 1 ]+"fixed_" + img_names[idx][pos_s + 1: ]
- cv.imwrite(fixedFileName, timelapser.getDst())
- else:
- blender.feed(cv.UMat(image_warped_s), mask_warped, corners[idx])
-if not timelapse:
- result=None
- result_mask=None
- result,result_mask = blender.blend(result,result_mask)
- cv.imwrite(result_name,result)
- zoomx =600/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()
+ blender.feed(cv.UMat(image_warped_s), mask_warped, corners[idx])
+ if not timelapse:
+ result=None
+ result_mask=None
+ result,result_mask = blender.blend(result,result_mask)
+ cv.imwrite(result_name,result)
+ zoomx =600/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()
projects / platform / upstream / opencv.git / commitdiff