diff --git a/samples/python/stitching_detailed.py b/samples/python/stitching_detailed.py index 26afd22609..645f659d54 100644 --- a/samples/python/stitching_detailed.py +++ b/samples/python/stitching_detailed.py @@ -64,344 +64,345 @@ import cv2 as cv 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.',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.',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") + 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() -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) - 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 - 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 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=[] - 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) - - 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) - 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: - 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]; + 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: - fixedFileName = img_names[idx][:pos_s + 1 ]+"fixed_" + img_names[idx][pos_s + 1: ] - cv.imwrite(fixedFileName, timelapser.getDst()) + print("Bad timelapse method") + exit() 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() + 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) + 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 + 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 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=[] + 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) + + 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) + 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: + 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: + 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()