diff --git a/doc/tutorials/stitching/stitcher/images/affinepano.jpg b/doc/tutorials/stitching/stitcher/images/affinepano.jpg new file mode 100644 index 0000000000..50fe159a61 Binary files /dev/null and b/doc/tutorials/stitching/stitcher/images/affinepano.jpg differ diff --git a/doc/tutorials/stitching/stitcher/images/compressedPlaneA2B1.jpg b/doc/tutorials/stitching/stitcher/images/compressedPlaneA2B1.jpg new file mode 100644 index 0000000000..abeb14c6c1 Binary files /dev/null and b/doc/tutorials/stitching/stitcher/images/compressedPlaneA2B1.jpg differ diff --git a/doc/tutorials/stitching/stitcher/images/fisheye.jpg b/doc/tutorials/stitching/stitcher/images/fisheye.jpg new file mode 100644 index 0000000000..a2adaa4a93 Binary files /dev/null and b/doc/tutorials/stitching/stitcher/images/fisheye.jpg differ diff --git a/doc/tutorials/stitching/stitcher/images/gvedit.jpg b/doc/tutorials/stitching/stitcher/images/gvedit.jpg new file mode 100644 index 0000000000..bf0d6e27b1 Binary files /dev/null and b/doc/tutorials/stitching/stitcher/images/gvedit.jpg differ diff --git a/doc/tutorials/stitching/stitcher/stitcher.markdown b/doc/tutorials/stitching/stitcher/stitcher.markdown index 1d4f27bdb9..a55ec6ca47 100644 --- a/doc/tutorials/stitching/stitcher/stitcher.markdown +++ b/doc/tutorials/stitching/stitcher/stitcher.markdown @@ -96,10 +96,63 @@ or (dataset from professional book scanner): Examples above expects POSIX platform, on windows you have to provide all files names explicitly (e.g. `boat1.jpg` `boat2.jpg`...) as windows command line does not support `*` expansion. -See also +Stitching detailed (python opencv >4.0.1) -------- If you want to study internals of the stitching pipeline or you want to experiment with detailed -configuration see -[stitching_detailed.cpp](https://github.com/opencv/opencv/tree/master/samples/cpp/stitching_detailed.cpp) -in `opencv/samples/cpp` folder. +configuration you can use stitching_detailed source code available in C++ or python + +

stitching_detailed

+@add_toggle_cpp +[stitching_detailed.cpp](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/stitching_detailed.cpp) +@end_toggle + +@add_toggle_python +[stitching_detailed.py](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/stitching_detailed.py) +@end_toggle + +stitching_detailed program uses command line to get stitching parameter. Many parameters exists. Above examples shows some command line parameters possible : + +boat5.jpg boat2.jpg boat3.jpg boat4.jpg boat1.jpg boat6.jpg --work_megapix 0.6 --features orb --matcher homography --estimator homography --match_conf 0.3 --conf_thresh 0.3 --ba ray --ba_refine_mask xxxxx --save_graph test.txt --wave_correct no --warp fisheye --blend multiband --expos_comp no --seam gc_colorgrad + +![](images/fisheye.jpg) + +Pairwise images are matched using an homography --matcher homography and estimator used for transformation estimation too --estimator homography + +Confidence for feature matching step is 0.3 : --match_conf 0.3. You can decrease this value if you have some difficulties to match images + +Threshold for two images are from the same panorama confidence is 0. : --conf_thresh 0.3 You can decrease this value if you have some difficulties to match images + +Bundle adjustment cost function is ray --ba ray + +Refinement mask for bundle adjustment is xxxxx ( --ba_refine_mask xxxxx) where 'x' means refine respective parameter and '_' means don't. Refine one, and has the following format: fx,skew,ppx,aspect,ppy + +Save matches graph represented in DOT language to test.txt ( --save_graph test.txt) : Labels description: Nm is number of matches, Ni is number of inliers, C is confidence + +![](images/gvedit.jpg) + +Perform wave effect correction is no (--wave_correct no) + +Warp surface type is fisheye (--warp fisheye) + +Blending method is multiband (--blend multiband) + +Exposure compensation method is not used (--expos_comp no) + +Seam estimation estimator is Minimum graph cut-based seam (--seam gc_colorgrad) + +you can use those arguments on command line too : + +boat5.jpg boat2.jpg boat3.jpg boat4.jpg boat1.jpg boat6.jpg --work_megapix 0.6 --features orb --matcher homography --estimator homography --match_conf 0.3 --conf_thresh 0.3 --ba ray --ba_refine_mask xxxxx --wave_correct horiz --warp compressedPlaneA2B1 --blend multiband --expos_comp channels_blocks --seam gc_colorgrad + +You will get : + +![](images/compressedPlaneA2B1.jpg) + +For images captured using a scanner or a drone ( affine motion) you can use those arguments on command line : + +newspaper1.jpg newspaper2.jpg --work_megapix 0.6 --features surf --matcher affine --estimator affine --match_conf 0.3 --conf_thresh 0.3 --ba affine --ba_refine_mask xxxxx --wave_correct no --warp affine + +![](images/affinepano.jpg) + +You can find all images in https://github.com/opencv/opencv_extra/tree/master/testdata/stitching diff --git a/modules/stitching/include/opencv2/stitching/detail/blenders.hpp b/modules/stitching/include/opencv2/stitching/detail/blenders.hpp index 872ba131b7..ec35aa7cbb 100644 --- a/modules/stitching/include/opencv2/stitching/detail/blenders.hpp +++ b/modules/stitching/include/opencv2/stitching/detail/blenders.hpp @@ -73,7 +73,7 @@ public: @param corners Source images top-left corners @param sizes Source image sizes */ - CV_WRAP void prepare(const std::vector &corners, const std::vector &sizes); + CV_WRAP virtual void prepare(const std::vector &corners, const std::vector &sizes); /** @overload */ CV_WRAP virtual void prepare(Rect dst_roi); /** @brief Processes the image. diff --git a/modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp b/modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp index 2d77dde340..ff05af1814 100644 --- a/modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp +++ b/modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp @@ -120,6 +120,8 @@ final transformation for each camera. */ class CV_EXPORTS_W AffineBasedEstimator : public Estimator { +public: + CV_WRAP AffineBasedEstimator(){} private: virtual bool estimate(const std::vector &features, const std::vector &pairwise_matches, diff --git a/modules/stitching/src/blenders.cpp b/modules/stitching/src/blenders.cpp index c0ae003277..811d7453cf 100644 --- a/modules/stitching/src/blenders.cpp +++ b/modules/stitching/src/blenders.cpp @@ -133,7 +133,6 @@ void Blender::blend(InputOutputArray dst, InputOutputArray dst_mask) dst_mask_.release(); } - void FeatherBlender::prepare(Rect dst_roi) { Blender::prepare(dst_roi); @@ -231,7 +230,6 @@ MultiBandBlender::MultiBandBlender(int try_gpu, int num_bands, int weight_type) weight_type_ = weight_type; } - void MultiBandBlender::prepare(Rect dst_roi) { dst_roi_final_ = dst_roi; diff --git a/samples/python/stitching_detailed.py b/samples/python/stitching_detailed.py index b1809a51d0..26afd22609 100644 --- a/samples/python/stitching_detailed.py +++ b/samples/python/stitching_detailed.py @@ -83,8 +83,11 @@ parser.add_argument('--seam_megapix',action = 'store', default = 0.1,help=' Reso 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=int,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('--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' ) @@ -119,10 +122,16 @@ 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 - + 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 @@ -180,7 +189,7 @@ for name in img_names: 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_create(False, try_cuda, match_conf) + matcher = cv.detail_AffineBestOf2NearestMatcher(False, try_cuda, match_conf) elif range_width==-1: matcher = cv.detail.BestOf2NearestMatcher_create(try_cuda, match_conf) else: @@ -189,14 +198,14 @@ p=matcher.apply2(features) matcher.collectGarbage() if save_graph: f = open(save_graph_to,"w") -# f.write(matchesGraphAsString(img_names, pairwise_matches, conf_thresh)) + 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(0,num_images): +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]]) @@ -273,26 +282,33 @@ for i in range(0,num_images): masks.append(um) warper = cv.PyRotationWarper(warp_type,warped_image_scale*seam_work_aspect) # warper peut etre nullptr? -for i in range(0,num_images): - K = cameras[i].K().astype(np.float32) +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[i],K,cameras[i].R,cv.INTER_LINEAR, cv.BORDER_REFLECT) + 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[i],K,cameras[i].R,cv.INTER_NEAREST, cv.BORDER_CONSTANT) - masks_warped.append(mask_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) -compensator=cv.detail.ExposureCompensator_createDefault(expos_comp_type) -compensator.feed(corners, images_warped, masks_warped) +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": @@ -332,7 +348,7 @@ for idx,name in enumerate(img_names): # https://github.com/opencv/opencv/blob/ma cameras[i].focal *= compose_work_aspect cameras[i].ppx *= compose_work_aspect cameras[i].ppy *= compose_work_aspect - sz = (full_img.shape[1] * compose_scale,full_img.shape[0] * compose_scale) + 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]) @@ -353,21 +369,20 @@ for idx,name in enumerate(img_names): # https://github.com/opencv/opencv/blob/ma 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(1) - dst_sz = cv.detail.resultRoi(corners,sizes) - blend_strength=1 + 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 == "MULTI_BAND": - blender = cv.detail.Blender_createDefault(cv.detail.Blender_MULTIBAND) + 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.Blender_createDefault(cv.detail.Blender_FEATHER) + elif blend_type == "feather": + blender = cv.detail_FeatherBlender() blender.setSharpness(1./blend_width) - blender.prepare(corners, sizes) + blender.prepare(dst_sz) elif timelapser==None and timelapse: - timelapser = cv.detail.createDefault(timelapse_type); + 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) @@ -379,9 +394,14 @@ for idx,name in enumerate(img_names): # https://github.com/opencv/opencv/blob/ma fixedFileName = img_names[idx][:pos_s + 1 ]+"fixed_" + img_names[idx][pos_s + 1: ] cv.imwrite(fixedFileName, timelapser.getDst()) else: - blender.feed(image_warped_s, mask_warped, corners[idx]) + 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()