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
+
+<H4>stitching_detailed</H4>
+@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
+
+
+
+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 decr\ 2ease 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
+
+
+
+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 :
+
+
+
+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
+
+
+
+You can find all images in https://github.com/opencv/opencv_extra/tree/master/testdata/stitching
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' )
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
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:
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]])
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":
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])
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)
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()
projects / platform / upstream / opencv.git / commitdiff
