opencv/samples/python/stitching_detailed.py

"""Rotation model images stitcher.
stitching_detailed img1 img2 [...imgN] [flags]
Flags:
    --preview
        Run stitching in the preview mode. Works faster than usual mode,
        but output image will have lower resolution.
    --try_cuda (yes|no)
        Try to use CUDA. The default value is 'no'. All default values
        are for CPU mode.
\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 cv2 as cv
import sys
import argparse

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:
        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
        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)
            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()