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Open Source Computer Vision Library
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369 lines
18 KiB
369 lines
18 KiB
""" |
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Stitching sample (advanced) |
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=========================== |
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Show how to use Stitcher API from python. |
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""" |
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# Python 2/3 compatibility |
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from __future__ import print_function |
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import numpy as np |
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import cv2 as cv |
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import sys |
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import argparse |
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parser = argparse.ArgumentParser(prog='stitching_detailed.py', description='Rotation model images stitcher') |
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parser.add_argument('img_names', nargs='+',help='files to stitch',type=str) |
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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' ) |
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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' ) |
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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' ) |
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parser.add_argument('--features',action = 'store', default = 'orb',help='Type of features used for images matching. The default is orb.',type=str,dest = 'features' ) |
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parser.add_argument('--matcher',action = 'store', default = 'homography',help='Matcher used for pairwise image matching.',type=str,dest = 'matcher' ) |
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parser.add_argument('--estimator',action = 'store', default = 'homography',help='Type of estimator used for transformation estimation.',type=str,dest = 'estimator' ) |
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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' ) |
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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' ) |
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parser.add_argument('--ba',action = 'store', default = 'ray',help='Bundle adjustment cost function. The default is ray.',type=str,dest = 'ba' ) |
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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' ) |
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parser.add_argument('--wave_correct',action = 'store', default = 'horiz',help='Perform wave effect correction. The default is "horiz"',type=str,dest = 'wave_correct' ) |
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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' ) |
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parser.add_argument('--warp',action = 'store', default = 'plane',help='Warp surface type. The default is "spherical".',type=str,dest = 'warp' ) |
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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' ) |
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parser.add_argument('--seam',action = 'store', default = 'no',help='Seam estimation method. The default is "gc_color".',type=str,dest = 'seam' ) |
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parser.add_argument('--compose_megapix',action = 'store', default = -1,help='Resolution for compositing step. Use -1 for original resolution.',type=float,dest = 'compose_megapix' ) |
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parser.add_argument('--expos_comp',action = 'store', default = 'no',help='Exposure compensation method. The default is "gain_blocks".',type=str,dest = 'expos_comp' ) |
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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' ) |
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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' ) |
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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' ) |
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parser.add_argument('--blend',action = 'store', default = 'multiband',help='Blending method. The default is "multiband".',type=str,dest = 'blend' ) |
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parser.add_argument('--blend_strength',action = 'store', default = 5,help='Blending strength from [0,100] range.',type=np.int32,dest = 'blend_strength' ) |
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parser.add_argument('--output',action = 'store', default = 'result.jpg',help='The default is "result.jpg"',type=str,dest = 'output' ) |
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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' ) |
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parser.add_argument('--rangewidth',action = 'store', default = -1,help='uses range_width to limit number of images to match with.',type=int,dest = 'rangewidth' ) |
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__doc__ += '\n' + parser.format_help() |
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def main(): |
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args = parser.parse_args() |
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img_names=args.img_names |
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print(img_names) |
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preview = args.preview |
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try_cuda = args.try_cuda |
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work_megapix = args.work_megapix |
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seam_megapix = args.seam_megapix |
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compose_megapix = args.compose_megapix |
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conf_thresh = args.conf_thresh |
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features_type = args.features |
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matcher_type = args.matcher |
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estimator_type = args.estimator |
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ba_cost_func = args.ba |
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ba_refine_mask = args.ba_refine_mask |
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wave_correct = args.wave_correct |
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if wave_correct=='no': |
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do_wave_correct= False |
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else: |
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do_wave_correct=True |
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if args.save_graph is None: |
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save_graph = False |
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else: |
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save_graph =True |
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save_graph_to = args.save_graph |
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warp_type = args.warp |
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if args.expos_comp=='no': |
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expos_comp_type = cv.detail.ExposureCompensator_NO |
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elif args.expos_comp=='gain': |
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expos_comp_type = cv.detail.ExposureCompensator_GAIN |
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elif args.expos_comp=='gain_blocks': |
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expos_comp_type = cv.detail.ExposureCompensator_GAIN_BLOCKS |
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elif args.expos_comp=='channel': |
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expos_comp_type = cv.detail.ExposureCompensator_CHANNELS |
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elif args.expos_comp=='channel_blocks': |
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expos_comp_type = cv.detail.ExposureCompensator_CHANNELS_BLOCKS |
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else: |
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print("Bad exposure compensation method") |
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exit() |
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expos_comp_nr_feeds = args.expos_comp_nr_feeds |
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expos_comp_nr_filtering = args.expos_comp_nr_filtering |
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expos_comp_block_size = args.expos_comp_block_size |
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match_conf = args.match_conf |
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seam_find_type = args.seam |
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blend_type = args.blend |
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blend_strength = args.blend_strength |
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result_name = args.output |
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if args.timelapse is not None: |
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timelapse = True |
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if args.timelapse=="as_is": |
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timelapse_type = cv.detail.Timelapser_AS_IS |
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elif args.timelapse=="crop": |
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timelapse_type = cv.detail.Timelapser_CROP |
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else: |
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print("Bad timelapse method") |
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exit() |
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else: |
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timelapse= False |
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range_width = args.rangewidth |
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if features_type=='orb': |
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finder= cv.ORB.create() |
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elif features_type=='surf': |
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finder= cv.xfeatures2d_SURF.create() |
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elif features_type=='sift': |
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finder= cv.xfeatures2d_SIFT.create() |
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else: |
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print ("Unknown descriptor type") |
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exit() |
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seam_work_aspect = 1 |
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full_img_sizes=[] |
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features=[] |
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images=[] |
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is_work_scale_set = False |
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is_seam_scale_set = False |
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is_compose_scale_set = False; |
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for name in img_names: |
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full_img = cv.imread(cv.samples.findFile(name)) |
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if full_img is None: |
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print("Cannot read image ", name) |
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exit() |
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full_img_sizes.append((full_img.shape[1],full_img.shape[0])) |
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if work_megapix < 0: |
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img = full_img |
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work_scale = 1 |
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is_work_scale_set = True |
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else: |
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if is_work_scale_set is False: |
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work_scale = min(1.0, np.sqrt(work_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1]))) |
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is_work_scale_set = True |
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img = cv.resize(src=full_img, dsize=None, fx=work_scale, fy=work_scale, interpolation=cv.INTER_LINEAR_EXACT) |
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if is_seam_scale_set is False: |
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seam_scale = min(1.0, np.sqrt(seam_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1]))) |
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seam_work_aspect = seam_scale / work_scale |
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is_seam_scale_set = True |
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imgFea= cv.detail.computeImageFeatures2(finder,img) |
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features.append(imgFea) |
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img = cv.resize(src=full_img, dsize=None, fx=seam_scale, fy=seam_scale, interpolation=cv.INTER_LINEAR_EXACT) |
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images.append(img) |
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if matcher_type== "affine": |
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matcher = cv.detail_AffineBestOf2NearestMatcher(False, try_cuda, match_conf) |
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elif range_width==-1: |
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matcher = cv.detail.BestOf2NearestMatcher_create(try_cuda, match_conf) |
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else: |
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matcher = cv.detail.BestOf2NearestRangeMatcher_create(range_width, try_cuda, match_conf) |
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p=matcher.apply2(features) |
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matcher.collectGarbage() |
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if save_graph: |
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f = open(save_graph_to,"w") |
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f.write(cv.detail.matchesGraphAsString(img_names, p, conf_thresh)) |
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f.close() |
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indices=cv.detail.leaveBiggestComponent(features,p,0.3) |
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img_subset =[] |
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img_names_subset=[] |
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full_img_sizes_subset=[] |
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num_images=len(indices) |
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for i in range(len(indices)): |
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img_names_subset.append(img_names[indices[i,0]]) |
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img_subset.append(images[indices[i,0]]) |
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full_img_sizes_subset.append(full_img_sizes[indices[i,0]]) |
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images = img_subset; |
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img_names = img_names_subset; |
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full_img_sizes = full_img_sizes_subset; |
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num_images = len(img_names) |
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if num_images < 2: |
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print("Need more images") |
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exit() |
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if estimator_type == "affine": |
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estimator = cv.detail_AffineBasedEstimator() |
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else: |
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estimator = cv.detail_HomographyBasedEstimator() |
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b, cameras =estimator.apply(features,p,None) |
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if not b: |
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print("Homography estimation failed.") |
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exit() |
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for cam in cameras: |
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cam.R=cam.R.astype(np.float32) |
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if ba_cost_func == "reproj": |
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adjuster = cv.detail_BundleAdjusterReproj() |
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elif ba_cost_func == "ray": |
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adjuster = cv.detail_BundleAdjusterRay() |
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elif ba_cost_func == "affine": |
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adjuster = cv.detail_BundleAdjusterAffinePartial() |
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elif ba_cost_func == "no": |
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adjuster = cv.detail_NoBundleAdjuster() |
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else: |
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print( "Unknown bundle adjustment cost function: ", ba_cost_func ) |
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exit() |
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adjuster.setConfThresh(1) |
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refine_mask=np.zeros((3,3),np.uint8) |
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if ba_refine_mask[0] == 'x': |
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refine_mask[0,0] = 1 |
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if ba_refine_mask[1] == 'x': |
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refine_mask[0,1] = 1 |
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if ba_refine_mask[2] == 'x': |
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refine_mask[0,2] = 1 |
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if ba_refine_mask[3] == 'x': |
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refine_mask[1,1] = 1 |
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if ba_refine_mask[4] == 'x': |
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refine_mask[1,2] = 1 |
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adjuster.setRefinementMask(refine_mask) |
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b,cameras = adjuster.apply(features,p,cameras) |
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if not b: |
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print("Camera parameters adjusting failed.") |
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exit() |
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focals=[] |
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for cam in cameras: |
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focals.append(cam.focal) |
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sorted(focals) |
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if len(focals)%2==1: |
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warped_image_scale = focals[len(focals) // 2] |
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else: |
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warped_image_scale = (focals[len(focals) // 2]+focals[len(focals) // 2-1])/2 |
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if do_wave_correct: |
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rmats=[] |
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for cam in cameras: |
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rmats.append(np.copy(cam.R)) |
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rmats = cv.detail.waveCorrect( rmats, cv.detail.WAVE_CORRECT_HORIZ) |
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for idx,cam in enumerate(cameras): |
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cam.R = rmats[idx] |
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corners=[] |
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mask=[] |
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masks_warped=[] |
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images_warped=[] |
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sizes=[] |
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masks=[] |
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for i in range(0,num_images): |
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um=cv.UMat(255*np.ones((images[i].shape[0],images[i].shape[1]),np.uint8)) |
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masks.append(um) |
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warper = cv.PyRotationWarper(warp_type,warped_image_scale*seam_work_aspect) # warper peut etre nullptr? |
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for idx in range(0,num_images): |
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K = cameras[idx].K().astype(np.float32) |
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swa = seam_work_aspect |
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K[0,0] *= swa |
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K[0,2] *= swa |
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K[1,1] *= swa |
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K[1,2] *= swa |
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corner,image_wp =warper.warp(images[idx],K,cameras[idx].R,cv.INTER_LINEAR, cv.BORDER_REFLECT) |
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corners.append(corner) |
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sizes.append((image_wp.shape[1],image_wp.shape[0])) |
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images_warped.append(image_wp) |
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p,mask_wp =warper.warp(masks[idx],K,cameras[idx].R,cv.INTER_NEAREST, cv.BORDER_CONSTANT) |
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masks_warped.append(mask_wp.get()) |
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images_warped_f=[] |
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for img in images_warped: |
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imgf=img.astype(np.float32) |
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images_warped_f.append(imgf) |
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if cv.detail.ExposureCompensator_CHANNELS == expos_comp_type: |
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compensator = cv.detail_ChannelsCompensator(expos_comp_nr_feeds) |
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# compensator.setNrGainsFilteringIterations(expos_comp_nr_filtering) |
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elif cv.detail.ExposureCompensator_CHANNELS_BLOCKS == expos_comp_type: |
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compensator=cv.detail_BlocksChannelsCompensator(expos_comp_block_size, expos_comp_block_size,expos_comp_nr_feeds) |
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# compensator.setNrGainsFilteringIterations(expos_comp_nr_filtering) |
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else: |
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compensator=cv.detail.ExposureCompensator_createDefault(expos_comp_type) |
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compensator.feed(corners=corners, images=images_warped, masks=masks_warped) |
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if seam_find_type == "no": |
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seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO) |
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elif seam_find_type == "voronoi": |
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seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM); |
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elif seam_find_type == "gc_color": |
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seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR") |
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elif seam_find_type == "gc_colorgrad": |
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seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR_GRAD") |
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elif seam_find_type == "dp_color": |
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seam_finder = cv.detail_DpSeamFinder("COLOR") |
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elif seam_find_type == "dp_colorgrad": |
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seam_finder = cv.detail_DpSeamFinder("COLOR_GRAD") |
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if seam_finder is None: |
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print("Can't create the following seam finder ",seam_find_type) |
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exit() |
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seam_finder.find(images_warped_f, corners,masks_warped ) |
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imgListe=[] |
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compose_scale=1 |
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corners=[] |
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sizes=[] |
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images_warped=[] |
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images_warped_f=[] |
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masks=[] |
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blender= None |
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timelapser=None |
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compose_work_aspect=1 |
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for idx,name in enumerate(img_names): # https://github.com/opencv/opencv/blob/master/samples/cpp/stitching_detailed.cpp#L725 ? |
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full_img = cv.imread(name) |
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if not is_compose_scale_set: |
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if compose_megapix > 0: |
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compose_scale = min(1.0, np.sqrt(compose_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1]))) |
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is_compose_scale_set = True; |
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compose_work_aspect = compose_scale / work_scale; |
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warped_image_scale *= compose_work_aspect |
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warper = cv.PyRotationWarper(warp_type,warped_image_scale) |
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for i in range(0,len(img_names)): |
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cameras[i].focal *= compose_work_aspect |
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cameras[i].ppx *= compose_work_aspect |
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cameras[i].ppy *= compose_work_aspect |
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sz = (full_img_sizes[i][0] * compose_scale,full_img_sizes[i][1]* compose_scale) |
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K = cameras[i].K().astype(np.float32) |
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roi = warper.warpRoi(sz, K, cameras[i].R); |
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corners.append(roi[0:2]) |
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sizes.append(roi[2:4]) |
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if abs(compose_scale - 1) > 1e-1: |
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img =cv.resize(src=full_img, dsize=None, fx=compose_scale, fy=compose_scale, interpolation=cv.INTER_LINEAR_EXACT) |
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else: |
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img = full_img; |
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img_size = (img.shape[1],img.shape[0]); |
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K=cameras[idx].K().astype(np.float32) |
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corner,image_warped =warper.warp(img,K,cameras[idx].R,cv.INTER_LINEAR, cv.BORDER_REFLECT) |
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mask =255*np.ones((img.shape[0],img.shape[1]),np.uint8) |
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p,mask_warped =warper.warp(mask,K,cameras[idx].R,cv.INTER_NEAREST, cv.BORDER_CONSTANT) |
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compensator.apply(idx,corners[idx],image_warped,mask_warped) |
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image_warped_s = image_warped.astype(np.int16) |
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image_warped=[] |
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dilated_mask = cv.dilate(masks_warped[idx],None) |
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seam_mask = cv.resize(dilated_mask,(mask_warped.shape[1],mask_warped.shape[0]),0,0,cv.INTER_LINEAR_EXACT) |
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mask_warped = cv.bitwise_and(seam_mask,mask_warped) |
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if blender==None and not timelapse: |
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blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO) |
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dst_sz = cv.detail.resultRoi(corners=corners,sizes=sizes) |
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blend_width = np.sqrt(dst_sz[2]*dst_sz[3]) * blend_strength / 100 |
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if blend_width < 1: |
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blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO) |
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elif blend_type == "multiband": |
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blender = cv.detail_MultiBandBlender() |
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blender.setNumBands((np.log(blend_width)/np.log(2.) - 1.).astype(np.int)) |
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elif blend_type == "feather": |
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blender = cv.detail_FeatherBlender() |
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blender.setSharpness(1./blend_width) |
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blender.prepare(dst_sz) |
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elif timelapser==None and timelapse: |
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timelapser = cv.detail.Timelapser_createDefault(timelapse_type) |
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timelapser.initialize(corners, sizes) |
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if timelapse: |
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matones=np.ones((image_warped_s.shape[0],image_warped_s.shape[1]), np.uint8) |
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timelapser.process(image_warped_s, matones, corners[idx]) |
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pos_s = img_names[idx].rfind("/"); |
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if pos_s == -1: |
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fixedFileName = "fixed_" + img_names[idx]; |
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else: |
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fixedFileName = img_names[idx][:pos_s + 1 ]+"fixed_" + img_names[idx][pos_s + 1: ] |
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cv.imwrite(fixedFileName, timelapser.getDst()) |
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else: |
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blender.feed(cv.UMat(image_warped_s), mask_warped, corners[idx]) |
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if not timelapse: |
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result=None |
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result_mask=None |
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result,result_mask = blender.blend(result,result_mask) |
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cv.imwrite(result_name,result) |
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zoomx = 600.0 / result.shape[1] |
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dst=cv.normalize(src=result,dst=None,alpha=255.,norm_type=cv.NORM_MINMAX,dtype=cv.CV_8U) |
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dst=cv.resize(dst,dsize=None,fx=zoomx,fy=zoomx) |
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cv.imshow(result_name,dst) |
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cv.waitKey() |
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print('Done') |
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if __name__ == '__main__': |
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print(__doc__) |
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main() |
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cv.destroyAllWindows()
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