Use Opencv stitching_detailed To Stitch Segmentation image

Thu, 29 Apr 2021 18:36:00 +0800

Use Opencv stitching_detailed To Stitch Segmentation image

Environment:

python version 3.7
opencv-python version 4.5.1.48

1.Download stitching_detailed file save it with file name stitching_detailed.py;

2.Run the command;

$python.exe stitching_detailed.py image_1.png image_2.png image_3.png image_4.png image_5.png image_6.png image_7.png image_8.png image_9.png origin.png --features=brisk --matcher=affine

Note that: image 1~9 is Segmentation images and origin.png is a full picture.

REFERENCE:

stitching_detailed

stitching_detailed.py source codes is follow:

"""
Stitching sample (advanced)
===========================
Show how to use Stitcher API from python.
"""

# Python 2/3 compatibility
from __future__ import print_function

import argparse
from collections import OrderedDict

import cv2 as cv
import numpy as np

EXPOS_COMP_CHOICES = OrderedDict()
EXPOS_COMP_CHOICES['gain_blocks'] = cv.detail.ExposureCompensator_GAIN_BLOCKS
EXPOS_COMP_CHOICES['gain'] = cv.detail.ExposureCompensator_GAIN
EXPOS_COMP_CHOICES['channel'] = cv.detail.ExposureCompensator_CHANNELS
EXPOS_COMP_CHOICES['channel_blocks'] = cv.detail.ExposureCompensator_CHANNELS_BLOCKS
EXPOS_COMP_CHOICES['no'] = cv.detail.ExposureCompensator_NO

BA_COST_CHOICES = OrderedDict()
BA_COST_CHOICES['ray'] = cv.detail_BundleAdjusterRay
BA_COST_CHOICES['reproj'] = cv.detail_BundleAdjusterReproj
BA_COST_CHOICES['affine'] = cv.detail_BundleAdjusterAffinePartial
BA_COST_CHOICES['no'] = cv.detail_NoBundleAdjuster

FEATURES_FIND_CHOICES = OrderedDict()
try:
 cv.xfeatures2d_SURF.create() # check if the function can be called
 FEATURES_FIND_CHOICES['surf'] = cv.xfeatures2d_SURF.create
except (AttributeError, cv.error) as e:
 print("SURF not available")
# if SURF not available, ORB is default
FEATURES_FIND_CHOICES['orb'] = cv.ORB.create
try:
 FEATURES_FIND_CHOICES['sift'] = cv.xfeatures2d_SIFT.create
except AttributeError:
 print("SIFT not available")
try:
 FEATURES_FIND_CHOICES['brisk'] = cv.BRISK_create
except AttributeError:
 print("BRISK not available")
try:
 FEATURES_FIND_CHOICES['akaze'] = cv.AKAZE_create
except AttributeError:
 print("AKAZE not available")

SEAM_FIND_CHOICES = OrderedDict()
SEAM_FIND_CHOICES['gc_color'] = cv.detail_GraphCutSeamFinder('COST_COLOR')
SEAM_FIND_CHOICES['gc_colorgrad'] = cv.detail_GraphCutSeamFinder('COST_COLOR_GRAD')
SEAM_FIND_CHOICES['dp_color'] = cv.detail_DpSeamFinder('COLOR')
SEAM_FIND_CHOICES['dp_colorgrad'] = cv.detail_DpSeamFinder('COLOR_GRAD')
SEAM_FIND_CHOICES['voronoi'] = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM)
SEAM_FIND_CHOICES['no'] = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)

ESTIMATOR_CHOICES = OrderedDict()
ESTIMATOR_CHOICES['homography'] = cv.detail_HomographyBasedEstimator
ESTIMATOR_CHOICES['affine'] = cv.detail_AffineBasedEstimator

WARP_CHOICES = (
 'spherical',
 'plane',
 'affine',
 'cylindrical',
 'fisheye',
 'stereographic',
 'compressedPlaneA2B1',
 'compressedPlaneA1.5B1',
 'compressedPlanePortraitA2B1',
 'compressedPlanePortraitA1.5B1',
 'paniniA2B1',
 'paniniA1.5B1',
 'paniniPortraitA2B1',
 'paniniPortraitA1.5B1',
 'mercator',
 'transverseMercator',
)

WAVE_CORRECT_CHOICES = OrderedDict()
WAVE_CORRECT_CHOICES['horiz'] = cv.detail.WAVE_CORRECT_HORIZ
WAVE_CORRECT_CHOICES['no'] = None
WAVE_CORRECT_CHOICES['vert'] = cv.detail.WAVE_CORRECT_VERT

BLEND_CHOICES = ('multiband', 'feather', 'no',)

parser = argparse.ArgumentParser(
 prog="stitching_detailed.py", description="Rotation model images stitcher"
)
parser.add_argument(
 'img_names', nargs='+',
 help="Files to stitch", type=str
)
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=list(FEATURES_FIND_CHOICES.keys())[0],
 help="Type of features used for images matching. The default is '%s'." % list(FEATURES_FIND_CHOICES.keys())[0],
 choices=FEATURES_FIND_CHOICES.keys(),
 type=str, dest='features'
)
parser.add_argument(
 '--matcher', action='store', default='homography',
 help="Matcher used for pairwise image matching. The default is 'homography'.",
 choices=('homography', 'affine'),
 type=str, dest='matcher'
)
parser.add_argument(
 '--estimator', action='store', default=list(ESTIMATOR_CHOICES.keys())[0],
 help="Type of estimator used for transformation estimation. The default is '%s'." % list(ESTIMATOR_CHOICES.keys())[0],
 choices=ESTIMATOR_CHOICES.keys(),
 type=str, dest='estimator'
)
parser.add_argument(
 '--match_conf', action='store',
 help="Confidence for feature matching step. The default is 0.3 for ORB and 0.65 for other feature types.",
 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=list(BA_COST_CHOICES.keys())[0],
 help="Bundle adjustment cost function. The default is '%s'." % list(BA_COST_CHOICES.keys())[0],
 choices=BA_COST_CHOICES.keys(),
 type=str, dest='ba'
)
parser.add_argument(
 '--ba_refine_mask', action='store', default='xxxxx',
 help="Set refinement mask for bundle adjustment. It looks like 'x_xxx', "
 "where 'x' means refine respective parameter and '_' means don't refine, "
 "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.",
 type=str, dest='ba_refine_mask'
)
parser.add_argument(
 '--wave_correct', action='store', default=list(WAVE_CORRECT_CHOICES.keys())[0],
 help="Perform wave effect correction. The default is '%s'" % list(WAVE_CORRECT_CHOICES.keys())[0],
 choices=WAVE_CORRECT_CHOICES.keys(),
 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=WARP_CHOICES[0],
 help="Warp surface type. The default is '%s'." % WARP_CHOICES[0],
 choices=WARP_CHOICES,
 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=list(SEAM_FIND_CHOICES.keys())[0],
 help="Seam estimation method. The default is '%s'." % list(SEAM_FIND_CHOICES.keys())[0],
 choices=SEAM_FIND_CHOICES.keys(),
 type=str, dest='seam'
)
parser.add_argument(
 '--compose_megapix', action='store', default=-1,
 help="Resolution for compositing step. Use -1 for original resolution. The default is -1",
 type=float, dest='compose_megapix'
)
parser.add_argument(
 '--expos_comp', action='store', default=list(EXPOS_COMP_CHOICES.keys())[0],
 help="Exposure compensation method. The default is '%s'." % list(EXPOS_COMP_CHOICES.keys())[0],
 choices=EXPOS_COMP_CHOICES.keys(),
 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. The default is 32.",
 type=np.int32, dest='expos_comp_block_size'
)
parser.add_argument(
 '--blend', action='store', default=BLEND_CHOICES[0],
 help="Blending method. The default is '%s'." % BLEND_CHOICES[0],
 choices=BLEND_CHOICES,
 type=str, dest='blend'
)
parser.add_argument(
 '--blend_strength', action='store', default=5,
 help="Blending strength from [0,100] range. The default is 5",
 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'
)

__doc__ += '\n' + parser.format_help()


def get_matcher(args):
 try_cuda = args.try_cuda
 matcher_type = args.matcher
 if args.match_conf is None:
 if args.features == 'orb':
 match_conf = 0.3
 else:
 match_conf = 0.65
 else:
 match_conf = args.match_conf
 range_width = args.rangewidth
 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)
 return matcher


def get_compensator(args):
 expos_comp_type = EXPOS_COMP_CHOICES[args.expos_comp]
 expos_comp_nr_feeds = args.expos_comp_nr_feeds
 expos_comp_block_size = args.expos_comp_block_size
 # expos_comp_nr_filtering = args.expos_comp_nr_filtering
 if expos_comp_type == cv.detail.ExposureCompensator_CHANNELS:
 compensator = cv.detail_ChannelsCompensator(expos_comp_nr_feeds)
 # compensator.setNrGainsFilteringIterations(expos_comp_nr_filtering)
 elif expos_comp_type == cv.detail.ExposureCompensator_CHANNELS_BLOCKS:
 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)
 return compensator


def main():
 args = parser.parse_args()
 img_names = args.img_names
 print(img_names)
 work_megapix = args.work_megapix
 seam_megapix = args.seam_megapix
 compose_megapix = args.compose_megapix
 conf_thresh = args.conf_thresh
 ba_refine_mask = args.ba_refine_mask
 wave_correct = WAVE_CORRECT_CHOICES[args.wave_correct]
 if args.save_graph is None:
 save_graph = False
 else:
 save_graph = True
 warp_type = args.warp
 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
 finder = FEATURES_FIND_CHOICES[args.features]()
 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(cv.samples.findFile(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
 img_feat = cv.detail.computeImageFeatures2(finder, img)
 features.append(img_feat)
 img = cv.resize(src=full_img, dsize=None, fx=seam_scale, fy=seam_scale, interpolation=cv.INTER_LINEAR_EXACT)
 images.append(img)

 matcher = get_matcher(args)
 p = matcher.apply2(features)
 matcher.collectGarbage()

 if save_graph:
 with open(args.save_graph, 'w') as fh:
 fh.write(cv.detail.matchesGraphAsString(img_names, p, conf_thresh))

 indices = cv.detail.leaveBiggestComponent(features, p, conf_thresh)
 img_subset = []
 img_names_subset = []
 full_img_sizes_subset = []
 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()

 estimator = ESTIMATOR_CHOICES[args.estimator]()
 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)

 adjuster = BA_COST_CHOICES[args.ba]()
 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)
 focals.sort()
 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 wave_correct is not None:
 rmats = []
 for cam in cameras:
 rmats.append(np.copy(cam.R))
 rmats = cv.detail.waveCorrect(rmats, wave_correct)
 for idx, cam in enumerate(cameras):
 cam.R = rmats[idx]
 corners = []
 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 could be 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)

 compensator = get_compensator(args)
 compensator.feed(corners=corners, images=images_warped, masks=masks_warped)

 seam_finder = SEAM_FIND_CHOICES[args.seam]
 seam_finder.find(images_warped_f, corners, masks_warped)
 compose_scale = 1
 corners = []
 sizes = []
 blender = None
 timelapser = None
 # https://github.com/opencv/opencv/blob/master/samples/cpp/stitching_detailed.cpp#L725 ?
 for idx, name in enumerate(img_names):
 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)
 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 is 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 is None and timelapse:
 timelapser = cv.detail.Timelapser_createDefault(timelapse_type)
 timelapser.initialize(corners, sizes)
 if timelapse:
 ma_tones = np.ones((image_warped_s.shape[0], image_warped_s.shape[1]), np.uint8)
 timelapser.process(image_warped_s, ma_tones, corners[idx])
 pos_s = img_names[idx].rfind("/")
 if pos_s == -1:
 fixed_file_name = "fixed_" + img_names[idx]
 else:
 fixed_file_name = img_names[idx][:pos_s + 1] + "fixed_" + img_names[idx][pos_s + 1:]
 cv.imwrite(fixed_file_name, 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)
 zoom_x = 600.0 / 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=zoom_x, fy=zoom_x)
 cv.imshow(result_name, dst)
 cv.waitKey()

 print("Done")


if __name__ == '__main__':
 print(__doc__)
 main()
 cv.destroyAllWindows()

OpenCV on Yaohong

Use Opencv stitching_detailed To Stitch Segmentation image

Use Opencv stitching_detailed To Stitch Segmentation image