# Python 2/3 compatibility
from __future__ import print_function
-import numpy as np
+import argparse
+from collections import OrderedDict
+
import cv2 as cv
+import numpy as np
-import sys
-import argparse
+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:
+ FEATURES_FIND_CHOICES['surf'] = cv.xfeatures2d_SURF.create
+except AttributeError:
+ 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)
-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('--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' )
+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 = ('horiz', 'no', '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'." % FEATURES_FIND_CHOICES.keys(),
+ choices=FEATURES_FIND_CHOICES.keys(),
+ type=str, dest='features'
+)
+parser.add_argument(
+ '--matcher', action='store', default='homography',
+ help="Matcher used for pairwise image matching.",
+ 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.",
+ 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=WAVE_CORRECT_CHOICES[0],
+ help="Perform wave effect correction. The default is '%s'" % WAVE_CORRECT_CHOICES[0],
+ choices=WAVE_CORRECT_CHOICES,
+ 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
+ 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
+ if wave_correct == 'no':
+ do_wave_correct = False
else:
- do_wave_correct=True
+ do_wave_correct = True
if args.save_graph is None:
save_graph = False
else:
- save_graph =True
- save_graph_to = args.save_graph
+ save_graph = True
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":
+ if args.timelapse == "as_is":
timelapse_type = cv.detail.Timelapser_AS_IS
- elif args.timelapse=="crop":
+ 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()
+ timelapse = False
+ finder = FEATURES_FIND_CHOICES[args.features]()
seam_work_aspect = 1
- full_img_sizes=[]
- features=[]
- images=[]
+ full_img_sizes = []
+ features = []
+ images = []
is_work_scale_set = False
is_seam_scale_set = False
is_compose_scale_set = False
if full_img is None:
print("Cannot read image ", name)
exit()
- full_img_sizes.append((full_img.shape[1],full_img.shape[0]))
+ 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])))
+ 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_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_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)
- 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 = get_matcher(args)
+ 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)
+ with open(args.save_graph, 'w') as fh:
+ fh.write(cv.detail.matchesGraphAsString(img_names, p, conf_thresh))
+
+ indices = cv.detail.leaveBiggestComponent(features, p, 0.3)
+ 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]])
+ 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
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)
+ 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)
-
- 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()
+ cam.R = cam.R.astype(np.float32)
+
+ adjuster = BA_COST_CHOICES[args.ba]()
adjuster.setConfThresh(1)
- refine_mask=np.zeros((3,3),np.uint8)
+ refine_mask = np.zeros((3, 3), np.uint8)
if ba_refine_mask[0] == 'x':
- refine_mask[0,0] = 1
+ refine_mask[0, 0] = 1
if ba_refine_mask[1] == 'x':
- refine_mask[0,1] = 1
+ refine_mask[0, 1] = 1
if ba_refine_mask[2] == 'x':
- refine_mask[0,2] = 1
+ refine_mask[0, 2] = 1
if ba_refine_mask[3] == 'x':
- refine_mask[1,1] = 1
+ refine_mask[1, 1] = 1
if ba_refine_mask[4] == 'x':
- refine_mask[1,2] = 1
+ refine_mask[1, 2] = 1
adjuster.setRefinementMask(refine_mask)
- b,cameras = adjuster.apply(features,p,cameras)
+ b, cameras = adjuster.apply(features, p, cameras)
if not b:
print("Camera parameters adjusting failed.")
exit()
- focals=[]
+ focals = []
for cam in cameras:
focals.append(cam.focal)
sorted(focals)
- if len(focals)%2==1:
+ 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
+ warped_image_scale = (focals[len(focals) // 2] + focals[len(focals) // 2 - 1]) / 2
if do_wave_correct:
- rmats=[]
+ 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):
+ 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))
+ 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 peut etre nullptr?
- for idx in range(0,num_images):
+ 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)
+ 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]))
+ 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)
+ 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=[]
+
+ images_warped_f = []
for img in images_warped:
- imgf=img.astype(np.float32)
+ 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 = get_compensator(args)
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)
+
+ 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])))
+ 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)):
+ 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)
+ 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)
+ 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)
+ _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:
+ 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
+ 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))
+ 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.setSharpness(1. / blend_width)
blender.prepare(dst_sz)
- elif timelapser==None and timelapse:
+ elif timelapser is 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])
+ 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:
- fixedFileName = "fixed_" + img_names[idx]
+ fixed_file_name = "fixed_" + img_names[idx]
else:
- fixedFileName = img_names[idx][:pos_s + 1 ]+"fixed_" + img_names[idx][pos_s + 1: ]
- cv.imwrite(fixedFileName, timelapser.getDst())
+ 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)
- zoomx = 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=zoomx,fy=zoomx)
- cv.imshow(result_name,dst)
+ 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')
+ print("Done")
if __name__ == '__main__':
projects / platform / upstream / opencv.git / commitdiff