Chiebot-Mirror
/
opencv
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Merge pull request #16257 from ianare:more-pythonic

Browse Source
pull/16368/head
Alexander Alekhin 5 years ago
parent ed377ac1e8 e4a64760db
commit 2b9cfe1e1c
1 changed files with 371 additions and 221 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 422
      samples/python/stitching_detailed.py

422
samples/python/stitching_detailed.py
Unescape View File

@ -8,56 +8,275 @@ Show how to use Stitcher API from python.
# 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)
ESTIMATOR_CHOICES = OrderedDict()
ESTIMATOR_CHOICES['homography'] = cv.detail_HomographyBasedEstimator
ESTIMATOR_CHOICES['affine'] = cv.detail_AffineBasedEstimator
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' )
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
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':
@ -68,26 +287,7 @@ def main():
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
@ -102,16 +302,7 @@ def main():
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()
finder = FEATURES_FIND_CHOICES[args.features]()
seam_work_aspect = 1
full_img_sizes = []
features = []
@ -138,27 +329,23 @@ def main():
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)
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()
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 = []
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]])
@ -171,10 +358,7 @@ def main():
print("Need more images")
exit()
if estimator_type == "affine":
estimator = cv.detail_AffineBasedEstimator()
else:
estimator = cv.detail_HomographyBasedEstimator()
estimator = ESTIMATOR_CHOICES[args.estimator]()
b, cameras = estimator.apply(features, p, None)
if not b:
print("Homography estimation failed.")
@ -182,17 +366,7 @@ def main():
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 = BA_COST_CHOICES[args.ba]()
adjuster.setConfThresh(1)
refine_mask = np.zeros((3, 3), np.uint8)
if ba_refine_mask[0] == 'x':
@ -226,7 +400,6 @@ def main():
for idx, cam in enumerate(cameras):
cam.R = rmats[idx]
corners = []
mask=[]
masks_warped = []
images_warped = []
sizes = []
@ -235,7 +408,7 @@ def main():
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?
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
@ -247,49 +420,26 @@ def main():
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 = 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 = SEAM_FIND_CHOICES[args.seam]
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 ?
# 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:
@ -308,7 +458,8 @@ def main():
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])
@ -318,11 +469,10 @@ def main():
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:
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
@ -335,18 +485,18 @@ def main():
blender = cv.detail_FeatherBlender()
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:
@ -354,13 +504,13 @@ def main():
result_mask = None
result, result_mask = blender.blend(result, result_mask)
cv.imwrite(result_name, result)
zoomx = 600.0 / result.shape[1]
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=zoomx,fy=zoomx)
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__':

Write Preview
Loading…
Cancel
Save