def test_simple(self):
img = self.get_sample('stitching/a1.png')
finder= cv.ORB.create()
imgFea = cv.detail.computeImageFeatures2(finder,img)
self.assertIsNotNone(imgFea)
matcher = cv.detail_BestOf2NearestMatcher(False, 0.3)
self.assertIsNotNone(matcher)
matcher = cv.detail_AffineBestOf2NearestMatcher(False, False, 0.3)
self.assertIsNotNone(matcher)
matcher = cv.detail_BestOf2NearestRangeMatcher(2, False, 0.3)
self.assertIsNotNone(matcher)
estimator = cv.detail_AffineBasedEstimator()
self.assertIsNotNone(estimator)
estimator = cv.detail_HomographyBasedEstimator()
self.assertIsNotNone(estimator)
adjuster = cv.detail_BundleAdjusterReproj()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_BundleAdjusterRay()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_BundleAdjusterAffinePartial()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_NoBundleAdjuster()
self.assertIsNotNone(adjuster)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_NO)
self.assertIsNotNone(compensator)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_GAIN)
self.assertIsNotNone(compensator)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_GAIN_BLOCKS)
self.assertIsNotNone(compensator)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR_GRAD")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_DpSeamFinder("COLOR")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_DpSeamFinder("COLOR_GRAD")
self.assertIsNotNone(seam_finder)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO)
self.assertIsNotNone(blender)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_FEATHER)
self.assertIsNotNone(blender)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_MULTI_BAND)
self.assertIsNotNone(blender)
timelapser = cv.detail.Timelapser_createDefault(cv.detail.Timelapser_AS_IS);
self.assertIsNotNone(timelapser)
timelapser = cv.detail.Timelapser_createDefault(cv.detail.Timelapser_CROP);
self.assertIsNotNone(timelapser)
def bundle_adjustment_cost_function_check(ba_cost_func):
adjuster = None
if ba_cost_func == "reproj":
adjuster = cv2.detail_BundleAdjusterReproj()
elif ba_cost_func == "ray":
adjuster = cv2.detail_BundleAdjusterRay()
elif ba_cost_func == "affine":
adjuster = cv2.detail_BundleAdjusterAffinePartial()
elif ba_cost_func == "no":
adjuster = cv2.detail_NoBundleAdjuster()
else:
print("Unknown bundle adjustment cost function: ", ba_cost_func)
exit()
return adjuster
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':
do_wave_correct= False
else:
do_wave_correct=True
if args.save_graph is None:
save_graph = False
else:
save_graph =True
save_graph_to = args.save_graph
warp_type = args.warp
if args.expos_comp=='no':
expos_comp_type = cv.detail.ExposureCompensator_NO
elif args.expos_comp=='gain':
expos_comp_type = cv.detail.ExposureCompensator_GAIN
elif args.expos_comp=='gain_blocks':
expos_comp_type = cv.detail.ExposureCompensator_GAIN_BLOCKS
elif args.expos_comp=='channel':
expos_comp_type = cv.detail.ExposureCompensator_CHANNELS
elif args.expos_comp=='channel_blocks':
expos_comp_type = cv.detail.ExposureCompensator_CHANNELS_BLOCKS
else:
print("Bad exposure compensation method")
exit()
expos_comp_nr_feeds = args.expos_comp_nr_feeds
expos_comp_nr_filtering = args.expos_comp_nr_filtering
expos_comp_block_size = args.expos_comp_block_size
match_conf = args.match_conf
seam_find_type = args.seam
blend_type = args.blend
blend_strength = args.blend_strength
result_name = args.output
if args.timelapse is not None:
timelapse = True
if args.timelapse=="as_is":
timelapse_type = cv.detail.Timelapser_AS_IS
elif args.timelapse=="crop":
timelapse_type = cv.detail.Timelapser_CROP
else:
print("Bad timelapse method")
exit()
else:
timelapse= False
range_width = args.rangewidth
if features_type=='orb':
finder= cv.ORB.create()
elif features_type=='surf':
finder= cv.xfeatures2d_SURF.create()
elif features_type=='sift':
finder= cv.xfeatures2d_SIFT.create()
else:
print ("Unknown descriptor type")
exit()
seam_work_aspect = 1
full_img_sizes=[]
features=[]
images=[]
is_work_scale_set = False
is_seam_scale_set = False
is_compose_scale_set = False;
for name in img_names:
full_img = cv.imread(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
imgFea= cv.detail.computeImageFeatures2(finder,img)
features.append(imgFea)
img = cv.resize(src=full_img, dsize=None, fx=seam_scale, fy=seam_scale, interpolation=cv.INTER_LINEAR_EXACT)
images.append(img)
if matcher_type== "affine":
matcher = cv.detail_AffineBestOf2NearestMatcher(False, try_cuda, match_conf)
elif range_width==-1:
matcher = cv.detail.BestOf2NearestMatcher_create(try_cuda, match_conf)
else:
matcher = cv.detail.BestOf2NearestRangeMatcher_create(range_width, try_cuda, match_conf)
p=matcher.apply2(features)
matcher.collectGarbage()
if save_graph:
f = open(save_graph_to,"w")
f.write(cv.detail.matchesGraphAsString(img_names, p, conf_thresh))
f.close()
indices=cv.detail.leaveBiggestComponent(features,p,0.3)
img_subset =[]
img_names_subset=[]
full_img_sizes_subset=[]
num_images=len(indices)
for i in range(len(indices)):
img_names_subset.append(img_names[indices[i,0]])
img_subset.append(images[indices[i,0]])
full_img_sizes_subset.append(full_img_sizes[indices[i,0]])
images = img_subset;
img_names = img_names_subset;
full_img_sizes = full_img_sizes_subset;
num_images = len(img_names)
if num_images < 2:
print("Need more images")
exit()
if estimator_type == "affine":
estimator = cv.detail_AffineBasedEstimator()
else:
estimator = cv.detail_HomographyBasedEstimator()
b, cameras =estimator.apply(features,p,None)
if not b:
print("Homography estimation failed.")
exit()
for cam in cameras:
cam.R=cam.R.astype(np.float32)
if ba_cost_func == "reproj":
adjuster = cv.detail_BundleAdjusterReproj()
elif ba_cost_func == "ray":
adjuster = cv.detail_BundleAdjusterRay()
elif ba_cost_func == "affine":
adjuster = cv.detail_BundleAdjusterAffinePartial()
elif ba_cost_func == "no":
adjuster = cv.detail_NoBundleAdjuster()
else:
print( "Unknown bundle adjustment cost function: ", ba_cost_func )
exit()
adjuster.setConfThresh(1)
refine_mask=np.zeros((3,3),np.uint8)
if ba_refine_mask[0] == 'x':
refine_mask[0,0] = 1
if ba_refine_mask[1] == 'x':
refine_mask[0,1] = 1
if ba_refine_mask[2] == 'x':
refine_mask[0,2] = 1
if ba_refine_mask[3] == 'x':
refine_mask[1,1] = 1
if ba_refine_mask[4] == 'x':
refine_mask[1,2] = 1
adjuster.setRefinementMask(refine_mask)
b,cameras = adjuster.apply(features,p,cameras)
if not b:
print("Camera parameters adjusting failed.")
exit()
focals=[]
for cam in cameras:
focals.append(cam.focal)
sorted(focals)
if len(focals)%2==1:
warped_image_scale = focals[len(focals) // 2]
else:
warped_image_scale = (focals[len(focals) // 2]+focals[len(focals) // 2-1])/2
if do_wave_correct:
rmats=[]
for cam in cameras:
rmats.append(np.copy(cam.R))
rmats = cv.detail.waveCorrect( rmats, cv.detail.WAVE_CORRECT_HORIZ)
for idx,cam in enumerate(cameras):
cam.R = rmats[idx]
corners=[]
mask=[]
masks_warped=[]
images_warped=[]
sizes=[]
masks=[]
for i in range(0,num_images):
um=cv.UMat(255*np.ones((images[i].shape[0],images[i].shape[1]),np.uint8))
masks.append(um)
warper = cv.PyRotationWarper(warp_type,warped_image_scale*seam_work_aspect) # warper peut etre nullptr?
for idx in range(0,num_images):
K = cameras[idx].K().astype(np.float32)
swa = seam_work_aspect
K[0,0] *= swa
K[0,2] *= swa
K[1,1] *= swa
K[1,2] *= swa
corner,image_wp =warper.warp(images[idx],K,cameras[idx].R,cv.INTER_LINEAR, cv.BORDER_REFLECT)
corners.append(corner)
sizes.append((image_wp.shape[1],image_wp.shape[0]))
images_warped.append(image_wp)
p,mask_wp =warper.warp(masks[idx],K,cameras[idx].R,cv.INTER_NEAREST, cv.BORDER_CONSTANT)
masks_warped.append(mask_wp.get())
images_warped_f=[]
for img in images_warped:
imgf=img.astype(np.float32)
images_warped_f.append(imgf)
if cv.detail.ExposureCompensator_CHANNELS == expos_comp_type:
compensator = cv.detail_ChannelsCompensator(expos_comp_nr_feeds)
# compensator.setNrGainsFilteringIterations(expos_comp_nr_filtering)
elif cv.detail.ExposureCompensator_CHANNELS_BLOCKS == expos_comp_type:
compensator=cv.detail_BlocksChannelsCompensator(expos_comp_block_size, expos_comp_block_size,expos_comp_nr_feeds)
# compensator.setNrGainsFilteringIterations(expos_comp_nr_filtering)
else:
compensator=cv.detail.ExposureCompensator_createDefault(expos_comp_type)
compensator.feed(corners=corners, images=images_warped, masks=masks_warped)
if seam_find_type == "no":
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
elif seam_find_type == "voronoi":
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM);
elif seam_find_type == "gc_color":
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR")
elif seam_find_type == "gc_colorgrad":
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR_GRAD")
elif seam_find_type == "dp_color":
seam_finder = cv.detail_DpSeamFinder("COLOR")
elif seam_find_type == "dp_colorgrad":
seam_finder = cv.detail_DpSeamFinder("COLOR_GRAD")
if seam_finder is None:
print("Can't create the following seam finder ",seam_find_type)
exit()
seam_finder.find(images_warped_f, corners,masks_warped )
imgListe=[]
compose_scale=1
corners=[]
sizes=[]
images_warped=[]
images_warped_f=[]
masks=[]
blender= None
timelapser=None
compose_work_aspect=1
for idx,name in enumerate(img_names): # https://github.com/opencv/opencv/blob/master/samples/cpp/stitching_detailed.cpp#L725 ?
full_img = cv.imread(name)
if not is_compose_scale_set:
if compose_megapix > 0:
compose_scale = min(1.0, np.sqrt(compose_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1])))
is_compose_scale_set = True;
compose_work_aspect = compose_scale / work_scale;
warped_image_scale *= compose_work_aspect
warper = cv.PyRotationWarper(warp_type,warped_image_scale)
for i in range(0,len(img_names)):
cameras[i].focal *= compose_work_aspect
cameras[i].ppx *= compose_work_aspect
cameras[i].ppy *= compose_work_aspect
sz = (full_img_sizes[i][0] * compose_scale,full_img_sizes[i][1]* compose_scale)
K = cameras[i].K().astype(np.float32)
roi = warper.warpRoi(sz, K, cameras[i].R);
corners.append(roi[0:2])
sizes.append(roi[2:4])
if abs(compose_scale - 1) > 1e-1:
img =cv.resize(src=full_img, dsize=None, fx=compose_scale, fy=compose_scale, interpolation=cv.INTER_LINEAR_EXACT)
else:
img = full_img;
img_size = (img.shape[1],img.shape[0]);
K=cameras[idx].K().astype(np.float32)
corner,image_warped =warper.warp(img,K,cameras[idx].R,cv.INTER_LINEAR, cv.BORDER_REFLECT)
mask =255*np.ones((img.shape[0],img.shape[1]),np.uint8)
p,mask_warped =warper.warp(mask,K,cameras[idx].R,cv.INTER_NEAREST, cv.BORDER_CONSTANT)
compensator.apply(idx,corners[idx],image_warped,mask_warped)
image_warped_s = image_warped.astype(np.int16)
image_warped=[]
dilated_mask = cv.dilate(masks_warped[idx],None)
seam_mask = cv.resize(dilated_mask,(mask_warped.shape[1],mask_warped.shape[0]),0,0,cv.INTER_LINEAR_EXACT)
mask_warped = cv.bitwise_and(seam_mask,mask_warped)
if blender==None and not timelapse:
blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO)
dst_sz = cv.detail.resultRoi(corners=corners,sizes=sizes)
blend_width = np.sqrt(dst_sz[2]*dst_sz[3]) * blend_strength / 100
if blend_width < 1:
blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO)
elif blend_type == "multiband":
blender = cv.detail_MultiBandBlender()
blender.setNumBands((np.log(blend_width)/np.log(2.) - 1.).astype(np.int))
elif blend_type == "feather":
blender = cv.detail_FeatherBlender()
blender.setSharpness(1./blend_width)
blender.prepare(dst_sz)
elif timelapser==None and timelapse:
timelapser = cv.detail.Timelapser_createDefault(timelapse_type)
timelapser.initialize(corners, sizes)
if timelapse:
matones=np.ones((image_warped_s.shape[0],image_warped_s.shape[1]), np.uint8)
timelapser.process(image_warped_s, matones, corners[idx])
pos_s = img_names[idx].rfind("/");
if pos_s == -1:
fixedFileName = "fixed_" + img_names[idx];
else:
fixedFileName = img_names[idx][:pos_s + 1 ]+"fixed_" + img_names[idx][pos_s + 1: ]
cv.imwrite(fixedFileName, timelapser.getDst())
else:
blender.feed(cv.UMat(image_warped_s), mask_warped, corners[idx])
if not timelapse:
result=None
result_mask=None
result,result_mask = blender.blend(result,result_mask)
cv.imwrite(result_name,result)
zoomx = 600.0 / 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)
cv.waitKey()
print('Done')
if estimator_type == "affine":
estimator = cv.detail_AffineBasedEstimator()
else:
estimator = cv.detail_HomographyBasedEstimator()
b, cameras =estimator.apply(features,p,None)
if not b:
print("Homography estimation failed.")
exit()
for cam in cameras:
cam.R=cam.R.astype(np.float32)
if ba_cost_func == "reproj":
adjuster = cv.detail_BundleAdjusterReproj()
elif ba_cost_func == "ray":
adjuster = cv.detail_BundleAdjusterRay()
elif ba_cost_func == "affine":
adjuster = cv.detail_BundleAdjusterAffinePartial()
elif ba_cost_func == "no":
adjuster = cv.detail_NoBundleAdjuster()
else:
print( "Unknown bundle adjustment cost function: ", ba_cost_func )
exit()
adjuster.setConfThresh(1)
refine_mask=np.zeros((3,3),np.uint8)
if ba_refine_mask[0] == 'x':
refine_mask[0,0] = 1
if ba_refine_mask[1] == 'x':
refine_mask[0,1] = 1
if ba_refine_mask[2] == 'x':
refine_mask[0,2] = 1
if estimator_type == "affine":
estimator = cv.detail_AffineBasedEstimator()
else:
estimator = cv.detail_HomographyBasedEstimator()
b, cameras = estimator.apply(features, p, None)
if not b:
print("Homography estimation failed.")
exit()
for cam in cameras:
cam.R = cam.R.astype(np.float32)
if ba_cost_func == "reproj":
adjuster = cv.detail_BundleAdjusterReproj()
elif ba_cost_func == "ray":
adjuster = cv.detail_BundleAdjusterRay()
elif ba_cost_func == "affine":
adjuster = cv.detail_BundleAdjusterAffinePartial()
elif ba_cost_func == "no":
adjuster = cv.detail_NoBundleAdjuster()
else:
print("Unknown bundle adjustment cost function: ", ba_cost_func)
exit()
adjuster.setConfThresh(1)
refine_mask = np.zeros((3, 3), np.uint8)
if ba_refine_mask[0] == 'x':
refine_mask[0, 0] = 1
if ba_refine_mask[1] == 'x':
refine_mask[0, 1] = 1
if ba_refine_mask[2] == 'x':
refine_mask[0, 2] = 1
class Stitch2:
finder = cv.ORB.create()
matcher = cv.detail.BestOfNearestMarcher_create(False, 0.3)
warp_type = "cylindrical"
p = list()
last_p = list()
estimator = cv.detail_HomographyBasedEstimator()
adjustor = cv.detail_BundleAdjusterRay()
seam_finder = cv.detail_DpSeamFinder("COLOR_GRAD")
all_cameras = list()
focals = list()
rmats = list()
work_scale = 0.6
seam_scale = 0.25
conf_thresh = 0.3
seam_work_aspect = 1
full_img_sizes = []
features = []
images = []
is_work_scale_set = False
is_seam_scale_set = False
is_compose_scale_set = False
do_wave_correct = True
images = list()
features = list()
def __init__(self, img1):
# first image push to list
self._get_refine_mask()
self._getWarper()
self._getCompensator()
self._getMask(img1)
self.__appendImg(img1)
def __appendImg(self, full_img):
self.full_img_sizes.append((full_img.shape[1], full_img.shape[0]))
self._getMask(full_img)
if self.work_megapix < 0:
img = full_img
self.work_scale = 1
self.is_work_scale_set = True
else:
if self.is_work_scale_set is False:
self.work_scale = min(
1.0,
np.sqrt(self.work_megapix * 1e6 /
(full_img.shape[0] * full_img.shape[1])))
self.is_work_scale_set = True
img = cv.resize(src=full_img,
dsize=None,
fx=self.work_scale,
fy=self.work_scale,
interpolation=cv.INTER_LINEAR_EXACT)
if self.is_seam_scale_set is False:
self.seam_scale = min(
1.0,
np.sqrt(self.seam_megapix * 1e6 /
(full_img.shape[0] * full_img.shape[1])))
self.seam_work_aspect = self.seam_scale / self.work_scale
self.is_seam_scale_set = True
img_feat = cv.detail.computeImageFeatures2(self.finder, img)
self.features.append(img_feat)
img = cv.resize(src=full_img,
dsize=None,
fx=self.seam_scale,
fy=self.seam_scale,
interpolation=cv.INTER_LINEAR_EXACT)
self.images.append(img)
def getNew(self, img2):
# get new images and calculate all
self.__appendImg(img2)
img_feat = cv.detail.computeImageFeatures2(self.finder, img2)
self.features.append(img_feat)
def matchImg(self):
# get matcher for last two features
_p = self.matcher.apply2(self.features[-2:])
self.last_p = _p
self.p.append(_p) # append the maching info to previous info
self.matcher.collectGarbage()
def _get_refine_mask(self):
# set mask
# apply to last two features?
self.refine_mask = np.zeros((3, 3), np.uint8)
self.refine_mask[0, 0] = 1
self.refine_mask[0, 1] = 1
self.refine_mask[0, 2] = 1
self.refine_mask[1, 1] = 1
self.refine_mask[1, 2] = 1
def estimateImg(self):
# cameras is the last two camera
b, cameras = self.estimator.apply(self.features[-2:], self.last_p,
None)
if not b:
print("Homography estimation failed.")
exit()
for cam in cameras:
cam.R = cam.R.astype(np.float32)
self.adjustor.setConfThresh(0.3)
self.adjuster.setRefinementMask(self.refine_mask)
b, cameras = self.adjuster.apply(self.features[-2:], self.last_p,
cameras)
if not b:
print("Camera parameters adjusting failed.")
return
# append last two/one? camera to all_cameras
self.all_cameras.append(cameras[-1])
# append last two/one? camera's focals
for cam in cameras:
self.focals.append(cam.focal)
self.rmats.append(np.copy(cam.R))
self.focals.sort()
if len(self.focals) % 2 == 1: # get median
self.warped_image_scale = self.focals[len(self.focals) // 2]
else:
self.warped_image_scale = (
self.focals[len(self.focals) // 2] +
self.focals[len(self.focals) // 2 - 1]) / 2
corners = []
masks_warped = []
images_warped = []
images_warped_f = []
sizes = []
masks = []
def waveCorrect(self):
if self.do_wave_correct:
self.rmats = cv.detail.waveCorrect(self.rmats,
cv.detail.WAVE_CORRECT_HORIZ)
for idx, cam in enumerate(self.all_cameras):
cam.R = self.rmats[idx]
def _getMask(self, img):
um = cv.UMat(255 * np.ones((img.shape[0], img.shape[1]), np.uint8))
self.masks.append(um)
def _getWarper(self):
self.warper = cv.PyRotationWarper(
self.warp_type, self.warped_image_scale * self.seam_work_aspect)
# warper could be nullptr?
def getNewMaskWarped(self):
idx = -1
K = self.all_cameras[idx].K().astype(np.float32)
swa = self.seam_work_aspect
K[0, 0] *= swa
K[0, 2] *= swa
K[1, 1] *= swa
K[1, 2] *= swa
corner, image_wp = self.warper.warp(self.images[idx], K,
self.all_cameras[idx].R,
cv.INTER_LINEAR, cv.BORDER_REFLECT)
self.corners.append(corner)
self.sizes.append((image_wp.shape[1], image_wp.shape[0]))
self.images_warped.append(image_wp)
p, mask_wp = self.warper.warp(self.masks[idx], K,
self.all_cameras[idx].R,
cv.INTER_NEAREST, cv.BORDER_CONSTANT)
self.masks_warped.append(mask_wp.get())
imgf = image_wp.astype(np.float32)
self.images_warped_f.append(imgf)
def _getCompensator(self):
expos_comp_block_size = 32
expos_comp_nr_feeds = 1
self.compensator = cv.detail_BlocksChannelsCompensator(
expos_comp_block_size, expos_comp_block_size, expos_comp_nr_feeds)
def applyCompensator(self):
self.compensator.feed(corners=self.corners,
images=self.images_warped,
masks=self.masks_warped)
def applySeamFinder(self):
self.seam_finder.find(self.images_warped_f, self.corners,
self.masks_warped)
def applyBlender(self):
for idx, full_img in enumerate(self.images):
if not self.is_compose_scale_set:
if self.compose_megapix > 0:
compose_scale = min(
1.0,
np.sqrt(self.compose_megapix * 1e6 /
(full_img.shape[0] * full_img.shape[1])))
self.is_compose_scale_set = True
compose_work_aspect = self.compose_scale / self.work_scale
self.warped_image_scale *= compose_work_aspect
# a new warper
warper = cv.PyRotationWarper(self.warp_type,
self.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])
def getAllMasksWarped(self):
warper = cv.PyRotationWarper(
self.warp_type, self.warped_image_scale *
self.seam_work_aspect) # warper could be nullptr?
num_images = self.images.count
for idx in range(0, num_images):
K = self.all_cameras[idx].K().astype(np.float32)
swa = self.seam_work_aspect
K[0, 0] *= swa
K[0, 2] *= swa
K[1, 1] *= swa
K[1, 2] *= swa
corner, image_wp = warper.warp(self.images[idx], K,
self.all_cameras[idx].R,
cv.INTER_LINEAR, cv.BORDER_REFLECT)
self.corners.append(corner)
self.sizes.append((image_wp.shape[1], image_wp.shape[0]))
self.images_warped.append(image_wp)
p, mask_wp = warper.warp(self.masks[idx], K,
self.all_cameras[idx].R, cv.INTER_NEAREST,
cv.BORDER_CONSTANT)
self.masks_warped.append(mask_wp.get())
for img in self.images_warped:
imgf = img.astype(np.float32)
self.images_warped_f.append(imgf)