mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
518 lines
20 KiB
518 lines
20 KiB
""" |
|
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['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] |
|
masks_warped = 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 = (int(round(full_img_sizes[i][0] * compose_scale)), |
|
int(round(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()
|
|
|