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Merge pull request #22005 from lukasalexanderweber:delete_stitching_tool

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Move stitching package and tool to a dedicated repository

* deleted moved files

* Update README.md
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Lukas-Alexander Weber 2022-05-26 21:30:24 +02:00 committed by GitHub
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4
apps/opencv_stitching_tool/README.md
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## In-Depth Stitching Tool for experiments and research ## MOVED: opencv_stitching_tool
Visit [opencv_stitching_tutorial](https://github.com/lukasalexanderweber/opencv_stitching_tutorial) for a detailed Tutorial As the stitching package is now available on [PyPI](https://pypi.org/project/stitching/) the tool and belonging package are now maintained [here](https://github.com/lukasalexanderweber/stitching). The Tutorial is maintained [here](https://github.com/lukasalexanderweber/stitching_tutorial).

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apps/opencv_stitching_tool/opencv_stitching/.gitignore vendored
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# python binary files
*.pyc
__pycache__
.pylint*

0
apps/opencv_stitching_tool/opencv_stitching/__init__.py
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apps/opencv_stitching_tool/opencv_stitching/blender.py
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import cv2 as cv
import numpy as np
class Blender:
BLENDER_CHOICES = ('multiband', 'feather', 'no',)
DEFAULT_BLENDER = 'multiband'
DEFAULT_BLEND_STRENGTH = 5
def __init__(self, blender_type=DEFAULT_BLENDER,
blend_strength=DEFAULT_BLEND_STRENGTH):
self.blender_type = blender_type
self.blend_strength = blend_strength
self.blender = None
def prepare(self, corners, sizes):
dst_sz = cv.detail.resultRoi(corners=corners, sizes=sizes)
blend_width = (np.sqrt(dst_sz[2] * dst_sz[3]) *
self.blend_strength / 100)
if self.blender_type == 'no' or blend_width < 1:
self.blender = cv.detail.Blender_createDefault(
cv.detail.Blender_NO
)
elif self.blender_type == "multiband":
self.blender = cv.detail_MultiBandBlender()
self.blender.setNumBands(int((np.log(blend_width) /
np.log(2.) - 1.)))
elif self.blender_type == "feather":
self.blender = cv.detail_FeatherBlender()
self.blender.setSharpness(1. / blend_width)
self.blender.prepare(dst_sz)
def feed(self, img, mask, corner):
"""https://docs.opencv.org/4.x/d6/d4a/classcv_1_1detail_1_1Blender.html#a64837308bcf4e414a6219beff6cbe37a""" # noqa
self.blender.feed(cv.UMat(img.astype(np.int16)), mask, corner)
def blend(self):
"""https://docs.opencv.org/4.x/d6/d4a/classcv_1_1detail_1_1Blender.html#aa0a91ce0d6046d3a63e0123cbb1b5c00""" # noqa
result = None
result_mask = None
result, result_mask = self.blender.blend(result, result_mask)
result = cv.convertScaleAbs(result)
return result, result_mask
@classmethod
def create_panorama(cls, imgs, masks, corners, sizes):
blender = cls("no")
blender.prepare(corners, sizes)
for img, mask, corner in zip(imgs, masks, corners):
blender.feed(img, mask, corner)
return blender.blend()

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apps/opencv_stitching_tool/opencv_stitching/camera_adjuster.py
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from collections import OrderedDict
import cv2 as cv
import numpy as np
from .stitching_error import StitchingError
class CameraAdjuster:
"""https://docs.opencv.org/4.x/d5/d56/classcv_1_1detail_1_1BundleAdjusterBase.html""" # noqa
CAMERA_ADJUSTER_CHOICES = OrderedDict()
CAMERA_ADJUSTER_CHOICES['ray'] = cv.detail_BundleAdjusterRay
CAMERA_ADJUSTER_CHOICES['reproj'] = cv.detail_BundleAdjusterReproj
CAMERA_ADJUSTER_CHOICES['affine'] = cv.detail_BundleAdjusterAffinePartial
CAMERA_ADJUSTER_CHOICES['no'] = cv.detail_NoBundleAdjuster
DEFAULT_CAMERA_ADJUSTER = list(CAMERA_ADJUSTER_CHOICES.keys())[0]
DEFAULT_REFINEMENT_MASK = "xxxxx"
def __init__(self,
adjuster=DEFAULT_CAMERA_ADJUSTER,
refinement_mask=DEFAULT_REFINEMENT_MASK):
self.adjuster = CameraAdjuster.CAMERA_ADJUSTER_CHOICES[adjuster]()
self.set_refinement_mask(refinement_mask)
self.adjuster.setConfThresh(1)
def set_refinement_mask(self, refinement_mask):
mask_matrix = np.zeros((3, 3), np.uint8)
if refinement_mask[0] == 'x':
mask_matrix[0, 0] = 1
if refinement_mask[1] == 'x':
mask_matrix[0, 1] = 1
if refinement_mask[2] == 'x':
mask_matrix[0, 2] = 1
if refinement_mask[3] == 'x':
mask_matrix[1, 1] = 1
if refinement_mask[4] == 'x':
mask_matrix[1, 2] = 1
self.adjuster.setRefinementMask(mask_matrix)
def adjust(self, features, pairwise_matches, estimated_cameras):
b, cameras = self.adjuster.apply(features,
pairwise_matches,
estimated_cameras)
if not b:
raise StitchingError("Camera parameters adjusting failed.")
return cameras

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apps/opencv_stitching_tool/opencv_stitching/camera_estimator.py
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from collections import OrderedDict
import cv2 as cv
import numpy as np
from .stitching_error import StitchingError
class CameraEstimator:
CAMERA_ESTIMATOR_CHOICES = OrderedDict()
CAMERA_ESTIMATOR_CHOICES['homography'] = cv.detail_HomographyBasedEstimator
CAMERA_ESTIMATOR_CHOICES['affine'] = cv.detail_AffineBasedEstimator
DEFAULT_CAMERA_ESTIMATOR = list(CAMERA_ESTIMATOR_CHOICES.keys())[0]
def __init__(self, estimator=DEFAULT_CAMERA_ESTIMATOR, **kwargs):
self.estimator = CameraEstimator.CAMERA_ESTIMATOR_CHOICES[estimator](
**kwargs
)
def estimate(self, features, pairwise_matches):
b, cameras = self.estimator.apply(features, pairwise_matches, None)
if not b:
raise StitchingError("Homography estimation failed.")
for cam in cameras:
cam.R = cam.R.astype(np.float32)
return cameras

28
apps/opencv_stitching_tool/opencv_stitching/camera_wave_corrector.py
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from collections import OrderedDict
import cv2 as cv
import numpy as np
class WaveCorrector:
"""https://docs.opencv.org/4.x/d7/d74/group__stitching__rotation.html#ga83b24d4c3e93584986a56d9e43b9cf7f""" # noqa
WAVE_CORRECT_CHOICES = OrderedDict()
WAVE_CORRECT_CHOICES['horiz'] = cv.detail.WAVE_CORRECT_HORIZ
WAVE_CORRECT_CHOICES['vert'] = cv.detail.WAVE_CORRECT_VERT
WAVE_CORRECT_CHOICES['auto'] = cv.detail.WAVE_CORRECT_AUTO
WAVE_CORRECT_CHOICES['no'] = None
DEFAULT_WAVE_CORRECTION = list(WAVE_CORRECT_CHOICES.keys())[0]
def __init__(self, wave_correct_kind=DEFAULT_WAVE_CORRECTION):
self.wave_correct_kind = WaveCorrector.WAVE_CORRECT_CHOICES[
wave_correct_kind
]
def correct(self, cameras):
if self.wave_correct_kind is not None:
rmats = [np.copy(cam.R) for cam in cameras]
rmats = cv.detail.waveCorrect(rmats, self.wave_correct_kind)
for idx, cam in enumerate(cameras):
cam.R = rmats[idx]
return cameras
return cameras

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apps/opencv_stitching_tool/opencv_stitching/cropper.py
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from collections import namedtuple
import cv2 as cv
from .blender import Blender
from .stitching_error import StitchingError
class Rectangle(namedtuple('Rectangle', 'x y width height')):
__slots__ = ()
@property
def area(self):
return self.width * self.height
@property
def corner(self):
return (self.x, self.y)
@property
def size(self):
return (self.width, self.height)
@property
def x2(self):
return self.x + self.width
@property
def y2(self):
return self.y + self.height
def times(self, x):
return Rectangle(*(int(round(i*x)) for i in self))
def draw_on(self, img, color=(0, 0, 255), size=1):
if len(img.shape) == 2:
img = cv.cvtColor(img, cv.COLOR_GRAY2RGB)
start_point = (self.x, self.y)
end_point = (self.x2-1, self.y2-1)
cv.rectangle(img, start_point, end_point, color, size)
return img
class Cropper:
DEFAULT_CROP = False
def __init__(self, crop=DEFAULT_CROP):
self.do_crop = crop
self.overlapping_rectangles = []
self.cropping_rectangles = []
def prepare(self, imgs, masks, corners, sizes):
if self.do_crop:
mask = self.estimate_panorama_mask(imgs, masks, corners, sizes)
self.compile_numba_functionality()
lir = self.estimate_largest_interior_rectangle(mask)
corners = self.get_zero_center_corners(corners)
rectangles = self.get_rectangles(corners, sizes)
self.overlapping_rectangles = self.get_overlaps(
rectangles, lir)
self.intersection_rectangles = self.get_intersections(
rectangles, self.overlapping_rectangles)
def crop_images(self, imgs, aspect=1):
for idx, img in enumerate(imgs):
yield self.crop_img(img, idx, aspect)
def crop_img(self, img, idx, aspect=1):
if self.do_crop:
intersection_rect = self.intersection_rectangles[idx]
scaled_intersection_rect = intersection_rect.times(aspect)
cropped_img = self.crop_rectangle(img, scaled_intersection_rect)
return cropped_img
return img
def crop_rois(self, corners, sizes, aspect=1):
if self.do_crop:
scaled_overlaps = \
[r.times(aspect) for r in self.overlapping_rectangles]
cropped_corners = [r.corner for r in scaled_overlaps]
cropped_corners = self.get_zero_center_corners(cropped_corners)
cropped_sizes = [r.size for r in scaled_overlaps]
return cropped_corners, cropped_sizes
return corners, sizes
@staticmethod
def estimate_panorama_mask(imgs, masks, corners, sizes):
_, mask = Blender.create_panorama(imgs, masks, corners, sizes)
return mask
def compile_numba_functionality(self):
# numba functionality is only imported if cropping
# is explicitely desired
try:
import numba
except ModuleNotFoundError:
raise StitchingError("Numba is needed for cropping but not installed")
from .largest_interior_rectangle import largest_interior_rectangle
self.largest_interior_rectangle = largest_interior_rectangle
def estimate_largest_interior_rectangle(self, mask):
lir = self.largest_interior_rectangle(mask)
lir = Rectangle(*lir)
return lir
@staticmethod
def get_zero_center_corners(corners):
min_corner_x = min([corner[0] for corner in corners])
min_corner_y = min([corner[1] for corner in corners])
return [(x - min_corner_x, y - min_corner_y) for x, y in corners]
@staticmethod
def get_rectangles(corners, sizes):
rectangles = []
for corner, size in zip(corners, sizes):
rectangle = Rectangle(*corner, *size)
rectangles.append(rectangle)
return rectangles
@staticmethod
def get_overlaps(rectangles, lir):
return [Cropper.get_overlap(r, lir) for r in rectangles]
@staticmethod
def get_overlap(rectangle1, rectangle2):
x1 = max(rectangle1.x, rectangle2.x)
y1 = max(rectangle1.y, rectangle2.y)
x2 = min(rectangle1.x2, rectangle2.x2)
y2 = min(rectangle1.y2, rectangle2.y2)
if x2 < x1 or y2 < y1:
raise StitchingError("Rectangles do not overlap!")
return Rectangle(x1, y1, x2-x1, y2-y1)
@staticmethod
def get_intersections(rectangles, overlapping_rectangles):
return [Cropper.get_intersection(r, overlap_r) for r, overlap_r
in zip(rectangles, overlapping_rectangles)]
@staticmethod
def get_intersection(rectangle, overlapping_rectangle):
x = abs(overlapping_rectangle.x - rectangle.x)
y = abs(overlapping_rectangle.y - rectangle.y)
width = overlapping_rectangle.width
height = overlapping_rectangle.height
return Rectangle(x, y, width, height)
@staticmethod
def crop_rectangle(img, rectangle):
return img[rectangle.y:rectangle.y2, rectangle.x:rectangle.x2]

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apps/opencv_stitching_tool/opencv_stitching/exposure_error_compensator.py
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from collections import OrderedDict
import cv2 as cv
class ExposureErrorCompensator:
COMPENSATOR_CHOICES = OrderedDict()
COMPENSATOR_CHOICES['gain_blocks'] = cv.detail.ExposureCompensator_GAIN_BLOCKS # noqa
COMPENSATOR_CHOICES['gain'] = cv.detail.ExposureCompensator_GAIN
COMPENSATOR_CHOICES['channel'] = cv.detail.ExposureCompensator_CHANNELS
COMPENSATOR_CHOICES['channel_blocks'] = cv.detail.ExposureCompensator_CHANNELS_BLOCKS # noqa
COMPENSATOR_CHOICES['no'] = cv.detail.ExposureCompensator_NO
DEFAULT_COMPENSATOR = list(COMPENSATOR_CHOICES.keys())[0]
DEFAULT_NR_FEEDS = 1
DEFAULT_BLOCK_SIZE = 32
def __init__(self,
compensator=DEFAULT_COMPENSATOR,
nr_feeds=DEFAULT_NR_FEEDS,
block_size=DEFAULT_BLOCK_SIZE):
if compensator == 'channel':
self.compensator = cv.detail_ChannelsCompensator(nr_feeds)
elif compensator == 'channel_blocks':
self.compensator = cv.detail_BlocksChannelsCompensator(
block_size, block_size, nr_feeds
)
else:
self.compensator = cv.detail.ExposureCompensator_createDefault(
ExposureErrorCompensator.COMPENSATOR_CHOICES[compensator]
)
def feed(self, *args):
"""https://docs.opencv.org/4.x/d2/d37/classcv_1_1detail_1_1ExposureCompensator.html#ae6b0cc69a7bc53818ddea53eddb6bdba""" # noqa
self.compensator.feed(*args)
def apply(self, *args):
"""https://docs.opencv.org/4.x/d2/d37/classcv_1_1detail_1_1ExposureCompensator.html#a473eaf1e585804c08d77c91e004f93aa""" # noqa
return self.compensator.apply(*args)

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apps/opencv_stitching_tool/opencv_stitching/feature_detector.py
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from collections import OrderedDict
import cv2 as cv
class FeatureDetector:
DETECTOR_CHOICES = OrderedDict()
try:
cv.xfeatures2d_SURF.create() # check if the function can be called
DETECTOR_CHOICES['surf'] = cv.xfeatures2d_SURF.create
except (AttributeError, cv.error):
print("SURF not available")
# if SURF not available, ORB is default
DETECTOR_CHOICES['orb'] = cv.ORB.create
try:
DETECTOR_CHOICES['sift'] = cv.SIFT_create
except AttributeError:
print("SIFT not available")
try:
DETECTOR_CHOICES['brisk'] = cv.BRISK_create
except AttributeError:
print("BRISK not available")
try:
DETECTOR_CHOICES['akaze'] = cv.AKAZE_create
except AttributeError:
print("AKAZE not available")
DEFAULT_DETECTOR = list(DETECTOR_CHOICES.keys())[0]
def __init__(self, detector=DEFAULT_DETECTOR, **kwargs):
self.detector = FeatureDetector.DETECTOR_CHOICES[detector](**kwargs)
def detect_features(self, img, *args, **kwargs):
return cv.detail.computeImageFeatures2(self.detector, img,
*args, **kwargs)
@staticmethod
def draw_keypoints(img, features, **kwargs):
kwargs.setdefault('color', (0, 255, 0))
keypoints = features.getKeypoints()
return cv.drawKeypoints(img, keypoints, None, **kwargs)

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apps/opencv_stitching_tool/opencv_stitching/feature_matcher.py
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import math
import cv2 as cv
import numpy as np
class FeatureMatcher:
MATCHER_CHOICES = ('homography', 'affine')
DEFAULT_MATCHER = 'homography'
DEFAULT_RANGE_WIDTH = -1
def __init__(self,
matcher_type=DEFAULT_MATCHER,
range_width=DEFAULT_RANGE_WIDTH,
**kwargs):
if matcher_type == "affine":
"""https://docs.opencv.org/4.x/d3/dda/classcv_1_1detail_1_1AffineBestOf2NearestMatcher.html""" # noqa
self.matcher = cv.detail_AffineBestOf2NearestMatcher(**kwargs)
elif range_width == -1:
"""https://docs.opencv.org/4.x/d4/d26/classcv_1_1detail_1_1BestOf2NearestMatcher.html""" # noqa
self.matcher = cv.detail_BestOf2NearestMatcher(**kwargs)
else:
"""https://docs.opencv.org/4.x/d8/d72/classcv_1_1detail_1_1BestOf2NearestRangeMatcher.html""" # noqa
self.matcher = cv.detail_BestOf2NearestRangeMatcher(
range_width, **kwargs
)
def match_features(self, features, *args, **kwargs):
pairwise_matches = self.matcher.apply2(features, *args, **kwargs)
self.matcher.collectGarbage()
return pairwise_matches
@staticmethod
def draw_matches_matrix(imgs, features, matches, conf_thresh=1,
inliers=False, **kwargs):
matches_matrix = FeatureMatcher.get_matches_matrix(matches)
for idx1, idx2 in FeatureMatcher.get_all_img_combinations(len(imgs)):
match = matches_matrix[idx1, idx2]
if match.confidence < conf_thresh:
continue
if inliers:
kwargs['matchesMask'] = match.getInliers()
yield idx1, idx2, FeatureMatcher.draw_matches(
imgs[idx1], features[idx1],
imgs[idx2], features[idx2],
match,
**kwargs
)
@staticmethod
def draw_matches(img1, features1, img2, features2, match1to2, **kwargs):
kwargs.setdefault('flags', cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
keypoints1 = features1.getKeypoints()
keypoints2 = features2.getKeypoints()
matches = match1to2.getMatches()
return cv.drawMatches(
img1, keypoints1, img2, keypoints2, matches, None, **kwargs
)
@staticmethod
def get_matches_matrix(pairwise_matches):
return FeatureMatcher.array_in_sqare_matrix(pairwise_matches)
@staticmethod
def get_confidence_matrix(pairwise_matches):
matches_matrix = FeatureMatcher.get_matches_matrix(pairwise_matches)
match_confs = [[m.confidence for m in row] for row in matches_matrix]
match_conf_matrix = np.array(match_confs)
return match_conf_matrix
@staticmethod
def array_in_sqare_matrix(array):
matrix_dimension = int(math.sqrt(len(array)))
rows = []
for i in range(0, len(array), matrix_dimension):
rows.append(array[i:i+matrix_dimension])
return np.array(rows)
def get_all_img_combinations(number_imgs):
ii, jj = np.triu_indices(number_imgs, k=1)
for i, j in zip(ii, jj):
yield i, j
@staticmethod
def get_match_conf(match_conf, feature_detector_type):
if match_conf is None:
match_conf = \
FeatureMatcher.get_default_match_conf(feature_detector_type)
return match_conf
@staticmethod
def get_default_match_conf(feature_detector_type):
if feature_detector_type == 'orb':
return 0.3
return 0.65

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apps/opencv_stitching_tool/opencv_stitching/image_handler.py
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import cv2 as cv
from .megapix_scaler import MegapixDownscaler
from .stitching_error import StitchingError
class ImageHandler:
DEFAULT_MEDIUM_MEGAPIX = 0.6
DEFAULT_LOW_MEGAPIX = 0.1
DEFAULT_FINAL_MEGAPIX = -1
def __init__(self,
medium_megapix=DEFAULT_MEDIUM_MEGAPIX,
low_megapix=DEFAULT_LOW_MEGAPIX,
final_megapix=DEFAULT_FINAL_MEGAPIX):
if medium_megapix < low_megapix:
raise StitchingError("Medium resolution megapix need to be "
"greater or equal than low resolution "
"megapix")
self.medium_scaler = MegapixDownscaler(medium_megapix)
self.low_scaler = MegapixDownscaler(low_megapix)
self.final_scaler = MegapixDownscaler(final_megapix)
self.scales_set = False
self.img_names = []
self.img_sizes = []
def set_img_names(self, img_names):
self.img_names = img_names
def resize_to_medium_resolution(self):
return self.read_and_resize_imgs(self.medium_scaler)
def resize_to_low_resolution(self, medium_imgs=None):
if medium_imgs and self.scales_set:
return self.resize_imgs_by_scaler(medium_imgs, self.low_scaler)
return self.read_and_resize_imgs(self.low_scaler)
def resize_to_final_resolution(self):
return self.read_and_resize_imgs(self.final_scaler)
def read_and_resize_imgs(self, scaler):
for img, size in self.input_images():
yield self.resize_img_by_scaler(scaler, size, img)
def resize_imgs_by_scaler(self, medium_imgs, scaler):
for img, size in zip(medium_imgs, self.img_sizes):
yield self.resize_img_by_scaler(scaler, size, img)
@staticmethod
def resize_img_by_scaler(scaler, size, img):
desired_size = scaler.get_scaled_img_size(size)
return cv.resize(img, desired_size,
interpolation=cv.INTER_LINEAR_EXACT)
def input_images(self):
self.img_sizes = []
for name in self.img_names:
img = self.read_image(name)
size = self.get_image_size(img)
self.img_sizes.append(size)
self.set_scaler_scales()
yield img, size
@staticmethod
def get_image_size(img):
"""(width, height)"""
return (img.shape[1], img.shape[0])
@staticmethod
def read_image(img_name):
img = cv.imread(img_name)
if img is None:
raise StitchingError("Cannot read image " + img_name)
return img
def set_scaler_scales(self):
if not self.scales_set:
first_img_size = self.img_sizes[0]
self.medium_scaler.set_scale_by_img_size(first_img_size)
self.low_scaler.set_scale_by_img_size(first_img_size)
self.final_scaler.set_scale_by_img_size(first_img_size)
self.scales_set = True
def get_medium_to_final_ratio(self):
return self.final_scaler.scale / self.medium_scaler.scale
def get_medium_to_low_ratio(self):
return self.low_scaler.scale / self.medium_scaler.scale
def get_final_to_low_ratio(self):
return self.low_scaler.scale / self.final_scaler.scale
def get_low_to_final_ratio(self):
return self.final_scaler.scale / self.low_scaler.scale
def get_final_img_sizes(self):
return [self.final_scaler.get_scaled_img_size(sz)
for sz in self.img_sizes]
def get_low_img_sizes(self):
return [self.low_scaler.get_scaled_img_size(sz)
for sz in self.img_sizes]

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apps/opencv_stitching_tool/opencv_stitching/largest_interior_rectangle.py
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import numpy as np
import numba as nb
import cv2 as cv
from .stitching_error import StitchingError
def largest_interior_rectangle(cells):
outline = get_outline(cells)
adjacencies = adjacencies_all_directions(cells)
s_map, _, saddle_candidates_map = create_maps(outline, adjacencies)
lir1 = biggest_span_in_span_map(s_map)
candidate_cells = cells_of_interest(saddle_candidates_map)
s_map = span_map(adjacencies[0], adjacencies[2], candidate_cells)
lir2 = biggest_span_in_span_map(s_map)
lir = biggest_rectangle(lir1, lir2)
return lir
def get_outline(cells):
contours, hierarchy = \
cv.findContours(cells, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
# TODO support multiple contours
# test that only one regular contour exists
if not hierarchy.shape == (1, 1, 4) or not np.all(hierarchy == -1):
raise StitchingError("Invalid Contour. Try without cropping.")
contour = contours[0][:, 0, :]
x_values = contour[:, 0].astype("uint32", order="C")
y_values = contour[:, 1].astype("uint32", order="C")
return x_values, y_values
@nb.njit('uint32[:,::1](uint8[:,::1], boolean)', parallel=True, cache=True)
def horizontal_adjacency(cells, direction):
result = np.zeros(cells.shape, dtype=np.uint32)
for y in nb.prange(cells.shape[0]):
span = 0
if direction:
iterator = range(cells.shape[1]-1, -1, -1)
else:
iterator = range(cells.shape[1])
for x in iterator:
if cells[y, x] > 0:
span += 1
else:
span = 0
result[y, x] = span
return result
@nb.njit('uint32[:,::1](uint8[:,::1], boolean)', parallel=True, cache=True)
def vertical_adjacency(cells, direction):
result = np.zeros(cells.shape, dtype=np.uint32)
for x in nb.prange(cells.shape[1]):
span = 0
if direction:
iterator = range(cells.shape[0]-1, -1, -1)
else:
iterator = range(cells.shape[0])
for y in iterator:
if cells[y, x] > 0:
span += 1
else:
span = 0
result[y, x] = span
return result
@nb.njit(cache=True)
def adjacencies_all_directions(cells):
h_left2right = horizontal_adjacency(cells, 1)
h_right2left = horizontal_adjacency(cells, 0)
v_top2bottom = vertical_adjacency(cells, 1)
v_bottom2top = vertical_adjacency(cells, 0)
return h_left2right, h_right2left, v_top2bottom, v_bottom2top
@nb.njit('uint32(uint32[:])', cache=True)
def predict_vector_size(array):
zero_indices = np.where(array == 0)[0]
if len(zero_indices) == 0:
if len(array) == 0:
return 0
return len(array)
return zero_indices[0]
@nb.njit('uint32[:](uint32[:,::1], uint32, uint32)', cache=True)
def h_vector_top2bottom(h_adjacency, x, y):
vector_size = predict_vector_size(h_adjacency[y:, x])
h_vector = np.zeros(vector_size, dtype=np.uint32)
h = np.Inf
for p in range(vector_size):
h = np.minimum(h_adjacency[y+p, x], h)
h_vector[p] = h
h_vector = np.unique(h_vector)[::-1]
return h_vector
@nb.njit('uint32[:](uint32[:,::1], uint32, uint32)', cache=True)
def h_vector_bottom2top(h_adjacency, x, y):
vector_size = predict_vector_size(np.flip(h_adjacency[:y+1, x]))
h_vector = np.zeros(vector_size, dtype=np.uint32)
h = np.Inf
for p in range(vector_size):
h = np.minimum(h_adjacency[y-p, x], h)
h_vector[p] = h
h_vector = np.unique(h_vector)[::-1]
return h_vector
@nb.njit(cache=True)
def h_vectors_all_directions(h_left2right, h_right2left, x, y):
h_l2r_t2b = h_vector_top2bottom(h_left2right, x, y)
h_r2l_t2b = h_vector_top2bottom(h_right2left, x, y)
h_l2r_b2t = h_vector_bottom2top(h_left2right, x, y)
h_r2l_b2t = h_vector_bottom2top(h_right2left, x, y)
return h_l2r_t2b, h_r2l_t2b, h_l2r_b2t, h_r2l_b2t
@nb.njit('uint32[:](uint32[:,::1], uint32, uint32)', cache=True)
def v_vector_left2right(v_adjacency, x, y):
vector_size = predict_vector_size(v_adjacency[y, x:])
v_vector = np.zeros(vector_size, dtype=np.uint32)
v = np.Inf
for q in range(vector_size):
v = np.minimum(v_adjacency[y, x+q], v)
v_vector[q] = v
v_vector = np.unique(v_vector)[::-1]
return v_vector
@nb.njit('uint32[:](uint32[:,::1], uint32, uint32)', cache=True)
def v_vector_right2left(v_adjacency, x, y):
vector_size = predict_vector_size(np.flip(v_adjacency[y, :x+1]))
v_vector = np.zeros(vector_size, dtype=np.uint32)
v = np.Inf
for q in range(vector_size):
v = np.minimum(v_adjacency[y, x-q], v)
v_vector[q] = v
v_vector = np.unique(v_vector)[::-1]
return v_vector
@nb.njit(cache=True)
def v_vectors_all_directions(v_top2bottom, v_bottom2top, x, y):
v_l2r_t2b = v_vector_left2right(v_top2bottom, x, y)
v_r2l_t2b = v_vector_right2left(v_top2bottom, x, y)
v_l2r_b2t = v_vector_left2right(v_bottom2top, x, y)
v_r2l_b2t = v_vector_right2left(v_bottom2top, x, y)
return v_l2r_t2b, v_r2l_t2b, v_l2r_b2t, v_r2l_b2t
@nb.njit('uint32[:,:](uint32[:], uint32[:])', cache=True)
def spans(h_vector, v_vector):
spans = np.stack((h_vector, v_vector[::-1]), axis=1)
return spans
@nb.njit('uint32[:](uint32[:,:])', cache=True)
def biggest_span(spans):
if len(spans) == 0:
return np.array([0, 0], dtype=np.uint32)
areas = spans[:, 0] * spans[:, 1]
biggest_span_index = np.where(areas == np.amax(areas))[0][0]
return spans[biggest_span_index]
@nb.njit(cache=True)
def spans_all_directions(h_vectors, v_vectors):
span_l2r_t2b = spans(h_vectors[0], v_vectors[0])
span_r2l_t2b = spans(h_vectors[1], v_vectors[1])
span_l2r_b2t = spans(h_vectors[2], v_vectors[2])
span_r2l_b2t = spans(h_vectors[3], v_vectors[3])
return span_l2r_t2b, span_r2l_t2b, span_l2r_b2t, span_r2l_b2t
@nb.njit(cache=True)
def get_n_directions(spans_all_directions):
n_directions = 1
for spans in spans_all_directions:
all_x_1 = np.all(spans[:, 0] == 1)
all_y_1 = np.all(spans[:, 1] == 1)
if not all_x_1 and not all_y_1:
n_directions += 1
return n_directions
@nb.njit(cache=True)
def get_xy_array(x, y, spans, mode=0):
"""0 - flip none, 1 - flip x, 2 - flip y, 3 - flip both"""
xy = spans.copy()
xy[:, 0] = x
xy[:, 1] = y
if mode == 1:
xy[:, 0] = xy[:, 0] - spans[:, 0] + 1
if mode == 2:
xy[:, 1] = xy[:, 1] - spans[:, 1] + 1
if mode == 3:
xy[:, 0] = xy[:, 0] - spans[:, 0] + 1
xy[:, 1] = xy[:, 1] - spans[:, 1] + 1
return xy
@nb.njit(cache=True)
def get_xy_arrays(x, y, spans_all_directions):
xy_l2r_t2b = get_xy_array(x, y, spans_all_directions[0], 0)
xy_r2l_t2b = get_xy_array(x, y, spans_all_directions[1], 1)
xy_l2r_b2t = get_xy_array(x, y, spans_all_directions[2], 2)
xy_r2l_b2t = get_xy_array(x, y, spans_all_directions[3], 3)
return xy_l2r_t2b, xy_r2l_t2b, xy_l2r_b2t, xy_r2l_b2t
@nb.njit(cache=True)
def point_on_outline(x, y, outline):
x_vals, y_vals = outline
x_true = x_vals == x
y_true = y_vals == y
both_true = np.logical_and(x_true, y_true)
return np.any(both_true)
@nb.njit('Tuple((uint32[:,:,::1], uint8[:,::1], uint8[:,::1]))'
'(UniTuple(uint32[:], 2), UniTuple(uint32[:,::1], 4))',
parallel=True, cache=True)
def create_maps(outline, adjacencies):
x_values, y_values = outline
h_left2right, h_right2left, v_top2bottom, v_bottom2top = adjacencies
shape = h_left2right.shape
span_map = np.zeros(shape + (2,), "uint32")
direction_map = np.zeros(shape, "uint8")
saddle_candidates_map = np.zeros(shape, "uint8")
for idx in nb.prange(len(x_values)):
x, y = x_values[idx], y_values[idx]
h_vectors = h_vectors_all_directions(h_left2right, h_right2left, x, y)
v_vectors = v_vectors_all_directions(v_top2bottom, v_bottom2top, x, y)
span_arrays = spans_all_directions(h_vectors, v_vectors)
n = get_n_directions(span_arrays)
direction_map[y, x] = n
xy_arrays = get_xy_arrays(x, y, span_arrays)
for direction_idx in range(4):
xy_array = xy_arrays[direction_idx]
span_array = span_arrays[direction_idx]
for span_idx in range(span_array.shape[0]):
x, y = xy_array[span_idx][0], xy_array[span_idx][1]
w, h = span_array[span_idx][0], span_array[span_idx][1]
if w*h > span_map[y, x, 0] * span_map[y, x, 1]:
span_map[y, x, :] = np.array([w, h], "uint32")
if n == 3 and not point_on_outline(x, y, outline):
saddle_candidates_map[y, x] = np.uint8(255)
return span_map, direction_map, saddle_candidates_map
def cells_of_interest(cells):
y_vals, x_vals = cells.nonzero()
x_vals = x_vals.astype("uint32", order="C")
y_vals = y_vals.astype("uint32", order="C")
return x_vals, y_vals
@nb.njit('uint32[:, :, :]'
'(uint32[:,::1], uint32[:,::1], UniTuple(uint32[:], 2))',
parallel=True, cache=True)
def span_map(h_adjacency_left2right,
v_adjacency_top2bottom,
cells_of_interest):
x_values, y_values = cells_of_interest
span_map = np.zeros(h_adjacency_left2right.shape + (2,), dtype=np.uint32)
for idx in nb.prange(len(x_values)):
x, y = x_values[idx], y_values[idx]
h_vector = h_vector_top2bottom(h_adjacency_left2right, x, y)
v_vector = v_vector_left2right(v_adjacency_top2bottom, x, y)
s = spans(h_vector, v_vector)
s = biggest_span(s)
span_map[y, x, :] = s
return span_map
@nb.njit('uint32[:](uint32[:, :, :])', cache=True)
def biggest_span_in_span_map(span_map):
areas = span_map[:, :, 0] * span_map[:, :, 1]
largest_rectangle_indices = np.where(areas == np.amax(areas))
x = largest_rectangle_indices[1][0]
y = largest_rectangle_indices[0][0]
span = span_map[y, x]
return np.array([x, y, span[0], span[1]], dtype=np.uint32)
def biggest_rectangle(*args):
biggest_rect = np.array([0, 0, 0, 0], dtype=np.uint32)
for rect in args:
if rect[2] * rect[3] > biggest_rect[2] * biggest_rect[3]:
biggest_rect = rect
return biggest_rect

38
apps/opencv_stitching_tool/opencv_stitching/megapix_scaler.py
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import numpy as np
class MegapixScaler:
def __init__(self, megapix):
self.megapix = megapix
self.is_scale_set = False
self.scale = None
def set_scale_by_img_size(self, img_size):
self.set_scale(
self.get_scale_by_resolution(img_size[0] * img_size[1])
)
def set_scale(self, scale):
self.scale = scale
self.is_scale_set = True
def get_scale_by_resolution(self, resolution):
if self.megapix > 0:
return np.sqrt(self.megapix * 1e6 / resolution)
return 1.0
def get_scaled_img_size(self, img_size):
width = int(round(img_size[0] * self.scale))
height = int(round(img_size[1] * self.scale))
return (width, height)
class MegapixDownscaler(MegapixScaler):
@staticmethod
def force_downscale(scale):
return min(1.0, scale)
def set_scale(self, scale):
scale = self.force_downscale(scale)
super().set_scale(scale)

126
apps/opencv_stitching_tool/opencv_stitching/seam_finder.py
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from collections import OrderedDict
import cv2 as cv
import numpy as np
from .blender import Blender
class SeamFinder:
"""https://docs.opencv.org/4.x/d7/d09/classcv_1_1detail_1_1SeamFinder.html""" # noqa
SEAM_FINDER_CHOICES = OrderedDict()
SEAM_FINDER_CHOICES['dp_color'] = cv.detail_DpSeamFinder('COLOR')
SEAM_FINDER_CHOICES['dp_colorgrad'] = cv.detail_DpSeamFinder('COLOR_GRAD')
SEAM_FINDER_CHOICES['voronoi'] = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM) # noqa
SEAM_FINDER_CHOICES['no'] = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO) # noqa
DEFAULT_SEAM_FINDER = list(SEAM_FINDER_CHOICES.keys())[0]
def __init__(self, finder=DEFAULT_SEAM_FINDER):
self.finder = SeamFinder.SEAM_FINDER_CHOICES[finder]
def find(self, imgs, corners, masks):
"""https://docs.opencv.org/4.x/d0/dd5/classcv_1_1detail_1_1DpSeamFinder.html#a7914624907986f7a94dd424209a8a609""" # noqa
imgs_float = [img.astype(np.float32) for img in imgs]
return self.finder.find(imgs_float, corners, masks)
@staticmethod
def resize(seam_mask, mask):
dilated_mask = cv.dilate(seam_mask, None)
resized_seam_mask = cv.resize(dilated_mask, (mask.shape[1],
mask.shape[0]),
0, 0, cv.INTER_LINEAR_EXACT)
return cv.bitwise_and(resized_seam_mask, mask)
@staticmethod
def draw_seam_mask(img, seam_mask, color=(0, 0, 0)):
seam_mask = cv.UMat.get(seam_mask)
overlayed_img = np.copy(img)
overlayed_img[seam_mask == 0] = color
return overlayed_img
@staticmethod
def draw_seam_polygons(panorama, blended_seam_masks, alpha=0.5):
return add_weighted_image(panorama, blended_seam_masks, alpha)
@staticmethod
def draw_seam_lines(panorama, blended_seam_masks,
linesize=1, color=(0, 0, 255)):
seam_lines = \
SeamFinder.exctract_seam_lines(blended_seam_masks, linesize)
panorama_with_seam_lines = panorama.copy()
panorama_with_seam_lines[seam_lines == 255] = color
return panorama_with_seam_lines
@staticmethod
def exctract_seam_lines(blended_seam_masks, linesize=1):
seam_lines = cv.Canny(np.uint8(blended_seam_masks), 100, 200)
seam_indices = (seam_lines == 255).nonzero()
seam_lines = remove_invalid_line_pixels(
seam_indices, seam_lines, blended_seam_masks
)
kernelsize = linesize + linesize - 1
kernel = np.ones((kernelsize, kernelsize), np.uint8)
return cv.dilate(seam_lines, kernel)
@staticmethod
def blend_seam_masks(seam_masks, corners, sizes):
imgs = colored_img_generator(sizes)
blended_seam_masks, _ = \
Blender.create_panorama(imgs, seam_masks, corners, sizes)
return blended_seam_masks
def colored_img_generator(sizes, colors=(
(255, 000, 000), # Blue
(000, 000, 255), # Red
(000, 255, 000), # Green
(000, 255, 255), # Yellow
(255, 000, 255), # Magenta
(128, 128, 255), # Pink
(128, 128, 128), # Gray
(000, 000, 128), # Brown
(000, 128, 255)) # Orange
):
for idx, size in enumerate(sizes):
if idx+1 > len(colors):
raise ValueError("Not enough default colors! Pass additional "
"colors to \"colors\" parameter")
yield create_img_by_size(size, colors[idx])
def create_img_by_size(size, color=(0, 0, 0)):
width, height = size
img = np.zeros((height, width, 3), np.uint8)
img[:] = color
return img
def add_weighted_image(img1, img2, alpha):
return cv.addWeighted(
img1, alpha, img2, (1.0 - alpha), 0.0
)
def remove_invalid_line_pixels(indices, lines, mask):
for x, y in zip(*indices):
if check_if_pixel_or_neighbor_is_black(mask, x, y):
lines[x, y] = 0
return lines
def check_if_pixel_or_neighbor_is_black(img, x, y):
check = [is_pixel_black(img, x, y),
is_pixel_black(img, x+1, y), is_pixel_black(img, x-1, y),
is_pixel_black(img, x, y+1), is_pixel_black(img, x, y-1)]
return any(check)
def is_pixel_black(img, x, y):
return np.all(get_pixel_value(img, x, y) == 0)
def get_pixel_value(img, x, y):
try:
return img[x, y]
except IndexError:
pass

236
apps/opencv_stitching_tool/opencv_stitching/stitcher.py
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@ -1,236 +0,0 @@
from types import SimpleNamespace
from .image_handler import ImageHandler
from .feature_detector import FeatureDetector
from .feature_matcher import FeatureMatcher
from .subsetter import Subsetter
from .camera_estimator import CameraEstimator
from .camera_adjuster import CameraAdjuster
from .camera_wave_corrector import WaveCorrector
from .warper import Warper
from .cropper import Cropper
from .exposure_error_compensator import ExposureErrorCompensator
from .seam_finder import SeamFinder
from .blender import Blender
from .timelapser import Timelapser
from .stitching_error import StitchingError
class Stitcher:
DEFAULT_SETTINGS = {
"medium_megapix": ImageHandler.DEFAULT_MEDIUM_MEGAPIX,
"detector": FeatureDetector.DEFAULT_DETECTOR,
"nfeatures": 500,
"matcher_type": FeatureMatcher.DEFAULT_MATCHER,
"range_width": FeatureMatcher.DEFAULT_RANGE_WIDTH,
"try_use_gpu": False,
"match_conf": None,
"confidence_threshold": Subsetter.DEFAULT_CONFIDENCE_THRESHOLD,
"matches_graph_dot_file": Subsetter.DEFAULT_MATCHES_GRAPH_DOT_FILE,
"estimator": CameraEstimator.DEFAULT_CAMERA_ESTIMATOR,
"adjuster": CameraAdjuster.DEFAULT_CAMERA_ADJUSTER,
"refinement_mask": CameraAdjuster.DEFAULT_REFINEMENT_MASK,
"wave_correct_kind": WaveCorrector.DEFAULT_WAVE_CORRECTION,
"warper_type": Warper.DEFAULT_WARP_TYPE,
"low_megapix": ImageHandler.DEFAULT_LOW_MEGAPIX,
"crop": Cropper.DEFAULT_CROP,
"compensator": ExposureErrorCompensator.DEFAULT_COMPENSATOR,
"nr_feeds": ExposureErrorCompensator.DEFAULT_NR_FEEDS,
"block_size": ExposureErrorCompensator.DEFAULT_BLOCK_SIZE,
"finder": SeamFinder.DEFAULT_SEAM_FINDER,
"final_megapix": ImageHandler.DEFAULT_FINAL_MEGAPIX,
"blender_type": Blender.DEFAULT_BLENDER,
"blend_strength": Blender.DEFAULT_BLEND_STRENGTH,
"timelapse": Timelapser.DEFAULT_TIMELAPSE}
def __init__(self, **kwargs):
self.initialize_stitcher(**kwargs)
def initialize_stitcher(self, **kwargs):
self.settings = Stitcher.DEFAULT_SETTINGS.copy()
self.validate_kwargs(kwargs)
self.settings.update(kwargs)
args = SimpleNamespace(**self.settings)
self.img_handler = ImageHandler(args.medium_megapix,
args.low_megapix,
args.final_megapix)
self.detector = \
FeatureDetector(args.detector, nfeatures=args.nfeatures)
match_conf = \
FeatureMatcher.get_match_conf(args.match_conf, args.detector)
self.matcher = FeatureMatcher(args.matcher_type, args.range_width,
try_use_gpu=args.try_use_gpu,
match_conf=match_conf)
self.subsetter = \
Subsetter(args.confidence_threshold, args.matches_graph_dot_file)
self.camera_estimator = CameraEstimator(args.estimator)
self.camera_adjuster = \
CameraAdjuster(args.adjuster, args.refinement_mask)
self.wave_corrector = WaveCorrector(args.wave_correct_kind)
self.warper = Warper(args.warper_type)
self.cropper = Cropper(args.crop)
self.compensator = \
ExposureErrorCompensator(args.compensator, args.nr_feeds,
args.block_size)
self.seam_finder = SeamFinder(args.finder)
self.blender = Blender(args.blender_type, args.blend_strength)
self.timelapser = Timelapser(args.timelapse)
def stitch(self, img_names):
self.initialize_registration(img_names)
imgs = self.resize_medium_resolution()
features = self.find_features(imgs)
matches = self.match_features(features)
imgs, features, matches = self.subset(imgs, features, matches)
cameras = self.estimate_camera_parameters(features, matches)
cameras = self.refine_camera_parameters(features, matches, cameras)
cameras = self.perform_wave_correction(cameras)
self.estimate_scale(cameras)
imgs = self.resize_low_resolution(imgs)
imgs, masks, corners, sizes = self.warp_low_resolution(imgs, cameras)
self.prepare_cropper(imgs, masks, corners, sizes)
imgs, masks, corners, sizes = \
self.crop_low_resolution(imgs, masks, corners, sizes)
self.estimate_exposure_errors(corners, imgs, masks)
seam_masks = self.find_seam_masks(imgs, corners, masks)
imgs = self.resize_final_resolution()
imgs, masks, corners, sizes = self.warp_final_resolution(imgs, cameras)
imgs, masks, corners, sizes = \
self.crop_final_resolution(imgs, masks, corners, sizes)
self.set_masks(masks)
imgs = self.compensate_exposure_errors(corners, imgs)
seam_masks = self.resize_seam_masks(seam_masks)
self.initialize_composition(corners, sizes)
self.blend_images(imgs, seam_masks, corners)
return self.create_final_panorama()
def initialize_registration(self, img_names):
self.img_handler.set_img_names(img_names)
def resize_medium_resolution(self):
return list(self.img_handler.resize_to_medium_resolution())
def find_features(self, imgs):
return [self.detector.detect_features(img) for img in imgs]
def match_features(self, features):
return self.matcher.match_features(features)
def subset(self, imgs, features, matches):
names, sizes, imgs, features, matches = \
self.subsetter.subset(self.img_handler.img_names,
self.img_handler.img_sizes,
imgs, features, matches)
self.img_handler.img_names, self.img_handler.img_sizes = names, sizes
return imgs, features, matches
def estimate_camera_parameters(self, features, matches):
return self.camera_estimator.estimate(features, matches)
def refine_camera_parameters(self, features, matches, cameras):
return self.camera_adjuster.adjust(features, matches, cameras)
def perform_wave_correction(self, cameras):
return self.wave_corrector.correct(cameras)
def estimate_scale(self, cameras):
self.warper.set_scale(cameras)
def resize_low_resolution(self, imgs=None):
return list(self.img_handler.resize_to_low_resolution(imgs))
def warp_low_resolution(self, imgs, cameras):
sizes = self.img_handler.get_low_img_sizes()
camera_aspect = self.img_handler.get_medium_to_low_ratio()
imgs, masks, corners, sizes = \
self.warp(imgs, cameras, sizes, camera_aspect)
return list(imgs), list(masks), corners, sizes
def warp_final_resolution(self, imgs, cameras):
sizes = self.img_handler.get_final_img_sizes()
camera_aspect = self.img_handler.get_medium_to_final_ratio()
return self.warp(imgs, cameras, sizes, camera_aspect)
def warp(self, imgs, cameras, sizes, aspect=1):
imgs = self.warper.warp_images(imgs, cameras, aspect)
masks = self.warper.create_and_warp_masks(sizes, cameras, aspect)
corners, sizes = self.warper.warp_rois(sizes, cameras, aspect)
return imgs, masks, corners, sizes
def prepare_cropper(self, imgs, masks, corners, sizes):
self.cropper.prepare(imgs, masks, corners, sizes)
def crop_low_resolution(self, imgs, masks, corners, sizes):
imgs, masks, corners, sizes = self.crop(imgs, masks, corners, sizes)
return list(imgs), list(masks), corners, sizes
def crop_final_resolution(self, imgs, masks, corners, sizes):
lir_aspect = self.img_handler.get_low_to_final_ratio()
return self.crop(imgs, masks, corners, sizes, lir_aspect)
def crop(self, imgs, masks, corners, sizes, aspect=1):
masks = self.cropper.crop_images(masks, aspect)
imgs = self.cropper.crop_images(imgs, aspect)
corners, sizes = self.cropper.crop_rois(corners, sizes, aspect)
return imgs, masks, corners, sizes
def estimate_exposure_errors(self, corners, imgs, masks):
self.compensator.feed(corners, imgs, masks)
def find_seam_masks(self, imgs, corners, masks):
return self.seam_finder.find(imgs, corners, masks)
def resize_final_resolution(self):
return self.img_handler.resize_to_final_resolution()
def compensate_exposure_errors(self, corners, imgs):
for idx, (corner, img) in enumerate(zip(corners, imgs)):
yield self.compensator.apply(idx, corner, img, self.get_mask(idx))
def resize_seam_masks(self, seam_masks):
for idx, seam_mask in enumerate(seam_masks):
yield SeamFinder.resize(seam_mask, self.get_mask(idx))
def set_masks(self, mask_generator):
self.masks = mask_generator
self.mask_index = -1
def get_mask(self, idx):
if idx == self.mask_index + 1:
self.mask_index += 1
self.mask = next(self.masks)
return self.mask
elif idx == self.mask_index:
return self.mask
else:
raise StitchingError("Invalid Mask Index!")
def initialize_composition(self, corners, sizes):
if self.timelapser.do_timelapse:
self.timelapser.initialize(corners, sizes)
else:
self.blender.prepare(corners, sizes)
def blend_images(self, imgs, masks, corners):
for idx, (img, mask, corner) in enumerate(zip(imgs, masks, corners)):
if self.timelapser.do_timelapse:
self.timelapser.process_and_save_frame(
self.img_handler.img_names[idx], img, corner
)
else:
self.blender.feed(img, mask, corner)
def create_final_panorama(self):
if not self.timelapser.do_timelapse:
panorama, _ = self.blender.blend()
return panorama
@staticmethod
def validate_kwargs(kwargs):
for arg in kwargs:
if arg not in Stitcher.DEFAULT_SETTINGS:
raise StitchingError("Invalid Argument: " + arg)

2
apps/opencv_stitching_tool/opencv_stitching/stitching_error.py
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class StitchingError(Exception):
pass

94
apps/opencv_stitching_tool/opencv_stitching/subsetter.py
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from itertools import chain
import math
import cv2 as cv
import numpy as np
from .feature_matcher import FeatureMatcher
from .stitching_error import StitchingError
class Subsetter:
DEFAULT_CONFIDENCE_THRESHOLD = 1
DEFAULT_MATCHES_GRAPH_DOT_FILE = None
def __init__(self,
confidence_threshold=DEFAULT_CONFIDENCE_THRESHOLD,
matches_graph_dot_file=DEFAULT_MATCHES_GRAPH_DOT_FILE):
self.confidence_threshold = confidence_threshold
self.save_file = matches_graph_dot_file
def subset(self, img_names, img_sizes, imgs, features, matches):
self.save_matches_graph_dot_file(img_names, matches)
indices = self.get_indices_to_keep(features, matches)
img_names = Subsetter.subset_list(img_names, indices)
img_sizes = Subsetter.subset_list(img_sizes, indices)
imgs = Subsetter.subset_list(imgs, indices)
features = Subsetter.subset_list(features, indices)
matches = Subsetter.subset_matches(matches, indices)
return img_names, img_sizes, imgs, features, matches
def save_matches_graph_dot_file(self, img_names, pairwise_matches):
if self.save_file:
with open(self.save_file, 'w') as filehandler:
filehandler.write(self.get_matches_graph(img_names,
pairwise_matches)
)
def get_matches_graph(self, img_names, pairwise_matches):
return cv.detail.matchesGraphAsString(img_names, pairwise_matches,
self.confidence_threshold)
def get_indices_to_keep(self, features, pairwise_matches):
indices = cv.detail.leaveBiggestComponent(features,
pairwise_matches,
self.confidence_threshold)
if len(indices) < 2:
raise StitchingError("No match exceeds the "
"given confidence theshold.")
return indices
@staticmethod
def subset_list(list_to_subset, indices):
return [list_to_subset[i] for i in indices]
@staticmethod
def subset_matches(pairwise_matches, indices):
indices_to_delete = Subsetter.get_indices_to_delete(
math.sqrt(len(pairwise_matches)),
indices
)
matches_matrix = FeatureMatcher.get_matches_matrix(pairwise_matches)
matches_matrix_subset = Subsetter.subset_matrix(matches_matrix,
indices_to_delete)
matches_subset = Subsetter.matrix_rows_to_list(matches_matrix_subset)
return matches_subset
@staticmethod
def get_indices_to_delete(nr_elements, indices_to_keep):
return list(set(range(int(nr_elements))) - set(indices_to_keep))
@staticmethod
def subset_matrix(matrix_to_subset, indices_to_delete):
for idx, idx_to_delete in enumerate(indices_to_delete):
matrix_to_subset = Subsetter.delete_index_from_matrix(
matrix_to_subset,
idx_to_delete-idx # matrix shape reduced by one at each step
)
return matrix_to_subset
@staticmethod
def delete_index_from_matrix(matrix, idx):
mask = np.ones(matrix.shape[0], bool)
mask[idx] = 0
return matrix[mask, :][:, mask]
@staticmethod
def matrix_rows_to_list(matrix):
return list(chain.from_iterable(matrix.tolist()))

13
apps/opencv_stitching_tool/opencv_stitching/test/.gitignore vendored
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# Ignore everything
*
# But not these files...
!.gitignore
!test_matcher.py
!test_stitcher.py
!test_megapix_scaler.py
!test_registration.py
!test_composition.py
!test_performance.py
!stitching_detailed.py
!SAMPLE_IMAGES_TO_DOWNLOAD.txt

5
apps/opencv_stitching_tool/opencv_stitching/test/SAMPLE_IMAGES_TO_DOWNLOAD.txt
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@ -1,5 +0,0 @@
https://github.com/opencv/opencv_extra/tree/4.x/testdata/stitching
s1.jpg s2.jpg
boat1.jpg boat2.jpg boat3.jpg boat4.jpg boat5.jpg boat6.jpg
budapest1.jpg budapest2.jpg budapest3.jpg budapest4.jpg budapest5.jpg budapest6.jpg

406
apps/opencv_stitching_tool/opencv_stitching/test/stitching_detailed.py
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"""
Stitching sample (advanced)
===========================
Show how to use Stitcher API from python.
"""
# Python 2/3 compatibility
from __future__ import print_function
from types import SimpleNamespace
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',)
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 = {
"img_names":["boat5.jpg", "boat2.jpg",
"boat3.jpg", "boat4.jpg",
"boat1.jpg", "boat6.jpg"],
"try_cuda": False,
"work_megapix": 0.6,
"features": "orb",
"matcher": "homography",
"estimator": "homography",
"match_conf": None,
"conf_thresh": 1.0,
"ba": "ray",
"ba_refine_mask": "xxxxx",
"wave_correct": "horiz",
"save_graph": None,
"warp": "spherical",
"seam_megapix": 0.1,
"seam": "dp_color",
"compose_megapix": 3,
"expos_comp": "gain_blocks",
"expos_comp_nr_feeds": 1,
"expos_comp_nr_filtering": 2,
"expos_comp_block_size": 32,
"blend": "multiband",
"blend_strength": 5,
"output": "time_test.jpg",
"timelapse": None,
"rangewidth": -1
}
args = SimpleNamespace(**args)
img_names = args.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/4.x/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.int64))
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)
return 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()
if __name__ == '__main__':
import tracemalloc
import time
tracemalloc.start()
start = time.time()
result = main()
current, peak = tracemalloc.get_traced_memory()
print(f"Current memory usage is {current / 10**6}MB; Peak was {peak / 10**6}MB")
tracemalloc.stop()
end = time.time()
print(end - start)

67
apps/opencv_stitching_tool/opencv_stitching/test/test_composition.py
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@ -1,67 +0,0 @@
import unittest
import os
import sys
import numpy as np
import cv2 as cv
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__),
'..', '..')))
from opencv_stitching.stitcher import Stitcher
class TestImageComposition(unittest.TestCase):
# visual test: look especially in the sky
def test_exposure_compensation(self):
img = cv.imread("s1.jpg")
img = increase_brightness(img, value=25)
cv.imwrite("s1_bright.jpg", img)
stitcher = Stitcher(compensator="no", blender_type="no")
result = stitcher.stitch(["s1_bright.jpg", "s2.jpg"])
cv.imwrite("without_exposure_comp.jpg", result)
stitcher = Stitcher(blender_type="no")
result = stitcher.stitch(["s1_bright.jpg", "s2.jpg"])
cv.imwrite("with_exposure_comp.jpg", result)
def test_timelapse(self):
stitcher = Stitcher(timelapse='as_is')
_ = stitcher.stitch(["s1.jpg", "s2.jpg"])
frame1 = cv.imread("fixed_s1.jpg")
max_image_shape_derivation = 3
np.testing.assert_allclose(frame1.shape[:2],
(700, 1811),
atol=max_image_shape_derivation)
left = cv.cvtColor(frame1[:, :1300, ], cv.COLOR_BGR2GRAY)
right = cv.cvtColor(frame1[:, 1300:, ], cv.COLOR_BGR2GRAY)
self.assertGreater(cv.countNonZero(left), 800000)
self.assertEqual(cv.countNonZero(right), 0)
def increase_brightness(img, value=30):
hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
h, s, v = cv.split(hsv)
lim = 255 - value
v[v > lim] = 255
v[v <= lim] += value
final_hsv = cv.merge((h, s, v))
img = cv.cvtColor(final_hsv, cv.COLOR_HSV2BGR)
return img
def starttest():
unittest.main()
if __name__ == "__main__":
starttest()

47
apps/opencv_stitching_tool/opencv_stitching/test/test_matcher.py
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import unittest
import os
import sys
import numpy as np
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__),
'..', '..')))
from opencv_stitching.feature_matcher import FeatureMatcher
# %%
class TestMatcher(unittest.TestCase):
def test_array_in_sqare_matrix(self):
array = np.zeros(9)
matrix = FeatureMatcher.array_in_sqare_matrix(array)
np.testing.assert_array_equal(matrix, np.array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]))
def test_get_all_img_combinations(self):
nimgs = 3
combinations = list(FeatureMatcher.get_all_img_combinations(nimgs))
self.assertEqual(combinations, [(0, 1), (0, 2), (1, 2)])
def test_get_match_conf(self):
explicit_match_conf = FeatureMatcher.get_match_conf(1, 'orb')
implicit_match_conf_orb = FeatureMatcher.get_match_conf(None, 'orb')
implicit_match_conf_other = FeatureMatcher.get_match_conf(None, 'surf')
self.assertEqual(explicit_match_conf, 1)
self.assertEqual(implicit_match_conf_orb, 0.3)
self.assertEqual(implicit_match_conf_other, 0.65)
def starttest():
unittest.main()
if __name__ == "__main__":
starttest()

58
apps/opencv_stitching_tool/opencv_stitching/test/test_megapix_scaler.py
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import unittest
import os
import sys
import cv2 as cv
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__),
'..', '..')))
from opencv_stitching.megapix_scaler import MegapixScaler, MegapixDownscaler
# %%
class TestScaler(unittest.TestCase):
def setUp(self):
self.img = cv.imread("s1.jpg")
self.size = (self.img.shape[1], self.img.shape[0])
def test_get_scale_by_resolution(self):
scaler = MegapixScaler(0.6)
scale = scaler.get_scale_by_resolution(1_200_000)
self.assertEqual(scale, 0.7071067811865476)
def test_get_scale_by_image(self):
scaler = MegapixScaler(0.6)
scaler.set_scale_by_img_size(self.size)
self.assertEqual(scaler.scale, 0.8294067854101966)
def test_get_scaled_img_size(self):
scaler = MegapixScaler(0.6)
scaler.set_scale_by_img_size(self.size)
size = scaler.get_scaled_img_size(self.size)
self.assertEqual(size, (1033, 581))
# 581*1033 = 600173 px = ~0.6 MP
def test_force_of_downscaling(self):
normal_scaler = MegapixScaler(2)
downscaler = MegapixDownscaler(2)
normal_scaler.set_scale_by_img_size(self.size)
downscaler.set_scale_by_img_size(self.size)
self.assertEqual(normal_scaler.scale, 1.5142826857233715)
self.assertEqual(downscaler.scale, 1.0)
def starttest():
unittest.main()
if __name__ == "__main__":
starttest()

65
apps/opencv_stitching_tool/opencv_stitching/test/test_performance.py
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import unittest
import os
import sys
import time
import tracemalloc
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__),
'..', '..')))
from opencv_stitching.stitcher import Stitcher
from stitching_detailed import main
# %%
class TestStitcher(unittest.TestCase):
@unittest.skip("skip performance test (not needed in every run)")
def test_performance(self):
print("Run new Stitcher class:")
start = time.time()
tracemalloc.start()
stitcher = Stitcher(final_megapix=3)
stitcher.stitch(["boat5.jpg", "boat2.jpg",
"boat3.jpg", "boat4.jpg",
"boat1.jpg", "boat6.jpg"])
_, peak_memory = tracemalloc.get_traced_memory()
tracemalloc.stop()
end = time.time()
time_needed = end - start
print(f"Peak was {peak_memory / 10**6} MB")
print(f"Time was {time_needed} s")
print("Run original stitching_detailed.py:")
start = time.time()
tracemalloc.start()
main()
_, peak_memory_detailed = tracemalloc.get_traced_memory()
tracemalloc.stop()
end = time.time()
time_needed_detailed = end - start
print(f"Peak was {peak_memory_detailed / 10**6} MB")
print(f"Time was {time_needed_detailed} s")
self.assertLessEqual(peak_memory / 10**6,
peak_memory_detailed / 10**6)
uncertainty_based_on_run = 0.25
self.assertLessEqual(time_needed - uncertainty_based_on_run,
time_needed_detailed)
def starttest():
unittest.main()
if __name__ == "__main__":
starttest()

100
apps/opencv_stitching_tool/opencv_stitching/test/test_registration.py
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import unittest
import os
import sys
import numpy as np
import cv2 as cv
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__),
'..', '..')))
from opencv_stitching.feature_detector import FeatureDetector
from opencv_stitching.feature_matcher import FeatureMatcher
from opencv_stitching.subsetter import Subsetter
class TestImageRegistration(unittest.TestCase):
def test_feature_detector(self):
img1 = cv.imread("s1.jpg")
default_number_of_keypoints = 500
detector = FeatureDetector("orb")
features = detector.detect_features(img1)
self.assertEqual(len(features.getKeypoints()),
default_number_of_keypoints)
other_keypoints = 1000
detector = FeatureDetector("orb", nfeatures=other_keypoints)
features = detector.detect_features(img1)
self.assertEqual(len(features.getKeypoints()), other_keypoints)
def test_feature_matcher(self):
img1, img2 = cv.imread("s1.jpg"), cv.imread("s2.jpg")
detector = FeatureDetector("orb")
features = [detector.detect_features(img1),
detector.detect_features(img2)]
matcher = FeatureMatcher()
pairwise_matches = matcher.match_features(features)
self.assertEqual(len(pairwise_matches), len(features)**2)
self.assertGreater(pairwise_matches[1].confidence, 2)
matches_matrix = FeatureMatcher.get_matches_matrix(pairwise_matches)
self.assertEqual(matches_matrix.shape, (2, 2))
conf_matrix = FeatureMatcher.get_confidence_matrix(pairwise_matches)
self.assertTrue(np.array_equal(
conf_matrix > 2,
np.array([[False, True], [True, False]])
))
def test_subsetting(self):
img1, img2 = cv.imread("s1.jpg"), cv.imread("s2.jpg")
img3, img4 = cv.imread("boat1.jpg"), cv.imread("boat2.jpg")
img5 = cv.imread("boat3.jpg")
img_names = ["s1.jpg", "s2.jpg", "boat1.jpg", "boat2.jpg", "boat3.jpg"]
detector = FeatureDetector("orb")
features = [detector.detect_features(img1),
detector.detect_features(img2),
detector.detect_features(img3),
detector.detect_features(img4),
detector.detect_features(img5)]
matcher = FeatureMatcher()
pairwise_matches = matcher.match_features(features)
subsetter = Subsetter(confidence_threshold=1,
matches_graph_dot_file="dot_graph.txt") # view in https://dreampuf.github.io # noqa
indices = subsetter.get_indices_to_keep(features, pairwise_matches)
indices_to_delete = subsetter.get_indices_to_delete(len(img_names),
indices)
np.testing.assert_array_equal(indices, np.array([2, 3, 4]))
np.testing.assert_array_equal(indices_to_delete, np.array([0, 1]))
subsetted_image_names = subsetter.subset_list(img_names, indices)
self.assertEqual(subsetted_image_names,
['boat1.jpg', 'boat2.jpg', 'boat3.jpg'])
matches_subset = subsetter.subset_matches(pairwise_matches, indices)
# FeatureMatcher.get_confidence_matrix(pairwise_matches)
# FeatureMatcher.get_confidence_matrix(subsetted_matches)
self.assertEqual(pairwise_matches[13].confidence,
matches_subset[1].confidence)
graph = subsetter.get_matches_graph(img_names, pairwise_matches)
self.assertTrue(graph.startswith("graph matches_graph{"))
subsetter.save_matches_graph_dot_file(img_names, pairwise_matches)
with open('dot_graph.txt', 'r') as file:
graph = file.read()
self.assertTrue(graph.startswith("graph matches_graph{"))
def starttest():
unittest.main()
if __name__ == "__main__":
starttest()

108
apps/opencv_stitching_tool/opencv_stitching/test/test_stitcher.py
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import unittest
import os
import sys
import numpy as np
import cv2 as cv
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__),
'..', '..')))
from opencv_stitching.stitcher import Stitcher
# %%
class TestStitcher(unittest.TestCase):
def test_stitcher_aquaduct(self):
stitcher = Stitcher(nfeatures=250)
result = stitcher.stitch(["s1.jpg", "s2.jpg"])
cv.imwrite("result.jpg", result)
max_image_shape_derivation = 3
np.testing.assert_allclose(result.shape[:2],
(700, 1811),
atol=max_image_shape_derivation)
@unittest.skip("skip boat test (high resuolution ran >30s)")
def test_stitcher_boat1(self):
settings = {"warper_type": "fisheye",
"wave_correct_kind": "no",
"finder": "dp_colorgrad",
"compensator": "no",
"confidence_threshold": 0.3}
stitcher = Stitcher(**settings)
result = stitcher.stitch(["boat5.jpg", "boat2.jpg",
"boat3.jpg", "boat4.jpg",
"boat1.jpg", "boat6.jpg"])
cv.imwrite("boat_fisheye.jpg", result)
max_image_shape_derivation = 600
np.testing.assert_allclose(result.shape[:2],
(14488, 7556),
atol=max_image_shape_derivation)
@unittest.skip("skip boat test (high resuolution ran >30s)")
def test_stitcher_boat2(self):
settings = {"warper_type": "compressedPlaneA2B1",
"finder": "dp_colorgrad",
"compensator": "channel_blocks",
"confidence_threshold": 0.3}
stitcher = Stitcher(**settings)
result = stitcher.stitch(["boat5.jpg", "boat2.jpg",
"boat3.jpg", "boat4.jpg",
"boat1.jpg", "boat6.jpg"])
cv.imwrite("boat_plane.jpg", result)
max_image_shape_derivation = 600
np.testing.assert_allclose(result.shape[:2],
(7400, 12340),
atol=max_image_shape_derivation)
def test_stitcher_boat_aquaduct_subset(self):
settings = {"final_megapix": 1, "crop": True}
stitcher = Stitcher(**settings)
result = stitcher.stitch(["boat5.jpg",
"s1.jpg", "s2.jpg",
"boat2.jpg",
"boat3.jpg", "boat4.jpg",
"boat1.jpg", "boat6.jpg"])
cv.imwrite("subset_low_res.jpg", result)
max_image_shape_derivation = 100
np.testing.assert_allclose(result.shape[:2],
(705, 3374),
atol=max_image_shape_derivation)
def test_stitcher_budapest(self):
settings = {"matcher_type": "affine",
"estimator": "affine",
"adjuster": "affine",
"warper_type": "affine",
"wave_correct_kind": "no",
"confidence_threshold": 0.3}
stitcher = Stitcher(**settings)
result = stitcher.stitch(["budapest1.jpg", "budapest2.jpg",
"budapest3.jpg", "budapest4.jpg",
"budapest5.jpg", "budapest6.jpg"])
cv.imwrite("budapest.jpg", result)
max_image_shape_derivation = 50
np.testing.assert_allclose(result.shape[:2],
(1155, 2310),
atol=max_image_shape_derivation)
def starttest():
unittest.main()
if __name__ == "__main__":
starttest()

50
apps/opencv_stitching_tool/opencv_stitching/timelapser.py
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import os
import cv2 as cv
import numpy as np
class Timelapser:
TIMELAPSE_CHOICES = ('no', 'as_is', 'crop',)
DEFAULT_TIMELAPSE = 'no'
def __init__(self, timelapse=DEFAULT_TIMELAPSE):
self.do_timelapse = True
self.timelapse_type = None
self.timelapser = None
if timelapse == "as_is":
self.timelapse_type = cv.detail.Timelapser_AS_IS
elif timelapse == "crop":
self.timelapse_type = cv.detail.Timelapser_CROP
else:
self.do_timelapse = False
if self.do_timelapse:
self.timelapser = cv.detail.Timelapser_createDefault(
self.timelapse_type
)
def initialize(self, *args):
"""https://docs.opencv.org/4.x/dd/dac/classcv_1_1detail_1_1Timelapser.html#aaf0f7c4128009f02473332a0c41f6345""" # noqa
self.timelapser.initialize(*args)
def process_and_save_frame(self, img_name, img, corner):
self.process_frame(img, corner)
cv.imwrite(self.get_fixed_filename(img_name), self.get_frame())
def process_frame(self, img, corner):
mask = np.ones((img.shape[0], img.shape[1]), np.uint8)
img = img.astype(np.int16)
self.timelapser.process(img, mask, corner)
def get_frame(self):
frame = self.timelapser.getDst()
frame = np.float32(cv.UMat.get(frame))
frame = cv.convertScaleAbs(frame)
return frame
@staticmethod
def get_fixed_filename(img_name):
dirname, filename = os.path.split(img_name)
return os.path.join(dirname, "fixed_" + filename)

79
apps/opencv_stitching_tool/opencv_stitching/warper.py
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from statistics import median
import cv2 as cv
import numpy as np
class Warper:
WARP_TYPE_CHOICES = ('spherical', 'plane', 'affine', 'cylindrical',
'fisheye', 'stereographic', 'compressedPlaneA2B1',
'compressedPlaneA1.5B1',
'compressedPlanePortraitA2B1',
'compressedPlanePortraitA1.5B1',
'paniniA2B1', 'paniniA1.5B1', 'paniniPortraitA2B1',
'paniniPortraitA1.5B1', 'mercator',
'transverseMercator')
DEFAULT_WARP_TYPE = 'spherical'
def __init__(self, warper_type=DEFAULT_WARP_TYPE):
self.warper_type = warper_type
self.scale = None
def set_scale(self, cameras):
focals = [cam.focal for cam in cameras]
self.scale = median(focals)
def warp_images(self, imgs, cameras, aspect=1):
for img, camera in zip(imgs, cameras):
yield self.warp_image(img, camera, aspect)
def warp_image(self, img, camera, aspect=1):
warper = cv.PyRotationWarper(self.warper_type, self.scale*aspect)
_, warped_image = warper.warp(img,
Warper.get_K(camera, aspect),
camera.R,
cv.INTER_LINEAR,
cv.BORDER_REFLECT)
return warped_image
def create_and_warp_masks(self, sizes, cameras, aspect=1):
for size, camera in zip(sizes, cameras):
yield self.create_and_warp_mask(size, camera, aspect)
def create_and_warp_mask(self, size, camera, aspect=1):
warper = cv.PyRotationWarper(self.warper_type, self.scale*aspect)
mask = 255 * np.ones((size[1], size[0]), np.uint8)
_, warped_mask = warper.warp(mask,
Warper.get_K(camera, aspect),
camera.R,
cv.INTER_NEAREST,
cv.BORDER_CONSTANT)
return warped_mask
def warp_rois(self, sizes, cameras, aspect=1):
roi_corners = []
roi_sizes = []
for size, camera in zip(sizes, cameras):
roi = self.warp_roi(size, camera, aspect)
roi_corners.append(roi[0:2])
roi_sizes.append(roi[2:4])
return roi_corners, roi_sizes
def warp_roi(self, size, camera, aspect=1):
warper = cv.PyRotationWarper(self.warper_type, self.scale*aspect)
K = Warper.get_K(camera, aspect)
return warper.warpRoi(size, K, camera.R)
@staticmethod
def get_K(camera, aspect=1):
K = camera.K().astype(np.float32)
""" Modification of intrinsic parameters needed if cameras were
obtained on different scale than the scale of the Images which should
be warped """
K[0, 0] *= aspect
K[0, 2] *= aspect
K[1, 1] *= aspect
K[1, 2] *= aspect
return K

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apps/opencv_stitching_tool/opencv_stitching_tool.py
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"""
Stitching sample (advanced)
===========================
Show how to use Stitcher API from python.
"""
# Python 2/3 compatibility
from __future__ import print_function
import argparse
import cv2 as cv
import numpy as np
from opencv_stitching.stitcher import Stitcher
from opencv_stitching.image_handler import ImageHandler
from opencv_stitching.feature_detector import FeatureDetector
from opencv_stitching.feature_matcher import FeatureMatcher
from opencv_stitching.subsetter import Subsetter
from opencv_stitching.camera_estimator import CameraEstimator
from opencv_stitching.camera_adjuster import CameraAdjuster
from opencv_stitching.camera_wave_corrector import WaveCorrector
from opencv_stitching.warper import Warper
from opencv_stitching.cropper import Cropper
from opencv_stitching.exposure_error_compensator import ExposureErrorCompensator # noqa
from opencv_stitching.seam_finder import SeamFinder
from opencv_stitching.blender import Blender
from opencv_stitching.timelapser import Timelapser
parser = argparse.ArgumentParser(
prog="opencv_stitching_tool.py",
description="Rotation model images stitcher"
)
parser.add_argument(
'img_names', nargs='+',
help="Files to stitch", type=str
)
parser.add_argument(
'--medium_megapix', action='store',
default=ImageHandler.DEFAULT_MEDIUM_MEGAPIX,
help="Resolution for image registration step. "
"The default is %s Mpx" % ImageHandler.DEFAULT_MEDIUM_MEGAPIX,
type=float, dest='medium_megapix'
)
parser.add_argument(
'--detector', action='store',
default=FeatureDetector.DEFAULT_DETECTOR,
help="Type of features used for images matching. "
"The default is '%s'." % FeatureDetector.DEFAULT_DETECTOR,
choices=FeatureDetector.DETECTOR_CHOICES.keys(),
type=str, dest='detector'
)
parser.add_argument(
'--nfeatures', action='store',
default=500,
help="Type of features used for images matching. "
"The default is 500.",
type=int, dest='nfeatures'
)
parser.add_argument(
'--matcher_type', action='store', default=FeatureMatcher.DEFAULT_MATCHER,
help="Matcher used for pairwise image matching. "
"The default is '%s'." % FeatureMatcher.DEFAULT_MATCHER,
choices=FeatureMatcher.MATCHER_CHOICES,
type=str, dest='matcher_type'
)
parser.add_argument(
'--range_width', action='store',
default=FeatureMatcher.DEFAULT_RANGE_WIDTH,
help="uses range_width to limit number of images to match with.",
type=int, dest='range_width'
)
parser.add_argument(
'--try_use_gpu', 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_use_gpu'
)
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(
'--confidence_threshold', action='store',
default=Subsetter.DEFAULT_CONFIDENCE_THRESHOLD,
help="Threshold for two images are from the same panorama confidence. "
"The default is '%s'." % Subsetter.DEFAULT_CONFIDENCE_THRESHOLD,
type=float, dest='confidence_threshold'
)
parser.add_argument(
'--matches_graph_dot_file', action='store',
default=Subsetter.DEFAULT_MATCHES_GRAPH_DOT_FILE,
help="Save matches graph represented in DOT language to <file_name> file.",
type=str, dest='matches_graph_dot_file'
)
parser.add_argument(
'--estimator', action='store',
default=CameraEstimator.DEFAULT_CAMERA_ESTIMATOR,
help="Type of estimator used for transformation estimation. "
"The default is '%s'." % CameraEstimator.DEFAULT_CAMERA_ESTIMATOR,
choices=CameraEstimator.CAMERA_ESTIMATOR_CHOICES.keys(),
type=str, dest='estimator'
)
parser.add_argument(
'--adjuster', action='store',
default=CameraAdjuster.DEFAULT_CAMERA_ADJUSTER,
help="Bundle adjustment cost function. "
"The default is '%s'." % CameraAdjuster.DEFAULT_CAMERA_ADJUSTER,
choices=CameraAdjuster.CAMERA_ADJUSTER_CHOICES.keys(),
type=str, dest='adjuster'
)
parser.add_argument(
'--refinement_mask', action='store',
default=CameraAdjuster.DEFAULT_REFINEMENT_MASK,
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 '%s'. "
"If bundle adjustment doesn't support estimation of selected "
"parameter then the respective flag is ignored."
"" % CameraAdjuster.DEFAULT_REFINEMENT_MASK,
type=str, dest='refinement_mask'
)
parser.add_argument(
'--wave_correct_kind', action='store',
default=WaveCorrector.DEFAULT_WAVE_CORRECTION,
help="Perform wave effect correction. "
"The default is '%s'" % WaveCorrector.DEFAULT_WAVE_CORRECTION,
choices=WaveCorrector.WAVE_CORRECT_CHOICES.keys(),
type=str, dest='wave_correct_kind'
)
parser.add_argument(
'--warper_type', action='store', default=Warper.DEFAULT_WARP_TYPE,
help="Warp surface type. The default is '%s'." % Warper.DEFAULT_WARP_TYPE,
choices=Warper.WARP_TYPE_CHOICES,
type=str, dest='warper_type'
)
parser.add_argument(
'--low_megapix', action='store', default=ImageHandler.DEFAULT_LOW_MEGAPIX,
help="Resolution for seam estimation and exposure estimation step. "
"The default is %s Mpx." % ImageHandler.DEFAULT_LOW_MEGAPIX,
type=float, dest='low_megapix'
)
parser.add_argument(
'--crop', action='store', default=Cropper.DEFAULT_CROP,
help="Crop black borders around images caused by warping using the "
"largest interior rectangle. "
"Default is '%s'." % Cropper.DEFAULT_CROP,
type=bool, dest='crop'
)
parser.add_argument(
'--compensator', action='store',
default=ExposureErrorCompensator.DEFAULT_COMPENSATOR,
help="Exposure compensation method. "
"The default is '%s'." % ExposureErrorCompensator.DEFAULT_COMPENSATOR,
choices=ExposureErrorCompensator.COMPENSATOR_CHOICES.keys(),
type=str, dest='compensator'
)
parser.add_argument(
'--nr_feeds', action='store',
default=ExposureErrorCompensator.DEFAULT_NR_FEEDS,
help="Number of exposure compensation feed.",
type=np.int32, dest='nr_feeds'
)
parser.add_argument(
'--block_size', action='store',
default=ExposureErrorCompensator.DEFAULT_BLOCK_SIZE,
help="BLock size in pixels used by the exposure compensator. "
"The default is '%s'." % ExposureErrorCompensator.DEFAULT_BLOCK_SIZE,
type=np.int32, dest='block_size'
)
parser.add_argument(
'--finder', action='store', default=SeamFinder.DEFAULT_SEAM_FINDER,
help="Seam estimation method. "
"The default is '%s'." % SeamFinder.DEFAULT_SEAM_FINDER,
choices=SeamFinder.SEAM_FINDER_CHOICES.keys(),
type=str, dest='finder'
)
parser.add_argument(
'--final_megapix', action='store',
default=ImageHandler.DEFAULT_FINAL_MEGAPIX,
help="Resolution for compositing step. Use -1 for original resolution. "
"The default is %s" % ImageHandler.DEFAULT_FINAL_MEGAPIX,
type=float, dest='final_megapix'
)
parser.add_argument(
'--blender_type', action='store', default=Blender.DEFAULT_BLENDER,
help="Blending method. The default is '%s'." % Blender.DEFAULT_BLENDER,
choices=Blender.BLENDER_CHOICES,
type=str, dest='blender_type'
)
parser.add_argument(
'--blend_strength', action='store', default=Blender.DEFAULT_BLEND_STRENGTH,
help="Blending strength from [0,100] range. "
"The default is '%s'." % Blender.DEFAULT_BLEND_STRENGTH,
type=np.int32, dest='blend_strength'
)
parser.add_argument(
'--timelapse', action='store', default=Timelapser.DEFAULT_TIMELAPSE,
help="Output warped images separately as frames of a time lapse movie, "
"with 'fixed_' prepended to input file names. "
"The default is '%s'." % Timelapser.DEFAULT_TIMELAPSE,
choices=Timelapser.TIMELAPSE_CHOICES,
type=str, dest='timelapse'
)
parser.add_argument(
'--output', action='store', default='result.jpg',
help="The default is 'result.jpg'",
type=str, dest='output'
)
__doc__ += '\n' + parser.format_help()
if __name__ == '__main__':
print(__doc__)
args = parser.parse_args()
args_dict = vars(args)
# Extract In- and Output
img_names = args_dict.pop("img_names")
img_names = [cv.samples.findFile(img_name) for img_name in img_names]
output = args_dict.pop("output")
stitcher = Stitcher(**args_dict)
panorama = stitcher.stitch(img_names)
cv.imwrite(output, panorama)
zoom_x = 600.0 / panorama.shape[1]
preview = cv.resize(panorama, dsize=None, fx=zoom_x, fy=zoom_x)
cv.imshow(output, preview)
cv.waitKey()
cv.destroyAllWindows()