mirror of https://github.com/opencv/opencv.git
Merge pull request #22005 from lukasalexanderweber:delete_stitching_tool
Move stitching package and tool to a dedicated repository * deleted moved files * Update README.mdpull/22048/head
Lukas-Alexander Weber
3 years ago
committed by
GitHub
parent
08c270f65a
commit
8ca394efaf
30 changed files with 2 additions and 2637 deletions
@ -1,3 +1,3 @@ |
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## In-Depth Stitching Tool for experiments and research |
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## MOVED: opencv_stitching_tool |
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|
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Visit [opencv_stitching_tutorial](https://github.com/lukasalexanderweber/opencv_stitching_tutorial) for a detailed Tutorial |
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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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@ -1,4 +0,0 @@ |
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# python binary files |
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*.pyc |
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__pycache__ |
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.pylint* |
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@ -1,56 +0,0 @@ |
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import cv2 as cv |
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import numpy as np |
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|
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|
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class Blender: |
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|
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BLENDER_CHOICES = ('multiband', 'feather', 'no',) |
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DEFAULT_BLENDER = 'multiband' |
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DEFAULT_BLEND_STRENGTH = 5 |
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|
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def __init__(self, blender_type=DEFAULT_BLENDER, |
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blend_strength=DEFAULT_BLEND_STRENGTH): |
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self.blender_type = blender_type |
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self.blend_strength = blend_strength |
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self.blender = None |
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|
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def prepare(self, corners, sizes): |
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dst_sz = cv.detail.resultRoi(corners=corners, sizes=sizes) |
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blend_width = (np.sqrt(dst_sz[2] * dst_sz[3]) * |
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self.blend_strength / 100) |
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|
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if self.blender_type == 'no' or blend_width < 1: |
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self.blender = cv.detail.Blender_createDefault( |
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cv.detail.Blender_NO |
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) |
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|
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elif self.blender_type == "multiband": |
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self.blender = cv.detail_MultiBandBlender() |
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self.blender.setNumBands(int((np.log(blend_width) / |
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np.log(2.) - 1.))) |
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|
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elif self.blender_type == "feather": |
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self.blender = cv.detail_FeatherBlender() |
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self.blender.setSharpness(1. / blend_width) |
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|
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self.blender.prepare(dst_sz) |
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|
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def feed(self, img, mask, corner): |
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"""https://docs.opencv.org/4.x/d6/d4a/classcv_1_1detail_1_1Blender.html#a64837308bcf4e414a6219beff6cbe37a""" # noqa |
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self.blender.feed(cv.UMat(img.astype(np.int16)), mask, corner) |
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|
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def blend(self): |
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"""https://docs.opencv.org/4.x/d6/d4a/classcv_1_1detail_1_1Blender.html#aa0a91ce0d6046d3a63e0123cbb1b5c00""" # noqa |
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result = None |
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result_mask = None |
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result, result_mask = self.blender.blend(result, result_mask) |
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result = cv.convertScaleAbs(result) |
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return result, result_mask |
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|
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@classmethod |
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def create_panorama(cls, imgs, masks, corners, sizes): |
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blender = cls("no") |
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blender.prepare(corners, sizes) |
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for img, mask, corner in zip(imgs, masks, corners): |
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blender.feed(img, mask, corner) |
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return blender.blend() |
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@ -1,49 +0,0 @@ |
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from collections import OrderedDict |
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import cv2 as cv |
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import numpy as np |
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from .stitching_error import StitchingError |
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class CameraAdjuster: |
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"""https://docs.opencv.org/4.x/d5/d56/classcv_1_1detail_1_1BundleAdjusterBase.html""" # noqa |
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CAMERA_ADJUSTER_CHOICES = OrderedDict() |
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CAMERA_ADJUSTER_CHOICES['ray'] = cv.detail_BundleAdjusterRay |
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CAMERA_ADJUSTER_CHOICES['reproj'] = cv.detail_BundleAdjusterReproj |
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CAMERA_ADJUSTER_CHOICES['affine'] = cv.detail_BundleAdjusterAffinePartial |
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CAMERA_ADJUSTER_CHOICES['no'] = cv.detail_NoBundleAdjuster |
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|
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DEFAULT_CAMERA_ADJUSTER = list(CAMERA_ADJUSTER_CHOICES.keys())[0] |
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DEFAULT_REFINEMENT_MASK = "xxxxx" |
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def __init__(self, |
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adjuster=DEFAULT_CAMERA_ADJUSTER, |
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refinement_mask=DEFAULT_REFINEMENT_MASK): |
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self.adjuster = CameraAdjuster.CAMERA_ADJUSTER_CHOICES[adjuster]() |
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self.set_refinement_mask(refinement_mask) |
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self.adjuster.setConfThresh(1) |
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def set_refinement_mask(self, refinement_mask): |
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mask_matrix = np.zeros((3, 3), np.uint8) |
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if refinement_mask[0] == 'x': |
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mask_matrix[0, 0] = 1 |
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if refinement_mask[1] == 'x': |
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mask_matrix[0, 1] = 1 |
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if refinement_mask[2] == 'x': |
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mask_matrix[0, 2] = 1 |
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if refinement_mask[3] == 'x': |
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mask_matrix[1, 1] = 1 |
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if refinement_mask[4] == 'x': |
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mask_matrix[1, 2] = 1 |
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self.adjuster.setRefinementMask(mask_matrix) |
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def adjust(self, features, pairwise_matches, estimated_cameras): |
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b, cameras = self.adjuster.apply(features, |
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pairwise_matches, |
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estimated_cameras) |
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if not b: |
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raise StitchingError("Camera parameters adjusting failed.") |
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return cameras |
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@ -1,27 +0,0 @@ |
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from collections import OrderedDict |
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import cv2 as cv |
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import numpy as np |
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from .stitching_error import StitchingError |
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class CameraEstimator: |
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CAMERA_ESTIMATOR_CHOICES = OrderedDict() |
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CAMERA_ESTIMATOR_CHOICES['homography'] = cv.detail_HomographyBasedEstimator |
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CAMERA_ESTIMATOR_CHOICES['affine'] = cv.detail_AffineBasedEstimator |
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DEFAULT_CAMERA_ESTIMATOR = list(CAMERA_ESTIMATOR_CHOICES.keys())[0] |
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def __init__(self, estimator=DEFAULT_CAMERA_ESTIMATOR, **kwargs): |
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self.estimator = CameraEstimator.CAMERA_ESTIMATOR_CHOICES[estimator]( |
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**kwargs |
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) |
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def estimate(self, features, pairwise_matches): |
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b, cameras = self.estimator.apply(features, pairwise_matches, None) |
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if not b: |
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raise StitchingError("Homography estimation failed.") |
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for cam in cameras: |
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cam.R = cam.R.astype(np.float32) |
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return cameras |
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@ -1,28 +0,0 @@ |
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from collections import OrderedDict |
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import cv2 as cv |
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import numpy as np |
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class WaveCorrector: |
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"""https://docs.opencv.org/4.x/d7/d74/group__stitching__rotation.html#ga83b24d4c3e93584986a56d9e43b9cf7f""" # noqa |
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WAVE_CORRECT_CHOICES = OrderedDict() |
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WAVE_CORRECT_CHOICES['horiz'] = cv.detail.WAVE_CORRECT_HORIZ |
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WAVE_CORRECT_CHOICES['vert'] = cv.detail.WAVE_CORRECT_VERT |
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WAVE_CORRECT_CHOICES['auto'] = cv.detail.WAVE_CORRECT_AUTO |
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WAVE_CORRECT_CHOICES['no'] = None |
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DEFAULT_WAVE_CORRECTION = list(WAVE_CORRECT_CHOICES.keys())[0] |
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def __init__(self, wave_correct_kind=DEFAULT_WAVE_CORRECTION): |
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self.wave_correct_kind = WaveCorrector.WAVE_CORRECT_CHOICES[ |
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wave_correct_kind |
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] |
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def correct(self, cameras): |
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if self.wave_correct_kind is not None: |
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rmats = [np.copy(cam.R) for cam in cameras] |
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rmats = cv.detail.waveCorrect(rmats, self.wave_correct_kind) |
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for idx, cam in enumerate(cameras): |
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cam.R = rmats[idx] |
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return cameras |
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return cameras |
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@ -1,149 +0,0 @@ |
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from collections import namedtuple |
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import cv2 as cv |
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from .blender import Blender |
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from .stitching_error import StitchingError |
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class Rectangle(namedtuple('Rectangle', 'x y width height')): |
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__slots__ = () |
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@property |
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def area(self): |
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return self.width * self.height |
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@property |
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def corner(self): |
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return (self.x, self.y) |
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@property |
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def size(self): |
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return (self.width, self.height) |
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@property |
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def x2(self): |
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return self.x + self.width |
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@property |
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def y2(self): |
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return self.y + self.height |
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def times(self, x): |
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return Rectangle(*(int(round(i*x)) for i in self)) |
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def draw_on(self, img, color=(0, 0, 255), size=1): |
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if len(img.shape) == 2: |
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img = cv.cvtColor(img, cv.COLOR_GRAY2RGB) |
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start_point = (self.x, self.y) |
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end_point = (self.x2-1, self.y2-1) |
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cv.rectangle(img, start_point, end_point, color, size) |
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return img |
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class Cropper: |
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DEFAULT_CROP = False |
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def __init__(self, crop=DEFAULT_CROP): |
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self.do_crop = crop |
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self.overlapping_rectangles = [] |
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self.cropping_rectangles = [] |
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def prepare(self, imgs, masks, corners, sizes): |
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if self.do_crop: |
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mask = self.estimate_panorama_mask(imgs, masks, corners, sizes) |
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self.compile_numba_functionality() |
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lir = self.estimate_largest_interior_rectangle(mask) |
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corners = self.get_zero_center_corners(corners) |
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rectangles = self.get_rectangles(corners, sizes) |
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self.overlapping_rectangles = self.get_overlaps( |
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rectangles, lir) |
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self.intersection_rectangles = self.get_intersections( |
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rectangles, self.overlapping_rectangles) |
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def crop_images(self, imgs, aspect=1): |
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for idx, img in enumerate(imgs): |
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yield self.crop_img(img, idx, aspect) |
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def crop_img(self, img, idx, aspect=1): |
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if self.do_crop: |
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intersection_rect = self.intersection_rectangles[idx] |
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scaled_intersection_rect = intersection_rect.times(aspect) |
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cropped_img = self.crop_rectangle(img, scaled_intersection_rect) |
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return cropped_img |
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return img |
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def crop_rois(self, corners, sizes, aspect=1): |
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if self.do_crop: |
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scaled_overlaps = \ |
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[r.times(aspect) for r in self.overlapping_rectangles] |
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cropped_corners = [r.corner for r in scaled_overlaps] |
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cropped_corners = self.get_zero_center_corners(cropped_corners) |
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cropped_sizes = [r.size for r in scaled_overlaps] |
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return cropped_corners, cropped_sizes |
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return corners, sizes |
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@staticmethod |
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def estimate_panorama_mask(imgs, masks, corners, sizes): |
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_, mask = Blender.create_panorama(imgs, masks, corners, sizes) |
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return mask |
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def compile_numba_functionality(self): |
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# numba functionality is only imported if cropping |
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# is explicitely desired |
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try: |
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import numba |
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except ModuleNotFoundError: |
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raise StitchingError("Numba is needed for cropping but not installed") |
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from .largest_interior_rectangle import largest_interior_rectangle |
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self.largest_interior_rectangle = largest_interior_rectangle |
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def estimate_largest_interior_rectangle(self, mask): |
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lir = self.largest_interior_rectangle(mask) |
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lir = Rectangle(*lir) |
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return lir |
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@staticmethod |
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def get_zero_center_corners(corners): |
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min_corner_x = min([corner[0] for corner in corners]) |
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min_corner_y = min([corner[1] for corner in corners]) |
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return [(x - min_corner_x, y - min_corner_y) for x, y in corners] |
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@staticmethod |
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def get_rectangles(corners, sizes): |
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rectangles = [] |
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for corner, size in zip(corners, sizes): |
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rectangle = Rectangle(*corner, *size) |
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rectangles.append(rectangle) |
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return rectangles |
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@staticmethod |
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def get_overlaps(rectangles, lir): |
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return [Cropper.get_overlap(r, lir) for r in rectangles] |
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@staticmethod |
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def get_overlap(rectangle1, rectangle2): |
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x1 = max(rectangle1.x, rectangle2.x) |
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y1 = max(rectangle1.y, rectangle2.y) |
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x2 = min(rectangle1.x2, rectangle2.x2) |
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y2 = min(rectangle1.y2, rectangle2.y2) |
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if x2 < x1 or y2 < y1: |
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raise StitchingError("Rectangles do not overlap!") |
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return Rectangle(x1, y1, x2-x1, y2-y1) |
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@staticmethod |
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def get_intersections(rectangles, overlapping_rectangles): |
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return [Cropper.get_intersection(r, overlap_r) for r, overlap_r |
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in zip(rectangles, overlapping_rectangles)] |
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@staticmethod |
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def get_intersection(rectangle, overlapping_rectangle): |
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x = abs(overlapping_rectangle.x - rectangle.x) |
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y = abs(overlapping_rectangle.y - rectangle.y) |
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width = overlapping_rectangle.width |
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height = overlapping_rectangle.height |
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return Rectangle(x, y, width, height) |
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@staticmethod |
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def crop_rectangle(img, rectangle): |
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return img[rectangle.y:rectangle.y2, rectangle.x:rectangle.x2] |
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@ -1,40 +0,0 @@ |
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from collections import OrderedDict |
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import cv2 as cv |
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class ExposureErrorCompensator: |
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COMPENSATOR_CHOICES = OrderedDict() |
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COMPENSATOR_CHOICES['gain_blocks'] = cv.detail.ExposureCompensator_GAIN_BLOCKS # noqa |
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COMPENSATOR_CHOICES['gain'] = cv.detail.ExposureCompensator_GAIN |
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COMPENSATOR_CHOICES['channel'] = cv.detail.ExposureCompensator_CHANNELS |
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COMPENSATOR_CHOICES['channel_blocks'] = cv.detail.ExposureCompensator_CHANNELS_BLOCKS # noqa |
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COMPENSATOR_CHOICES['no'] = cv.detail.ExposureCompensator_NO |
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DEFAULT_COMPENSATOR = list(COMPENSATOR_CHOICES.keys())[0] |
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DEFAULT_NR_FEEDS = 1 |
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DEFAULT_BLOCK_SIZE = 32 |
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def __init__(self, |
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compensator=DEFAULT_COMPENSATOR, |
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nr_feeds=DEFAULT_NR_FEEDS, |
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block_size=DEFAULT_BLOCK_SIZE): |
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if compensator == 'channel': |
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self.compensator = cv.detail_ChannelsCompensator(nr_feeds) |
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elif compensator == 'channel_blocks': |
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self.compensator = cv.detail_BlocksChannelsCompensator( |
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block_size, block_size, nr_feeds |
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) |
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else: |
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self.compensator = cv.detail.ExposureCompensator_createDefault( |
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ExposureErrorCompensator.COMPENSATOR_CHOICES[compensator] |
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) |
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|
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def feed(self, *args): |
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"""https://docs.opencv.org/4.x/d2/d37/classcv_1_1detail_1_1ExposureCompensator.html#ae6b0cc69a7bc53818ddea53eddb6bdba""" # noqa |
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self.compensator.feed(*args) |
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|
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def apply(self, *args): |
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"""https://docs.opencv.org/4.x/d2/d37/classcv_1_1detail_1_1ExposureCompensator.html#a473eaf1e585804c08d77c91e004f93aa""" # noqa |
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return self.compensator.apply(*args) |
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@ -1,44 +0,0 @@ |
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from collections import OrderedDict |
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import cv2 as cv |
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class FeatureDetector: |
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DETECTOR_CHOICES = OrderedDict() |
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try: |
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cv.xfeatures2d_SURF.create() # check if the function can be called |
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DETECTOR_CHOICES['surf'] = cv.xfeatures2d_SURF.create |
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except (AttributeError, cv.error): |
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print("SURF not available") |
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# if SURF not available, ORB is default |
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DETECTOR_CHOICES['orb'] = cv.ORB.create |
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try: |
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DETECTOR_CHOICES['sift'] = cv.SIFT_create |
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except AttributeError: |
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print("SIFT not available") |
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try: |
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DETECTOR_CHOICES['brisk'] = cv.BRISK_create |
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except AttributeError: |
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print("BRISK not available") |
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try: |
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DETECTOR_CHOICES['akaze'] = cv.AKAZE_create |
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except AttributeError: |
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print("AKAZE not available") |
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DEFAULT_DETECTOR = list(DETECTOR_CHOICES.keys())[0] |
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def __init__(self, detector=DEFAULT_DETECTOR, **kwargs): |
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self.detector = FeatureDetector.DETECTOR_CHOICES[detector](**kwargs) |
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def detect_features(self, img, *args, **kwargs): |
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return cv.detail.computeImageFeatures2(self.detector, img, |
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*args, **kwargs) |
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|
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@staticmethod |
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def draw_keypoints(img, features, **kwargs): |
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kwargs.setdefault('color', (0, 255, 0)) |
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keypoints = features.getKeypoints() |
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return cv.drawKeypoints(img, keypoints, None, **kwargs) |
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@ -1,98 +0,0 @@ |
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import math |
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import cv2 as cv |
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import numpy as np |
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|
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|
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class FeatureMatcher: |
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|
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MATCHER_CHOICES = ('homography', 'affine') |
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DEFAULT_MATCHER = 'homography' |
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DEFAULT_RANGE_WIDTH = -1 |
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|
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def __init__(self, |
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matcher_type=DEFAULT_MATCHER, |
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range_width=DEFAULT_RANGE_WIDTH, |
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**kwargs): |
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|
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if matcher_type == "affine": |
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"""https://docs.opencv.org/4.x/d3/dda/classcv_1_1detail_1_1AffineBestOf2NearestMatcher.html""" # noqa |
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self.matcher = cv.detail_AffineBestOf2NearestMatcher(**kwargs) |
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elif range_width == -1: |
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"""https://docs.opencv.org/4.x/d4/d26/classcv_1_1detail_1_1BestOf2NearestMatcher.html""" # noqa |
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self.matcher = cv.detail_BestOf2NearestMatcher(**kwargs) |
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else: |
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"""https://docs.opencv.org/4.x/d8/d72/classcv_1_1detail_1_1BestOf2NearestRangeMatcher.html""" # noqa |
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self.matcher = cv.detail_BestOf2NearestRangeMatcher( |
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range_width, **kwargs |
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) |
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|
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def match_features(self, features, *args, **kwargs): |
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pairwise_matches = self.matcher.apply2(features, *args, **kwargs) |
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self.matcher.collectGarbage() |
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return pairwise_matches |
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|
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@staticmethod |
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def draw_matches_matrix(imgs, features, matches, conf_thresh=1, |
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inliers=False, **kwargs): |
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matches_matrix = FeatureMatcher.get_matches_matrix(matches) |
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for idx1, idx2 in FeatureMatcher.get_all_img_combinations(len(imgs)): |
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match = matches_matrix[idx1, idx2] |
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if match.confidence < conf_thresh: |
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continue |
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if inliers: |
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kwargs['matchesMask'] = match.getInliers() |
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yield idx1, idx2, FeatureMatcher.draw_matches( |
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imgs[idx1], features[idx1], |
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imgs[idx2], features[idx2], |
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match, |
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**kwargs |
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) |
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|
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@staticmethod |
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def draw_matches(img1, features1, img2, features2, match1to2, **kwargs): |
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kwargs.setdefault('flags', cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS) |
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|
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keypoints1 = features1.getKeypoints() |
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keypoints2 = features2.getKeypoints() |
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matches = match1to2.getMatches() |
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|
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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 |
||||
@ -1,105 +0,0 @@ |
||||
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] |
||||
@ -1,303 +0,0 @@ |
||||
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 |
||||
@ -1,38 +0,0 @@ |
||||
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) |
||||
@ -1,126 +0,0 @@ |
||||
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 |
||||
@ -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) |
||||
@ -1,2 +0,0 @@ |
||||
class StitchingError(Exception): |
||||
pass |
||||
@ -1,94 +0,0 @@ |
||||
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())) |
||||
@ -1,13 +0,0 @@ |
||||
# 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 |
||||
@ -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 |
||||
@ -1,406 +0,0 @@ |
||||
""" |
||||
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) |
||||
@ -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() |
||||
@ -1,47 +0,0 @@ |
||||
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() |
||||
@ -1,58 +0,0 @@ |
||||
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() |
||||
@ -1,65 +0,0 @@ |
||||
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() |
||||
@ -1,100 +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.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() |
||||
@ -1,108 +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 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() |
||||
@ -1,50 +0,0 @@ |
||||
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) |
||||
@ -1,79 +0,0 @@ |
||||
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 |
||||
@ -1,238 +0,0 @@ |
||||
""" |
||||
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() |
||||
Loading…
Reference in new issue