opencv
diff --git a/‎apps/opencv_stitching_tool/README.md
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diff --git a/‎apps/opencv_stitching_tool/opencv_stitching/.gitignore
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diff --git a/‎apps/opencv_stitching_tool/opencv_stitching/__init__.py
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diff --git a/‎apps/opencv_stitching_tool/opencv_stitching/blender.py
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diff --git a/‎apps/opencv_stitching_tool/opencv_stitching/camera_adjuster.py
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diff --git a/‎apps/opencv_stitching_tool/opencv_stitching/camera_estimator.py
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diff --git a/‎apps/opencv_stitching_tool/opencv_stitching/camera_wave_corrector.py
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diff --git a/‎apps/opencv_stitching_tool/opencv_stitching/exposure_error_compensator.py
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diff --git a/‎apps/opencv_stitching_tool/opencv_stitching/feature_detector.py
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diff --git a/‎apps/opencv_stitching_tool/opencv_stitching/feature_matcher.py
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+## In-Depth Stitching Tool for experiments and research
+
+Visit [opencv_stitching_tutorial](https://github.com/lukasalexanderweber/opencv_stitching_tutorial) for a detailed Tutorial
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+# python binary files
+*.pyc
+__pycache__
+.pylint*
@@ -0,0 +1,48 @@
+import cv2 as cv
+import numpy as np
+
+
+class Blender:
+
+    BLENDER_CHOICES = ('multiband', 'feather', 'no',)
+    DEFAULT_BLENDER = 'multiband'
+    DEFAULT_BLEND_STRENGTH = 5
+
+    def __init__(self, blender_type=DEFAULT_BLENDER,
+                 blend_strength=DEFAULT_BLEND_STRENGTH):
+        self.blender_type = blender_type
+        self.blend_strength = blend_strength
+        self.blender = None
+
+    def prepare(self, corners, sizes):
+        dst_sz = cv.detail.resultRoi(corners=corners, sizes=sizes)
+        blend_width = (np.sqrt(dst_sz[2] * dst_sz[3]) *
+                       self.blend_strength / 100)
+
+        if self.blender_type == 'no' or blend_width < 1:
+            self.blender = cv.detail.Blender_createDefault(
+                cv.detail.Blender_NO
+                )
+
+        elif self.blender_type == "multiband":
+            self.blender = cv.detail_MultiBandBlender()
+            self.blender.setNumBands((np.log(blend_width) /
+                                      np.log(2.) - 1.).astype(np.int))
+
+        elif self.blender_type == "feather":
+            self.blender = cv.detail_FeatherBlender()
+            self.blender.setSharpness(1. / blend_width)
+
+        self.blender.prepare(dst_sz)
+
+    def feed(self, img, mask, corner):
+        """https://docs.opencv.org/master/d6/d4a/classcv_1_1detail_1_1Blender.html#a64837308bcf4e414a6219beff6cbe37a"""  # noqa
+        self.blender.feed(cv.UMat(img.astype(np.int16)), mask, corner)
+
+    def blend(self):
+        """https://docs.opencv.org/master/d6/d4a/classcv_1_1detail_1_1Blender.html#aa0a91ce0d6046d3a63e0123cbb1b5c00"""  # noqa
+        result = None
+        result_mask = None
+        result, result_mask = self.blender.blend(result, result_mask)
+        result = cv.convertScaleAbs(result)
+        return result
@@ -0,0 +1,49 @@
+from collections import OrderedDict
+import cv2 as cv
+import numpy as np
+
+from .stitching_error import StitchingError
+
+
+class CameraAdjuster:
+    """https://docs.opencv.org/master/d5/d56/classcv_1_1detail_1_1BundleAdjusterBase.html"""  # noqa
+
+    CAMERA_ADJUSTER_CHOICES = OrderedDict()
+    CAMERA_ADJUSTER_CHOICES['ray'] = cv.detail_BundleAdjusterRay
+    CAMERA_ADJUSTER_CHOICES['reproj'] = cv.detail_BundleAdjusterReproj
+    CAMERA_ADJUSTER_CHOICES['affine'] = cv.detail_BundleAdjusterAffinePartial
+    CAMERA_ADJUSTER_CHOICES['no'] = cv.detail_NoBundleAdjuster
+
+    DEFAULT_CAMERA_ADJUSTER = list(CAMERA_ADJUSTER_CHOICES.keys())[0]
+    DEFAULT_REFINEMENT_MASK = "xxxxx"
+
+    def __init__(self,
+                 adjuster=DEFAULT_CAMERA_ADJUSTER,
+                 refinement_mask=DEFAULT_REFINEMENT_MASK):
+
+        self.adjuster = CameraAdjuster.CAMERA_ADJUSTER_CHOICES[adjuster]()
+        self.set_refinement_mask(refinement_mask)
+        self.adjuster.setConfThresh(1)
+
+    def set_refinement_mask(self, refinement_mask):
+        mask_matrix = np.zeros((3, 3), np.uint8)
+        if refinement_mask[0] == 'x':
+            mask_matrix[0, 0] = 1
+        if refinement_mask[1] == 'x':
+            mask_matrix[0, 1] = 1
+        if refinement_mask[2] == 'x':
+            mask_matrix[0, 2] = 1
+        if refinement_mask[3] == 'x':
+            mask_matrix[1, 1] = 1
+        if refinement_mask[4] == 'x':
+            mask_matrix[1, 2] = 1
+        self.adjuster.setRefinementMask(mask_matrix)
+
+    def adjust(self, features, pairwise_matches, estimated_cameras):
+        b, cameras = self.adjuster.apply(features,
+                                         pairwise_matches,
+                                         estimated_cameras)
+        if not b:
+            raise StitchingError("Camera parameters adjusting failed.")
+
+        return cameras
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+from collections import OrderedDict
+import cv2 as cv
+import numpy as np
+
+from .stitching_error import StitchingError
+
+
+class CameraEstimator:
+
+    CAMERA_ESTIMATOR_CHOICES = OrderedDict()
+    CAMERA_ESTIMATOR_CHOICES['homography'] = cv.detail_HomographyBasedEstimator
+    CAMERA_ESTIMATOR_CHOICES['affine'] = cv.detail_AffineBasedEstimator
+
+    DEFAULT_CAMERA_ESTIMATOR = list(CAMERA_ESTIMATOR_CHOICES.keys())[0]
+
+    def __init__(self, estimator=DEFAULT_CAMERA_ESTIMATOR, **kwargs):
+        self.estimator = CameraEstimator.CAMERA_ESTIMATOR_CHOICES[estimator](
+            **kwargs
+            )
+
+    def estimate(self, features, pairwise_matches):
+        b, cameras = self.estimator.apply(features, pairwise_matches, None)
+        if not b:
+            raise StitchingError("Homography estimation failed.")
+        for cam in cameras:
+            cam.R = cam.R.astype(np.float32)
+        return cameras
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+from collections import OrderedDict
+import cv2 as cv
+import numpy as np
+
+
+class WaveCorrector:
+    """https://docs.opencv.org/master/d7/d74/group__stitching__rotation.html#ga83b24d4c3e93584986a56d9e43b9cf7f"""  # noqa
+    WAVE_CORRECT_CHOICES = OrderedDict()
+    WAVE_CORRECT_CHOICES['horiz'] = cv.detail.WAVE_CORRECT_HORIZ
+    WAVE_CORRECT_CHOICES['vert'] = cv.detail.WAVE_CORRECT_VERT
+    WAVE_CORRECT_CHOICES['auto'] = cv.detail.WAVE_CORRECT_AUTO
+    WAVE_CORRECT_CHOICES['no'] = None
+
+    DEFAULT_WAVE_CORRECTION = list(WAVE_CORRECT_CHOICES.keys())[0]
+
+    def __init__(self, wave_correct_kind=DEFAULT_WAVE_CORRECTION):
+        self.wave_correct_kind = WaveCorrector.WAVE_CORRECT_CHOICES[
+            wave_correct_kind
+            ]
+
+    def correct(self, cameras):
+        if self.wave_correct_kind is not None:
+            rmats = [np.copy(cam.R) for cam in cameras]
+            rmats = cv.detail.waveCorrect(rmats, self.wave_correct_kind)
+            for idx, cam in enumerate(cameras):
+                cam.R = rmats[idx]
+            return cameras
+        return cameras
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+from collections import OrderedDict
+import cv2 as cv
+
+
+class ExposureErrorCompensator:
+
+    COMPENSATOR_CHOICES = OrderedDict()
+    COMPENSATOR_CHOICES['gain_blocks'] = cv.detail.ExposureCompensator_GAIN_BLOCKS  # noqa
+    COMPENSATOR_CHOICES['gain'] = cv.detail.ExposureCompensator_GAIN
+    COMPENSATOR_CHOICES['channel'] = cv.detail.ExposureCompensator_CHANNELS
+    COMPENSATOR_CHOICES['channel_blocks'] = cv.detail.ExposureCompensator_CHANNELS_BLOCKS  # noqa
+    COMPENSATOR_CHOICES['no'] = cv.detail.ExposureCompensator_NO
+
+    DEFAULT_COMPENSATOR = list(COMPENSATOR_CHOICES.keys())[0]
+    DEFAULT_NR_FEEDS = 1
+    DEFAULT_BLOCK_SIZE = 32
+
+    def __init__(self,
+                 compensator=DEFAULT_COMPENSATOR,
+                 nr_feeds=DEFAULT_NR_FEEDS,
+                 block_size=DEFAULT_BLOCK_SIZE):
+
+        if compensator == 'channel':
+            self.compensator = cv.detail_ChannelsCompensator(nr_feeds)
+        elif compensator == 'channel_blocks':
+            self.compensator = cv.detail_BlocksChannelsCompensator(
+                block_size, block_size, nr_feeds
+                )
+        else:
+            self.compensator = cv.detail.ExposureCompensator_createDefault(
+                ExposureErrorCompensator.COMPENSATOR_CHOICES[compensator]
+                )
+
+    def feed(self, *args):
+        """https://docs.opencv.org/master/d2/d37/classcv_1_1detail_1_1ExposureCompensator.html#ae6b0cc69a7bc53818ddea53eddb6bdba"""  # noqa
+        self.compensator.feed(*args)
+
+    def apply(self, *args):
+        """https://docs.opencv.org/master/d2/d37/classcv_1_1detail_1_1ExposureCompensator.html#a473eaf1e585804c08d77c91e004f93aa"""  # noqa
+        return self.compensator.apply(*args)
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+from collections import OrderedDict
+import cv2 as cv
+
+
+class FeatureDetector:
+    DETECTOR_CHOICES = OrderedDict()
+    try:
+        cv.xfeatures2d_SURF.create()  # check if the function can be called
+        DETECTOR_CHOICES['surf'] = cv.xfeatures2d_SURF.create
+    except (AttributeError, cv.error):
+        print("SURF not available")
+
+    # if SURF not available, ORB is default
+    DETECTOR_CHOICES['orb'] = cv.ORB.create
+
+    try:
+        DETECTOR_CHOICES['sift'] = cv.SIFT_create
+    except AttributeError:
+        print("SIFT not available")
+
+    try:
+        DETECTOR_CHOICES['brisk'] = cv.BRISK_create
+    except AttributeError:
+        print("BRISK not available")
+
+    try:
+        DETECTOR_CHOICES['akaze'] = cv.AKAZE_create
+    except AttributeError:
+        print("AKAZE not available")
+
+    DEFAULT_DETECTOR = list(DETECTOR_CHOICES.keys())[0]
+
+    def __init__(self, detector=DEFAULT_DETECTOR, **kwargs):
+        self.detector = FeatureDetector.DETECTOR_CHOICES[detector](**kwargs)
+
+    def detect_features(self, img, *args, **kwargs):
+        return cv.detail.computeImageFeatures2(self.detector, img,
+                                               *args, **kwargs)
+
+    @staticmethod
+    def draw_keypoints(img, features, **kwargs):
+        kwargs.setdefault('color', (0, 255, 0))
+        keypoints = features.getKeypoints()
+        return cv.drawKeypoints(img, keypoints, None, **kwargs)
@@ -0,0 +1,98 @@
+import math
+import cv2 as cv
+import numpy as np
+
+
+class FeatureMatcher:
+
+    MATCHER_CHOICES = ('homography', 'affine')
+    DEFAULT_MATCHER = 'homography'
+    DEFAULT_RANGE_WIDTH = -1
+
+    def __init__(self,
+                 matcher_type=DEFAULT_MATCHER,
+                 range_width=DEFAULT_RANGE_WIDTH,
+                 **kwargs):
+
+        if matcher_type == "affine":
+            """https://docs.opencv.org/master/d3/dda/classcv_1_1detail_1_1AffineBestOf2NearestMatcher.html"""  # noqa
+            self.matcher = cv.detail_AffineBestOf2NearestMatcher(**kwargs)
+        elif range_width == -1:
+            """https://docs.opencv.org/master/d4/d26/classcv_1_1detail_1_1BestOf2NearestMatcher.html"""  # noqa
+            self.matcher = cv.detail.BestOf2NearestMatcher_create(**kwargs)
+        else:
+            """https://docs.opencv.org/master/d8/d72/classcv_1_1detail_1_1BestOf2NearestRangeMatcher.html"""  # noqa
+            self.matcher = cv.detail.BestOf2NearestRangeMatcher_create(
+                range_width, **kwargs
+                )
+
+    def match_features(self, features, *args, **kwargs):
+        pairwise_matches = self.matcher.apply2(features, *args, **kwargs)
+        self.matcher.collectGarbage()
+        return pairwise_matches
+
+    @staticmethod
+    def draw_matches_matrix(imgs, features, matches, conf_thresh=1,
+                            inliers=False, **kwargs):
+        matches_matrix = FeatureMatcher.get_matches_matrix(matches)
+        for idx1, idx2 in FeatureMatcher.get_all_img_combinations(len(imgs)):
+            match = matches_matrix[idx1, idx2]
+            if match.confidence < conf_thresh:
+                continue
+            if inliers:
+                kwargs['matchesMask'] = match.getInliers()
+            yield idx1, idx2, FeatureMatcher.draw_matches(
+                imgs[idx1], features[idx1],
+                imgs[idx2], features[idx2],
+                match,
+                **kwargs
+                )
+
+    @staticmethod
+    def draw_matches(img1, features1, img2, features2, match1to2, **kwargs):
+        kwargs.setdefault('flags', cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
+
+        keypoints1 = features1.getKeypoints()
+        keypoints2 = features2.getKeypoints()
+        matches = match1to2.getMatches()
+
+        return cv.drawMatches(
+            img1, keypoints1, img2, keypoints2, matches, None, **kwargs
+            )
+
+    @staticmethod
+    def get_matches_matrix(pairwise_matches):
+        return FeatureMatcher.array_in_sqare_matrix(pairwise_matches)
+
+    @staticmethod
+    def get_confidence_matrix(pairwise_matches):
+        matches_matrix = FeatureMatcher.get_matches_matrix(pairwise_matches)
+        match_confs = [[m.confidence for m in row] for row in matches_matrix]
+        match_conf_matrix = np.array(match_confs)
+        return match_conf_matrix
+
+    @staticmethod
+    def array_in_sqare_matrix(array):
+        matrix_dimension = int(math.sqrt(len(array)))
+        rows = []
+        for i in range(0, len(array), matrix_dimension):
+            rows.append(array[i:i+matrix_dimension])
+        return np.array(rows)
+
+    def get_all_img_combinations(number_imgs):
+        ii, jj = np.triu_indices(number_imgs, k=1)
+        for i, j in zip(ii, jj):
+            yield i, j
+
+    @staticmethod
+    def get_match_conf(match_conf, feature_detector_type):
+        if match_conf is None:
+            match_conf = \
+                FeatureMatcher.get_default_match_conf(feature_detector_type)
+        return match_conf
+
+    @staticmethod
+    def get_default_match_conf(feature_detector_type):
+        if feature_detector_type == 'orb':
+            return 0.3
+        return 0.65
Original file line number	Diff line number	Diff line change
`@@ -0,0 +1,3 @@`
	`1`	`+## In-Depth Stitching Tool for experiments and research`
	`2`	`+`
	`3`	`+Visit [opencv_stitching_tutorial](https://github.com/lukasalexanderweber/opencv_stitching_tutorial) for a detailed Tutorial`