run pre-commit hooks against all files (#12)

Author: lukasalexanderweber

.pre-commit-config.yaml

@@ -1,3 +1,8 @@
+# Apply to all files without commiting:
+#   pre-commit run --all-files
+# Update this file:
+#   pre-commit autoupdate
+
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.2.0
@@ -6,15 +11,7 @@ repos:
       - id: trailing-whitespace
       - id: end-of-file-fixer
       - id: mixed-line-ending
-      - id: check-symlinks
       - id: check-added-large-files
-      - id: check-json
-      - id: check-symlinks
-      - id: check-added-large-files
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: "v2.6.2"
-    hooks:
-      - id: prettier
   - repo: https://github.com/psf/black
     rev: 22.3.0
     hooks:
@@ -27,12 +24,13 @@ repos:
     rev: 3.9.2
     hooks:
       - id: flake8
-  - repo: https://github.com/detailyang/pre-commit-shell
-    rev: 1.0.5
-    hooks:
-      - id: shell-lint
-        args: [--format=json]
+        args:
+          - "--max-line-length=88"
+          - "--ignore=E203"
   - repo: https://github.com/timothycrosley/isort
     rev: 5.10.1
     hooks:
       - id: isort
+        args:
+          - "--profile=black"
+          - "--filter-files"

README.md

@@ -22,15 +22,18 @@ pip install stitching
 
 ## Usage
 
-The command line interface ([cli](https://github.com/lukasalexanderweber/stitching/tree/main/stitching/cli/stitch.py)) is available after installation
+The command line interface
+([cli](https://github.com/lukasalexanderweber/stitching/tree/main/stitching/cli/stitch.py))
+is available after installation
 
 `stitch -h`               show the help
 
 `stitch *`                stitches all files in the actual dir
 
-`stitch img_dir/IMG*.jpg` stitches all files in the img_dir directory starting with "IMG" and ending with ".jpg"
+`stitch img_dir/IMG*.jpg` stitches all files in the img_dir directory
+starting with "IMG" and ending with ".jpg"
 
-`stitch img1.jpg img2.jpg img3.jpg` 
+`stitch img1.jpg img2.jpg img3.jpg`
 stitches the 3 explicit files of the current dir
 
 Or use the Stitcher class in your script

stitching/__init__.py

@@ -1,3 +1,3 @@
-from .stitcher import Stitcher
+from .stitcher import Stitcher  # noqa: F401
 
 __version__ = "0.2.0"

stitching/blender.py

@@ -3,36 +3,37 @@
 
 
 class Blender:
-    """https://docs.opencv.org/4.x/d6/d4a/classcv_1_1detail_1_1Blender.html"""  # noqa
-
-    BLENDER_CHOICES = ('multiband', 'feather', 'no',)
-    DEFAULT_BLENDER = 'multiband'
+    """https://docs.opencv.org/4.x/d6/d4a/classcv_1_1detail_1_1Blender.html"""
+
+    BLENDER_CHOICES = (
+        "multiband",
+        "feather",
+        "no",
+    )
+    DEFAULT_BLENDER = "multiband"
     DEFAULT_BLEND_STRENGTH = 5
 
-    def __init__(self, blender_type=DEFAULT_BLENDER,
-                 blend_strength=DEFAULT_BLEND_STRENGTH):
+    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)
+        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
-                )
+        if self.blender_type == "no" or blend_width < 1:
+            self.blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO)
 
         elif self.blender_type == "multiband":
             self.blender = cv.detail_MultiBandBlender()
-            self.blender.setNumBands(int((np.log(blend_width) /
-                                          np.log(2.) - 1.)))
+            self.blender.setNumBands(int((np.log(blend_width) / np.log(2.0) - 1.0)))
 
         elif self.blender_type == "feather":
             self.blender = cv.detail_FeatherBlender()
-            self.blender.setSharpness(1. / blend_width)
+            self.blender.setSharpness(1.0 / blend_width)
 
         self.blender.prepare(dst_sz)
 

stitching/camera_adjuster.py

@@ -1,48 +1,47 @@
 from collections import OrderedDict
+
 import cv2 as cv
 import numpy as np
 
 from .stitching_error import StitchingError
 
 
 class CameraAdjuster:
-    """https://docs.opencv.org/4.x/d5/d56/classcv_1_1detail_1_1BundleAdjusterBase.html"""  # noqa
+    """https://docs.opencv.org/4.x/d5/d56/classcv_1_1detail_1_1BundleAdjusterBase.html"""  # noqa: E501
 
     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
+    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):
+    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':
+        if refinement_mask[0] == "x":
             mask_matrix[0, 0] = 1
-        if refinement_mask[1] == 'x':
+        if refinement_mask[1] == "x":
             mask_matrix[0, 1] = 1
-        if refinement_mask[2] == 'x':
+        if refinement_mask[2] == "x":
             mask_matrix[0, 2] = 1
-        if refinement_mask[3] == 'x':
+        if refinement_mask[3] == "x":
             mask_matrix[1, 1] = 1
-        if refinement_mask[4] == 'x':
+        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)
+        b, cameras = self.adjuster.apply(features, pairwise_matches, estimated_cameras)
         if not b:
             raise StitchingError("Camera parameters adjusting failed.")
 

stitching/camera_estimator.py

@@ -1,23 +1,22 @@
 from collections import OrderedDict
+
 import cv2 as cv
 import numpy as np
 
 from .stitching_error import StitchingError
 
 
 class CameraEstimator:
-    """https://docs.opencv.org/4.x/df/d15/classcv_1_1detail_1_1Estimator.html"""  # noqa
+    """https://docs.opencv.org/4.x/df/d15/classcv_1_1detail_1_1Estimator.html"""
 
     CAMERA_ESTIMATOR_CHOICES = OrderedDict()
-    CAMERA_ESTIMATOR_CHOICES['homography'] = cv.detail_HomographyBasedEstimator
-    CAMERA_ESTIMATOR_CHOICES['affine'] = cv.detail_AffineBasedEstimator
+    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
-            )
+        self.estimator = CameraEstimator.CAMERA_ESTIMATOR_CHOICES[estimator](**kwargs)
 
     def estimate(self, features, pairwise_matches):
         b, cameras = self.estimator.apply(features, pairwise_matches, None)

stitching/camera_wave_corrector.py

@@ -1,23 +1,22 @@
 from collections import OrderedDict
+
 import cv2 as cv
 import numpy as np
 
 
 class WaveCorrector:
-    """https://docs.opencv.org/4.x/d7/d74/group__stitching__rotation.html#ga8faf9588aebd5aeb6f8c649c82beb1fb"""  # noqa
+    """https://docs.opencv.org/4.x/d7/d74/group__stitching__rotation.html#ga8faf9588aebd5aeb6f8c649c82beb1fb"""  # noqa: E501
 
     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
+    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
-            ]
+        self.wave_correct_kind = WaveCorrector.WAVE_CORRECT_CHOICES[wave_correct_kind]
 
     def correct(self, cameras):
         if self.wave_correct_kind is not None:

stitching/cli/stitch.py

@@ -29,7 +29,7 @@
     "--affine",
     action="store_true",
     help="Overwrites multiple parameters to optimize the stitching for "
-    "scans and images captured by specialized devices."
+    "scans and images captured by specialized devices.",
 )
 parser.add_argument(
     "--medium_megapix",
@@ -267,12 +267,14 @@ def main():
 
     # Set parameters for 'affine' mode
     affine_mode = args_dict.pop("affine")
-    affine_settings = {"estimator": "affine",
-                       "wave_correct_kind": "no",
-                       "matcher_type": "affine",
-                       "adjuster": "affine",
-                       "warper_type": "affine",
-                       "compensator": "no"}
+    affine_settings = {
+        "estimator": "affine",
+        "wave_correct_kind": "no",
+        "matcher_type": "affine",
+        "adjuster": "affine",
+        "warper_type": "affine",
+        "compensator": "no",
+    }
     if affine_mode:
         args_dict.update(affine_settings)
 

stitching/cropper.py

@@ -1,12 +1,13 @@
 from collections import namedtuple
+
 import cv2 as cv
 import numpy as np
 
 from .blender import Blender
 from .stitching_error import StitchingError
 
 
-class Rectangle(namedtuple('Rectangle', 'x y width height')):
+class Rectangle(namedtuple("Rectangle", "x y width height")):
     __slots__ = ()
 
     @property
@@ -30,13 +31,13 @@ def y2(self):
         return self.y + self.height
 
     def times(self, x):
-        return Rectangle(*(int(round(i*x)) for i in self))
+        return Rectangle(*(int(round(i * x)) for i in self))
 
     def draw_on(self, img, color=(0, 0, 255), size=1):
         if len(img.shape) == 2:
             img = cv.cvtColor(img, cv.COLOR_GRAY2RGB)
         start_point = (self.x, self.y)
-        end_point = (self.x2-1, self.y2-1)
+        end_point = (self.x2 - 1, self.y2 - 1)
         cv.rectangle(img, start_point, end_point, color, size)
         return img
 
@@ -56,10 +57,10 @@ def prepare(self, imgs, masks, corners, sizes):
             lir = self.estimate_largest_interior_rectangle(mask)
             corners = self.get_zero_center_corners(corners)
             rectangles = self.get_rectangles(corners, sizes)
-            self.overlapping_rectangles = self.get_overlaps(
-                rectangles, lir)
+            self.overlapping_rectangles = self.get_overlaps(rectangles, lir)
             self.intersection_rectangles = self.get_intersections(
-                rectangles, self.overlapping_rectangles)
+                rectangles, self.overlapping_rectangles
+            )
 
     def crop_images(self, imgs, aspect=1):
         for idx, img in enumerate(imgs):
@@ -75,8 +76,7 @@ def crop_img(self, img, idx, aspect=1):
 
     def crop_rois(self, corners, sizes, aspect=1):
         if self.do_crop:
-            scaled_overlaps = \
-                [r.times(aspect) for r in self.overlapping_rectangles]
+            scaled_overlaps = [r.times(aspect) for r in self.overlapping_rectangles]
             cropped_corners = [r.corner for r in scaled_overlaps]
             cropped_corners = self.get_zero_center_corners(cropped_corners)
             cropped_sizes = [r.size for r in scaled_overlaps]
@@ -93,8 +93,7 @@ def estimate_largest_interior_rectangle(self, mask):
         # is explicitely desired (needs some time to compile at the first run!)
         import largestinteriorrectangle
 
-        contours, hierarchy = \
-            cv.findContours(mask, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
+        contours, hierarchy = cv.findContours(mask, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
         if not hierarchy.shape == (1, 1, 4) or not np.all(hierarchy == -1):
             raise StitchingError("Invalid Contour. Try without cropping.")
         contour = contours[0][:, 0, :]
@@ -129,12 +128,14 @@ def get_overlap(rectangle1, rectangle2):
         y2 = min(rectangle1.y2, rectangle2.y2)
         if x2 < x1 or y2 < y1:
             raise StitchingError("Rectangles do not overlap!")
-        return Rectangle(x1, y1, x2-x1, y2-y1)
+        return Rectangle(x1, y1, x2 - x1, y2 - y1)
 
     @staticmethod
     def get_intersections(rectangles, overlapping_rectangles):
-        return [Cropper.get_intersection(r, overlap_r) for r, overlap_r
-                in zip(rectangles, overlapping_rectangles)]
+        return [
+            Cropper.get_intersection(r, overlap_r)
+            for r, overlap_r in zip(rectangles, overlapping_rectangles)
+        ]
 
     @staticmethod
     def get_intersection(rectangle, overlapping_rectangle):
@@ -146,4 +147,4 @@ def get_intersection(rectangle, overlapping_rectangle):
 
     @staticmethod
     def crop_rectangle(img, rectangle):
-        return img[rectangle.y:rectangle.y2, rectangle.x:rectangle.x2]
+        return img[rectangle.y : rectangle.y2, rectangle.x : rectangle.x2]