[Utils] Migrate datasets/pepelines/box_utils.py

.dev_scripts/covignore.cfg

@@ -22,6 +22,13 @@ mmocr/models/textdet/detectors/single_stage_text_detector.py
 
 # It will be removed after all utils are moved to mmocr.utils
 mmocr/core/evaluation/utils.py
+mmocr/models/textdet/postprocessors/utils.py
+mmocr/utils/bbox_utils.py
+mmocr/datasets/pipelines/crop.py
+
+# It will be removed after all models have been refactored
+mmocr/utils/ocr.py
+mmocr/datasets/kie_dataset.py
 
 
 # It will be removed after all models have been refactored

mmocr/datasets/pipelines/__init__.py

@@ -1,5 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from .box_utils import sort_vertex, sort_vertex8
 from .formatting import PackTextDetInputs, PackTextRecogInputs
 from .kie_transforms import ResizeNoImg
 from .loading import LoadOCRAnnotations
@@ -22,10 +21,10 @@ __all__ = [
     'ToTensorOCR', 'DBNetTargets', 'PANetTargets', 'RandomRotate',
     'ScaleAspectJitter', 'MultiRotateAugOCR', 'OCRSegTargets', 'FancyPCA',
     'RandomPaddingOCR', 'ImgAug', 'RandomRotateImageBox', 'OpencvToPil',
-    'PilToOpencv', 'SourceImagePad', 'TextSnakeTargets', 'sort_vertex',
+    'PilToOpencv', 'SourceImagePad', 'TextSnakeTargets', 'FCENetTargets',
-    'sort_vertex8', 'FCENetTargets', 'TextDetRandomCropFlip', 'ResizeNoImg',
+    'TextDetRandomCropFlip', 'ResizeNoImg', 'PyramidRescale',
-    'PyramidRescale', 'TorchVisionWrapper', 'Resize', 'RandomCrop',
+    'TorchVisionWrapper', 'Resize', 'RandomCrop', 'TextDetRandomCrop',
-    'TextDetRandomCrop', 'RandomCrop', 'PackTextDetInputs',
+    'RandomCrop', 'PackTextDetInputs', 'PackTextRecogInputs',
-    'PackTextRecogInputs', 'RescaleToHeight', 'PadToWidth',
+    'RescaleToHeight', 'PadToWidth', 'ShortScaleAspectJitter', 'RandomFlip',
-    'ShortScaleAspectJitter', 'RandomFlip', 'BoundedScaleAspectJitter'
+    'BoundedScaleAspectJitter'
 ]

mmocr/datasets/pipelines/box_utils.py

@@ -1,53 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numpy as np
-
-import mmocr.utils as utils
-
-
-def sort_vertex(points_x, points_y):
-    """Sort box vertices in clockwise order from left-top first.
-
-    Args:
-        points_x (list[float]): x of four vertices.
-        points_y (list[float]): y of four vertices.
-    Returns:
-        sorted_points_x (list[float]): x of sorted four vertices.
-        sorted_points_y (list[float]): y of sorted four vertices.
-    """
-    assert utils.is_type_list(points_x, (float, int))
-    assert utils.is_type_list(points_y, (float, int))
-    assert len(points_x) == 4
-    assert len(points_y) == 4
-    vertices = np.stack((points_x, points_y), axis=-1).astype(np.float32)
-    vertices = _sort_vertex(vertices)
-    sorted_points_x = list(vertices[:, 0])
-    sorted_points_y = list(vertices[:, 1])
-    return sorted_points_x, sorted_points_y
-
-
-def _sort_vertex(vertices):
-    assert vertices.ndim == 2
-    assert vertices.shape[-1] == 2
-    N = vertices.shape[0]
-    if N == 0:
-        return vertices
-
-    center = np.mean(vertices, axis=0)
-    directions = vertices - center
-    angles = np.arctan2(directions[:, 1], directions[:, 0])
-    sort_idx = np.argsort(angles)
-    vertices = vertices[sort_idx]
-
-    left_top = np.min(vertices, axis=0)
-    dists = np.linalg.norm(left_top - vertices, axis=-1, ord=2)
-    lefttop_idx = np.argmin(dists)
-    indexes = (np.arange(N, dtype=np.int) + lefttop_idx) % N
-    return vertices[indexes]
-
-
-def sort_vertex8(points):
-    """Sort vertex with 8 points [x1 y1 x2 y2 x3 y3 x4 y4]"""
-    assert len(points) == 8
-    vertices = _sort_vertex(np.array(points, dtype=np.float32).reshape(-1, 2))
-    sorted_box = list(vertices.flatten())
-    return sorted_box

mmocr/datasets/pipelines/crop.py

@@ -4,7 +4,6 @@ import numpy as np
 from shapely.geometry import LineString, Point
 
 import mmocr.utils as utils
-from .box_utils import sort_vertex
 
 
 def box_jitter(points_x, points_y, jitter_ratio_x=0.5, jitter_ratio_y=0.1):
@@ -56,7 +55,7 @@ def warp_img(src_img,
     points_x = [min(max(x, 0), w) for x in box[0:8:2]]
     points_y = [min(max(y, 0), h) for y in box[1:9:2]]
 
-    points_x, points_y = sort_vertex(points_x, points_y)
+    points_x, points_y = utils.sort_vertex(points_x, points_y)
 
     if jitter_flag:
         box_jitter(

mmocr/utils/__init__.py

@@ -3,7 +3,7 @@ from mmcv.utils import Registry, build_from_cfg
 
 from .api_utils import disable_text_recog_aug_test
 from .bbox_utils import (bbox2poly, box_center_distance, box_diag,
-                         rescale_bboxes)
+                         rescale_bboxes, sort_vertex, sort_vertex8)
 from .box_util import (bezier_to_polygon, is_on_same_line, sort_points,
                        stitch_boxes_into_lines)
 from .check_argument import (equal_len, is_2dlist, is_3dlist, is_none_or_type,
@@ -36,8 +36,9 @@ __all__ = [
     'rescale_bboxes', 'bbox2poly', 'crop_polygon', 'is_poly_inside_rect',
     'poly2bbox', 'poly_intersection', 'poly_iou', 'poly_make_valid',
     'poly_union', 'poly2shapely', 'polys2shapely', 'register_all_modules',
-    'dist_points2line', 'offset_polygon', 'disable_text_recog_aug_test',
+    'dist_points2line', 'offset_polygon', 'sort_vertex8', 'sort_vertex',
-    'box_center_distance', 'box_diag', 'compute_hmean', 'filter_2dlist_result',
+    'disable_text_recog_aug_test', 'box_center_distance', 'box_diag',
-    'many2one_match_ic13', 'one2one_match_ic13', 'select_top_boundary',
+    'compute_hmean', 'filter_2dlist_result', 'many2one_match_ic13',
-    'point_distance', 'points_center', 'boundary_iou'
+    'one2one_match_ic13', 'select_top_boundary', 'boundary_iou',
+    'point_distance', 'points_center'
 ]

mmocr/utils/bbox_utils.py

@@ -4,6 +4,7 @@ from typing import Tuple
 import numpy as np
 from numpy.typing import ArrayLike
 
+from mmocr.utils.check_argument import is_type_list
 from mmocr.utils.point_utils import point_distance, points_center
 
 
@@ -78,6 +79,58 @@ def bbox2poly(bbox: ArrayLike) -> np.array:
     ])
 
 
+def sort_vertex(points_x, points_y):
+    # TODO Add typehints & docstring & test
+    """Sort box vertices in clockwise order from left-top first.
+
+    Args:
+        points_x (list[float]): x of four vertices.
+        points_y (list[float]): y of four vertices.
+    Returns:
+        sorted_points_x (list[float]): x of sorted four vertices.
+        sorted_points_y (list[float]): y of sorted four vertices.
+    """
+    assert is_type_list(points_x, (float, int))
+    assert is_type_list(points_y, (float, int))
+    assert len(points_x) == 4
+    assert len(points_y) == 4
+    vertices = np.stack((points_x, points_y), axis=-1).astype(np.float32)
+    vertices = _sort_vertex(vertices)
+    sorted_points_x = list(vertices[:, 0])
+    sorted_points_y = list(vertices[:, 1])
+    return sorted_points_x, sorted_points_y
+
+
+def _sort_vertex(vertices):
+    # TODO Add typehints & docstring & test
+    assert vertices.ndim == 2
+    assert vertices.shape[-1] == 2
+    N = vertices.shape[0]
+    if N == 0:
+        return vertices
+
+    center = np.mean(vertices, axis=0)
+    directions = vertices - center
+    angles = np.arctan2(directions[:, 1], directions[:, 0])
+    sort_idx = np.argsort(angles)
+    vertices = vertices[sort_idx]
+
+    left_top = np.min(vertices, axis=0)
+    dists = np.linalg.norm(left_top - vertices, axis=-1, ord=2)
+    lefttop_idx = np.argmin(dists)
+    indexes = (np.arange(N, dtype=np.int) + lefttop_idx) % N
+    return vertices[indexes]
+
+
+def sort_vertex8(points):
+    # TODO Add typehints & docstring & test
+    """Sort vertex with 8 points [x1 y1 x2 y2 x3 y3 x4 y4]"""
+    assert len(points) == 8
+    vertices = _sort_vertex(np.array(points, dtype=np.float32).reshape(-1, 2))
+    sorted_box = list(vertices.flatten())
+    return sorted_box
+
+
 def box_center_distance(b1, b2):
     # TODO typehints & docstring & test
     assert isinstance(b1, np.ndarray)