Merge pull request #2339 from xiexinch/resize-shortest-edge

[Feature] Add ResizeShortestEdge transform

mmseg/__init__.py

@@ -7,7 +7,7 @@ from packaging.version import parse
 
 from .version import __version__, version_info
 
-MMCV_MIN = '2.0.0rc1'
+MMCV_MIN = '2.0.0rc3'
 MMCV_MAX = '2.1.0'
 MMENGINE_MIN = '0.1.0'
 MMENGINE_MAX = '1.0.0'

mmseg/datasets/__init__.py

@@ -22,7 +22,8 @@ from .transforms import (CLAHE, AdjustGamma, GenerateEdge, LoadAnnotations,
                          LoadBiomedicalImageFromFile, LoadImageFromNDArray,
                          PackSegInputs, PhotoMetricDistortion, RandomCrop,
                          RandomCutOut, RandomMosaic, RandomRotate, Rerange,
-                         ResizeToMultiple, RGB2Gray, SegRescale)
+                         ResizeShortestEdge, ResizeToMultiple, RGB2Gray,
+                         SegRescale)
 from .voc import PascalVOCDataset
 
 __all__ = [
@@ -36,5 +37,5 @@ __all__ = [
     'RandomCutOut', 'RandomMosaic', 'PackSegInputs', 'ResizeToMultiple',
     'LoadImageFromNDArray', 'LoadBiomedicalImageFromFile',
     'LoadBiomedicalAnnotation', 'LoadBiomedicalData', 'GenerateEdge',
-    'DecathlonDataset', 'LIPDataset'
+    'DecathlonDataset', 'LIPDataset', 'ResizeShortestEdge'
 ]

mmseg/datasets/transforms/__init__.py

@@ -5,13 +5,15 @@ from .loading import (LoadAnnotations, LoadBiomedicalAnnotation,
                       LoadImageFromNDArray)
 from .transforms import (CLAHE, AdjustGamma, GenerateEdge,
                          PhotoMetricDistortion, RandomCrop, RandomCutOut,
-                         RandomMosaic, RandomRotate, Rerange, ResizeToMultiple,
+                         RandomMosaic, RandomRotate, Rerange,
-                         RGB2Gray, SegRescale)
+                         ResizeShortestEdge, ResizeToMultiple, RGB2Gray,
+                         SegRescale)
 
 __all__ = [
     'LoadAnnotations', 'RandomCrop', 'SegRescale', 'PhotoMetricDistortion',
     'RandomRotate', 'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray',
     'RandomCutOut', 'RandomMosaic', 'PackSegInputs', 'ResizeToMultiple',
     'LoadImageFromNDArray', 'LoadBiomedicalImageFromFile',
-    'LoadBiomedicalAnnotation', 'LoadBiomedicalData', 'GenerateEdge'
+    'LoadBiomedicalAnnotation', 'LoadBiomedicalData', 'GenerateEdge',
+    'ResizeShortestEdge'
 ]

mmseg/datasets/transforms/transforms.py

@@ -1226,3 +1226,87 @@ class GenerateEdge(BaseTransform):
         repr_str += f'edge_width={self.edge_width}, '
         repr_str += f'ignore_index={self.ignore_index})'
         return repr_str
+
+
+@TRANSFORMS.register_module()
+class ResizeShortestEdge(BaseTransform):
+    """Resize the image and mask while keeping the aspect ratio unchanged.
+
+    Modified from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/transforms/augmentation_impl.py#L130 # noqa:E501
+    Copyright (c) Facebook, Inc. and its affiliates.
+    Licensed under the Apache-2.0 License
+
+    This transform attempts to scale the shorter edge to the given
+    `scale`, as long as the longer edge does not exceed `max_size`.
+    If `max_size` is reached, then downscale so that the longer
+    edge does not exceed `max_size`.
+
+    Required Keys:
+
+    - img
+    - gt_seg_map (optional)
+
+    Modified Keys:
+
+    - img
+    - img_shape
+    - gt_seg_map (optional))
+
+    Added Keys:
+
+    - scale
+    - scale_factor
+    - keep_ratio
+
+
+    Args:
+        scale (Union[int, Tuple[int, int]]): The target short edge length.
+            If it's tuple, will select the min value as the short edge length.
+        max_size (int): The maximum allowed longest edge length.
+    """
+
+    def __init__(self, scale: Union[int, Tuple[int, int]],
+                 max_size: int) -> None:
+        super().__init__()
+        self.scale = scale
+        self.max_size = max_size
+
+        # Create a empty Resize object
+        self.resize = TRANSFORMS.build({
+            'type': 'Resize',
+            'scale': 0,
+            'keep_ratio': True
+        })
+
+    def _get_output_shape(self, img, short_edge_length) -> Tuple[int, int]:
+        """Compute the target image shape with the given `short_edge_length`.
+
+        Args:
+            img (np.ndarray): The input image.
+            short_edge_length (Union[int, Tuple[int, int]]): The target short
+                edge length. If it's tuple, will select the min value as the
+                short edge length.
+        """
+        h, w = img.shape[:2]
+        if isinstance(short_edge_length, int):
+            size = short_edge_length * 1.0
+        elif isinstance(short_edge_length, tuple):
+            size = min(short_edge_length) * 1.0
+        scale = size / min(h, w)
+        if h < w:
+            new_h, new_w = size, scale * w
+        else:
+            new_h, new_w = scale * h, size
+
+        if max(new_h, new_w) > self.max_size:
+            scale = self.max_size * 1.0 / max(new_h, new_w)
+            new_h *= scale
+            new_w *= scale
+
+        new_h = int(new_h + 0.5)
+        new_w = int(new_w + 0.5)
+        return (new_w, new_h)
+
+    def transform(self, results: Dict) -> Dict:
+        self.resize.scale = self._get_output_shape(results['img'], self.scale)
+        return self.resize(results)

tests/test_datasets/test_transform.py

@@ -10,6 +10,9 @@ from PIL import Image
 from mmseg.datasets.transforms import *  # noqa
 from mmseg.datasets.transforms import PhotoMetricDistortion, RandomCrop
 from mmseg.registry import TRANSFORMS
+from mmseg.utils import register_all_modules
+
+register_all_modules()
 
 
 def test_resize():
@@ -71,6 +74,34 @@ def test_resize():
     resized_results = resize_module(results.copy())
     assert max(resized_results['img_shape'][:2]) <= 1333 * 1.1
 
+    # test RandomChoiceResize, which `resize_type` is `ResizeShortestEdge`
+    transform = dict(
+        type='RandomChoiceResize',
+        scales=[128, 256, 512],
+        resize_type='ResizeShortestEdge',
+        max_size=1333)
+    resize_module = TRANSFORMS.build(transform)
+    resized_results = resize_module(results.copy())
+    assert resized_results['img_shape'][0] in [128, 256, 512]
+
+    transform = dict(
+        type='RandomChoiceResize',
+        scales=[512],
+        resize_type='ResizeShortestEdge',
+        max_size=512)
+    resize_module = TRANSFORMS.build(transform)
+    resized_results = resize_module(results.copy())
+    assert resized_results['img_shape'][1] == 512
+
+    transform = dict(
+        type='RandomChoiceResize',
+        scales=[(128, 256), (256, 512), (512, 1024)],
+        resize_type='ResizeShortestEdge',
+        max_size=1333)
+    resize_module = TRANSFORMS.build(transform)
+    resized_results = resize_module(results.copy())
+    assert resized_results['img_shape'][0] in [128, 256, 512]
+
     # test scale=None and scale_factor is tuple.
     # img shape: (288, 512, 3)
     transform = dict(