[Feature] Add Mosaic transform

mmseg/datasets/pipelines/__init__.py

@@ -6,13 +6,14 @@
 from .test_time_aug import MultiScaleFlipAug
 from .transforms import (CLAHE, AdjustGamma, Normalize, Pad,
                          PhotoMetricDistortion, RandomCrop, RandomCutOut,
-                         RandomFlip, RandomRotate, Rerange, Resize, RGB2Gray,
-                         SegRescale)
+                         RandomFlip, RandomMosaic, RandomRotate, Rerange,
+                         Resize, RGB2Gray, SegRescale)
 
 __all__ = [
     'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToDataContainer',
     'Transpose', 'Collect', 'LoadAnnotations', 'LoadImageFromFile',
     'MultiScaleFlipAug', 'Resize', 'RandomFlip', 'Pad', 'RandomCrop',
     'Normalize', 'SegRescale', 'PhotoMetricDistortion', 'RandomRotate',
-    'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray', 'RandomCutOut'
+    'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray', 'RandomCutOut',
+    'RandomMosaic'
 ]

mmseg/datasets/pipelines/transforms.py

@@ -1,4 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
 import mmcv
 import numpy as np
 from mmcv.utils import deprecated_api_warning, is_tuple_of
@@ -1040,3 +1042,261 @@ def __repr__(self):
         repr_str += f'fill_in={self.fill_in}, '
         repr_str += f'seg_fill_in={self.seg_fill_in})'
         return repr_str
+
+
+@PIPELINES.register_module()
+class RandomMosaic(object):
+    """Mosaic augmentation. Given 4 images, mosaic transform combines them into
+    one output image. The output image is composed of the parts from each sub-
+    image.
+
+    .. code:: text
+                        mosaic transform
+                           center_x
+                +------------------------------+
+                |       pad        |  pad      |
+                |      +-----------+           |
+                |      |           |           |
+                |      |  image1   |--------+  |
+                |      |           |        |  |
+                |      |           | image2 |  |
+     center_y   |----+-------------+-----------|
+                |    |   cropped   |           |
+                |pad |   image3    |  image4   |
+                |    |             |           |
+                +----|-------------+-----------+
+                     |             |
+                     +-------------+
+     The mosaic transform steps are as follows:
+         1. Choose the mosaic center as the intersections of 4 images
+         2. Get the left top image according to the index, and randomly
+            sample another 3 images from the custom dataset.
+         3. Sub image will be cropped if image is larger than mosaic patch
+    Args:
+        prob (float): mosaic probability.
+        img_scale (Sequence[int]): Image size after mosaic pipeline of
+            a single image. The size of the output image is four times
+            that of a single image. The output image comprises 4 single images.
+            Default: (640, 640).
+        center_ratio_range (Sequence[float]): Center ratio range of mosaic
+            output. Default: (0.5, 1.5).
+        pad_val (int): Pad value. Default: 0.
+        seg_pad_val (int): Pad value of segmentation map. Default: 255.
+    """
+
+    def __init__(self,
+                 prob,
+                 img_scale=(640, 640),
+                 center_ratio_range=(0.5, 1.5),
+                 pad_val=0,
+                 seg_pad_val=255):
+        assert 0 <= prob and prob <= 1
+        assert isinstance(img_scale, tuple)
+        self.prob = prob
+        self.img_scale = img_scale
+        self.center_ratio_range = center_ratio_range
+        self.pad_val = pad_val
+        self.seg_pad_val = seg_pad_val
+
+    def __call__(self, results):
+        """Call function to make a mosaic of image.
+
+        Args:
+            results (dict): Result dict.
+        Returns:
+            dict: Result dict with mosaic transformed.
+        """
+        mosaic = True if np.random.rand() < self.prob else False
+        if mosaic:
+            results = self._mosaic_transform_img(results)
+            results = self._mosaic_transform_seg(results)
+        return results
+
+    def get_indexes(self, dataset):
+        """Call function to collect indexes.
+
+        Args:
+            dataset (:obj:`MultiImageMixDataset`): The dataset.
+        Returns:
+            list: indexes.
+        """
+
+        indexes = [random.randint(0, len(dataset)) for _ in range(3)]
+        return indexes
+
+    def _mosaic_transform_img(self, results):
+        """Mosaic transform function.
+
+        Args:
+            results (dict): Result dict.
+        Returns:
+            dict: Updated result dict.
+        """
+
+        assert 'mix_results' in results
+        if len(results['img'].shape) == 3:
+            mosaic_img = np.full(
+                (int(self.img_scale[0] * 2), int(self.img_scale[1] * 2), 3),
+                self.pad_val,
+                dtype=results['img'].dtype)
+        else:
+            mosaic_img = np.full(
+                (int(self.img_scale[0] * 2), int(self.img_scale[1] * 2)),
+                self.pad_val,
+                dtype=results['img'].dtype)
+
+        # mosaic center x, y
+        self.center_x = int(
+            random.uniform(*self.center_ratio_range) * self.img_scale[1])
+        self.center_y = int(
+            random.uniform(*self.center_ratio_range) * self.img_scale[0])
+        center_position = (self.center_x, self.center_y)
+
+        loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
+        for i, loc in enumerate(loc_strs):
+            if loc == 'top_left':
+                result_patch = copy.deepcopy(results)
+            else:
+                result_patch = copy.deepcopy(results['mix_results'][i - 1])
+
+            img_i = result_patch['img']
+            h_i, w_i = img_i.shape[:2]
+            # keep_ratio resize
+            scale_ratio_i = min(self.img_scale[0] / h_i,
+                                self.img_scale[1] / w_i)
+            img_i = mmcv.imresize(
+                img_i, (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)))
+
+            # compute the combine parameters
+            paste_coord, crop_coord = self._mosaic_combine(
+                loc, center_position, img_i.shape[:2][::-1])
+            x1_p, y1_p, x2_p, y2_p = paste_coord
+            x1_c, y1_c, x2_c, y2_c = crop_coord
+
+            # crop and paste image
+            mosaic_img[y1_p:y2_p, x1_p:x2_p] = img_i[y1_c:y2_c, x1_c:x2_c]
+
+        results['img'] = mosaic_img
+        results['img_shape'] = mosaic_img.shape
+        results['ori_shape'] = mosaic_img.shape
+
+        return results
+
+    def _mosaic_transform_seg(self, results):
+        """Mosaic transform function for label annotations.
+
+        Args:
+            results (dict): Result dict.
+        Returns:
+            dict: Updated result dict.
+        """
+
+        assert 'mix_results' in results
+        for key in results.get('seg_fields', []):
+            mosaic_seg = np.full(
+                (int(self.img_scale[0] * 2), int(self.img_scale[1] * 2)),
+                self.seg_pad_val,
+                dtype=results[key].dtype)
+
+            # mosaic center x, y
+            center_position = (self.center_x, self.center_y)
+
+            loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
+            for i, loc in enumerate(loc_strs):
+                if loc == 'top_left':
+                    result_patch = copy.deepcopy(results)
+                else:
+                    result_patch = copy.deepcopy(results['mix_results'][i - 1])
+
+                gt_seg_i = result_patch[key]
+                h_i, w_i = gt_seg_i.shape[:2]
+                # keep_ratio resize
+                scale_ratio_i = min(self.img_scale[0] / h_i,
+                                    self.img_scale[1] / w_i)
+                gt_seg_i = mmcv.imresize(
+                    gt_seg_i,
+                    (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)),
+                    interpolation='nearest')
+
+                # compute the combine parameters
+                paste_coord, crop_coord = self._mosaic_combine(
+                    loc, center_position, gt_seg_i.shape[:2][::-1])
+                x1_p, y1_p, x2_p, y2_p = paste_coord
+                x1_c, y1_c, x2_c, y2_c = crop_coord
+
+                # crop and paste image
+                mosaic_seg[y1_p:y2_p, x1_p:x2_p] = gt_seg_i[y1_c:y2_c,
+                                                            x1_c:x2_c]
+
+            results[key] = mosaic_seg
+
+        return results
+
+    def _mosaic_combine(self, loc, center_position_xy, img_shape_wh):
+        """Calculate global coordinate of mosaic image and local coordinate of
+        cropped sub-image.
+        Args:
+            loc (str): Index for the sub-image, loc in ('top_left',
+              'top_right', 'bottom_left', 'bottom_right').
+            center_position_xy (Sequence[float]): Mixing center for 4 images,
+                (x, y).
+            img_shape_wh (Sequence[int]): Width and height of sub-image
+        Returns:
+            tuple[tuple[float]]: Corresponding coordinate of pasting and
+                cropping
+                - paste_coord (tuple): paste corner coordinate in mosaic image.
+                - crop_coord (tuple): crop corner coordinate in mosaic image.
+        """
+
+        assert loc in ('top_left', 'top_right', 'bottom_left', 'bottom_right')
+        if loc == 'top_left':
+            # index0 to top left part of image
+            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
+                             max(center_position_xy[1] - img_shape_wh[1], 0), \
+                             center_position_xy[0], \
+                             center_position_xy[1]
+            crop_coord = img_shape_wh[0] - (x2 - x1), img_shape_wh[1] - (
+                y2 - y1), img_shape_wh[0], img_shape_wh[1]
+
+        elif loc == 'top_right':
+            # index1 to top right part of image
+            x1, y1, x2, y2 = center_position_xy[0], \
+                             max(center_position_xy[1] - img_shape_wh[1], 0), \
+                             min(center_position_xy[0] + img_shape_wh[0],
+                                 self.img_scale[1] * 2), \
+                             center_position_xy[1]
+            crop_coord = 0, img_shape_wh[1] - (y2 - y1), min(
+                img_shape_wh[0], x2 - x1), img_shape_wh[1]
+
+        elif loc == 'bottom_left':
+            # index2 to bottom left part of image
+            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
+                             center_position_xy[1], \
+                             center_position_xy[0], \
+                             min(self.img_scale[0] * 2, center_position_xy[1] +
+                                 img_shape_wh[1])
+            crop_coord = img_shape_wh[0] - (x2 - x1), 0, img_shape_wh[0], min(
+                y2 - y1, img_shape_wh[1])
+
+        else:
+            # index3 to bottom right part of image
+            x1, y1, x2, y2 = center_position_xy[0], \
+                             center_position_xy[1], \
+                             min(center_position_xy[0] + img_shape_wh[0],
+                                 self.img_scale[1] * 2), \
+                             min(self.img_scale[0] * 2, center_position_xy[1] +
+                                 img_shape_wh[1])
+            crop_coord = 0, 0, min(img_shape_wh[0],
+                                   x2 - x1), min(y2 - y1, img_shape_wh[1])
+
+        paste_coord = x1, y1, x2, y2
+        return paste_coord, crop_coord
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(prob={self.prob}, '
+        repr_str += f'img_scale={self.img_scale}, '
+        repr_str += f'center_ratio_range={self.center_ratio_range}, '
+        repr_str += f'pad_val={self.pad_val}, '
+        repr_str += f'seg_pad_val={self.pad_val})'
+        return repr_str

tests/test_data/test_transform.py

@@ -614,3 +614,52 @@ def test_cutout():
     cutout_result = cutout_module(copy.deepcopy(results))
     assert cutout_result['img'].sum() > img.sum()
     assert cutout_result['gt_semantic_seg'].sum() > seg.sum()
+
+
+def test_mosaic():
+    # test prob
+    with pytest.raises(AssertionError):
+        transform = dict(type='RandomMosaic', prob=1.5)
+        build_from_cfg(transform, PIPELINES)
+    # test assertion for invalid img_scale
+    with pytest.raises(AssertionError):
+        transform = dict(type='RandomMosaic', prob=1, img_scale=640)
+        build_from_cfg(transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+    seg = np.array(
+        Image.open(osp.join(osp.dirname(__file__), '../data/seg.png')))
+
+    results['img'] = img
+    results['gt_semantic_seg'] = seg
+    results['seg_fields'] = ['gt_semantic_seg']
+
+    transform = dict(type='RandomMosaic', prob=1, img_scale=(10, 12))
+    mosaic_module = build_from_cfg(transform, PIPELINES)
+    assert 'Mosaic' in repr(mosaic_module)
+
+    # test assertion for invalid mix_results
+    with pytest.raises(AssertionError):
+        mosaic_module(results)
+
+    results['mix_results'] = [copy.deepcopy(results)] * 3
+    results = mosaic_module(results)
+    assert results['img'].shape[:2] == (20, 24)
+
+    results = dict()
+    results['img'] = img[:, :, 0]
+    results['gt_semantic_seg'] = seg
+    results['seg_fields'] = ['gt_semantic_seg']
+
+    transform = dict(type='RandomMosaic', prob=0, img_scale=(10, 12))
+    mosaic_module = build_from_cfg(transform, PIPELINES)
+    results['mix_results'] = [copy.deepcopy(results)] * 3
+    results = mosaic_module(results)
+    assert results['img'].shape[:2] == img.shape[:2]
+
+    transform = dict(type='RandomMosaic', prob=1, img_scale=(10, 12))
+    mosaic_module = build_from_cfg(transform, PIPELINES)
+    results = mosaic_module(results)
+    assert results['img'].shape[:2] == (20, 24)