add imgaug support

paddlex/cv/datasets/coco.py

@@ -100,7 +100,7 @@ class CocoDetection(VOCDetection):
             gt_score = np.ones((num_bbox, 1), dtype=np.float32)
             is_crowd = np.zeros((num_bbox, 1), dtype=np.int32)
             difficult = np.zeros((num_bbox, 1), dtype=np.int32)
-            gt_poly = [None] * num_bbox
+            gt_poly = None
 
             for i, box in enumerate(bboxes):
                 catid = box['category_id']
@@ -108,6 +108,8 @@ class CocoDetection(VOCDetection):
                 gt_bbox[i, :] = box['clean_bbox']
                 is_crowd[i][0] = box['iscrowd']
                 if 'segmentation' in box:
+                    if gt_poly is None:
+                        gt_poly = [None] * num_bbox
                     gt_poly[i] = box['segmentation']
 
             im_info = {
@@ -119,9 +121,11 @@ class CocoDetection(VOCDetection):
                 'gt_class': gt_class,
                 'gt_bbox': gt_bbox,
                 'gt_score': gt_score,
-                'gt_poly': gt_poly,
                 'difficult': difficult
             }
+            if gt_poly is not None:
+                label_info['gt_poly'] = gt_poly
+
             coco_rec = (im_info, label_info)
             self.file_list.append([im_fname, coco_rec])
 

paddlex/cv/datasets/voc.py

@@ -153,7 +153,6 @@ class VOCDetection(Dataset):
                     'gt_class': gt_class,
                     'gt_bbox': gt_bbox,
                     'gt_score': gt_score,
-                    'gt_poly': [],
                     'difficult': difficult
                 }
                 voc_rec = (im_info, label_info)

paddlex/cv/transforms/cls_transforms.py

@@ -13,13 +13,22 @@
 # limitations under the License.
 
 from .ops import *
+from .imgaug_support import execute_imgaug
 import random
 import os.path as osp
 import numpy as np
 from PIL import Image, ImageEnhance
 
 
-class Compose:
+class ClsTransform:
+    """分类Transform的基类
+    """
+
+    def __init__(self):
+        pass
+
+
+class Compose(ClsTransform):
     """根据数据预处理/增强算子对输入数据进行操作。
        所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。
 
@@ -39,6 +48,15 @@ class Compose:
                             'must be equal or larger than 1!')
         self.transforms = transforms
 
+        # 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
+        for op in self.transforms:
+            if not isinstance(op, ClsTransform):
+                import imgaug.augmenters as iaa
+                if not isinstance(op, iaa.Augmenter):
+                    raise Exception(
+                        "Elements in transforms should be defined in 'paddlex.cls.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
+                    )
+
     def __call__(self, im, label=None):
         """
         Args:
@@ -48,20 +66,34 @@ class Compose:
             tuple: 根据网络所需字段所组成的tuple；
                 字段由transforms中的最后一个数据预处理操作决定。
         """
-        try:
-            im = cv2.imread(im).astype('float32')
-        except:
-            raise TypeError('Can\'t read The image file {}!'.format(im))
+        if isinstance(im, np.ndarray):
+            if len(im.shape) != 3:
+                raise Exception(
+                    "im should be 3-dimension, but now is {}-dimensions".
+                    format(len(im.shape)))
+        else:
+            try:
+                im = cv2.imread(im).astype('float32')
+            except:
+                raise TypeError('Can\'t read The image file {}!'.format(im))
         im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
         for op in self.transforms:
-            outputs = op(im, label)
-            im = outputs[0]
-            if len(outputs) == 2:
-                label = outputs[1]
+            if isinstance(op, ClsTransform):
+                outputs = op(im, label)
+                im = outputs[0]
+                if len(outputs) == 2:
+                    label = outputs[1]
+            else:
+                import imgaug.augmenters as iaa
+                if isinstance(op, iaa.Augmenter):
+                    im, = execute_imgaug(op, im)
+                output = (im, )
+                if label is not None:
+                    output = (im, label)
         return outputs
 
 
-class RandomCrop:
+class RandomCrop(ClsTransform):
     """对图像进行随机剪裁，模型训练时的数据增强操作。
 
     1. 根据lower_scale、lower_ratio、upper_ratio计算随机剪裁的高、宽。
@@ -104,7 +136,7 @@ class RandomCrop:
             return (im, label)
 
 
-class RandomHorizontalFlip:
+class RandomHorizontalFlip(ClsTransform):
     """以一定的概率对图像进行随机水平翻转，模型训练时的数据增强操作。
 
     Args:
@@ -132,7 +164,7 @@ class RandomHorizontalFlip:
             return (im, label)
 
 
-class RandomVerticalFlip:
+class RandomVerticalFlip(ClsTransform):
     """以一定的概率对图像进行随机垂直翻转，模型训练时的数据增强操作。
 
     Args:
@@ -160,7 +192,7 @@ class RandomVerticalFlip:
             return (im, label)
 
 
-class Normalize:
+class Normalize(ClsTransform):
     """对图像进行标准化。
 
     1. 对图像进行归一化到区间[0.0, 1.0]。
@@ -195,7 +227,7 @@ class Normalize:
             return (im, label)
 
 
-class ResizeByShort:
+class ResizeByShort(ClsTransform):
     """根据图像短边对图像重新调整大小（resize）。
 
     1. 获取图像的长边和短边长度。
@@ -242,7 +274,7 @@ class ResizeByShort:
             return (im, label)
 
 
-class CenterCrop:
+class CenterCrop(ClsTransform):
     """以图像中心点扩散裁剪长宽为`crop_size`的正方形
 
     1. 计算剪裁的起始点。
@@ -272,7 +304,7 @@ class CenterCrop:
             return (im, label)
 
 
-class RandomRotate:
+class RandomRotate(ClsTransform):
     def __init__(self, rotate_range=30, prob=0.5):
         """以一定的概率对图像在[-rotate_range, rotaterange]角度范围内进行旋转，模型训练时的数据增强操作。
 
@@ -306,7 +338,7 @@ class RandomRotate:
             return (im, label)
 
 
-class RandomDistort:
+class RandomDistort(ClsTransform):
     """以一定的概率对图像进行随机像素内容变换，模型训练时的数据增强操作。
 
     1. 对变换的操作顺序进行随机化操作。
@@ -397,7 +429,7 @@ class RandomDistort:
             return (im, label)
 
 
-class ArrangeClassifier:
+class ArrangeClassifier(ClsTransform):
     """获取训练/验证/预测所需信息。注意：此操作不需用户自己显示调用
 
     Args:

paddlex/cv/transforms/det_transforms.py

@@ -24,11 +24,20 @@ import numpy as np
 import cv2
 from PIL import Image, ImageEnhance
 
+from .imgaug_support import execute_imgaug
 from .ops import *
 from .box_utils import *
 
 
-class Compose:
+class DetTransform:
+    """检测数据处理基类
+    """
+
+    def __init__(self):
+        pass
+
+
+class Compose(DetTransform):
     """根据数据预处理/增强列表对输入数据进行操作。
        所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。
 
@@ -49,8 +58,16 @@ class Compose:
         self.transforms = transforms
         self.use_mixup = False
         for t in self.transforms:
-            if t.__class__.__name__ == 'MixupImage':
+            if type(t).__name__ == 'MixupImage':
                 self.use_mixup = True
+        # 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
+        for op in self.transforms:
+            if not isinstance(op, DetTransform):
+                import imgaug.augmenters as iaa
+                if not isinstance(op, iaa.Augmenter):
+                    raise Exception(
+                        "Elements in transforms should be defined in 'paddlex.det.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
+                    )
 
     def __call__(self, im, im_info=None, label_info=None):
         """
@@ -84,11 +101,18 @@ class Compose:
         def decode_image(im_file, im_info, label_info):
             if im_info is None:
                 im_info = dict()
-            try:
-                im = cv2.imread(im_file).astype('float32')
-            except:
-                raise TypeError(
-                    'Can\'t read The image file {}!'.format(im_file))
+            if isinstance(im_file, np.ndarray):
+                if len(im_file.shape) != 3:
+                    raise Exception(
+                        "im should be 3-dimensions, but now is {}-dimensions".
+                        format(len(im_file.shape)))
+                im = im_file
+            else:
+                try:
+                    im = cv2.imread(im_file).astype('float32')
+                except:
+                    raise TypeError(
+                        'Can\'t read The image file {}!'.format(im_file))
             im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
             # make default im_info with [h, w, 1]
             im_info['im_resize_info'] = np.array(
@@ -117,12 +141,28 @@ class Compose:
         for op in self.transforms:
             if im is None:
                 return None
-            outputs = op(im, im_info, label_info)
-            im = outputs[0]
+            if isinstance(op, DetTransform):
+                outputs = op(im, im_info, label_info)
+                im = outputs[0]
+            else:
+                if label_info is not None:
+                    gt_poly = label_info.get('gt_poly', None)
+                    gt_bbox = label_info['gt_bbox']
+                    if gt_poly is None:
+                        im, aug_bbox = execute_imgaug(op, im, bboxes=gt_bbox)
+                    else:
+                        im, aug_bbox, aug_poly = execute_imgaug(
+                            op, im, bboxes=gt_bbox, polygons=gt_poly)
+                        label_info['gt_poly'] = aug_poly
+                    label_info['gt_bbox'] = aug_bbox
+                    outputs = (im, im_info, label_info)
+                else:
+                    im, = execute_imgaug(op, im)
+                    outputs = (im, im_info)
         return outputs
 
 
-class ResizeByShort:
+class ResizeByShort(DetTransform):
     """根据图像的短边调整图像大小（resize）。
 
     1. 获取图像的长边和短边长度。
@@ -194,7 +234,7 @@ class ResizeByShort:
             return (im, im_info, label_info)
 
 
-class Padding:
+class Padding(DetTransform):
     """1.将图像的长和宽padding至coarsest_stride的倍数。如输入图像为[300, 640],
        `coarest_stride`为32，则由于300不为32的倍数，因此在图像最右和最下使用0值
        进行padding，最终输出图像为[320, 640]。
@@ -290,7 +330,7 @@ class Padding:
             return (padding_im, im_info, label_info)
 
 
-class Resize:
+class Resize(DetTransform):
     """调整图像大小（resize）。
 
     - 当目标大小（target_size）类型为int时，根据插值方式，
@@ -369,7 +409,7 @@ class Resize:
             return (im, im_info, label_info)
 
 
-class RandomHorizontalFlip:
+class RandomHorizontalFlip(DetTransform):
     """随机翻转图像、标注框、分割信息，模型训练时的数据增强操作。
 
     1. 随机采样一个0-1之间的小数，当小数小于水平翻转概率时，
@@ -447,7 +487,7 @@ class RandomHorizontalFlip:
             return (im, im_info, label_info)
 
 
-class Normalize:
+class Normalize(DetTransform):
     """对图像进行标准化。
 
     1. 归一化图像到到区间[0.0, 1.0]。
@@ -491,7 +531,7 @@ class Normalize:
             return (im, im_info, label_info)
 
 
-class RandomDistort:
+class RandomDistort(DetTransform):
     """以一定的概率对图像进行随机像素内容变换，模型训练时的数据增强操作
 
     1. 对变换的操作顺序进行随机化操作。
@@ -585,7 +625,7 @@ class RandomDistort:
             return (im, im_info, label_info)
 
 
-class MixupImage:
+class MixupImage(DetTransform):
     """对图像进行mixup操作,模型训练时的数据增强操作，目前仅YOLOv3模型支持该transform。
 
     当label_info中不存在mixup字段时，直接返回，否则进行下述操作：
@@ -714,7 +754,7 @@ class MixupImage:
             return (im, im_info, label_info)
 
 
-class RandomExpand:
+class RandomExpand(DetTransform):
     """随机扩张图像，模型训练时的数据增强操作。
     1. 随机选取扩张比例（扩张比例大于1时才进行扩张）。
     2. 计算扩张后图像大小。
@@ -796,7 +836,7 @@ class RandomExpand:
         return (canvas, im_info, label_info)
 
 
-class RandomCrop:
+class RandomCrop(DetTransform):
     """随机裁剪图像。
     1. 若allow_no_crop为True，则在thresholds加入’no_crop’。
     2. 随机打乱thresholds。
@@ -944,7 +984,7 @@ class RandomCrop:
         return (im, im_info, label_info)
 
 
-class ArrangeFasterRCNN:
+class ArrangeFasterRCNN(DetTransform):
     """获取FasterRCNN模型训练/验证/预测所需信息。
 
     Args:
@@ -1019,7 +1059,7 @@ class ArrangeFasterRCNN:
         return outputs
 
 
-class ArrangeMaskRCNN:
+class ArrangeMaskRCNN(DetTransform):
     """获取MaskRCNN模型训练/验证/预测所需信息。
 
     Args:
@@ -1103,7 +1143,7 @@ class ArrangeMaskRCNN:
         return outputs
 
 
-class ArrangeYOLOv3:
+class ArrangeYOLOv3(DetTransform):
     """获取YOLOv3模型训练/验证/预测所需信息。
 
     Args:

paddlex/cv/transforms/imgaug_support.py

@@ -0,0 +1,220 @@
+import numpy as np
+
+#def execute_imgaug(augmenter, im, bboxes=None, polygons=None, segment_map=None):
+#    import imgaug.augmentables.polys as polys
+#    import imgaug.augmentables.bbs as bbs
+#
+#    # 预处理，将bboxes, polygons转换成imgaug格式
+#    aug_bboxes = None
+#    if bboxes is not None:
+#        aug_bboxes = list()
+#        for i in range(len(bboxes)):
+#            x1 = bboxes[i, 0]-1
+#            y1 = bboxes[i, 1]
+#            x2 = bboxes[i, 2]
+#            y2 = bboxes[i, 3]
+#            aug_bboxes.append(bbs.BoundingBox(x1, y1, x2, y2))
+#
+#    aug_polygons = None
+#    lod_info = list()
+#    if polygons is not None:
+#        aug_polygons = list()
+#        for i in range(len(polygons)):
+#            num = len(polygons[i])
+#            lod_info.append(num)
+#            for j in range(num):
+#                points = np.reshape(polygons[i][j], (-1, 2))
+#                aug_polygons.append(polys.Polygon(points))
+#
+#    aug_segment_map = None
+#    if segment_map is not None:
+#        if len(segment_map.shape) == 2:
+#            h, w = segment_map.shape
+#            aug_segment_map = np.reshape(segment_map, (1, h, w, 1))
+#        elif len(segment_map.shape) == 3:
+#            h, w, c = segment_map.shape
+#            aug_segment_map = np.reshape(segment_map, (1, h, w, c))
+#        else:
+#            raise Exception("Only support 2-dimensions for 3-dimensions for segment_map")
+#
+#    aug_im = im.astype('uint8')
+#    aug_im, aug_bboxes, aug_polygons, aug_seg_map = augmenter.augment(image=aug_im, bounding_boxes=aug_bboxes,
+#                                                                  polygons=aug_polygons,
+#                                                                  segmentation_maps=aug_segment_map)
+#
+#    if aug_polygons is not None:
+#        assert len(aug_bboxes) == len(lod_info), "length of bboxes should be equal to the length of polygons"
+#
+#    if aug_bboxes is not None:
+#        # 裁剪掉在图像之外的bbox和polygon
+#        for i in range(len(aug_bboxes)):
+#            aug_bboxes[i] = aug_bboxes[i].clip_out_of_image(aug_im)
+#        if aug_polygons is not None:
+#            for i in range(len(aug_polygons)):
+#                aug_polygons[i] = aug_polygons[i].clip_out_of_image(aug_im)
+#
+#        # 过滤掉无效的bbox和polygon，并转换为训练数据格式
+#        converted_bboxes = list()
+#        converted_polygons = list()
+#        poly_index = 0
+#        for i in range(len(aug_bboxes)):
+#            # 过滤width或height不足1像素的框
+#            if aug_bboxes[i].width < 1 or aug_bboxes[i].height < 1:
+#                continue
+#            if aug_polygons is None:
+#                converted_bboxes.append([aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2, aug_bboxes[i].y2])
+#                continue
+#
+#            # 如若有polygons，将会继续执行下面代码
+#            polygons_this_box = list()
+#            for ps in aug_polygons[poly_index:poly_index+lod_info[i]]:
+#                if len(ps) == 0:
+#                    continue
+#                for p in ps:
+#                    # 没有3个point的polygon被过滤
+#                    if len(p.exterior) < 3:
+#                        continue
+#                    polygons_this_box.append(p.exterior.flatten().tolist())
+#            poly_index += lod_info[i]
+#
+#            if len(polygons_this_box) == 0:
+#                continue
+#            converted_bboxes.append([aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2, aug_bboxes[i].y2])
+#            converted_polygons.append(polygons_this_box)
+#        if len(converted_bboxes) == 0:
+#			# 若完成增强后，没有可用的目标，则仍然返回原图进行训练
+#            aug_im = im
+#            converted_bboxes = bboxes
+#            converted_polygons = polygons
+#
+#    result = [aug_im.astype('float32')]
+#    if bboxes is not None:
+#        result.append(np.array(converted_bboxes))
+#    if polygons is not None:
+#        result.append(converted_polygons)
+#    if segment_map is not None:
+#        n, h, w, c = aug_seg_map.shape
+#        if len(segment_map.shape) == 2:
+#            aug_seg_map = np.reshape(aug_seg_map, (h, w))
+#        elif len(segment_map.shape) == 3:
+#            aug_seg_map = np.reshape(aug_seg_map, (h, w, c))
+#        result.append(aug_seg_map)
+#    return result
+
+import numpy as np
+
+
+def execute_imgaug(augmenter, im, bboxes=None, polygons=None,
+                   segment_map=None):
+    # 预处理，将bboxes, polygons转换成imgaug格式
+    import imgaug.augmentables.polys as polys
+    import imgaug.augmentables.bbs as bbs
+
+    aug_im = im.astype('uint8')
+
+    aug_bboxes = None
+    if bboxes is not None:
+        aug_bboxes = list()
+        for i in range(len(bboxes)):
+            x1 = bboxes[i, 0] - 1
+            y1 = bboxes[i, 1]
+            x2 = bboxes[i, 2]
+            y2 = bboxes[i, 3]
+            aug_bboxes.append(bbs.BoundingBox(x1, y1, x2, y2))
+
+    aug_polygons = None
+    lod_info = list()
+    if polygons is not None:
+        aug_polygons = list()
+        for i in range(len(polygons)):
+            num = len(polygons[i])
+            lod_info.append(num)
+            for j in range(num):
+                points = np.reshape(polygons[i][j], (-1, 2))
+                aug_polygons.append(polys.Polygon(points))
+
+    aug_segment_map = None
+    if segment_map is not None:
+        if len(segment_map.shape) == 2:
+            h, w = segment_map.shape
+            aug_segment_map = np.reshape(segment_map, (1, h, w, 1))
+        elif len(segment_map.shape) == 3:
+            h, w, c = segment_map.shape
+            aug_segment_map = np.reshape(segment_map, (1, h, w, c))
+        else:
+            raise Exception(
+                "Only support 2-dimensions for 3-dimensions for segment_map")
+
+    aug_im, aug_bboxes, aug_polygons, aug_seg_map = augmenter.augment(
+        image=aug_im,
+        bounding_boxes=aug_bboxes,
+        polygons=aug_polygons,
+        segmentation_maps=aug_segment_map)
+
+    aug_im = aug_im.astype('float32')
+
+    if aug_polygons is not None:
+        assert len(aug_bboxes) == len(
+            lod_info
+        ), "Number of aug_bboxes should be equal to number of aug_polygons"
+
+    if aug_bboxes is not None:
+        # 裁剪掉在图像之外的bbox和polygon
+        for i in range(len(aug_bboxes)):
+            aug_bboxes[i] = aug_bboxes[i].clip_out_of_image(aug_im)
+        if aug_polygons is not None:
+            for i in range(len(aug_polygons)):
+                aug_polygons[i] = aug_polygons[i].clip_out_of_image(aug_im)
+
+        # 过滤掉无效的bbox和polygon，并转换为训练数据格式
+        converted_bboxes = list()
+        converted_polygons = list()
+        poly_index = 0
+        for i in range(len(aug_bboxes)):
+            # 过滤width或height不足1像素的框
+            if aug_bboxes[i].width < 1 or aug_bboxes[i].height < 1:
+                continue
+            if aug_polygons is None:
+                converted_bboxes.append([
+                    aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
+                    aug_bboxes[i].y2
+                ])
+                continue
+
+            # 如若有polygons，将会继续执行下面代码
+            polygons_this_box = list()
+            for ps in aug_polygons[poly_index:poly_index + lod_info[i]]:
+                if len(ps) == 0:
+                    continue
+                for p in ps:
+                    # 没有3个point的polygon被过滤
+                    if len(p.exterior) < 3:
+                        continue
+                    polygons_this_box.append(p.exterior.flatten().tolist())
+            poly_index += lod_info[i]
+
+            if len(polygons_this_box) == 0:
+                continue
+            converted_bboxes.append([
+                aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
+                aug_bboxes[i].y2
+            ])
+            converted_polygons.append(polygons_this_box)
+        if len(converted_bboxes) == 0:
+            aug_im = im
+            converted_bboxes = bboxes
+            converted_polygons = polygons
+
+    result = [aug_im]
+    if bboxes is not None:
+        result.append(np.array(converted_bboxes))
+    if polygons is not None:
+        result.append(converted_polygons)
+    if segment_map is not None:
+        n, h, w, c = aug_seg_map.shape
+        if len(segment_map.shape) == 2:
+            aug_seg_map = np.reshape(aug_seg_map, (h, w))
+        elif len(segment_map.shape) == 3:
+            aug_seg_map = np.reshape(aug_seg_map, (h, w, c))
+        result.append(aug_seg_map)
+    return result

paddlex/cv/transforms/seg_transforms.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 from .ops import *
+from .imgaug_support import execute_imgaug
 import random
 import os.path as osp
 import numpy as np
@@ -22,7 +23,15 @@ import cv2
 from collections import OrderedDict
 
 
-class Compose:
+class SegTransform:
+    """ 分割transform基类
+    """
+
+    def __init__(self):
+        pass
+
+
+class Compose(SegTransform):
     """根据数据预处理/增强算子对输入数据进行操作。
        所有操作的输入图像流形状均是[H, W, C]，其中H为图像高，W为图像宽，C为图像通道数。
 
@@ -43,6 +52,14 @@ class Compose:
                             'must be equal or larger than 1!')
         self.transforms = transforms
         self.to_rgb = False
+        # 检查transforms里面的操作，目前支持PaddleX定义的或者是imgaug操作
+        for op in self.transforms:
+            if not isinstance(op, SegTransform):
+                import imgaug.augmenters as iaa
+                if not isinstance(op, iaa.Augmenter):
+                    raise Exception(
+                        "Elements in transforms should be defined in 'paddlex.seg.transforms' or class of imgaug.augmenters.Augmenter, see docs here: https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/"
+                    )
 
     def __call__(self, im, im_info=None, label=None):
         """
@@ -60,26 +77,40 @@ class Compose:
 
         if im_info is None:
             im_info = list()
-        try:
-            im = cv2.imread(im).astype('float32')
-        except:
-            raise ValueError('Can\'t read The image file {}!'.format(im))
+        if isinstance(im, np.ndarray):
+            if len(im.shape) != 3:
+                raise Exception(
+                    "im should be 3-dimensions, but now is {}-dimensions".
+                    format(len(im.shape)))
+        else:
+            try:
+                im = cv2.imread(im).astype('float32')
+            except:
+                raise ValueError('Can\'t read The image file {}!'.format(im))
         if self.to_rgb:
             im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
         if label is not None:
             if not isinstance(label, np.ndarray):
                 label = np.asarray(Image.open(label))
         for op in self.transforms:
-            outputs = op(im, im_info, label)
-            im = outputs[0]
-            if len(outputs) >= 2:
-                im_info = outputs[1]
-            if len(outputs) == 3:
-                label = outputs[2]
+            if isinstance(op, SegTransform):
+                outputs = op(im, im_info, label)
+                im = outputs[0]
+                if len(outputs) >= 2:
+                    im_info = outputs[1]
+                if len(outputs) == 3:
+                    label = outputs[2]
+            else:
+                if label is not None:
+                    im, label = execute_imgaug(op, im, segment_map=label)
+                    outputs = (im, im_info, label)
+                else:
+                    im, = execute_imgaug(op, im)
+                    outputs = (im, im_info)
         return outputs
 
 
-class RandomHorizontalFlip:
+class RandomHorizontalFlip(SegTransform):
     """以一定的概率对图像进行水平翻转。当存在标注图像时，则同步进行翻转。
 
     Args:
@@ -115,7 +146,7 @@ class RandomHorizontalFlip:
             return (im, im_info, label)
 
 
-class RandomVerticalFlip:
+class RandomVerticalFlip(SegTransform):
     """以一定的概率对图像进行垂直翻转。当存在标注图像时，则同步进行翻转。
 
     Args:
@@ -150,7 +181,7 @@ class RandomVerticalFlip:
             return (im, im_info, label)
 
 
-class Resize:
+class Resize(SegTransform):
     """调整图像大小（resize），当存在标注图像时，则同步进行处理。
 
     - 当目标大小（target_size）类型为int时，根据插值方式，
@@ -260,7 +291,7 @@ class Resize:
             return (im, im_info, label)
 
 
-class ResizeByLong:
+class ResizeByLong(SegTransform):
     """对图像长边resize到固定值，短边按比例进行缩放。当存在标注图像时，则同步进行处理。
 
     Args:
@@ -301,7 +332,7 @@ class ResizeByLong:
             return (im, im_info, label)
 
 
-class ResizeByShort:
+class ResizeByShort(SegTransform):
     """根据图像的短边调整图像大小（resize）。
 
     1. 获取图像的长边和短边长度。
@@ -378,7 +409,7 @@ class ResizeByShort:
             return (im, im_info, label)
 
 
-class ResizeRangeScaling:
+class ResizeRangeScaling(SegTransform):
     """对图像长边随机resize到指定范围内，短边按比例进行缩放。当存在标注图像时，则同步进行处理。
 
     Args:
@@ -427,7 +458,7 @@ class ResizeRangeScaling:
             return (im, im_info, label)
 
 
-class ResizeStepScaling:
+class ResizeStepScaling(SegTransform):
     """对图像按照某一个比例resize，这个比例以scale_step_size为步长
     在[min_scale_factor, max_scale_factor]随机变动。当存在标注图像时，则同步进行处理。
 
@@ -502,7 +533,7 @@ class ResizeStepScaling:
             return (im, im_info, label)
 
 
-class Normalize:
+class Normalize(SegTransform):
     """对图像进行标准化。
     1.尺度缩放到 [0,1]。
     2.对图像进行减均值除以标准差操作。
@@ -550,7 +581,7 @@ class Normalize:
             return (im, im_info, label)
 
 
-class Padding:
+class Padding(SegTransform):
     """对图像或标注图像进行padding，padding方向为右和下。
     根据提供的值对图像或标注图像进行padding操作。
 
@@ -642,7 +673,7 @@ class Padding:
             return (im, im_info, label)
 
 
-class RandomPaddingCrop:
+class RandomPaddingCrop(SegTransform):
     """对图像和标注图进行随机裁剪，当所需要的裁剪尺寸大于原图时，则进行padding操作。
 
     Args:
@@ -741,7 +772,7 @@ class RandomPaddingCrop:
             return (im, im_info, label)
 
 
-class RandomBlur:
+class RandomBlur(SegTransform):
     """以一定的概率对图像进行高斯模糊。
 
     Args：
@@ -787,7 +818,7 @@ class RandomBlur:
             return (im, im_info, label)
 
 
-class RandomRotate:
+class RandomRotate(SegTransform):
     """对图像进行随机旋转, 模型训练时的数据增强操作。
     在旋转区间[-rotate_range, rotate_range]内，对图像进行随机旋转，当存在标注图像时，同步进行，
     并对旋转后的图像和标注图像进行相应的padding。
@@ -859,7 +890,7 @@ class RandomRotate:
             return (im, im_info, label)
 
 
-class RandomScaleAspect:
+class RandomScaleAspect(SegTransform):
     """裁剪并resize回原始尺寸的图像和标注图像。
     按照一定的面积比和宽高比对图像进行裁剪，并reszie回原始图像的图像，当存在标注图时，同步进行。
 
@@ -922,7 +953,7 @@ class RandomScaleAspect:
             return (im, im_info, label)
 
 
-class RandomDistort:
+class RandomDistort(SegTransform):
     """对图像进行随机失真。
 
     1. 对变换的操作顺序进行随机化操作。
@@ -1018,7 +1049,7 @@ class RandomDistort:
             return (im, im_info, label)
 
 
-class ArrangeSegmenter:
+class ArrangeSegmenter(SegTransform):
     """获取训练/验证/预测所需的信息。
 
     Args: