update im_info format

dygraph/infer.py

@@ -107,14 +107,16 @@ def infer(model, test_dataset=None, model_dir=None, save_dir='output'):
         pred, _ = model(im, mode='test')
         pred = pred.numpy()
         pred = np.squeeze(pred).astype('uint8')
-        keys = list(im_info.keys())
-        for k in keys[::-1]:
-            if k == 'shape_before_resize':
-                h, w = im_info[k][0], im_info[k][1]
+        for info in im_info[::-1]:
+            if info[0] == 'resize':
+                h, w = info[1][0], info[1][1]
                 pred = cv2.resize(pred, (w, h), cv2.INTER_NEAREST)
-            elif k == 'shape_before_padding':
-                h, w = im_info[k][0], im_info[k][1]
+            elif info[0] == 'padding':
+                h, w = info[1][0], info[1][1]
                 pred = pred[0:h, 0:w]
+            else:
+                raise Exception("Unexpected info '{}' in im_info".format(
+                    info[0]))
 
         im_file = im_path.replace(test_dataset.data_dir, '')
         if im_file[0] == '/':

dygraph/transforms/transforms.py

@@ -12,12 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .functional import *
 import random
+from collections import OrderedDict
+
 import numpy as np
 from PIL import Image
 import cv2
-from collections import OrderedDict
+
+from .functional import *
 
 
 class Compose:
@@ -33,6 +35,7 @@ class Compose:
         ValueError: transforms元素个数小于1。
 
     """
+
     def __init__(self, transforms, to_rgb=True):
         if not isinstance(transforms, list):
             raise TypeError('The transforms must be a list!')
@@ -56,7 +59,7 @@ class Compose:
         """
 
         if im_info is None:
-            im_info = dict()
+            im_info = list()
         if isinstance(im, str):
             im = cv2.imread(im).astype('float32')
         if isinstance(label, str):
@@ -86,6 +89,7 @@ class RandomHorizontalFlip:
         prob (float): 随机水平翻转的概率。默认值为0.5。
 
     """
+
     def __init__(self, prob=0.5):
         self.prob = prob
 
@@ -117,6 +121,7 @@ class RandomVerticalFlip:
     Args:
         prob (float): 随机垂直翻转的概率。默认值为0.1。
     """
+
     def __init__(self, prob=0.1):
         self.prob = prob
 
@@ -207,8 +212,8 @@ class Resize:
             ValueError: 数据长度不匹配。
         """
         if im_info is None:
-            im_info = OrderedDict()
-        im_info['shape_before_resize'] = im.shape[:2]
+            im_info = list()
+        im_info.append(('resize', im.shape[:2]))
         if not isinstance(im, np.ndarray):
             raise TypeError("Resize: image type is not numpy.")
         if len(im.shape) != 3:
@@ -233,6 +238,7 @@ class ResizeByLong:
     Args:
         long_size (int): resize后图像的长边大小。
     """
+
     def __init__(self, long_size):
         self.long_size = long_size
 
@@ -251,9 +257,9 @@ class ResizeByLong:
                     -shape_before_resize (tuple): 保存resize之前图像的形状(h, w）。
         """
         if im_info is None:
-            im_info = OrderedDict()
+            im_info = list()
 
-        im_info['shape_before_resize'] = im.shape[:2]
+        im_info.append(('resize', im.shape[:2]))
         im = resize_long(im, self.long_size)
         if label is not None:
             label = resize_long(label, self.long_size, cv2.INTER_NEAREST)
@@ -265,7 +271,7 @@ class ResizeByLong:
 
 
 class ResizeRangeScaling:
-    """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。
+    """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。一般用于训练
 
     Args:
         min_value (int): 图像长边resize后的最小值。默认值400。
@@ -274,6 +280,7 @@ class ResizeRangeScaling:
     Raises:
         ValueError: min_value大于max_value
     """
+
     def __init__(self, min_value=400, max_value=600):
         if min_value > max_value:
             raise ValueError('min_value must be less than max_value, '
@@ -311,7 +318,7 @@ class ResizeRangeScaling:
 
 class ResizeStepScaling:
     """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。
+    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。一般用于训练
 
     Args:
         min_scale_factor（float), resize最小尺度。默认值0.75。
@@ -321,6 +328,7 @@ class ResizeStepScaling:
     Raises:
         ValueError: min_scale_factor大于max_scale_factor
     """
+
     def __init__(self,
                  min_scale_factor=0.75,
                  max_scale_factor=1.25,
@@ -386,6 +394,7 @@ class Normalize:
     Raises:
         ValueError: mean或std不是list对象。std包含0。
     """
+
     def __init__(self, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
         self.mean = mean
         self.std = std
@@ -431,6 +440,7 @@ class Padding:
         TypeError: target_size不是int|list|tuple。
         ValueError:  target_size为list|tuple时元素个数不等于2。
     """
+
     def __init__(self,
                  target_size,
                  im_padding_value=[127.5, 127.5, 127.5],
@@ -466,8 +476,8 @@ class Padding:
             ValueError: 输入图像im或label的形状大于目标值
         """
         if im_info is None:
-            im_info = OrderedDict()
-        im_info['shape_before_padding'] = im.shape[:2]
+            im_info = list()
+        im_info.append(('padding', im.shape[:2]))
 
         im_height, im_width = im.shape[0], im.shape[1]
         if isinstance(self.target_size, int):
@@ -483,7 +493,8 @@ class Padding:
                 'the size of image should be less than target_size, but the size of image ({}, {}), is larger than target_size ({}, {})'
                 .format(im_width, im_height, target_width, target_height))
         else:
-            im = cv2.copyMakeBorder(im,
+            im = cv2.copyMakeBorder(
+                im,
                 0,
                 pad_height,
                 0,
@@ -491,7 +502,8 @@ class Padding:
                 cv2.BORDER_CONSTANT,
                 value=self.im_padding_value)
             if label is not None:
-                label = cv2.copyMakeBorder(label,
+                label = cv2.copyMakeBorder(
+                    label,
                     0,
                     pad_height,
                     0,
@@ -516,6 +528,7 @@ class RandomPaddingCrop:
         TypeError: crop_size不是int/list/tuple。
         ValueError:  target_size为list/tuple时元素个数不等于2。
     """
+
     def __init__(self,
                  crop_size=512,
                  im_padding_value=[127.5, 127.5, 127.5],
@@ -564,7 +577,8 @@ class RandomPaddingCrop:
             pad_height = max(crop_height - img_height, 0)
             pad_width = max(crop_width - img_width, 0)
             if (pad_height > 0 or pad_width > 0):
-                im = cv2.copyMakeBorder(im,
+                im = cv2.copyMakeBorder(
+                    im,
                     0,
                     pad_height,
                     0,
@@ -572,7 +586,8 @@ class RandomPaddingCrop:
                     cv2.BORDER_CONSTANT,
                     value=self.im_padding_value)
                 if label is not None:
-                    label = cv2.copyMakeBorder(label,
+                    label = cv2.copyMakeBorder(
+                        label,
                         0,
                         pad_height,
                         0,
@@ -586,11 +601,11 @@ class RandomPaddingCrop:
                 h_off = np.random.randint(img_height - crop_height + 1)
                 w_off = np.random.randint(img_width - crop_width + 1)
 
-                im = im[h_off:(crop_height + h_off), w_off:(w_off +
-                                                            crop_width), :]
+                im = im[h_off:(crop_height + h_off), w_off:(
+                    w_off + crop_width), :]
                 if label is not None:
-                    label = label[h_off:(crop_height +
-                                         h_off), w_off:(w_off + crop_width)]
+                    label = label[h_off:(crop_height + h_off), w_off:(
+                        w_off + crop_width)]
         if label is None:
             return (im, im_info)
         else:
@@ -603,6 +618,7 @@ class RandomBlur:
     Args：
         prob (float): 图像模糊概率。默认为0.1。
     """
+
     def __init__(self, prob=0.1):
         self.prob = prob
 
@@ -650,6 +666,7 @@ class RandomRotation:
         label_padding_value (int): 标注图像padding的值。默认为255。
 
     """
+
     def __init__(self,
                  max_rotation=15,
                  im_padding_value=[127.5, 127.5, 127.5],
@@ -686,13 +703,15 @@ class RandomRotation:
             r[0, 2] += (nw / 2) - cx
             r[1, 2] += (nh / 2) - cy
             dsize = (nw, nh)
-            im = cv2.warpAffine(im,
+            im = cv2.warpAffine(
+                im,
                 r,
                 dsize=dsize,
                 flags=cv2.INTER_LINEAR,
                 borderMode=cv2.BORDER_CONSTANT,
                 borderValue=self.im_padding_value)
-            label = cv2.warpAffine(label,
+            label = cv2.warpAffine(
+                label,
                 r,
                 dsize=dsize,
                 flags=cv2.INTER_NEAREST,
@@ -713,6 +732,7 @@ class RandomScaleAspect:
         min_scale (float)：裁取图像占原始图像的面积比，取值[0，1]，为0时则返回原图。默认为0.5。
         aspect_ratio (float): 裁取图像的宽高比范围，非负值，为0时返回原图。默认为0.33。
     """
+
     def __init__(self, min_scale=0.5, aspect_ratio=0.33):
         self.min_scale = min_scale
         self.aspect_ratio = aspect_ratio
@@ -751,9 +771,11 @@ class RandomScaleAspect:
 
                     im = im[h1:(h1 + dh), w1:(w1 + dw), :]
                     label = label[h1:(h1 + dh), w1:(w1 + dw)]
-                    im = cv2.resize(im, (img_width, img_height),
+                    im = cv2.resize(
+                        im, (img_width, img_height),
                         interpolation=cv2.INTER_LINEAR)
-                    label = cv2.resize(label, (img_width, img_height),
+                    label = cv2.resize(
+                        label, (img_width, img_height),
                         interpolation=cv2.INTER_NEAREST)
                     break
         if label is None:
@@ -778,6 +800,7 @@ class RandomDistort:
         hue_range (int): 色调因子的范围。默认为18。
         hue_prob (float): 随机调整色调的概率。默认为0.5。
     """
+
     def __init__(self,
                  brightness_range=0.5,
                  brightness_prob=0.5,