image preprcoess module

python/paddle/v2/__init__.py

@@ -32,11 +32,12 @@ import networks
 import py_paddle.swig_paddle as api
 import minibatch
 import plot
+import image
 
 __all__ = [
     'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer',
     'event', 'data_type', 'attr', 'pooling', 'data_feeder', 'dataset', 'reader',
-    'topology', 'networks', 'infer', 'plot', 'evaluator'
+    'topology', 'networks', 'infer', 'plot', 'evaluator', 'image'
 ]
 
 

python/paddle/v2/image.py

+import numpy as np
+try:
+    import cv2
+except ImportError:
+    cv2 = None
+
+from cv2 import resize
+
+__all__ = [
+    "load_image", "resize_short", "to_chw", "center_crop", "random_crop",
+    "left_right_flip", "simple_transform", "load_and_transform"
+]
+"""
+This file contains some common interface for image preprocess.
+Many users are confused about the image layout. We introduce
+the image layout firstly.
+
+- CHW Layout
+  - The abbreviations: C=channel, H=Height, W=Width
+  - The default image layout is HWC opened by cv2 or PIL.
+    PaddlePaddle only support the image layout with CHW.
+    CHW is simply a transpose of HWC. It must transpose
+    the input image.
+
+- Color format: RGB or BGR
+  OpenCV use BGR color format. PIL use RGB color format. Both
+  formats can be used for training. But it must be noted that,
+  the format should be keep consistent between the training and
+  inference peroid.
+"""
+
+
+def load_image(file, is_color=True):
+    """
+    Load an color or gray image from the file path.
+
+    Example usage:
+    
+    .. code-block:: python
+        im = load_image('cat.jpg')
+
+    :param file: the input image path.
+    :type file: string
+    :param is_color: If set is_color True, it will load and
+                     return a color image. Otherwise, it will
+                     load and return a gray image.
+    """
+    flag = cv2.CV_LOAD_IMAGE_COLOR if is_color else \
+           cv2.CV_LOAD_IMAGE_GRAYSCALE
+    im = cv2.imread(file, flag)
+    return im
+
+
+def resize_short(im, size):
+    """ 
+    Resize an image so that the length of shorter edge is size.
+
+    Example usage:
+    
+    .. code-block:: python
+        im = load_image('cat.jpg')
+        im = resize_short(im, 256)
+    
+    :param im: the input image with HWC layout.
+    :type im: ndarray
+    :param size: the shorter edge size of image after resizing.
+    :type size: int
+    """
+    assert im.shape[-1] == 1 or im.shape[-1] == 3
+    h, w = im.shape[:2]
+    h_new, w_new = size, size
+    if h > w:
+        h_new = size * h / w
+    else:
+        w_new = size * w / h
+    im = resize(im, (h_new, w_new), interpolation=cv2.INTER_CUBIC)
+    return im
+
+
+def to_chw(im, order=(2, 0, 1)):
+    """
+    Transpose the input image order. The image layout is HWC format
+    opened by cv2 or PIL. Transposed the input image to CHW layouts
+    by order (2,0,1).
+
+    Example usage:
+    
+    .. code-block:: python
+        im = load_image('cat.jpg')
+        im = resize_short(im, 256)
+        im = to_chw(im)
+    
+    :param im: the input image with HWC layout.
+    :type im: ndarray
+    :param order: the transposed order.
+    :type order: tuple|list 
+    """
+    assert len(im.shape) == len(order)
+    im = im.transpose(order)
+    return im
+
+
+def center_crop(im, size, is_color=True):
+    """
+    Crop the center of image with size.
+
+    Example usage:
+    
+    .. code-block:: python
+        im = center_crop(im, 224)
+    
+    :param im: the input image with HWC layout.
+    :type im: ndarray
+    :param size: the cropping size
+    :type size: int
+    :param is_color: whether the image is color or not.
+    :type is_color: bool
+    """
+    h, w = im.shape[:2]
+    h_start = (h - size) / 2
+    w_start = (w - size) / 2
+    h_end, w_end = h_start + size, w_start + size
+    if is_color:
+        im = im[h_start:h_end, w_start:w_end, :]
+    else:
+        im = im[h_start:h_end, w_start:w_end]
+    return im
+
+
+def random_crop(im, size, is_color=True):
+    """
+    Randomly crop input image with size.
+
+    Example usage:
+    
+    .. code-block:: python
+        im = random_crop(im, 224)
+    
+    :param im: the input image with HWC layout.
+    :type im: ndarray
+    :param size: the cropping size
+    :type size: int
+    :param is_color: whether the image is color or not.
+    :type is_color: bool
+    """
+    h, w = im.shape[:2]
+    h_start = np.random.randint(0, h - size + 1)
+    w_start = np.random.randint(0, w - size + 1)
+    h_end, w_end = h_start + size, w_start + size
+    if is_color:
+        im = im[h_start:h_end, w_start:w_end, :]
+    else:
+        im = im[h_start:h_end, w_start:w_end]
+    return im
+
+
+def left_right_flip(im):
+    """
+    Flip an image along the horizontal direction.
+    Return the flipped image.
+
+    Example usage:
+    
+    .. code-block:: python
+        im = left_right_flip(im)
+    
+    :paam im: input image with HWC layout
+    :type im: ndarray
+    """
+    if len(im.shape) == 3:
+        return im[:, ::-1, :]
+    else:
+        return im[:, ::-1, :]
+
+
+def simple_transform(im, resize_size, crop_size, is_train, is_color=True):
+    """
+    Simply data argumentation for traing. These operations includes
+    resizing, croping and flipping.
+
+    :param im: The input image with HWC layout.
+    :type im: ndarray
+    :param resize_size: The shorter edge length of the resized image.
+    :type resize_size: int
+    :param crop_size: The cropping size.
+    :type crop_size: int
+    :param is_train: Whether it is training or not.
+    :type is_train: bool
+    """
+    im = resize_short(im, resize_size)
+    if is_train:
+        im = random_crop(im, crop_size)
+        if np.random.randint(2) == 0:
+            im = left_right_flip(im)
+    else:
+        im = center_crop(im, crop_size)
+    im = to_chw(im)
+
+    return im
+
+
+def load_and_transform(filename,
+                       resize_size,
+                       crop_size,
+                       is_train,
+                       is_color=True):
+    """
+    Load image from the input file `filename` and transform image for
+    data argumentation. Please refer the `simple_transform` interface
+    for the transform operation.
+
+    :param filename: The file name of input image.
+    :type filename: string
+    :param resize_size: The shorter edge length of the resized image.
+    :type resize_size: int
+    :param crop_size: The cropping size.
+    :type crop_size: int
+    :param is_train: Whether it is training or not.
+    :type is_train: bool
+    """
+    im = load_image(filename)
+    im = simple_transform(im, resize_size, crop_size, is_train, is_color)
+    return im

python/paddle/v2/tests/CMakeLists.txt

-add_python_test(test_v2_api test_data_feeder.py test_parameters.py test_layer.py test_rnn_layer.py test_topology.py)
+add_python_test(test_v2_api test_data_feeder.py test_parameters.py
+test_layer.py test_rnn_layer.py test_topology.py test_image)

python/paddle/v2/tests/test_image.py

+# Copyright PaddlePaddle contributors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import unittest
+import numpy as np
+
+import paddle.v2.image as image
+
+
+class Image(unittest.TestCase):
+    def test_resize_flip_chw(self):
+        # resize
+        im = image.load_image('cat.jpg')
+        im = image.resize_short(im, 256)
+        self.assertEqual(256, min(im.shape[:2]))
+        self.assertEqual(3, im.shape[2])
+
+        # flip
+        im = left_right_flip(im)
+        im2 = np.flip(im, 1)
+        self.assertEqual(im.all(), im2.all())
+
+        # to_chw
+        h, w, c = im.shape
+        im = image.to_chw(im)
+        self.assertEqual(c, im.shape[0])
+        self.assertEqual(h, im.shape[1])
+        self.assertEqual(w, im.shape[2])
+
+
+if __name__ == '__main__':
+    unittest.main()

python/setup.py.in

@@ -18,6 +18,7 @@ setup(name='paddle',
           "numpy",
           "protobuf==${PROTOBUF_VERSION}",
           "matplotlib",
+          "opencv-python",
       ],
       packages=packages,
       package_dir={