Merge pull request #2242 from wanghaoshuang/flowers_reader

Add flowers dataset for image classification model

python/paddle/v2/dataset/flowers.py

+# Copyright (c) 2016 PaddlePaddle Authors. 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.
+"""
+This module will download dataset from
+http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html 
+and parse train/test set intopaddle reader creators.
+
+This set contains images of flowers belonging to 102 different categories. 
+The images were acquired by searching the web and taking pictures. There are a
+minimum of 40 images for each category.
+
+The database was used in:
+
+Nilsback, M-E. and Zisserman, A. Automated flower classification over a large
+ number of classes.Proceedings of the Indian Conference on Computer Vision, 
+Graphics and Image Processing (2008) 
+http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}.
+
+"""
+import cPickle
+import itertools
+from common import download
+import tarfile
+import scipy.io as scio
+from paddle.v2.image import *
+import os
+import numpy as np
+import paddle.v2 as paddle
+from multiprocessing import cpu_count
+__all__ = ['train', 'test', 'valid']
+
+DATA_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz'
+LABEL_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/imagelabels.mat'
+SETID_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/setid.mat'
+DATA_MD5 = '52808999861908f626f3c1f4e79d11fa'
+LABEL_MD5 = 'e0620be6f572b9609742df49c70aed4d'
+SETID_MD5 = 'a5357ecc9cb78c4bef273ce3793fc85c'
+
+
+def default_mapper(sample):
+    '''
+    map image bytes data to type needed by model input layer
+    '''
+    img, label = sample
+    img = paddle.image.load_image_bytes(img)
+    img = paddle.image.simple_transform(img, 256, 224, True)
+    return img.flatten().astype('float32'), label
+
+
+def reader_creator(data_file,
+                   label_file,
+                   setid_file,
+                   dataset_name,
+                   mapper=default_mapper,
+                   buffered_size=1024):
+    '''
+    1. read images from tar file and 
+        merge images into batch files in 102flowers.tgz_batch/
+    2. get a reader to read sample from batch file
+    
+    :param data_file: downloaded data file 
+    :type data_file: string
+    :param label_file: downloaded label file 
+    :type label_file: string
+    :param setid_file: downloaded setid file containing information
+                        about how to split dataset
+    :type setid_file: string
+    :param dataset_name: data set name (tstid|trnid|valid)
+    :type dataset_name: string
+    :param mapper: a function to map image bytes data to type 
+                    needed by model input layer
+    :type mapper: callable
+    :param buffered_size: the size of buffer used to process images
+    :type buffered_size: int
+    :return: data reader
+    :rtype: callable
+    '''
+    labels = scio.loadmat(label_file)['labels'][0]
+    indexes = scio.loadmat(setid_file)[dataset_name][0]
+    img2label = {}
+    for i in indexes:
+        img = "jpg/image_%05d.jpg" % i
+        img2label[img] = labels[i - 1]
+    file_list = batch_images_from_tar(data_file, dataset_name, img2label)
+
+    def reader():
+        for file in open(file_list):
+            file = file.strip()
+            batch = None
+            with open(file, 'r') as f:
+                batch = cPickle.load(f)
+            data = batch['data']
+            labels = batch['label']
+            for sample, label in itertools.izip(data, batch['label']):
+                yield sample, int(label)
+
+    return paddle.reader.xmap_readers(mapper, reader,
+                                      cpu_count(), buffered_size)
+
+
+def train(mapper=default_mapper, buffered_size=1024):
+    '''
+    Create flowers training set reader. 
+    It returns a reader, each sample in the reader is   
+    image pixels in [0, 1] and label in [1, 102] 
+    translated from original color image by steps:
+    1. resize to 256*256
+    2. random crop to 224*224
+    3. flatten
+    :param mapper:  a function to map sample.
+    :type mapper: callable
+    :param buffered_size: the size of buffer used to process images
+    :type buffered_size: int
+    :return: train data reader
+    :rtype: callable
+    '''
+    return reader_creator(
+        download(DATA_URL, 'flowers', DATA_MD5),
+        download(LABEL_URL, 'flowers', LABEL_MD5),
+        download(SETID_URL, 'flowers', SETID_MD5), 'trnid', mapper,
+        buffered_size)
+
+
+def test(mapper=default_mapper, buffered_size=1024):
+    '''
+    Create flowers test set reader. 
+    It returns a reader, each sample in the reader is   
+    image pixels in [0, 1] and label in [1, 102] 
+    translated from original color image by steps:
+    1. resize to 256*256
+    2. random crop to 224*224
+    3. flatten
+    :param mapper:  a function to map sample.
+    :type mapper: callable
+    :param buffered_size: the size of buffer used to process images
+    :type buffered_size: int
+    :return: test data reader
+    :rtype: callable
+    '''
+    return reader_creator(
+        download(DATA_URL, 'flowers', DATA_MD5),
+        download(LABEL_URL, 'flowers', LABEL_MD5),
+        download(SETID_URL, 'flowers', SETID_MD5), 'tstid', mapper,
+        buffered_size)
+
+
+def valid(mapper=default_mapper, buffered_size=1024):
+    '''
+    Create flowers validation set reader. 
+    It returns a reader, each sample in the reader is   
+    image pixels in [0, 1] and label in [1, 102] 
+    translated from original color image by steps:
+    1. resize to 256*256
+    2. random crop to 224*224
+    3. flatten
+    :param mapper:  a function to map sample.
+    :type mapper: callable
+    :param buffered_size: the size of buffer used to process images
+    :type buffered_size: int
+    :return: test data reader
+    :rtype: callable
+    '''
+    return reader_creator(
+        download(DATA_URL, 'flowers', DATA_MD5),
+        download(LABEL_URL, 'flowers', LABEL_MD5),
+        download(SETID_URL, 'flowers', SETID_MD5), 'valid', mapper,
+        buffered_size)
+
+
+def fetch():
+    download(DATA_URL, 'flowers', DATA_MD5)
+    download(LABEL_URL, 'flowers', LABEL_MD5)
+    download(SETID_URL, 'flowers', SETID_MD5)

python/paddle/v2/dataset/tests/flowers_test.py

+# Copyright (c) 2016 PaddlePaddle Authors. 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 paddle.v2.dataset.flowers
+import unittest
+
+
+class TestFlowers(unittest.TestCase):
+    def check_reader(self, reader):
+        sum = 0
+        label = 0
+        size = 224 * 224 * 3
+        for l in reader():
+            self.assertEqual(l[0].size, size)
+            if l[1] > label:
+                label = l[1]
+            sum += 1
+        return sum, label
+
+    def test_train(self):
+        instances, max_label_value = self.check_reader(
+            paddle.v2.dataset.flowers.train())
+        self.assertEqual(instances, 1020)
+        self.assertEqual(max_label_value, 102)
+
+    def test_test(self):
+        instances, max_label_value = self.check_reader(
+            paddle.v2.dataset.flowers.test())
+        self.assertEqual(instances, 6149)
+        self.assertEqual(max_label_value, 102)
+
+    def test_valid(self):
+        instances, max_label_value = self.check_reader(
+            paddle.v2.dataset.flowers.valid())
+        self.assertEqual(instances, 1020)
+        self.assertEqual(max_label_value, 102)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/image.py

 import numpy as np
 try:
     import cv2
-except:
-    print(
-        "import cv2 error, please install opencv-python: pip install opencv-python"
-    )
+except ImportError:
+    cv2 = None
+import os
+import tarfile
+import cPickle
 
 __all__ = [
-    "load_image", "resize_short", "to_chw", "center_crop", "random_crop",
-    "left_right_flip", "simple_transform", "load_and_transform"
+    "load_image_bytes", "load_image", "resize_short", "to_chw", "center_crop",
+    "random_crop", "left_right_flip", "simple_transform", "load_and_transform",
+    "batch_images_from_tar"
 ]
 """
 This file contains some common interfaces for image preprocess.
@@ -28,6 +30,90 @@ the image layout as follows.
 """
 
 
+def batch_images_from_tar(data_file,
+                          dataset_name,
+                          img2label,
+                          num_per_batch=1024):
+    """
+    Read images from tar file and batch them into batch file.
+    param data_file: path of image tar file
+    type data_file: string
+    param dataset_name: 'train','test' or 'valid'
+    type dataset_name: string
+    param img2label: a dic with image file name as key 
+                    and image's label as value
+    type img2label: dic
+    param num_per_batch: image number per batch file
+    type num_per_batch: int
+    return: path of list file containing paths of batch file
+    rtype: string
+    """
+    batch_dir = data_file + "_batch"
+    out_path = "%s/%s" % (batch_dir, dataset_name)
+    meta_file = "%s/%s.txt" % (batch_dir, dataset_name)
+
+    if os.path.exists(out_path):
+        return meta_file
+    else:
+        os.makedirs(out_path)
+
+    tf = tarfile.open(data_file)
+    mems = tf.getmembers()
+    data = []
+    labels = []
+    file_id = 0
+    for mem in mems:
+        if mem.name in img2label:
+            data.append(tf.extractfile(mem).read())
+            labels.append(img2label[mem.name])
+            if len(data) == num_per_batch:
+                output = {}
+                output['label'] = labels
+                output['data'] = data
+                cPickle.dump(
+                    output,
+                    open('%s/batch_%d' % (out_path, file_id), 'w'),
+                    protocol=cPickle.HIGHEST_PROTOCOL)
+                file_id += 1
+                data = []
+                labels = []
+    if len(data) > 0:
+        output = {}
+        output['label'] = labels
+        output['data'] = data
+        cPickle.dump(
+            output,
+            open('%s/batch_%d' % (out_path, file_id), 'w'),
+            protocol=cPickle.HIGHEST_PROTOCOL)
+
+    with open(meta_file, 'a') as meta:
+        for file in os.listdir(out_path):
+            meta.write(os.path.abspath("%s/%s" % (out_path, file)) + "\n")
+    return meta_file
+
+
+def load_image_bytes(bytes, is_color=True):
+    """
+    Load an color or gray image from bytes array.
+
+    Example usage:
+    
+    .. code-block:: python
+        with open('cat.jpg') as f:
+            im = load_image_bytes(f.read())
+
+    :param bytes: the input image bytes array.
+    :type file: str
+    :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 = 1 if is_color else 0
+    file_bytes = np.asarray(bytearray(bytes), dtype=np.uint8)
+    img = cv2.imdecode(file_bytes, flag)
+    return img
+
+
 def load_image(file, is_color=True):
     """
     Load an color or gray image from the file path.

python/paddle/v2/reader/decorator.py

@@ -14,7 +14,7 @@
 
 __all__ = [
     'map_readers', 'buffered', 'compose', 'chain', 'shuffle',
-    'ComposeNotAligned', 'firstn'
+    'ComposeNotAligned', 'firstn', 'xmap_readers'
 ]
 
 import itertools
@@ -224,3 +224,74 @@ def firstn(reader, n):
             yield item
 
     return firstn_reader
+
+
+class XmapEndSignal():
+    pass
+
+
+def xmap_readers(mapper, reader, process_num, buffer_size):
+    """
+    Use multiprocess to map samples from reader by a mapper defined by user.
+    And this function contains a buffered decorator.
+    :param mapper:  a function to map sample.
+    :type mapper: callable
+    :param reader: the data reader to read from
+    :type reader: callable
+    :param process_num: process number to handle original sample 
+    :type process_num: int
+    :param buffer_size: max buffer size
+    :type buffer_size: int
+    :return: the decarated reader
+    :rtype: callable
+    """
+    end = XmapEndSignal()
+    in_queue = Queue(buffer_size)
+    out_queue = Queue(buffer_size)
+
+    # define a worker to read samples from reader to in_queue
+    def read_worker(reader, in_queue):
+        for i in reader():
+            in_queue.put(i)
+        in_queue.put(end)
+
+    # start a read worker in a thread
+    t = Thread(target=read_worker, args=(reader, in_queue))
+    t.daemon = True
+    t.start()
+
+    # define a worker to handle samples from in_queue by mapper
+    # and put mapped samples into out_queue
+    def handle_worker(in_queue, out_queue, mapper):
+        sample = in_queue.get()
+        while not isinstance(sample, XmapEndSignal):
+            r = mapper(sample)
+            out_queue.put(r)
+            sample = in_queue.get()
+        in_queue.put(end)
+        out_queue.put(end)
+
+    # start several handle_workers
+    workers = []
+    for i in xrange(process_num):
+        worker = Thread(
+            target=handle_worker, args=(in_queue, out_queue, mapper))
+        worker.daemon = True
+        workers.append(worker)
+    for w in workers:
+        w.start()
+
+    def xreader():
+        sample = out_queue.get()
+        while not isinstance(sample, XmapEndSignal):
+            yield sample
+            sample = out_queue.get()
+        finish = 1
+        while finish < process_num:
+            sample = out_queue.get()
+            if isinstance(sample, XmapEndSignal):
+                finish += 1
+            else:
+                yield sample
+
+    return xreader