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279 lines
9.7 KiB
279 lines
9.7 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os, sys
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import numpy as np
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from PIL import Image
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import six
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from six.moves import cStringIO as StringIO
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import multiprocessing
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import functools
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import itertools
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from paddle.utils.image_util import *
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from paddle.trainer.config_parser import logger
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try:
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import cv2
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except ImportError:
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logger.warning("OpenCV2 is not installed, using PIL to process")
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cv2 = None
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__all__ = ["CvTransformer", "PILTransformer", "MultiProcessImageTransformer"]
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class CvTransformer(ImageTransformer):
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"""
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CvTransformer used python-opencv to process image.
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"""
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def __init__(
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self,
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min_size=None,
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crop_size=None,
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transpose=(2, 0, 1), # transpose to C * H * W
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channel_swap=None,
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mean=None,
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is_train=True,
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is_color=True):
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ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
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self.min_size = min_size
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self.crop_size = crop_size
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self.is_train = is_train
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def resize(self, im, min_size):
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row, col = im.shape[:2]
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new_row, new_col = min_size, min_size
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if row > col:
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new_row = min_size * row / col
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else:
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new_col = min_size * col / row
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im = cv2.resize(im, (new_row, new_col), interpolation=cv2.INTER_CUBIC)
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return im
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def crop_and_flip(self, im):
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"""
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Return cropped image.
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The size of the cropped image is inner_size * inner_size.
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im: (H x W x K) ndarrays
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"""
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row, col = im.shape[:2]
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start_h, start_w = 0, 0
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if self.is_train:
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start_h = np.random.randint(0, row - self.crop_size + 1)
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start_w = np.random.randint(0, col - self.crop_size + 1)
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else:
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start_h = (row - self.crop_size) / 2
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start_w = (col - self.crop_size) / 2
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end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
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if self.is_color:
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im = im[start_h:end_h, start_w:end_w, :]
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else:
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im = im[start_h:end_h, start_w:end_w]
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if (self.is_train) and (np.random.randint(2) == 0):
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if self.is_color:
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im = im[:, ::-1, :]
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else:
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im = im[:, ::-1]
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return im
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def transform(self, im):
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im = self.resize(im, self.min_size)
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im = self.crop_and_flip(im)
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# transpose, swap channel, sub mean
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im = im.astype('float32')
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ImageTransformer.transformer(self, im)
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return im
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def load_image_from_string(self, data):
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flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
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im = cv2.imdecode(np.fromstring(data, np.uint8), flag)
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return im
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def transform_from_string(self, data):
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im = self.load_image_from_string(data)
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return self.transform(im)
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def load_image_from_file(self, file):
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flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
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im = cv2.imread(file, flag)
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return im
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def transform_from_file(self, file):
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im = self.load_image_from_file(file)
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return self.transform(im)
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class PILTransformer(ImageTransformer):
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"""
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PILTransformer used PIL to process image.
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"""
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def __init__(
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self,
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min_size=None,
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crop_size=None,
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transpose=(2, 0, 1), # transpose to C * H * W
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channel_swap=None,
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mean=None,
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is_train=True,
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is_color=True):
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ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
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self.min_size = min_size
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self.crop_size = crop_size
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self.is_train = is_train
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def resize(self, im, min_size):
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row, col = im.size[:2]
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new_row, new_col = min_size, min_size
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if row > col:
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new_row = min_size * row / col
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else:
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new_col = min_size * col / row
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im = im.resize((new_row, new_col), Image.ANTIALIAS)
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return im
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def crop_and_flip(self, im):
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"""
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Return cropped image.
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The size of the cropped image is inner_size * inner_size.
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"""
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row, col = im.size[:2]
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start_h, start_w = 0, 0
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if self.is_train:
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start_h = np.random.randint(0, row - self.crop_size + 1)
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start_w = np.random.randint(0, col - self.crop_size + 1)
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else:
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start_h = (row - self.crop_size) / 2
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start_w = (col - self.crop_size) / 2
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end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
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im = im.crop((start_h, start_w, end_h, end_w))
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if (self.is_train) and (np.random.randint(2) == 0):
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im = im.transpose(Image.FLIP_LEFT_RIGHT)
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return im
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def transform(self, im):
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im = self.resize(im, self.min_size)
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im = self.crop_and_flip(im)
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im = np.array(im, dtype=np.float32) # convert to numpy.array
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# transpose, swap channel, sub mean
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ImageTransformer.transformer(self, im)
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return im
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def load_image_from_string(self, data):
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im = Image.open(StringIO(data))
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return im
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def transform_from_string(self, data):
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im = self.load_image_from_string(data)
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return self.transform(im)
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def load_image_from_file(self, file):
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im = Image.open(file)
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return im
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def transform_from_file(self, file):
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im = self.load_image_from_file(file)
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return self.transform(im)
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def job(is_img_string, transformer, data_label_pack):
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(data, label) = data_label_pack
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if is_img_string:
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return transformer.transform_from_string(data), label
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else:
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return transformer.transform_from_file(data), label
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class MultiProcessImageTransformer(object):
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def __init__(self,
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procnum=10,
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resize_size=None,
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crop_size=None,
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transpose=(2, 0, 1),
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channel_swap=None,
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mean=None,
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is_train=True,
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is_color=True,
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is_img_string=True):
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"""
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Processing image with multi-process. If it is used in PyDataProvider,
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the simple usage for CNN is as follows:
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.. code-block:: python
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def hool(settings, is_train, **kwargs):
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settings.is_train = is_train
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settings.mean_value = np.array([103.939,116.779,123.68], dtype=np.float32)
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settings.input_types = [
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dense_vector(3 * 224 * 224),
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integer_value(1)]
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settings.transformer = MultiProcessImageTransformer(
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procnum=10,
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resize_size=256,
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crop_size=224,
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transpose=(2, 0, 1),
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mean=settings.mean_values,
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is_train=settings.is_train)
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@provider(init_hook=hook, pool_size=20480)
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def process(settings, file_list):
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with open(file_list, 'r') as fdata:
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for line in fdata:
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data_dic = np.load(line.strip()) # load the data batch pickled by Pickle.
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data = data_dic['data']
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labels = data_dic['label']
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labels = np.array(labels, dtype=np.float32)
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for im, lab in settings.dp.run(data, labels):
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yield [im.astype('float32'), int(lab)]
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:param procnum: processor number.
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:type procnum: int
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:param resize_size: the shorter edge size of image after resizing.
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:type resize_size: int
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:param crop_size: the croping size.
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:type crop_size: int
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:param transpose: the transpose order, Paddle only allow C * H * W order.
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:type transpose: tuple or list
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:param channel_swap: the channel swap order, RGB or BRG.
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:type channel_swap: tuple or list
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:param mean: the mean values of image, per-channel mean or element-wise mean.
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:type mean: array, The dimension is 1 for per-channel mean.
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The dimension is 3 for element-wise mean.
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:param is_train: training peroid or testing peroid.
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:type is_train: bool.
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:param is_color: the image is color or gray.
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:type is_color: bool.
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:param is_img_string: The input can be the file name of image or image string.
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:type is_img_string: bool.
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"""
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self.procnum = procnum
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self.pool = multiprocessing.Pool(procnum)
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self.is_img_string = is_img_string
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if cv2 is not None:
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self.transformer = CvTransformer(resize_size, crop_size, transpose,
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channel_swap, mean, is_train,
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is_color)
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else:
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self.transformer = PILTransformer(resize_size, crop_size, transpose,
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channel_swap, mean, is_train,
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is_color)
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def run(self, data, label):
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fun = functools.partial(job, self.is_img_string, self.transformer)
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return self.pool.imap_unordered(
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fun, six.moves.zip(data, label), chunksize=100 * self.procnum)
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