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data_provider.py
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data_provider.py
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# 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 image_util
from paddle.utils.image_util import *
import random
from PIL import Image
import numpy as np
import xml.etree.ElementTree
import os
class Settings(object):
def __init__(self, data_dir, label_file, resize_h, resize_w, mean_value):
self._data_dir = data_dir
self._label_list = []
label_fpath = os.path.join(data_dir, label_file)
for line in open(label_fpath):
self._label_list.append(line.strip())
self._resize_height = resize_h
self._resize_width = resize_w
self._img_mean = np.array(mean_value)[:, np.newaxis, np.newaxis].astype(
'float32')
@property
def data_dir(self):
return self._data_dir
@property
def label_list(self):
return self._label_list
@property
def resize_h(self):
return self._resize_height
@property
def resize_w(self):
return self._resize_width
@property
def img_mean(self):
return self._img_mean
def _reader_creator(settings, file_list, mode, shuffle):
def reader():
with open(file_list) as flist:
lines = [line.strip() for line in flist]
if shuffle:
random.shuffle(lines)
for line in lines:
if mode == 'train' or mode == 'test':
img_path, label_path = line.split()
img_path = os.path.join(settings.data_dir, img_path)
label_path = os.path.join(settings.data_dir, label_path)
elif mode == 'infer':
img_path = os.path.join(settings.data_dir, line)
img = Image.open(img_path)
img_width, img_height = img.size
img = np.array(img)
# layout: label | xmin | ymin | xmax | ymax | difficult
if mode == 'train' or mode == 'test':
bbox_labels = []
root = xml.etree.ElementTree.parse(label_path).getroot()
for object in root.findall('object'):
bbox_sample = []
# start from 1
bbox_sample.append(
float(
settings.label_list.index(
object.find('name').text)))
bbox = object.find('bndbox')
difficult = float(object.find('difficult').text)
bbox_sample.append(
float(bbox.find('xmin').text) / img_width)
bbox_sample.append(
float(bbox.find('ymin').text) / img_height)
bbox_sample.append(
float(bbox.find('xmax').text) / img_width)
bbox_sample.append(
float(bbox.find('ymax').text) / img_height)
bbox_sample.append(difficult)
bbox_labels.append(bbox_sample)
sample_labels = bbox_labels
if mode == 'train':
batch_sampler = []
# hard-code here
batch_sampler.append(
image_util.sampler(1, 1, 1.0, 1.0, 1.0, 1.0, 0.0,
0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.1,
0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.3,
0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.5,
0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.7,
0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.9,
0.0))
batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.0,
1.0))
""" random crop """
sampled_bbox = image_util.generate_batch_samples(
batch_sampler, bbox_labels, img_width, img_height)
if len(sampled_bbox) > 0:
idx = int(random.uniform(0, len(sampled_bbox)))
img, sample_labels = image_util.crop_image(
img, bbox_labels, sampled_bbox[idx], img_width,
img_height)
img = Image.fromarray(img)
img = img.resize((settings.resize_w, settings.resize_h),
Image.ANTIALIAS)
img = np.array(img)
if mode == 'train':
mirror = int(random.uniform(0, 2))
if mirror == 1:
img = img[:, ::-1, :]
for i in xrange(len(sample_labels)):
tmp = sample_labels[i][1]
sample_labels[i][1] = 1 - sample_labels[i][3]
sample_labels[i][3] = 1 - tmp
if len(img.shape) == 3:
img = np.swapaxes(img, 1, 2)
img = np.swapaxes(img, 1, 0)
img = img.astype('float32')
img -= settings.img_mean
img = img.flatten()
if mode == 'train' or mode == 'test':
if mode == 'train' and len(sample_labels) == 0: continue
yield img.astype('float32'), sample_labels
elif mode == 'infer':
yield img.astype('float32')
return reader
def train(settings, file_list, shuffle=True):
return _reader_creator(settings, file_list, 'train', shuffle)
def test(settings, file_list):
return _reader_creator(settings, file_list, 'test', False)
def infer(settings, file_list):
return _reader_creator(settings, file_list, 'infer', False)