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reader.py
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reader.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# 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 os
import sys
import cv2
import math
import random
import functools
try:
import cPickle as pickle
from cStringIO import StringIO
except ImportError:
import pickle
from io import BytesIO
import numpy as np
import paddle
from PIL import Image, ImageEnhance, ImageDraw
import logging
logger = logging.getLogger(__name__)
python_ver = sys.version_info
class KineticsReader(object):
"""
Data reader for kinetics dataset of two format mp4 and pkl.
1. mp4, the original format of kinetics400
2. pkl, the mp4 was decoded previously and stored as pkl
In both case, load the data, and then get the frame data in the form of numpy and label as an integer.
dataset cfg: format
num_classes
seg_num
short_size
target_size
num_reader_threads
buf_size
image_mean
image_std
batch_size
list
"""
def __init__(self, name, mode, cfg):
self.cfg = cfg
self.mode = mode
self.name = name
self.format = cfg.MODEL.format
self.num_classes = self.get_config_from_sec('model', 'num_classes')
self.seg_num = self.get_config_from_sec('model', 'seg_num')
self.seglen = self.get_config_from_sec('model', 'seglen')
self.seg_num = self.get_config_from_sec(mode, 'seg_num', self.seg_num)
self.short_size = self.get_config_from_sec(mode, 'short_size')
self.target_size = self.get_config_from_sec(mode, 'target_size')
self.num_reader_threads = self.get_config_from_sec(mode,
'num_reader_threads')
self.buf_size = self.get_config_from_sec(mode, 'buf_size')
self.enable_ce = self.get_config_from_sec(mode, 'enable_ce')
self.img_mean = np.array(cfg.MODEL.image_mean).reshape(
[3, 1, 1]).astype(np.float32)
self.img_std = np.array(cfg.MODEL.image_std).reshape(
[3, 1, 1]).astype(np.float32)
# set batch size and file list
self.batch_size = cfg[mode.upper()]['batch_size']
self.filelist = cfg[mode.upper()]['filelist']
if self.enable_ce:
random.seed(0)
np.random.seed(0)
def get_config_from_sec(self, sec, item, default=None):
if sec.upper() not in self.cfg:
return default
return self.cfg[sec.upper()].get(item, default)
def create_reader(self):
_reader = self._reader_creator(self.filelist, self.mode, seg_num=self.seg_num, seglen=self.seglen,
short_size=self.short_size, target_size=self.target_size,
img_mean=self.img_mean, img_std=self.img_std,
shuffle=(self.mode == 'train'),
num_threads=self.num_reader_threads,
buf_size=self.buf_size, format=self.format)
def _batch_reader():
batch_out = []
for imgs, label in _reader():
if imgs is None:
continue
batch_out.append((imgs, label))
if len(batch_out) == self.batch_size:
yield batch_out
batch_out = []
return _batch_reader
def _reader_creator(self,
pickle_list,
mode,
seg_num,
seglen,
short_size,
target_size,
img_mean,
img_std,
shuffle=False,
num_threads=1,
buf_size=1024,
format='pkl'):
def decode_mp4(sample, mode, seg_num, seglen, short_size, target_size,
img_mean, img_std):
sample = sample[0].split(' ')
mp4_path = sample[0]
# when infer, we store vid as label
label = int(sample[1])
try:
imgs = mp4_loader(mp4_path, seg_num, seglen, mode)
if len(imgs) < 1:
logger.error('{} frame length {} less than 1.'.format(
mp4_path, len(imgs)))
return None, None
except:
logger.error('Error when loading {}'.format(mp4_path))
return None, None
return imgs_transform(imgs, label, mode, seg_num, seglen, \
short_size, target_size, img_mean, img_std)
def decode_pickle(sample, mode, seg_num, seglen, short_size,
target_size, img_mean, img_std):
pickle_path = sample[0]
try:
if python_ver < (3, 0):
data_loaded = pickle.load(open(pickle_path, 'rb'))
else:
data_loaded = pickle.load(
open(pickle_path, 'rb'), encoding='bytes')
vid, label, frames = data_loaded
if len(frames) < 1:
logger.error('{} frame length {} less than 1.'.format(
pickle_path, len(frames)))
return None, None
except:
logger.info('Error when loading {}'.format(pickle_path))
return None, None
if mode == 'train' or mode == 'valid' or mode == 'test':
ret_label = label
elif mode == 'infer':
ret_label = vid
imgs = video_loader(frames, seg_num, seglen, mode)
return imgs_transform(imgs, ret_label, mode, seg_num, seglen, \
short_size, target_size, img_mean, img_std)
def imgs_transform(imgs, label, mode, seg_num, seglen, short_size,
target_size, img_mean, img_std):
imgs = group_scale(imgs, short_size)
if mode == 'train':
if self.name == "TSM":
imgs = group_multi_scale_crop(imgs, short_size)
imgs = group_random_crop(imgs, target_size)
imgs = group_random_flip(imgs)
imgs = group_random_brightness(imgs)
imgs = group_random_contrast(imgs)
imgs = group_random_saturation(imgs)
#添加数据增强部分,提升分类精度
else:
imgs = group_center_crop(imgs, target_size)
np_imgs = (np.array(imgs[0]).astype('float32').transpose(
(2, 0, 1))).reshape(1, 3, target_size, target_size) / 255
for i in range(len(imgs) - 1):
img = (np.array(imgs[i + 1]).astype('float32').transpose(
(2, 0, 1))).reshape(1, 3, target_size, target_size) / 255
np_imgs = np.concatenate((np_imgs, img))
imgs = np_imgs
imgs -= img_mean
imgs /= img_std
imgs = np.reshape(imgs,
(seg_num, seglen * 3, target_size, target_size))
return imgs, label
def reader():
with open(pickle_list) as flist:
lines = [line.strip() for line in flist]
if shuffle:
random.shuffle(lines)
for line in lines:
pickle_path = line.strip()
yield [pickle_path]
if format == 'pkl':
decode_func = decode_pickle
elif format == 'mp4':
decode_func = decode_mp4
else:
raise "Not implemented format {}".format(format)
mapper = functools.partial(
decode_func,
mode=mode,
seg_num=seg_num,
seglen=seglen,
short_size=short_size,
target_size=target_size,
img_mean=img_mean,
img_std=img_std)
return paddle.reader.xmap_readers(mapper, reader, num_threads, buf_size)
def group_multi_scale_crop(img_group, target_size, scales=None, \
max_distort=1, fix_crop=True, more_fix_crop=True):
scales = scales if scales is not None else [1, .875, .75, .66]
input_size = [target_size, target_size]
im_size = img_group[0].size
# get random crop offset
def _sample_crop_size(im_size):
image_w, image_h = im_size[0], im_size[1]
base_size = min(image_w, image_h)
crop_sizes = [int(base_size * x) for x in scales]
crop_h = [
input_size[1] if abs(x - input_size[1]) < 3 else x
for x in crop_sizes
]
crop_w = [
input_size[0] if abs(x - input_size[0]) < 3 else x
for x in crop_sizes
]
pairs = []
for i, h in enumerate(crop_h):
for j, w in enumerate(crop_w):
if abs(i - j) <= max_distort:
pairs.append((w, h))
crop_pair = random.choice(pairs)
if not fix_crop:
w_offset = random.randint(0, image_w - crop_pair[0])
h_offset = random.randint(0, image_h - crop_pair[1])
else:
w_step = (image_w - crop_pair[0]) / 4
h_step = (image_h - crop_pair[1]) / 4
ret = list()
ret.append((0, 0)) # upper left
if w_step != 0:
ret.append((4 * w_step, 0)) # upper right
if h_step != 0:
ret.append((0, 4 * h_step)) # lower left
if h_step != 0 and w_step != 0:
ret.append((4 * w_step, 4 * h_step)) # lower right
if h_step != 0 or w_step != 0:
ret.append((2 * w_step, 2 * h_step)) # center
if more_fix_crop:
ret.append((0, 2 * h_step)) # center left
ret.append((4 * w_step, 2 * h_step)) # center right
ret.append((2 * w_step, 4 * h_step)) # lower center
ret.append((2 * w_step, 0 * h_step)) # upper center
ret.append((1 * w_step, 1 * h_step)) # upper left quarter
ret.append((3 * w_step, 1 * h_step)) # upper right quarter
ret.append((1 * w_step, 3 * h_step)) # lower left quarter
ret.append((3 * w_step, 3 * h_step)) # lower righ quarter
w_offset, h_offset = random.choice(ret)
return crop_pair[0], crop_pair[1], w_offset, h_offset
crop_w, crop_h, offset_w, offset_h = _sample_crop_size(im_size)
crop_img_group = [
img.crop((offset_w, offset_h, offset_w + crop_w, offset_h + crop_h))
for img in img_group
]
ret_img_group = [
img.resize((input_size[0], input_size[1]), Image.BILINEAR)
for img in crop_img_group
]
return ret_img_group
def group_random_crop(img_group, target_size):
w, h = img_group[0].size
th, tw = target_size, target_size
assert (w >= target_size) and (h >= target_size), \
"image width({}) and height({}) should be larger than crop size".format(w, h, target_size)
out_images = []
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
for img in img_group:
if w == tw and h == th:
out_images.append(img)
else:
out_images.append(img.crop((x1, y1, x1 + tw, y1 + th)))
return out_images
def group_random_flip(img_group):
v = random.random()
if v < 0.5:
ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group]
return ret
else:
return img_group
def group_random_brightness(img_group):
prob = np.random.uniform(0, 1)
if prob < 0.5:
brightness_delta = 0.125
delta = np.random.uniform(-brightness_delta, brightness_delta) + 1
ret = [ImageEnhance.Brightness(img).enhance(delta) for img in img_group]
return ret
else:
return img_group
def group_random_contrast(img_group):
prob = np.random.uniform(0, 1)
if prob < 0.5:
contrast_delta = 0.5
delta = np.random.uniform(-contrast_delta, contrast_delta) + 1
ret = [ImageEnhance.Contrast(img).enhance(delta) for img in img_group]
return ret
else:
return img_group
def group_random_saturation(img_group):
prob = np.random.uniform(0, 1)
if prob < 0.5:
saturation_delta = 0.5
delta = np.random.uniform(-saturation_delta, saturation_delta) + 1
ret = [ImageEnhance.Color(img).enhance(delta) for img in img_group]
return ret
else:
return img_group
def group_center_crop(img_group, target_size):
img_crop = []
for img in img_group:
w, h = img.size
th, tw = target_size, target_size
assert (w >= target_size) and (h >= target_size), \
"image width({}) and height({}) should be larger than crop size".format(w, h, target_size)
x1 = int(round((w - tw) / 2.))
y1 = int(round((h - th) / 2.))
img_crop.append(img.crop((x1, y1, x1 + tw, y1 + th)))
return img_crop
def group_scale(imgs, target_size):
resized_imgs = []
for i in range(len(imgs)):
img = imgs[i]
w, h = img.size
if (w <= h and w == target_size) or (h <= w and h == target_size):
resized_imgs.append(img)
continue
if w < h:
ow = target_size
oh = int(target_size * 4.0 / 3.0)
resized_imgs.append(img.resize((ow, oh), Image.BILINEAR))
else:
oh = target_size
ow = int(target_size * 4.0 / 3.0)
resized_imgs.append(img.resize((ow, oh), Image.BILINEAR))
return resized_imgs
def imageloader(buf):
if isinstance(buf, str):
img = Image.open(buf)
else:
img = Image.open(BytesIO(buf))
return img.convert('RGB')
def video_loader(frames, nsample, seglen, mode):
videolen = len(frames)
average_dur = int(videolen / nsample)
imgs = []
for i in range(nsample):
idx = 0
if mode == 'train':
if average_dur >= seglen:
idx = random.randint(0, average_dur - seglen)
idx += i * average_dur
elif average_dur >= 1:
idx += i * average_dur
else:
idx = i
else:
if average_dur >= seglen:
idx = (average_dur - seglen) // 2
idx += i * average_dur
elif average_dur >= 1:
idx += i * average_dur
else:
idx = i
for jj in range(idx, idx + seglen):
imgbuf = frames[int(jj % videolen)]
img = imageloader(imgbuf)
imgs.append(img)
return imgs
def mp4_loader(filepath, nsample, seglen, mode):
cap = cv2.VideoCapture(filepath)
videolen = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
sampledFrames = []
for i in range(videolen):
ret, frame = cap.read()
# maybe first frame is empty
if ret == False:
continue
img = frame[:, :, ::-1]
sampledFrames.append(img)
average_dur = int(len(sampledFrames) / nsample)
imgs = []
for i in range(nsample):
idx = 0
if mode == 'train':
if average_dur >= seglen:
idx = random.randint(0, average_dur - seglen)
idx += i * average_dur
elif average_dur >= 1:
idx += i * average_dur
else:
idx = i
else:
if average_dur >= seglen:
idx = (average_dur - 1) // 2
idx += i * average_dur
elif average_dur >= 1:
idx += i * average_dur
else:
idx = i
for jj in range(idx, idx + seglen):
imgbuf = sampledFrames[int(jj % len(sampledFrames))]
img = Image.fromarray(imgbuf, mode='RGB')
imgs.append(img)
return imgs