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reader.py
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reader.py
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import os
import math
import random
import functools
import numpy as np
import paddle
from PIL import Image, ImageEnhance
random.seed(0)
DATA_DIM = 224
THREAD = 8
BUF_SIZE = 102400
DATA_DIR = 'data/ILSVRC2012'
TRAIN_LIST = 'data/ILSVRC2012/train_list.txt'
TEST_LIST = 'data/ILSVRC2012/val_list.txt'
img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
def resize_short(img, target_size):
percent = float(target_size) / min(img.size[0], img.size[1])
resized_width = int(round(img.size[0] * percent))
resized_height = int(round(img.size[1] * percent))
img = img.resize((resized_width, resized_height), Image.LANCZOS)
return img
def crop_image(img, target_size, center):
width, height = img.size
size = target_size
if center == True:
w_start = (width - size) / 2
h_start = (height - size) / 2
else:
w_start = random.randint(0, width - size)
h_start = random.randint(0, height - size)
w_end = w_start + size
h_end = h_start + size
img = img.crop((w_start, h_start, w_end, h_end))
return img
def random_crop(img, size, scale=[0.08, 1.0], ratio=[3. / 4., 4. / 3.]):
aspect_ratio = math.sqrt(random.uniform(*ratio))
w = 1. * aspect_ratio
h = 1. / aspect_ratio
bound = min((float(img.size[0]) / img.size[1]) / (w**2),
(float(img.size[1]) / img.size[0]) / (h**2))
scale_max = min(scale[1], bound)
scale_min = min(scale[0], bound)
target_area = img.size[0] * img.size[1] * random.uniform(scale_min,
scale_max)
target_size = math.sqrt(target_area)
w = int(target_size * w)
h = int(target_size * h)
i = random.randint(0, img.size[0] - w)
j = random.randint(0, img.size[1] - h)
img = img.crop((i, j, i + w, j + h))
img = img.resize((size, size), Image.LANCZOS)
return img
def rotate_image(img):
angle = random.randint(-10, 10)
img = img.rotate(angle)
return img
def distort_color(img):
def random_brightness(img, lower=0.5, upper=1.5):
e = random.uniform(lower, upper)
return ImageEnhance.Brightness(img).enhance(e)
def random_contrast(img, lower=0.5, upper=1.5):
e = random.uniform(lower, upper)
return ImageEnhance.Contrast(img).enhance(e)
def random_color(img, lower=0.5, upper=1.5):
e = random.uniform(lower, upper)
return ImageEnhance.Color(img).enhance(e)
ops = [random_brightness, random_contrast, random_color]
random.shuffle(ops)
img = ops[0](img)
img = ops[1](img)
img = ops[2](img)
return img
def process_image(sample, mode, color_jitter, rotate):
img_path = sample[0]
img = Image.open(img_path)
if mode == 'train':
if rotate: img = rotate_image(img)
img = random_crop(img, DATA_DIM)
else:
img = resize_short(img, target_size=256)
img = crop_image(img, target_size=DATA_DIM, center=True)
if mode == 'train':
if color_jitter:
img = distort_color(img)
if random.randint(0, 1) == 1:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
if img.mode != 'RGB':
img = img.convert('RGB')
img = np.array(img).astype('float32').transpose((2, 0, 1)) / 255
img -= img_mean
img /= img_std
if mode == 'train' or mode == 'val':
return img, sample[1]
elif mode == 'test':
return [img]
def _reader_creator(file_list,
mode,
shuffle=False,
color_jitter=False,
rotate=False):
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 == 'val':
img_path, label = line.split()
img_path = os.path.join(DATA_DIR, img_path)
yield img_path, int(label)
elif mode == 'test':
img_path = os.path.join(DATA_DIR, line)
yield [img_path]
mapper = functools.partial(
process_image, mode=mode, color_jitter=color_jitter, rotate=rotate)
return paddle.reader.xmap_readers(mapper, reader, THREAD, BUF_SIZE)
def train(file_list=TRAIN_LIST):
return _reader_creator(
file_list, 'train', shuffle=True, color_jitter=False, rotate=False)
def val(file_list=TEST_LIST):
return _reader_creator(file_list, 'val', shuffle=False)
def test(file_list=TEST_LIST):
return _reader_creator(file_list, 'test', shuffle=False)