|
|
|
|
# Copyright (c) 2020 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 cv2
|
|
|
|
|
import numpy as np
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class DecodeImage(object):
|
|
|
|
|
def __init__(self, to_rgb=True):
|
|
|
|
|
self.to_rgb = to_rgb
|
|
|
|
|
|
|
|
|
|
def __call__(self, img):
|
|
|
|
|
data = np.frombuffer(img, dtype='uint8')
|
|
|
|
|
img = cv2.imdecode(data, 1)
|
|
|
|
|
if self.to_rgb:
|
|
|
|
|
assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
|
|
|
|
|
img = img[:, :, ::-1]
|
|
|
|
|
|
|
|
|
|
return img
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class ResizeImage(object):
|
|
|
|
|
def __init__(self, resize_short=None, interpolation=1):
|
|
|
|
|
self.resize_short = resize_short
|
|
|
|
|
self.interpolation = interpolation
|
|
|
|
|
|
|
|
|
|
def __call__(self, img):
|
|
|
|
|
img_h, img_w = img.shape[:2]
|
|
|
|
|
percent = float(self.resize_short) / min(img_w, img_h)
|
|
|
|
|
w = int(round(img_w * percent))
|
|
|
|
|
h = int(round(img_h * percent))
|
|
|
|
|
return cv2.resize(img, (w, h), interpolation=self.interpolation)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class CropImage(object):
|
|
|
|
|
def __init__(self, size):
|
|
|
|
|
if type(size) is int:
|
|
|
|
|
self.size = (size, size)
|
|
|
|
|
else:
|
|
|
|
|
self.size = size
|
|
|
|
|
|
|
|
|
|
def __call__(self, img):
|
|
|
|
|
w, h = self.size
|
|
|
|
|
img_h, img_w = img.shape[:2]
|
|
|
|
|
w_start = (img_w - w) // 2
|
|
|
|
|
h_start = (img_h - h) // 2
|
|
|
|
|
|
|
|
|
|
w_end = w_start + w
|
|
|
|
|
h_end = h_start + h
|
|
|
|
|
return img[h_start:h_end, w_start:w_end, :]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class NormalizeImage(object):
|
|
|
|
|
def __init__(self, scale=None, mean=None, std=None):
|
|
|
|
|
self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
|
|
|
|
|
mean = mean if mean is not None else [0.485, 0.456, 0.406]
|
|
|
|
|
std = std if std is not None else [0.229, 0.224, 0.225]
|
|
|
|
|
|
|
|
|
|
shape = (1, 1, 3)
|
|
|
|
|
self.mean = np.array(mean).reshape(shape).astype('float32')
|
|
|
|
|
self.std = np.array(std).reshape(shape).astype('float32')
|
|
|
|
|
|
|
|
|
|
def __call__(self, img):
|
|
|
|
|
return (img.astype('float32') * self.scale - self.mean) / self.std
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class ToTensor(object):
|
|
|
|
|
def __init__(self):
|
|
|
|
|
pass
|
|
|
|
|
|
|
|
|
|
def __call__(self, img):
|
|
|
|
|
img = img.transpose((2, 0, 1))
|
|
|
|
|
return img
|
