# 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
def decode_image(im_file, im_info):
"""read rgb image
Args:
im_file (str|np.ndarray): input can be image path or np.ndarray
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
if isinstance(im_file, str):
with open(im_file, 'rb') as f:
im_read = f.read()
data = np.frombuffer(im_read, dtype='uint8')
im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
else:
im = im_file
im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32)
im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32)
return im, im_info
class DetResize(object):
"""resize image by target_size and max_size
Args:
target_size (int): the target size of image
keep_ratio (bool): whether keep_ratio or not, default true
interp (int): method of resize
"""
def __init__(
self,
target_size,
keep_ratio=True,
interp=cv2.INTER_LINEAR, ):
if isinstance(target_size, int):
target_size = [target_size, target_size]
self.target_size = target_size
self.keep_ratio = keep_ratio
self.interp = interp
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
assert len(self.target_size) == 2
assert self.target_size[0] > 0 and self.target_size[1] > 0
im_channel = im.shape[2]
im_scale_y, im_scale_x = self.generate_scale(im)
# set image_shape
im_info['input_shape'][1] = int(im_scale_y * im.shape[0])
im_info['input_shape'][2] = int(im_scale_x * im.shape[1])
im = cv2.resize(
im,
None,
None,
fx=im_scale_x,
fy=im_scale_y,
interpolation=self.interp)
im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
im_info['scale_factor'] = np.array(
[im_scale_y, im_scale_x]).astype('float32')
return im, im_info
def generate_scale(self, im):
"""
Args:
im (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
"""
origin_shape = im.shape[:2]
im_c = im.shape[2]
if self.keep_ratio:
im_size_min = np.min(origin_shape)
im_size_max = np.max(origin_shape)
target_size_min = np.min(self.target_size)
target_size_max = np.max(self.target_size)
im_scale = float(target_size_min) / float(im_size_min)
if np.round(im_scale * im_size_max) > target_size_max:
im_scale = float(target_size_max) / float(im_size_max)
im_scale_x = im_scale
im_scale_y = im_scale
else:
resize_h, resize_w = self.target_size
im_scale_y = resize_h / float(origin_shape[0])
im_scale_x = resize_w / float(origin_shape[1])
return im_scale_y, im_scale_x
class DetNormalizeImage(object):
"""normalize image
Args:
mean (list): im - mean
std (list): im / std
is_scale (bool): whether need im / 255
is_channel_first (bool): if True: image shape is CHW, else: HWC
"""
def __init__(self, mean, std, is_scale=True):
self.mean = mean
self.std = std
self.is_scale = is_scale
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im = im.astype(np.float32, copy=False)
mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
std = np.array(self.std)[np.newaxis, np.newaxis, :]
if self.is_scale:
im = im / 255.0
im -= mean
im /= std
return im, im_info
class DetPermute(object):
"""permute image
Args:
to_bgr (bool): whether convert RGB to BGR
channel_first (bool): whether convert HWC to CHW
"""
def __init__(self, ):
super().__init__()
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im = im.transpose((2, 0, 1)).copy()
return im, im_info
class DetPadStride(object):
""" padding image for model with FPN , instead PadBatch(pad_to_stride, pad_gt) in original config
Args:
stride (bool): model with FPN need image shape % stride == 0
"""
def __init__(self, stride=0):
self.coarsest_stride = stride
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
coarsest_stride = self.coarsest_stride
if coarsest_stride <= 0:
return im, im_info
im_c, im_h, im_w = im.shape
pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
padding_im[:, :im_h, :im_w] = im
return padding_im, im_info
def det_preprocess(im, im_info, preprocess_ops):
for operator in preprocess_ops:
im, im_info = operator(im, im_info)
return im, im_info