287 lines
9.6 KiB
Python
287 lines
9.6 KiB
Python
# Copyright (c) 2021 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 Resize(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)
|
|
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 NormalizeImage(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 Permute(object):
|
|
"""permute image
|
|
Args:
|
|
to_bgr (bool): whether convert RGB to BGR
|
|
channel_first (bool): whether convert HWC to CHW
|
|
"""
|
|
|
|
def __init__(self, ):
|
|
super(Permute, self).__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 PadStride(object):
|
|
""" padding image for model with FPN, instead PadBatch(pad_to_stride) 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
|
|
|
|
|
|
class LetterBoxResize(object):
|
|
def __init__(self, target_size):
|
|
"""
|
|
Resize image to target size, convert normalized xywh to pixel xyxy
|
|
format ([x_center, y_center, width, height] -> [x0, y0, x1, y1]).
|
|
Args:
|
|
target_size (int|list): image target size.
|
|
"""
|
|
super(LetterBoxResize, self).__init__()
|
|
if isinstance(target_size, int):
|
|
target_size = [target_size, target_size]
|
|
self.target_size = target_size
|
|
|
|
def letterbox(self, img, height, width, color=(127.5, 127.5, 127.5)):
|
|
# letterbox: resize a rectangular image to a padded rectangular
|
|
shape = img.shape[:2] # [height, width]
|
|
ratio_h = float(height) / shape[0]
|
|
ratio_w = float(width) / shape[1]
|
|
ratio = min(ratio_h, ratio_w)
|
|
new_shape = (round(shape[1] * ratio),
|
|
round(shape[0] * ratio)) # [width, height]
|
|
padw = (width - new_shape[0]) / 2
|
|
padh = (height - new_shape[1]) / 2
|
|
top, bottom = round(padh - 0.1), round(padh + 0.1)
|
|
left, right = round(padw - 0.1), round(padw + 0.1)
|
|
|
|
img = cv2.resize(
|
|
img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
|
|
img = cv2.copyMakeBorder(
|
|
img, top, bottom, left, right, cv2.BORDER_CONSTANT,
|
|
value=color) # padded rectangular
|
|
return img, ratio, padw, padh
|
|
|
|
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
|
|
height, width = self.target_size
|
|
h, w = im.shape[:2]
|
|
im, ratio, padw, padh = self.letterbox(im, height=height, width=width)
|
|
|
|
new_shape = [round(h * ratio), round(w * ratio)]
|
|
im_info['im_shape'] = np.array(new_shape, dtype=np.float32)
|
|
im_info['scale_factor'] = np.array([ratio, ratio], dtype=np.float32)
|
|
return im, im_info
|
|
|
|
|
|
class Pad(object):
|
|
def __init__(self, size, fill_value=[114.0, 114.0, 114.0]):
|
|
"""
|
|
Pad image to a specified size.
|
|
Args:
|
|
size (list[int]): image target size
|
|
fill_value (list[float]): rgb value of pad area, default (114.0, 114.0, 114.0)
|
|
"""
|
|
super(Pad, self).__init__()
|
|
if isinstance(size, int):
|
|
size = [size, size]
|
|
self.size = size
|
|
self.fill_value = fill_value
|
|
|
|
def __call__(self, im, im_info):
|
|
im_h, im_w = im.shape[:2]
|
|
h, w = self.size
|
|
if h == im_h and w == im_w:
|
|
im = im.astype(np.float32)
|
|
return im, im_info
|
|
|
|
canvas = np.ones((h, w, 3), dtype=np.float32)
|
|
canvas *= np.array(self.fill_value, dtype=np.float32)
|
|
canvas[0:im_h, 0:im_w, :] = im.astype(np.float32)
|
|
im = canvas
|
|
return im, im_info
|
|
|
|
|
|
def preprocess(im, preprocess_ops):
|
|
# process image by preprocess_ops
|
|
im_info = {
|
|
'scale_factor': np.array(
|
|
[1., 1.], dtype=np.float32),
|
|
'im_shape': None,
|
|
}
|
|
im, im_info = decode_image(im, im_info)
|
|
for operator in preprocess_ops:
|
|
im, im_info = operator(im, im_info)
|
|
return im, im_info
|