550 lines
18 KiB
Python
550 lines
18 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import cv2
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import numpy as np
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import imgaug.augmenters as iaa
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from keypoint_preprocess import get_affine_transform
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from PIL import Image
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def decode_image(im_file, im_info):
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"""read rgb image
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Args:
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im_file (str|np.ndarray): input can be image path or np.ndarray
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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if isinstance(im_file, str):
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with open(im_file, 'rb') as f:
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im_read = f.read()
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data = np.frombuffer(im_read, dtype='uint8')
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im = cv2.imdecode(data, 1) # BGR mode, but need RGB mode
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im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
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else:
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im = im_file
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im_info['im_shape'] = np.array(im.shape[:2], dtype=np.float32)
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im_info['scale_factor'] = np.array([1., 1.], dtype=np.float32)
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return im, im_info
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class Resize_Mult32(object):
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"""resize image by target_size and max_size
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Args:
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target_size (int): the target size of image
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keep_ratio (bool): whether keep_ratio or not, default true
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interp (int): method of resize
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"""
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def __init__(self, limit_side_len, limit_type, interp=cv2.INTER_LINEAR):
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self.limit_side_len = limit_side_len
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self.limit_type = limit_type
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self.interp = interp
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def __call__(self, im, im_info):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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im_channel = im.shape[2]
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im_scale_y, im_scale_x = self.generate_scale(im)
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im = cv2.resize(
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im,
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None,
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None,
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fx=im_scale_x,
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fy=im_scale_y,
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interpolation=self.interp)
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im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
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im_info['scale_factor'] = np.array(
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[im_scale_y, im_scale_x]).astype('float32')
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return im, im_info
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def generate_scale(self, img):
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"""
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Args:
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img (np.ndarray): image (np.ndarray)
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Returns:
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im_scale_x: the resize ratio of X
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im_scale_y: the resize ratio of Y
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"""
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limit_side_len = self.limit_side_len
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h, w, c = img.shape
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# limit the max side
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if self.limit_type == 'max':
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if h > w:
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ratio = float(limit_side_len) / h
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else:
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ratio = float(limit_side_len) / w
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elif self.limit_type == 'min':
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if h < w:
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ratio = float(limit_side_len) / h
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else:
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ratio = float(limit_side_len) / w
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elif self.limit_type == 'resize_long':
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ratio = float(limit_side_len) / max(h, w)
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else:
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raise Exception('not support limit type, image ')
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resize_h = int(h * ratio)
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resize_w = int(w * ratio)
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resize_h = max(int(round(resize_h / 32) * 32), 32)
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resize_w = max(int(round(resize_w / 32) * 32), 32)
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im_scale_y = resize_h / float(h)
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im_scale_x = resize_w / float(w)
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return im_scale_y, im_scale_x
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class Resize(object):
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"""resize image by target_size and max_size
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Args:
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target_size (int): the target size of image
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keep_ratio (bool): whether keep_ratio or not, default true
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interp (int): method of resize
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"""
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def __init__(self, target_size, keep_ratio=True, interp=cv2.INTER_LINEAR):
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if isinstance(target_size, int):
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target_size = [target_size, target_size]
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self.target_size = target_size
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self.keep_ratio = keep_ratio
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self.interp = interp
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def __call__(self, im, im_info):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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assert len(self.target_size) == 2
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assert self.target_size[0] > 0 and self.target_size[1] > 0
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im_channel = im.shape[2]
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im_scale_y, im_scale_x = self.generate_scale(im)
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im = cv2.resize(
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im,
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None,
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None,
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fx=im_scale_x,
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fy=im_scale_y,
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interpolation=self.interp)
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im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
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im_info['scale_factor'] = np.array(
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[im_scale_y, im_scale_x]).astype('float32')
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return im, im_info
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def generate_scale(self, im):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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Returns:
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im_scale_x: the resize ratio of X
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im_scale_y: the resize ratio of Y
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"""
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origin_shape = im.shape[:2]
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im_c = im.shape[2]
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if self.keep_ratio:
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im_size_min = np.min(origin_shape)
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im_size_max = np.max(origin_shape)
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target_size_min = np.min(self.target_size)
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target_size_max = np.max(self.target_size)
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im_scale = float(target_size_min) / float(im_size_min)
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if np.round(im_scale * im_size_max) > target_size_max:
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im_scale = float(target_size_max) / float(im_size_max)
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im_scale_x = im_scale
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im_scale_y = im_scale
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else:
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resize_h, resize_w = self.target_size
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im_scale_y = resize_h / float(origin_shape[0])
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im_scale_x = resize_w / float(origin_shape[1])
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return im_scale_y, im_scale_x
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class ShortSizeScale(object):
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"""
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Scale images by short size.
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Args:
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short_size(float | int): Short size of an image will be scaled to the short_size.
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fixed_ratio(bool): Set whether to zoom according to a fixed ratio. default: True
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do_round(bool): Whether to round up when calculating the zoom ratio. default: False
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backend(str): Choose pillow or cv2 as the graphics processing backend. default: 'pillow'
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"""
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def __init__(self,
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short_size,
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fixed_ratio=True,
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keep_ratio=None,
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do_round=False,
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backend='pillow'):
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self.short_size = short_size
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assert (fixed_ratio and not keep_ratio) or (
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not fixed_ratio
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), "fixed_ratio and keep_ratio cannot be true at the same time"
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self.fixed_ratio = fixed_ratio
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self.keep_ratio = keep_ratio
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self.do_round = do_round
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assert backend in [
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'pillow', 'cv2'
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], "Scale's backend must be pillow or cv2, but get {backend}"
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self.backend = backend
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def __call__(self, img):
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"""
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Performs resize operations.
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Args:
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img (PIL.Image): a PIL.Image.
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return:
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resized_img: a PIL.Image after scaling.
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"""
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result_img = None
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if isinstance(img, np.ndarray):
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h, w, _ = img.shape
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elif isinstance(img, Image.Image):
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w, h = img.size
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else:
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raise NotImplementedError
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if w <= h:
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ow = self.short_size
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if self.fixed_ratio: # default is True
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oh = int(self.short_size * 4.0 / 3.0)
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elif not self.keep_ratio: # no
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oh = self.short_size
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else:
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scale_factor = self.short_size / w
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oh = int(h * float(scale_factor) +
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0.5) if self.do_round else int(h * self.short_size / w)
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ow = int(w * float(scale_factor) +
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0.5) if self.do_round else int(w * self.short_size / h)
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else:
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oh = self.short_size
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if self.fixed_ratio:
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ow = int(self.short_size * 4.0 / 3.0)
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elif not self.keep_ratio: # no
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ow = self.short_size
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else:
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scale_factor = self.short_size / h
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oh = int(h * float(scale_factor) +
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0.5) if self.do_round else int(h * self.short_size / w)
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ow = int(w * float(scale_factor) +
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0.5) if self.do_round else int(w * self.short_size / h)
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if type(img) == np.ndarray:
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img = Image.fromarray(img, mode='RGB')
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if self.backend == 'pillow':
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result_img = img.resize((ow, oh), Image.BILINEAR)
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elif self.backend == 'cv2' and (self.keep_ratio is not None):
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result_img = cv2.resize(
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img, (ow, oh), interpolation=cv2.INTER_LINEAR)
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else:
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result_img = Image.fromarray(
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cv2.resize(
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np.asarray(img), (ow, oh), interpolation=cv2.INTER_LINEAR))
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return result_img
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class NormalizeImage(object):
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"""normalize image
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Args:
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mean (list): im - mean
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std (list): im / std
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is_scale (bool): whether need im / 255
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norm_type (str): type in ['mean_std', 'none']
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"""
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def __init__(self, mean, std, is_scale=True, norm_type='mean_std'):
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self.mean = mean
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self.std = std
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self.is_scale = is_scale
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self.norm_type = norm_type
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def __call__(self, im, im_info):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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im = im.astype(np.float32, copy=False)
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if self.is_scale:
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scale = 1.0 / 255.0
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im *= scale
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if self.norm_type == 'mean_std':
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mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
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std = np.array(self.std)[np.newaxis, np.newaxis, :]
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im -= mean
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im /= std
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return im, im_info
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class Permute(object):
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"""permute image
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Args:
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to_bgr (bool): whether convert RGB to BGR
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channel_first (bool): whether convert HWC to CHW
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"""
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def __init__(self, ):
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super(Permute, self).__init__()
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def __call__(self, im, im_info):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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im = im.transpose((2, 0, 1)).copy()
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return im, im_info
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class PadStride(object):
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""" padding image for model with FPN, instead PadBatch(pad_to_stride) in original config
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Args:
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stride (bool): model with FPN need image shape % stride == 0
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"""
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def __init__(self, stride=0):
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self.coarsest_stride = stride
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def __call__(self, im, im_info):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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coarsest_stride = self.coarsest_stride
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if coarsest_stride <= 0:
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return im, im_info
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im_c, im_h, im_w = im.shape
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pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
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pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
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padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
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padding_im[:, :im_h, :im_w] = im
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return padding_im, im_info
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class LetterBoxResize(object):
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def __init__(self, target_size):
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"""
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Resize image to target size, convert normalized xywh to pixel xyxy
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format ([x_center, y_center, width, height] -> [x0, y0, x1, y1]).
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Args:
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target_size (int|list): image target size.
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"""
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super(LetterBoxResize, self).__init__()
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if isinstance(target_size, int):
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target_size = [target_size, target_size]
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self.target_size = target_size
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def letterbox(self, img, height, width, color=(127.5, 127.5, 127.5)):
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# letterbox: resize a rectangular image to a padded rectangular
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shape = img.shape[:2] # [height, width]
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ratio_h = float(height) / shape[0]
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ratio_w = float(width) / shape[1]
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ratio = min(ratio_h, ratio_w)
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new_shape = (round(shape[1] * ratio),
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round(shape[0] * ratio)) # [width, height]
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padw = (width - new_shape[0]) / 2
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padh = (height - new_shape[1]) / 2
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top, bottom = round(padh - 0.1), round(padh + 0.1)
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left, right = round(padw - 0.1), round(padw + 0.1)
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img = cv2.resize(
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img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
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img = cv2.copyMakeBorder(
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img, top, bottom, left, right, cv2.BORDER_CONSTANT,
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value=color) # padded rectangular
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return img, ratio, padw, padh
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def __call__(self, im, im_info):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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assert len(self.target_size) == 2
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assert self.target_size[0] > 0 and self.target_size[1] > 0
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height, width = self.target_size
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h, w = im.shape[:2]
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im, ratio, padw, padh = self.letterbox(im, height=height, width=width)
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new_shape = [round(h * ratio), round(w * ratio)]
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im_info['im_shape'] = np.array(new_shape, dtype=np.float32)
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im_info['scale_factor'] = np.array([ratio, ratio], dtype=np.float32)
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return im, im_info
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class Pad(object):
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def __init__(self, size, fill_value=[114.0, 114.0, 114.0]):
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"""
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Pad image to a specified size.
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Args:
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size (list[int]): image target size
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fill_value (list[float]): rgb value of pad area, default (114.0, 114.0, 114.0)
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"""
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super(Pad, self).__init__()
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if isinstance(size, int):
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size = [size, size]
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self.size = size
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self.fill_value = fill_value
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def __call__(self, im, im_info):
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im_h, im_w = im.shape[:2]
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h, w = self.size
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if h == im_h and w == im_w:
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im = im.astype(np.float32)
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return im, im_info
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canvas = np.ones((h, w, 3), dtype=np.float32)
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canvas *= np.array(self.fill_value, dtype=np.float32)
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canvas[0:im_h, 0:im_w, :] = im.astype(np.float32)
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im = canvas
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return im, im_info
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class WarpAffine(object):
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"""Warp affine the image
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"""
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def __init__(self,
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keep_res=False,
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pad=31,
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input_h=512,
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input_w=512,
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scale=0.4,
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shift=0.1,
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down_ratio=4):
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self.keep_res = keep_res
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self.pad = pad
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self.input_h = input_h
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self.input_w = input_w
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self.scale = scale
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self.shift = shift
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self.down_ratio = down_ratio
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def __call__(self, im, im_info):
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"""
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Args:
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im (np.ndarray): image (np.ndarray)
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im_info (dict): info of image
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Returns:
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im (np.ndarray): processed image (np.ndarray)
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im_info (dict): info of processed image
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"""
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img = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
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h, w = img.shape[:2]
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if self.keep_res:
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# True in detection eval/infer
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input_h = (h | self.pad) + 1
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input_w = (w | self.pad) + 1
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s = np.array([input_w, input_h], dtype=np.float32)
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c = np.array([w // 2, h // 2], dtype=np.float32)
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else:
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# False in centertrack eval_mot/eval_mot
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s = max(h, w) * 1.0
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input_h, input_w = self.input_h, self.input_w
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c = np.array([w / 2., h / 2.], dtype=np.float32)
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trans_input = get_affine_transform(c, s, 0, [input_w, input_h])
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img = cv2.resize(img, (w, h))
|
|
inp = cv2.warpAffine(
|
|
img, trans_input, (input_w, input_h), flags=cv2.INTER_LINEAR)
|
|
|
|
if not self.keep_res:
|
|
out_h = input_h // self.down_ratio
|
|
out_w = input_w // self.down_ratio
|
|
trans_output = get_affine_transform(c, s, 0, [out_w, out_h])
|
|
|
|
im_info.update({
|
|
'center': c,
|
|
'scale': s,
|
|
'out_height': out_h,
|
|
'out_width': out_w,
|
|
'inp_height': input_h,
|
|
'inp_width': input_w,
|
|
'trans_input': trans_input,
|
|
'trans_output': trans_output,
|
|
})
|
|
return inp, im_info
|
|
|
|
|
|
class CULaneResize(object):
|
|
def __init__(self, img_h, img_w, cut_height, prob=0.5):
|
|
super(CULaneResize, self).__init__()
|
|
self.img_h = img_h
|
|
self.img_w = img_w
|
|
self.cut_height = cut_height
|
|
self.prob = prob
|
|
|
|
def __call__(self, im, im_info):
|
|
# cut
|
|
im = im[self.cut_height:, :, :]
|
|
# resize
|
|
transform = iaa.Sometimes(self.prob,
|
|
iaa.Resize({
|
|
"height": self.img_h,
|
|
"width": self.img_w
|
|
}))
|
|
im = transform(image=im.copy().astype(np.uint8))
|
|
|
|
im = im.astype(np.float32) / 255.
|
|
# check transpose is need whether the func decode_image is equal to CULaneDataSet cv.imread
|
|
im = im.transpose(2, 0, 1)
|
|
|
|
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
|