469 lines
16 KiB
Python
469 lines
16 KiB
Python
"""
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# 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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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import sys
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import six
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import cv2
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import numpy as np
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import math
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class DecodeImage(object):
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""" decode image """
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def __init__(self,
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img_mode='RGB',
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channel_first=False,
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ignore_orientation=False,
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**kwargs):
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self.img_mode = img_mode
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self.channel_first = channel_first
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self.ignore_orientation = ignore_orientation
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def __call__(self, data):
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img = data['image']
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if six.PY2:
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assert type(img) is str and len(
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img) > 0, "invalid input 'img' in DecodeImage"
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else:
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assert type(img) is bytes and len(
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img) > 0, "invalid input 'img' in DecodeImage"
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img = np.frombuffer(img, dtype='uint8')
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if self.ignore_orientation:
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img = cv2.imdecode(img, cv2.IMREAD_IGNORE_ORIENTATION |
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cv2.IMREAD_COLOR)
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else:
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img = cv2.imdecode(img, 1)
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if img is None:
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return None
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if self.img_mode == 'GRAY':
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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elif self.img_mode == 'RGB':
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assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
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img = img[:, :, ::-1]
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if self.channel_first:
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img = img.transpose((2, 0, 1))
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data['image'] = img
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return data
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class NRTRDecodeImage(object):
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""" decode image """
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def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
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self.img_mode = img_mode
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self.channel_first = channel_first
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def __call__(self, data):
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img = data['image']
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if six.PY2:
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assert type(img) is str and len(
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img) > 0, "invalid input 'img' in DecodeImage"
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else:
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assert type(img) is bytes and len(
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img) > 0, "invalid input 'img' in DecodeImage"
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img = np.frombuffer(img, dtype='uint8')
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img = cv2.imdecode(img, 1)
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if img is None:
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return None
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if self.img_mode == 'GRAY':
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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elif self.img_mode == 'RGB':
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assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
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img = img[:, :, ::-1]
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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if self.channel_first:
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img = img.transpose((2, 0, 1))
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data['image'] = img
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return data
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class NormalizeImage(object):
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""" normalize image such as substract mean, divide std
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"""
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def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs):
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if isinstance(scale, str):
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scale = eval(scale)
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self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
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mean = mean if mean is not None else [0.485, 0.456, 0.406]
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std = std if std is not None else [0.229, 0.224, 0.225]
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shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
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self.mean = np.array(mean).reshape(shape).astype('float32')
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self.std = np.array(std).reshape(shape).astype('float32')
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def __call__(self, data):
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img = data['image']
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from PIL import Image
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if isinstance(img, Image.Image):
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img = np.array(img)
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assert isinstance(img,
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np.ndarray), "invalid input 'img' in NormalizeImage"
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data['image'] = (
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img.astype('float32') * self.scale - self.mean) / self.std
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return data
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class ToCHWImage(object):
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""" convert hwc image to chw image
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"""
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def __init__(self, **kwargs):
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pass
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def __call__(self, data):
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img = data['image']
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from PIL import Image
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if isinstance(img, Image.Image):
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img = np.array(img)
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data['image'] = img.transpose((2, 0, 1))
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return data
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class Fasttext(object):
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def __init__(self, path="None", **kwargs):
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import fasttext
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self.fast_model = fasttext.load_model(path)
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def __call__(self, data):
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label = data['label']
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fast_label = self.fast_model[label]
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data['fast_label'] = fast_label
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return data
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class KeepKeys(object):
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def __init__(self, keep_keys, **kwargs):
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self.keep_keys = keep_keys
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def __call__(self, data):
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data_list = []
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for key in self.keep_keys:
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data_list.append(data[key])
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return data_list
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class Pad(object):
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def __init__(self, size=None, size_div=32, **kwargs):
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if size is not None and not isinstance(size, (int, list, tuple)):
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raise TypeError("Type of target_size is invalid. Now is {}".format(
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type(size)))
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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.size_div = size_div
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def __call__(self, data):
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img = data['image']
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img_h, img_w = img.shape[0], img.shape[1]
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if self.size:
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resize_h2, resize_w2 = self.size
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assert (
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img_h < resize_h2 and img_w < resize_w2
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), '(h, w) of target size should be greater than (img_h, img_w)'
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else:
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resize_h2 = max(
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int(math.ceil(img.shape[0] / self.size_div) * self.size_div),
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self.size_div)
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resize_w2 = max(
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int(math.ceil(img.shape[1] / self.size_div) * self.size_div),
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self.size_div)
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img = cv2.copyMakeBorder(
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img,
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0,
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resize_h2 - img_h,
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0,
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resize_w2 - img_w,
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cv2.BORDER_CONSTANT,
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value=0)
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data['image'] = img
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return data
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class Resize(object):
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def __init__(self, size=(640, 640), **kwargs):
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self.size = size
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def resize_image(self, img):
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resize_h, resize_w = self.size
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ori_h, ori_w = img.shape[:2] # (h, w, c)
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ratio_h = float(resize_h) / ori_h
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ratio_w = float(resize_w) / ori_w
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img = cv2.resize(img, (int(resize_w), int(resize_h)))
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return img, [ratio_h, ratio_w]
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def __call__(self, data):
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img = data['image']
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if 'polys' in data:
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text_polys = data['polys']
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img_resize, [ratio_h, ratio_w] = self.resize_image(img)
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if 'polys' in data:
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new_boxes = []
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for box in text_polys:
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new_box = []
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for cord in box:
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new_box.append([cord[0] * ratio_w, cord[1] * ratio_h])
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new_boxes.append(new_box)
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data['polys'] = np.array(new_boxes, dtype=np.float32)
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data['image'] = img_resize
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return data
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class DetResizeForTest(object):
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def __init__(self, **kwargs):
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super(DetResizeForTest, self).__init__()
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self.resize_type = 0
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if 'image_shape' in kwargs:
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self.image_shape = kwargs['image_shape']
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self.resize_type = 1
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elif 'limit_side_len' in kwargs:
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self.limit_side_len = kwargs['limit_side_len']
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self.limit_type = kwargs.get('limit_type', 'min')
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elif 'resize_long' in kwargs:
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self.resize_type = 2
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self.resize_long = kwargs.get('resize_long', 960)
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else:
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self.limit_side_len = 736
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self.limit_type = 'min'
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def __call__(self, data):
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img = data['image']
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src_h, src_w, _ = img.shape
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if self.resize_type == 0:
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# img, shape = self.resize_image_type0(img)
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img, [ratio_h, ratio_w] = self.resize_image_type0(img)
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elif self.resize_type == 2:
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img, [ratio_h, ratio_w] = self.resize_image_type2(img)
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else:
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# img, shape = self.resize_image_type1(img)
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img, [ratio_h, ratio_w] = self.resize_image_type1(img)
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data['image'] = img
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data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
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return data
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def resize_image_type1(self, img):
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resize_h, resize_w = self.image_shape
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ori_h, ori_w = img.shape[:2] # (h, w, c)
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ratio_h = float(resize_h) / ori_h
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ratio_w = float(resize_w) / ori_w
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img = cv2.resize(img, (int(resize_w), int(resize_h)))
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# return img, np.array([ori_h, ori_w])
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return img, [ratio_h, ratio_w]
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def resize_image_type0(self, img):
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"""
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resize image to a size multiple of 32 which is required by the network
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args:
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img(array): array with shape [h, w, c]
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return(tuple):
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img, (ratio_h, ratio_w)
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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 max(h, w) > limit_side_len:
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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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else:
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ratio = 1.
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elif self.limit_type == 'min':
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if min(h, w) < limit_side_len:
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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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else:
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ratio = 1.
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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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try:
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if int(resize_w) <= 0 or int(resize_h) <= 0:
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return None, (None, None)
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img = cv2.resize(img, (int(resize_w), int(resize_h)))
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except:
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print(img.shape, resize_w, resize_h)
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sys.exit(0)
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ratio_h = resize_h / float(h)
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ratio_w = resize_w / float(w)
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return img, [ratio_h, ratio_w]
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def resize_image_type2(self, img):
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h, w, _ = img.shape
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resize_w = w
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resize_h = h
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if resize_h > resize_w:
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ratio = float(self.resize_long) / resize_h
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else:
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ratio = float(self.resize_long) / resize_w
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resize_h = int(resize_h * ratio)
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resize_w = int(resize_w * ratio)
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max_stride = 128
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resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
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resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
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img = cv2.resize(img, (int(resize_w), int(resize_h)))
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ratio_h = resize_h / float(h)
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ratio_w = resize_w / float(w)
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return img, [ratio_h, ratio_w]
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class E2EResizeForTest(object):
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def __init__(self, **kwargs):
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super(E2EResizeForTest, self).__init__()
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self.max_side_len = kwargs['max_side_len']
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self.valid_set = kwargs['valid_set']
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def __call__(self, data):
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img = data['image']
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src_h, src_w, _ = img.shape
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if self.valid_set == 'totaltext':
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im_resized, [ratio_h, ratio_w] = self.resize_image_for_totaltext(
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img, max_side_len=self.max_side_len)
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else:
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im_resized, (ratio_h, ratio_w) = self.resize_image(
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img, max_side_len=self.max_side_len)
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data['image'] = im_resized
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data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
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return data
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def resize_image_for_totaltext(self, im, max_side_len=512):
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h, w, _ = im.shape
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resize_w = w
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resize_h = h
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ratio = 1.25
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if h * ratio > max_side_len:
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ratio = float(max_side_len) / resize_h
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resize_h = int(resize_h * ratio)
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resize_w = int(resize_w * ratio)
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max_stride = 128
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resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
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resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
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im = cv2.resize(im, (int(resize_w), int(resize_h)))
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ratio_h = resize_h / float(h)
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ratio_w = resize_w / float(w)
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return im, (ratio_h, ratio_w)
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def resize_image(self, im, max_side_len=512):
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"""
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resize image to a size multiple of max_stride which is required by the network
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:param im: the resized image
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:param max_side_len: limit of max image size to avoid out of memory in gpu
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:return: the resized image and the resize ratio
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"""
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h, w, _ = im.shape
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resize_w = w
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resize_h = h
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# Fix the longer side
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if resize_h > resize_w:
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ratio = float(max_side_len) / resize_h
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else:
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ratio = float(max_side_len) / resize_w
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resize_h = int(resize_h * ratio)
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resize_w = int(resize_w * ratio)
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max_stride = 128
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resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
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resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
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im = cv2.resize(im, (int(resize_w), int(resize_h)))
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ratio_h = resize_h / float(h)
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ratio_w = resize_w / float(w)
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return im, (ratio_h, ratio_w)
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class KieResize(object):
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def __init__(self, **kwargs):
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super(KieResize, self).__init__()
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self.max_side, self.min_side = kwargs['img_scale'][0], kwargs[
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'img_scale'][1]
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def __call__(self, data):
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img = data['image']
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points = data['points']
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src_h, src_w, _ = img.shape
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im_resized, scale_factor, [ratio_h, ratio_w
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], [new_h, new_w] = self.resize_image(img)
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resize_points = self.resize_boxes(img, points, scale_factor)
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data['ori_image'] = img
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data['ori_boxes'] = points
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data['points'] = resize_points
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data['image'] = im_resized
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data['shape'] = np.array([new_h, new_w])
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return data
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def resize_image(self, img):
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norm_img = np.zeros([1024, 1024, 3], dtype='float32')
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scale = [512, 1024]
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h, w = img.shape[:2]
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max_long_edge = max(scale)
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max_short_edge = min(scale)
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scale_factor = min(max_long_edge / max(h, w),
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max_short_edge / min(h, w))
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resize_w, resize_h = int(w * float(scale_factor) + 0.5), int(h * float(
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scale_factor) + 0.5)
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max_stride = 32
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resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
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resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
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im = cv2.resize(img, (resize_w, resize_h))
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new_h, new_w = im.shape[:2]
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w_scale = new_w / w
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h_scale = new_h / h
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scale_factor = np.array(
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[w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
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norm_img[:new_h, :new_w, :] = im
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return norm_img, scale_factor, [h_scale, w_scale], [new_h, new_w]
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def resize_boxes(self, im, points, scale_factor):
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points = points * scale_factor
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img_shape = im.shape[:2]
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points[:, 0::2] = np.clip(points[:, 0::2], 0, img_shape[1])
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points[:, 1::2] = np.clip(points[:, 1::2], 0, img_shape[0])
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return points
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