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712 lines
24 KiB
Python
712 lines
24 KiB
Python
#
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# Copyright 2024 The InfiniFlow 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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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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from PIL import Image
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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 isinstance(img, str) and len(
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img) > 0, "invalid input 'img' in DecodeImage"
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else:
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assert isinstance(img, 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]' % (
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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 StandardizeImage(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 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 LinearResize(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 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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self.keep_ratio = False
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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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if 'keep_ratio' in kwargs:
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self.keep_ratio = kwargs['keep_ratio']
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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 sum([src_h, src_w]) < 64:
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img = self.image_padding(img)
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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 image_padding(self, im, value=0):
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h, w, c = im.shape
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im_pad = np.zeros((max(32, h), max(32, w), c), np.uint8) + value
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im_pad[:h, :w, :] = im
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return im_pad
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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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if self.keep_ratio is True:
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resize_w = ori_w * resize_h / ori_h
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N = math.ceil(resize_w / 32)
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resize_w = N * 32
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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 BaseException:
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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)
|
|
|
|
max_stride = 128
|
|
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
|
|
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
|
|
im = cv2.resize(im, (int(resize_w), int(resize_h)))
|
|
ratio_h = resize_h / float(h)
|
|
ratio_w = resize_w / float(w)
|
|
|
|
return im, (ratio_h, ratio_w)
|
|
|
|
|
|
class KieResize(object):
|
|
def __init__(self, **kwargs):
|
|
super(KieResize, self).__init__()
|
|
self.max_side, self.min_side = kwargs['img_scale'][0], kwargs[
|
|
'img_scale'][1]
|
|
|
|
def __call__(self, data):
|
|
img = data['image']
|
|
points = data['points']
|
|
src_h, src_w, _ = img.shape
|
|
im_resized, scale_factor, [ratio_h, ratio_w
|
|
], [new_h, new_w] = self.resize_image(img)
|
|
resize_points = self.resize_boxes(img, points, scale_factor)
|
|
data['ori_image'] = img
|
|
data['ori_boxes'] = points
|
|
data['points'] = resize_points
|
|
data['image'] = im_resized
|
|
data['shape'] = np.array([new_h, new_w])
|
|
return data
|
|
|
|
def resize_image(self, img):
|
|
norm_img = np.zeros([1024, 1024, 3], dtype='float32')
|
|
scale = [512, 1024]
|
|
h, w = img.shape[:2]
|
|
max_long_edge = max(scale)
|
|
max_short_edge = min(scale)
|
|
scale_factor = min(max_long_edge / max(h, w),
|
|
max_short_edge / min(h, w))
|
|
resize_w, resize_h = int(w * float(scale_factor) + 0.5), int(h * float(
|
|
scale_factor) + 0.5)
|
|
max_stride = 32
|
|
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
|
|
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
|
|
im = cv2.resize(img, (resize_w, resize_h))
|
|
new_h, new_w = im.shape[:2]
|
|
w_scale = new_w / w
|
|
h_scale = new_h / h
|
|
scale_factor = np.array(
|
|
[w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
|
|
norm_img[:new_h, :new_w, :] = im
|
|
return norm_img, scale_factor, [h_scale, w_scale], [new_h, new_w]
|
|
|
|
def resize_boxes(self, im, points, scale_factor):
|
|
points = points * scale_factor
|
|
img_shape = im.shape[:2]
|
|
points[:, 0::2] = np.clip(points[:, 0::2], 0, img_shape[1])
|
|
points[:, 1::2] = np.clip(points[:, 1::2], 0, img_shape[0])
|
|
return points
|
|
|
|
|
|
class SRResize(object):
|
|
def __init__(self,
|
|
imgH=32,
|
|
imgW=128,
|
|
down_sample_scale=4,
|
|
keep_ratio=False,
|
|
min_ratio=1,
|
|
mask=False,
|
|
infer_mode=False,
|
|
**kwargs):
|
|
self.imgH = imgH
|
|
self.imgW = imgW
|
|
self.keep_ratio = keep_ratio
|
|
self.min_ratio = min_ratio
|
|
self.down_sample_scale = down_sample_scale
|
|
self.mask = mask
|
|
self.infer_mode = infer_mode
|
|
|
|
def __call__(self, data):
|
|
imgH = self.imgH
|
|
imgW = self.imgW
|
|
images_lr = data["image_lr"]
|
|
transform2 = ResizeNormalize(
|
|
(imgW // self.down_sample_scale, imgH // self.down_sample_scale))
|
|
images_lr = transform2(images_lr)
|
|
data["img_lr"] = images_lr
|
|
if self.infer_mode:
|
|
return data
|
|
|
|
images_HR = data["image_hr"]
|
|
label_strs = data["label"]
|
|
transform = ResizeNormalize((imgW, imgH))
|
|
images_HR = transform(images_HR)
|
|
data["img_hr"] = images_HR
|
|
return data
|
|
|
|
|
|
class ResizeNormalize(object):
|
|
def __init__(self, size, interpolation=Image.BICUBIC):
|
|
self.size = size
|
|
self.interpolation = interpolation
|
|
|
|
def __call__(self, img):
|
|
img = img.resize(self.size, self.interpolation)
|
|
img_numpy = np.array(img).astype("float32")
|
|
img_numpy = img_numpy.transpose((2, 0, 1)) / 255
|
|
return img_numpy
|
|
|
|
|
|
class GrayImageChannelFormat(object):
|
|
"""
|
|
format gray scale image's channel: (3,h,w) -> (1,h,w)
|
|
Args:
|
|
inverse: inverse gray image
|
|
"""
|
|
|
|
def __init__(self, inverse=False, **kwargs):
|
|
self.inverse = inverse
|
|
|
|
def __call__(self, data):
|
|
img = data['image']
|
|
img_single_channel = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
|
img_expanded = np.expand_dims(img_single_channel, 0)
|
|
|
|
if self.inverse:
|
|
data['image'] = np.abs(img_expanded - 1)
|
|
else:
|
|
data['image'] = img_expanded
|
|
|
|
data['src_image'] = img
|
|
return data
|
|
|
|
|
|
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
|
|
|
|
|
|
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
|
|
|
|
|
|
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
|