mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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534 lines
16 KiB
Python
534 lines
16 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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 math
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import cv2
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import numpy as np
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import random
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from PIL import Image
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from .text_image_aug import tia_perspective, tia_stretch, tia_distort
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class RecAug(object):
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def __init__(self, use_tia=True, aug_prob=0.4, **kwargs):
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self.use_tia = use_tia
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self.aug_prob = aug_prob
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def __call__(self, data):
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img = data['image']
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img = warp(img, 10, self.use_tia, self.aug_prob)
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data['image'] = img
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return data
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class ClsResizeImg(object):
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def __init__(self, image_shape, **kwargs):
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self.image_shape = image_shape
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def __call__(self, data):
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img = data['image']
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norm_img = resize_norm_img(img, self.image_shape)
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data['image'] = norm_img
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return data
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class NRTRRecResizeImg(object):
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def __init__(self, image_shape, resize_type, padding=False, **kwargs):
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self.image_shape = image_shape
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self.resize_type = resize_type
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self.padding = padding
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def __call__(self, data):
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img = data['image']
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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image_shape = self.image_shape
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if self.padding:
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imgC, imgH, imgW = image_shape
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# todo: change to 0 and modified image shape
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h = img.shape[0]
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w = img.shape[1]
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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norm_img = np.expand_dims(resized_image, -1)
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norm_img = norm_img.transpose((2, 0, 1))
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resized_image = norm_img.astype(np.float32) / 128. - 1.
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padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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data['image'] = padding_im
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return data
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if self.resize_type == 'PIL':
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image_pil = Image.fromarray(np.uint8(img))
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img = image_pil.resize(self.image_shape, Image.ANTIALIAS)
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img = np.array(img)
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if self.resize_type == 'OpenCV':
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img = cv2.resize(img, self.image_shape)
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norm_img = np.expand_dims(img, -1)
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norm_img = norm_img.transpose((2, 0, 1))
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data['image'] = norm_img.astype(np.float32) / 128. - 1.
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return data
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class RecResizeImg(object):
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def __init__(self,
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image_shape,
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infer_mode=False,
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character_dict_path='./ppocr/utils/ppocr_keys_v1.txt',
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padding=True,
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**kwargs):
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self.image_shape = image_shape
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self.infer_mode = infer_mode
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self.character_dict_path = character_dict_path
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self.padding = padding
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def __call__(self, data):
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img = data['image']
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if self.infer_mode and self.character_dict_path is not None:
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norm_img = resize_norm_img_chinese(img, self.image_shape)
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else:
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norm_img = resize_norm_img(img, self.image_shape, self.padding)
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data['image'] = norm_img
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return data
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class SRNRecResizeImg(object):
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def __init__(self, image_shape, num_heads, max_text_length, **kwargs):
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self.image_shape = image_shape
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self.num_heads = num_heads
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self.max_text_length = max_text_length
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def __call__(self, data):
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img = data['image']
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norm_img = resize_norm_img_srn(img, self.image_shape)
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data['image'] = norm_img
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[encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
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srn_other_inputs(self.image_shape, self.num_heads, self.max_text_length)
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data['encoder_word_pos'] = encoder_word_pos
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data['gsrm_word_pos'] = gsrm_word_pos
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data['gsrm_slf_attn_bias1'] = gsrm_slf_attn_bias1
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data['gsrm_slf_attn_bias2'] = gsrm_slf_attn_bias2
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return data
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class SARRecResizeImg(object):
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def __init__(self, image_shape, width_downsample_ratio=0.25, **kwargs):
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self.image_shape = image_shape
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self.width_downsample_ratio = width_downsample_ratio
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def __call__(self, data):
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img = data['image']
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norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar(
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img, self.image_shape, self.width_downsample_ratio)
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data['image'] = norm_img
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data['resized_shape'] = resize_shape
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data['pad_shape'] = pad_shape
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data['valid_ratio'] = valid_ratio
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return data
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def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25):
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imgC, imgH, imgW_min, imgW_max = image_shape
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h = img.shape[0]
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w = img.shape[1]
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valid_ratio = 1.0
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# make sure new_width is an integral multiple of width_divisor.
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width_divisor = int(1 / width_downsample_ratio)
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# resize
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ratio = w / float(h)
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resize_w = math.ceil(imgH * ratio)
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if resize_w % width_divisor != 0:
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resize_w = round(resize_w / width_divisor) * width_divisor
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if imgW_min is not None:
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resize_w = max(imgW_min, resize_w)
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if imgW_max is not None:
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valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
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resize_w = min(imgW_max, resize_w)
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resized_image = cv2.resize(img, (resize_w, imgH))
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resized_image = resized_image.astype('float32')
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# norm
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if image_shape[0] == 1:
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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else:
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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resize_shape = resized_image.shape
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padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
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padding_im[:, :, 0:resize_w] = resized_image
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pad_shape = padding_im.shape
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return padding_im, resize_shape, pad_shape, valid_ratio
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def resize_norm_img(img, image_shape, padding=True):
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imgC, imgH, imgW = image_shape
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h = img.shape[0]
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w = img.shape[1]
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if not padding:
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resized_image = cv2.resize(
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img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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resized_w = imgW
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else:
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype('float32')
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if image_shape[0] == 1:
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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else:
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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def resize_norm_img_chinese(img, image_shape):
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imgC, imgH, imgW = image_shape
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# todo: change to 0 and modified image shape
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max_wh_ratio = imgW * 1.0 / imgH
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h, w = img.shape[0], img.shape[1]
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ratio = w * 1.0 / h
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max_wh_ratio = max(max_wh_ratio, ratio)
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imgW = int(32 * max_wh_ratio)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype('float32')
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if image_shape[0] == 1:
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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else:
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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def resize_norm_img_srn(img, image_shape):
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imgC, imgH, imgW = image_shape
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img_black = np.zeros((imgH, imgW))
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im_hei = img.shape[0]
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im_wid = img.shape[1]
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if im_wid <= im_hei * 1:
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img_new = cv2.resize(img, (imgH * 1, imgH))
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elif im_wid <= im_hei * 2:
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img_new = cv2.resize(img, (imgH * 2, imgH))
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elif im_wid <= im_hei * 3:
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img_new = cv2.resize(img, (imgH * 3, imgH))
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else:
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img_new = cv2.resize(img, (imgW, imgH))
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img_np = np.asarray(img_new)
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img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
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img_black[:, 0:img_np.shape[1]] = img_np
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img_black = img_black[:, :, np.newaxis]
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row, col, c = img_black.shape
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c = 1
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return np.reshape(img_black, (c, row, col)).astype(np.float32)
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def srn_other_inputs(image_shape, num_heads, max_text_length):
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imgC, imgH, imgW = image_shape
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feature_dim = int((imgH / 8) * (imgW / 8))
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encoder_word_pos = np.array(range(0, feature_dim)).reshape(
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(feature_dim, 1)).astype('int64')
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gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
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(max_text_length, 1)).astype('int64')
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gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
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gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
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[1, max_text_length, max_text_length])
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gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1,
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[num_heads, 1, 1]) * [-1e9]
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gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
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[1, max_text_length, max_text_length])
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gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2,
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[num_heads, 1, 1]) * [-1e9]
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return [
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encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2
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]
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def flag():
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"""
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flag
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"""
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return 1 if random.random() > 0.5000001 else -1
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def cvtColor(img):
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"""
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cvtColor
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"""
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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delta = 0.001 * random.random() * flag()
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hsv[:, :, 2] = hsv[:, :, 2] * (1 + delta)
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new_img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
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return new_img
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def blur(img):
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"""
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blur
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"""
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h, w, _ = img.shape
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if h > 10 and w > 10:
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return cv2.GaussianBlur(img, (5, 5), 1)
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else:
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return img
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def jitter(img):
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"""
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jitter
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"""
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w, h, _ = img.shape
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if h > 10 and w > 10:
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thres = min(w, h)
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s = int(random.random() * thres * 0.01)
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src_img = img.copy()
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for i in range(s):
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img[i:, i:, :] = src_img[:w - i, :h - i, :]
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return img
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else:
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return img
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def add_gasuss_noise(image, mean=0, var=0.1):
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"""
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Gasuss noise
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"""
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noise = np.random.normal(mean, var**0.5, image.shape)
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out = image + 0.5 * noise
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out = np.clip(out, 0, 255)
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out = np.uint8(out)
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return out
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def get_crop(image):
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"""
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random crop
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"""
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h, w, _ = image.shape
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top_min = 1
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top_max = 8
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top_crop = int(random.randint(top_min, top_max))
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top_crop = min(top_crop, h - 1)
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crop_img = image.copy()
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ratio = random.randint(0, 1)
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if ratio:
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crop_img = crop_img[top_crop:h, :, :]
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else:
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crop_img = crop_img[0:h - top_crop, :, :]
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return crop_img
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class Config:
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"""
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Config
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"""
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def __init__(self, use_tia):
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self.anglex = random.random() * 30
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self.angley = random.random() * 15
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self.anglez = random.random() * 10
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self.fov = 42
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self.r = 0
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self.shearx = random.random() * 0.3
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self.sheary = random.random() * 0.05
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self.borderMode = cv2.BORDER_REPLICATE
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self.use_tia = use_tia
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def make(self, w, h, ang):
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"""
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make
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"""
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self.anglex = random.random() * 5 * flag()
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self.angley = random.random() * 5 * flag()
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self.anglez = -1 * random.random() * int(ang) * flag()
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self.fov = 42
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self.r = 0
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self.shearx = 0
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self.sheary = 0
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self.borderMode = cv2.BORDER_REPLICATE
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self.w = w
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self.h = h
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self.perspective = self.use_tia
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self.stretch = self.use_tia
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self.distort = self.use_tia
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self.crop = True
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self.affine = False
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self.reverse = True
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self.noise = True
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self.jitter = True
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self.blur = True
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self.color = True
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def rad(x):
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"""
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rad
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"""
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return x * np.pi / 180
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def get_warpR(config):
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"""
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get_warpR
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"""
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anglex, angley, anglez, fov, w, h, r = \
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config.anglex, config.angley, config.anglez, config.fov, config.w, config.h, config.r
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if w > 69 and w < 112:
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anglex = anglex * 1.5
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z = np.sqrt(w**2 + h**2) / 2 / np.tan(rad(fov / 2))
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# Homogeneous coordinate transformation matrix
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rx = np.array([[1, 0, 0, 0],
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[0, np.cos(rad(anglex)), -np.sin(rad(anglex)), 0], [
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0,
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-np.sin(rad(anglex)),
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np.cos(rad(anglex)),
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0,
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], [0, 0, 0, 1]], np.float32)
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ry = np.array([[np.cos(rad(angley)), 0, np.sin(rad(angley)), 0],
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[0, 1, 0, 0], [
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-np.sin(rad(angley)),
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0,
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np.cos(rad(angley)),
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0,
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], [0, 0, 0, 1]], np.float32)
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rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0, 0],
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[-np.sin(rad(anglez)), np.cos(rad(anglez)), 0, 0],
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[0, 0, 1, 0], [0, 0, 0, 1]], np.float32)
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r = rx.dot(ry).dot(rz)
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# generate 4 points
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pcenter = np.array([h / 2, w / 2, 0, 0], np.float32)
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p1 = np.array([0, 0, 0, 0], np.float32) - pcenter
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p2 = np.array([w, 0, 0, 0], np.float32) - pcenter
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p3 = np.array([0, h, 0, 0], np.float32) - pcenter
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p4 = np.array([w, h, 0, 0], np.float32) - pcenter
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dst1 = r.dot(p1)
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dst2 = r.dot(p2)
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dst3 = r.dot(p3)
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dst4 = r.dot(p4)
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list_dst = np.array([dst1, dst2, dst3, dst4])
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org = np.array([[0, 0], [w, 0], [0, h], [w, h]], np.float32)
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dst = np.zeros((4, 2), np.float32)
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# Project onto the image plane
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dst[:, 0] = list_dst[:, 0] * z / (z - list_dst[:, 2]) + pcenter[0]
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dst[:, 1] = list_dst[:, 1] * z / (z - list_dst[:, 2]) + pcenter[1]
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warpR = cv2.getPerspectiveTransform(org, dst)
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dst1, dst2, dst3, dst4 = dst
|
|
r1 = int(min(dst1[1], dst2[1]))
|
|
r2 = int(max(dst3[1], dst4[1]))
|
|
c1 = int(min(dst1[0], dst3[0]))
|
|
c2 = int(max(dst2[0], dst4[0]))
|
|
|
|
try:
|
|
ratio = min(1.0 * h / (r2 - r1), 1.0 * w / (c2 - c1))
|
|
|
|
dx = -c1
|
|
dy = -r1
|
|
T1 = np.float32([[1., 0, dx], [0, 1., dy], [0, 0, 1.0 / ratio]])
|
|
ret = T1.dot(warpR)
|
|
except:
|
|
ratio = 1.0
|
|
T1 = np.float32([[1., 0, 0], [0, 1., 0], [0, 0, 1.]])
|
|
ret = T1
|
|
return ret, (-r1, -c1), ratio, dst
|
|
|
|
|
|
def get_warpAffine(config):
|
|
"""
|
|
get_warpAffine
|
|
"""
|
|
anglez = config.anglez
|
|
rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0],
|
|
[-np.sin(rad(anglez)), np.cos(rad(anglez)), 0]], np.float32)
|
|
return rz
|
|
|
|
|
|
def warp(img, ang, use_tia=True, prob=0.4):
|
|
"""
|
|
warp
|
|
"""
|
|
h, w, _ = img.shape
|
|
config = Config(use_tia=use_tia)
|
|
config.make(w, h, ang)
|
|
new_img = img
|
|
|
|
if config.distort:
|
|
img_height, img_width = img.shape[0:2]
|
|
if random.random() <= prob and img_height >= 20 and img_width >= 20:
|
|
new_img = tia_distort(new_img, random.randint(3, 6))
|
|
|
|
if config.stretch:
|
|
img_height, img_width = img.shape[0:2]
|
|
if random.random() <= prob and img_height >= 20 and img_width >= 20:
|
|
new_img = tia_stretch(new_img, random.randint(3, 6))
|
|
|
|
if config.perspective:
|
|
if random.random() <= prob:
|
|
new_img = tia_perspective(new_img)
|
|
|
|
if config.crop:
|
|
img_height, img_width = img.shape[0:2]
|
|
if random.random() <= prob and img_height >= 20 and img_width >= 20:
|
|
new_img = get_crop(new_img)
|
|
|
|
if config.blur:
|
|
if random.random() <= prob:
|
|
new_img = blur(new_img)
|
|
if config.color:
|
|
if random.random() <= prob:
|
|
new_img = cvtColor(new_img)
|
|
if config.jitter:
|
|
new_img = jitter(new_img)
|
|
if config.noise:
|
|
if random.random() <= prob:
|
|
new_img = add_gasuss_noise(new_img)
|
|
if config.reverse:
|
|
if random.random() <= prob:
|
|
new_img = 255 - new_img
|
|
return new_img
|