# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import cv2 import numpy as np import random import copy from PIL import Image from .text_image_aug import tia_perspective, tia_stretch, tia_distort from .abinet_aug import CVGeometry, CVDeterioration, CVColorJitter from paddle.vision.transforms import Compose class RecAug(object): def __init__(self, tia_prob=0.4, crop_prob=0.4, reverse_prob=0.4, noise_prob=0.4, jitter_prob=0.4, blur_prob=0.4, hsv_aug_prob=0.4, **kwargs): self.tia_prob = tia_prob self.bda = BaseDataAugmentation(crop_prob, reverse_prob, noise_prob, jitter_prob, blur_prob, hsv_aug_prob) def __call__(self, data): img = data['image'] h, w, _ = img.shape # tia if random.random() <= self.tia_prob: if h >= 20 and w >= 20: img = tia_distort(img, random.randint(3, 6)) img = tia_stretch(img, random.randint(3, 6)) img = tia_perspective(img) # bda data['image'] = img data = self.bda(data) return data class BaseDataAugmentation(object): def __init__(self, crop_prob=0.4, reverse_prob=0.4, noise_prob=0.4, jitter_prob=0.4, blur_prob=0.4, hsv_aug_prob=0.4, **kwargs): self.crop_prob = crop_prob self.reverse_prob = reverse_prob self.noise_prob = noise_prob self.jitter_prob = jitter_prob self.blur_prob = blur_prob self.hsv_aug_prob = hsv_aug_prob def __call__(self, data): img = data['image'] h, w, _ = img.shape if random.random() <= self.crop_prob and h >= 20 and w >= 20: img = get_crop(img) if random.random() <= self.blur_prob: img = blur(img) if random.random() <= self.hsv_aug_prob: img = hsv_aug(img) if random.random() <= self.jitter_prob: img = jitter(img) if random.random() <= self.noise_prob: img = add_gasuss_noise(img) if random.random() <= self.reverse_prob: img = 255 - img data['image'] = img return data class ABINetRecAug(object): def __init__(self, geometry_p=0.5, deterioration_p=0.25, colorjitter_p=0.25, **kwargs): self.transforms = Compose([ CVGeometry( degrees=45, translate=(0.0, 0.0), scale=(0.5, 2.), shear=(45, 15), distortion=0.5, p=geometry_p), CVDeterioration( var=20, degrees=6, factor=4, p=deterioration_p), CVColorJitter( brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1, p=colorjitter_p) ]) def __call__(self, data): img = data['image'] img = self.transforms(img) data['image'] = img return data class RecConAug(object): def __init__(self, prob=0.5, image_shape=(32, 320, 3), max_text_length=25, ext_data_num=1, **kwargs): self.ext_data_num = ext_data_num self.prob = prob self.max_text_length = max_text_length self.image_shape = image_shape self.max_wh_ratio = self.image_shape[1] / self.image_shape[0] def merge_ext_data(self, data, ext_data): ori_w = round(data['image'].shape[1] / data['image'].shape[0] * self.image_shape[0]) ext_w = round(ext_data['image'].shape[1] / ext_data['image'].shape[0] * self.image_shape[0]) data['image'] = cv2.resize(data['image'], (ori_w, self.image_shape[0])) ext_data['image'] = cv2.resize(ext_data['image'], (ext_w, self.image_shape[0])) data['image'] = np.concatenate( [data['image'], ext_data['image']], axis=1) data["label"] += ext_data["label"] return data def __call__(self, data): rnd_num = random.random() if rnd_num > self.prob: return data for idx, ext_data in enumerate(data["ext_data"]): if len(data["label"]) + len(ext_data[ "label"]) > self.max_text_length: break concat_ratio = data['image'].shape[1] / data['image'].shape[ 0] + ext_data['image'].shape[1] / ext_data['image'].shape[0] if concat_ratio > self.max_wh_ratio: break data = self.merge_ext_data(data, ext_data) data.pop("ext_data") return data class ClsResizeImg(object): def __init__(self, image_shape, **kwargs): self.image_shape = image_shape def __call__(self, data): img = data['image'] norm_img, _ = resize_norm_img(img, self.image_shape) data['image'] = norm_img return data class RecResizeImg(object): def __init__(self, image_shape, infer_mode=False, character_dict_path='./ppocr/utils/ppocr_keys_v1.txt', padding=True, **kwargs): self.image_shape = image_shape self.infer_mode = infer_mode self.character_dict_path = character_dict_path self.padding = padding def __call__(self, data): img = data['image'] if self.infer_mode and self.character_dict_path is not None: norm_img, valid_ratio = resize_norm_img_chinese(img, self.image_shape) else: norm_img, valid_ratio = resize_norm_img(img, self.image_shape, self.padding) data['image'] = norm_img data['valid_ratio'] = valid_ratio return data class VLRecResizeImg(object): def __init__(self, image_shape, infer_mode=False, character_dict_path='./ppocr/utils/ppocr_keys_v1.txt', padding=True, **kwargs): self.image_shape = image_shape self.infer_mode = infer_mode self.character_dict_path = character_dict_path self.padding = padding def __call__(self, data): img = data['image'] imgC, imgH, imgW = self.image_shape resized_image = cv2.resize( img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) resized_w = imgW resized_image = resized_image.astype('float32') if self.image_shape[0] == 1: resized_image = resized_image / 255 norm_img = resized_image[np.newaxis, :] else: norm_img = resized_image.transpose((2, 0, 1)) / 255 valid_ratio = min(1.0, float(resized_w / imgW)) data['image'] = norm_img data['valid_ratio'] = valid_ratio return data class RFLRecResizeImg(object): def __init__(self, image_shape, padding=True, interpolation=1, **kwargs): self.image_shape = image_shape self.padding = padding self.interpolation = interpolation if self.interpolation == 0: self.interpolation = cv2.INTER_NEAREST elif self.interpolation == 1: self.interpolation = cv2.INTER_LINEAR elif self.interpolation == 2: self.interpolation = cv2.INTER_CUBIC elif self.interpolation == 3: self.interpolation = cv2.INTER_AREA else: raise Exception("Unsupported interpolation type !!!") def __call__(self, data): img = data['image'] img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) norm_img, valid_ratio = resize_norm_img( img, self.image_shape, self.padding, self.interpolation) data['image'] = norm_img data['valid_ratio'] = valid_ratio return data class SRNRecResizeImg(object): def __init__(self, image_shape, num_heads, max_text_length, **kwargs): self.image_shape = image_shape self.num_heads = num_heads self.max_text_length = max_text_length def __call__(self, data): img = data['image'] norm_img = resize_norm_img_srn(img, self.image_shape) data['image'] = norm_img [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \ srn_other_inputs(self.image_shape, self.num_heads, self.max_text_length) data['encoder_word_pos'] = encoder_word_pos data['gsrm_word_pos'] = gsrm_word_pos data['gsrm_slf_attn_bias1'] = gsrm_slf_attn_bias1 data['gsrm_slf_attn_bias2'] = gsrm_slf_attn_bias2 return data class SARRecResizeImg(object): def __init__(self, image_shape, width_downsample_ratio=0.25, **kwargs): self.image_shape = image_shape self.width_downsample_ratio = width_downsample_ratio def __call__(self, data): img = data['image'] norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar( img, self.image_shape, self.width_downsample_ratio) data['image'] = norm_img data['resized_shape'] = resize_shape data['pad_shape'] = pad_shape data['valid_ratio'] = valid_ratio return data class PRENResizeImg(object): def __init__(self, image_shape, **kwargs): """ Accroding to original paper's realization, it's a hard resize method here. So maybe you should optimize it to fit for your task better. """ self.dst_h, self.dst_w = image_shape def __call__(self, data): img = data['image'] resized_img = cv2.resize( img, (self.dst_w, self.dst_h), interpolation=cv2.INTER_LINEAR) resized_img = resized_img.transpose((2, 0, 1)) / 255 resized_img -= 0.5 resized_img /= 0.5 data['image'] = resized_img.astype(np.float32) return data class SPINRecResizeImg(object): def __init__(self, image_shape, interpolation=2, mean=(127.5, 127.5, 127.5), std=(127.5, 127.5, 127.5), **kwargs): self.image_shape = image_shape self.mean = np.array(mean, dtype=np.float32) self.std = np.array(std, dtype=np.float32) self.interpolation = interpolation def __call__(self, data): img = data['image'] img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # different interpolation type corresponding the OpenCV if self.interpolation == 0: interpolation = cv2.INTER_NEAREST elif self.interpolation == 1: interpolation = cv2.INTER_LINEAR elif self.interpolation == 2: interpolation = cv2.INTER_CUBIC elif self.interpolation == 3: interpolation = cv2.INTER_AREA else: raise Exception("Unsupported interpolation type !!!") # Deal with the image error during image loading if img is None: return None img = cv2.resize(img, tuple(self.image_shape), interpolation) img = np.array(img, np.float32) img = np.expand_dims(img, -1) img = img.transpose((2, 0, 1)) # normalize the image img = img.copy().astype(np.float32) mean = np.float64(self.mean.reshape(1, -1)) stdinv = 1 / np.float64(self.std.reshape(1, -1)) img -= mean img *= stdinv data['image'] = img return data class GrayRecResizeImg(object): def __init__(self, image_shape, resize_type, inter_type='Image.ANTIALIAS', scale=True, padding=False, **kwargs): self.image_shape = image_shape self.resize_type = resize_type self.padding = padding self.inter_type = eval(inter_type) self.scale = scale def __call__(self, data): img = data['image'] img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) image_shape = self.image_shape if self.padding: imgC, imgH, imgW = image_shape # todo: change to 0 and modified image shape h = img.shape[0] w = img.shape[1] ratio = w / float(h) if math.ceil(imgH * ratio) > imgW: resized_w = imgW else: resized_w = int(math.ceil(imgH * ratio)) resized_image = cv2.resize(img, (resized_w, imgH)) norm_img = np.expand_dims(resized_image, -1) norm_img = norm_img.transpose((2, 0, 1)) resized_image = norm_img.astype(np.float32) / 128. - 1. padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) padding_im[:, :, 0:resized_w] = resized_image data['image'] = padding_im return data if self.resize_type == 'PIL': image_pil = Image.fromarray(np.uint8(img)) img = image_pil.resize(self.image_shape, self.inter_type) img = np.array(img) if self.resize_type == 'OpenCV': img = cv2.resize(img, self.image_shape) norm_img = np.expand_dims(img, -1) norm_img = norm_img.transpose((2, 0, 1)) if self.scale: data['image'] = norm_img.astype(np.float32) / 128. - 1. else: data['image'] = norm_img.astype(np.float32) / 255. return data class ABINetRecResizeImg(object): def __init__(self, image_shape, **kwargs): self.image_shape = image_shape def __call__(self, data): img = data['image'] norm_img, valid_ratio = resize_norm_img_abinet(img, self.image_shape) data['image'] = norm_img data['valid_ratio'] = valid_ratio return data class SVTRRecResizeImg(object): def __init__(self, image_shape, padding=True, **kwargs): self.image_shape = image_shape self.padding = padding def __call__(self, data): img = data['image'] norm_img, valid_ratio = resize_norm_img(img, self.image_shape, self.padding) data['image'] = norm_img data['valid_ratio'] = valid_ratio return data class RobustScannerRecResizeImg(object): def __init__(self, image_shape, max_text_length, width_downsample_ratio=0.25, **kwargs): self.image_shape = image_shape self.width_downsample_ratio = width_downsample_ratio self.max_text_length = max_text_length def __call__(self, data): img = data['image'] norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar( img, self.image_shape, self.width_downsample_ratio) word_positons = np.array(range(0, self.max_text_length)).astype('int64') data['image'] = norm_img data['resized_shape'] = resize_shape data['pad_shape'] = pad_shape data['valid_ratio'] = valid_ratio data['word_positons'] = word_positons return data class GrayImageChannelFormat(object): """ format gray scale image's channel: (3,h,w) -> (1,h,w) Args: normalize: True/False when True convert image dynamic range [0,255]->[0,1] inverse: inverse gray image """ def __init__(self, normalize=True, inverse=False, **kwargs): self.normalize = normalize self.inverse = inverse def __call__(self, data): img = data['image'] img_single_channel = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img_single_channel = np.expand_dims(img_single_channel, 0) if self.normalize: img_single_channel = img_single_channel / 255.0 if self.inverse: data['image'] = np.abs(img_single_channel - 1).astype('float32') else: data['image'] = img_single_channel.astype('float32') data['src_image'] = img return data def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25): imgC, imgH, imgW_min, imgW_max = image_shape h = img.shape[0] w = img.shape[1] valid_ratio = 1.0 # make sure new_width is an integral multiple of width_divisor. width_divisor = int(1 / width_downsample_ratio) # resize ratio = w / float(h) resize_w = math.ceil(imgH * ratio) if resize_w % width_divisor != 0: resize_w = round(resize_w / width_divisor) * width_divisor if imgW_min is not None: resize_w = max(imgW_min, resize_w) if imgW_max is not None: valid_ratio = min(1.0, 1.0 * resize_w / imgW_max) resize_w = min(imgW_max, resize_w) resized_image = cv2.resize(img, (resize_w, imgH)) resized_image = resized_image.astype('float32') # norm if image_shape[0] == 1: resized_image = resized_image / 255 resized_image = resized_image[np.newaxis, :] else: resized_image = resized_image.transpose((2, 0, 1)) / 255 resized_image -= 0.5 resized_image /= 0.5 resize_shape = resized_image.shape padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32) padding_im[:, :, 0:resize_w] = resized_image pad_shape = padding_im.shape return padding_im, resize_shape, pad_shape, valid_ratio def resize_norm_img(img, image_shape, padding=True, interpolation=cv2.INTER_LINEAR): imgC, imgH, imgW = image_shape h = img.shape[0] w = img.shape[1] if not padding: resized_image = cv2.resize( img, (imgW, imgH), interpolation=interpolation) resized_w = imgW else: ratio = w / float(h) if math.ceil(imgH * ratio) > imgW: resized_w = imgW else: resized_w = int(math.ceil(imgH * ratio)) resized_image = cv2.resize(img, (resized_w, imgH)) resized_image = resized_image.astype('float32') if image_shape[0] == 1: resized_image = resized_image / 255 resized_image = resized_image[np.newaxis, :] else: resized_image = resized_image.transpose((2, 0, 1)) / 255 resized_image -= 0.5 resized_image /= 0.5 padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) padding_im[:, :, 0:resized_w] = resized_image valid_ratio = min(1.0, float(resized_w / imgW)) return padding_im, valid_ratio def resize_norm_img_chinese(img, image_shape): imgC, imgH, imgW = image_shape # todo: change to 0 and modified image shape max_wh_ratio = imgW * 1.0 / imgH h, w = img.shape[0], img.shape[1] ratio = w * 1.0 / h max_wh_ratio = min(max(max_wh_ratio, ratio), max_wh_ratio) imgW = int(imgH * max_wh_ratio) if math.ceil(imgH * ratio) > imgW: resized_w = imgW else: resized_w = int(math.ceil(imgH * ratio)) resized_image = cv2.resize(img, (resized_w, imgH)) resized_image = resized_image.astype('float32') if image_shape[0] == 1: resized_image = resized_image / 255 resized_image = resized_image[np.newaxis, :] else: resized_image = resized_image.transpose((2, 0, 1)) / 255 resized_image -= 0.5 resized_image /= 0.5 padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) padding_im[:, :, 0:resized_w] = resized_image valid_ratio = min(1.0, float(resized_w / imgW)) return padding_im, valid_ratio def resize_norm_img_srn(img, image_shape): imgC, imgH, imgW = image_shape img_black = np.zeros((imgH, imgW)) im_hei = img.shape[0] im_wid = img.shape[1] if im_wid <= im_hei * 1: img_new = cv2.resize(img, (imgH * 1, imgH)) elif im_wid <= im_hei * 2: img_new = cv2.resize(img, (imgH * 2, imgH)) elif im_wid <= im_hei * 3: img_new = cv2.resize(img, (imgH * 3, imgH)) else: img_new = cv2.resize(img, (imgW, imgH)) img_np = np.asarray(img_new) img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) img_black[:, 0:img_np.shape[1]] = img_np img_black = img_black[:, :, np.newaxis] row, col, c = img_black.shape c = 1 return np.reshape(img_black, (c, row, col)).astype(np.float32) def resize_norm_img_abinet(img, image_shape): imgC, imgH, imgW = image_shape resized_image = cv2.resize( img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) resized_w = imgW resized_image = resized_image.astype('float32') resized_image = resized_image / 255. mean = np.array([0.485, 0.456, 0.406]) std = np.array([0.229, 0.224, 0.225]) resized_image = ( resized_image - mean[None, None, ...]) / std[None, None, ...] resized_image = resized_image.transpose((2, 0, 1)) resized_image = resized_image.astype('float32') valid_ratio = min(1.0, float(resized_w / imgW)) return resized_image, valid_ratio def srn_other_inputs(image_shape, num_heads, max_text_length): imgC, imgH, imgW = image_shape feature_dim = int((imgH / 8) * (imgW / 8)) encoder_word_pos = np.array(range(0, feature_dim)).reshape( (feature_dim, 1)).astype('int64') gsrm_word_pos = np.array(range(0, max_text_length)).reshape( (max_text_length, 1)).astype('int64') gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape( [1, max_text_length, max_text_length]) gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, [num_heads, 1, 1]) * [-1e9] gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape( [1, max_text_length, max_text_length]) gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, [num_heads, 1, 1]) * [-1e9] return [ encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2 ] def flag(): """ flag """ return 1 if random.random() > 0.5000001 else -1 def hsv_aug(img): """ cvtColor """ hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) delta = 0.001 * random.random() * flag() hsv[:, :, 2] = hsv[:, :, 2] * (1 + delta) new_img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) return new_img def blur(img): """ blur """ h, w, _ = img.shape if h > 10 and w > 10: return cv2.GaussianBlur(img, (5, 5), 1) else: return img def jitter(img): """ jitter """ w, h, _ = img.shape if h > 10 and w > 10: thres = min(w, h) s = int(random.random() * thres * 0.01) src_img = img.copy() for i in range(s): img[i:, i:, :] = src_img[:w - i, :h - i, :] return img else: return img def add_gasuss_noise(image, mean=0, var=0.1): """ Gasuss noise """ noise = np.random.normal(mean, var**0.5, image.shape) out = image + 0.5 * noise out = np.clip(out, 0, 255) out = np.uint8(out) return out def get_crop(image): """ random crop """ h, w, _ = image.shape top_min = 1 top_max = 8 top_crop = int(random.randint(top_min, top_max)) top_crop = min(top_crop, h - 1) crop_img = image.copy() ratio = random.randint(0, 1) if ratio: crop_img = crop_img[top_crop:h, :, :] else: crop_img = crop_img[0:h - top_crop, :, :] return crop_img def rad(x): """ rad """ return x * np.pi / 180 def get_warpR(config): """ get_warpR """ anglex, angley, anglez, fov, w, h, r = \ config.anglex, config.angley, config.anglez, config.fov, config.w, config.h, config.r if w > 69 and w < 112: anglex = anglex * 1.5 z = np.sqrt(w**2 + h**2) / 2 / np.tan(rad(fov / 2)) # Homogeneous coordinate transformation matrix rx = np.array([[1, 0, 0, 0], [0, np.cos(rad(anglex)), -np.sin(rad(anglex)), 0], [ 0, -np.sin(rad(anglex)), np.cos(rad(anglex)), 0, ], [0, 0, 0, 1]], np.float32) ry = np.array([[np.cos(rad(angley)), 0, np.sin(rad(angley)), 0], [0, 1, 0, 0], [ -np.sin(rad(angley)), 0, np.cos(rad(angley)), 0, ], [0, 0, 0, 1]], np.float32) rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0, 0], [-np.sin(rad(anglez)), np.cos(rad(anglez)), 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) r = rx.dot(ry).dot(rz) # generate 4 points pcenter = np.array([h / 2, w / 2, 0, 0], np.float32) p1 = np.array([0, 0, 0, 0], np.float32) - pcenter p2 = np.array([w, 0, 0, 0], np.float32) - pcenter p3 = np.array([0, h, 0, 0], np.float32) - pcenter p4 = np.array([w, h, 0, 0], np.float32) - pcenter dst1 = r.dot(p1) dst2 = r.dot(p2) dst3 = r.dot(p3) dst4 = r.dot(p4) list_dst = np.array([dst1, dst2, dst3, dst4]) org = np.array([[0, 0], [w, 0], [0, h], [w, h]], np.float32) dst = np.zeros((4, 2), np.float32) # Project onto the image plane dst[:, 0] = list_dst[:, 0] * z / (z - list_dst[:, 2]) + pcenter[0] dst[:, 1] = list_dst[:, 1] * z / (z - list_dst[:, 2]) + pcenter[1] warpR = cv2.getPerspectiveTransform(org, dst) 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