# encoding=utf8 # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # 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 io import logging import os import sys # __dir__ = os.path.dirname(os.path.abspath(__file__)) import zlib import requests # sys.path.append(__dir__) # sys.path.append(os.path.abspath(os.path.join(__dir__, '../..'))) sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../../") os.environ["FLAGS_allocator_strategy"] = 'auto_growth' import cv2 import numpy as np import math import time import traceback os.environ['FLAGS_eager_delete_tensor_gb'] = '0' import paddle import ocr.tools.infer.utility as utility from ocr.ppocr.postprocess import build_post_process from ocr.ppocr.utils.logging import get_logger from ocr.ppocr.utils.utility import get_image_file_list, check_and_read_gif from format_convert.utils import judge_error_code, log, namespace_to_dict from format_convert import _global logger = get_logger() class TextRecognizer(object): shrink_memory_count = 0 def __init__(self, args): self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")] self.character_type = args.rec_char_type self.rec_batch_num = args.rec_batch_num self.rec_algorithm = args.rec_algorithm postprocess_params = { 'name': 'CTCLabelDecode', "character_type": args.rec_char_type, "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char } if self.rec_algorithm == "SRN": postprocess_params = { 'name': 'SRNLabelDecode', "character_type": args.rec_char_type, "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char } elif self.rec_algorithm == "RARE": postprocess_params = { 'name': 'AttnLabelDecode', "character_type": args.rec_char_type, "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char } self.postprocess_op = build_post_process(postprocess_params) self.predictor, self.input_tensor, self.output_tensors = \ utility.create_predictor(args, 'rec', logger) def resize_norm_img(self, img, max_wh_ratio): h, w = img.shape[:2] imgC, imgH, imgW = self.rec_image_shape assert imgC == img.shape[2] if max_wh_ratio < 0.1: if h > imgW: resized_image = cv2.resize(img, (w, imgW)) else: resized_image = img else: if self.character_type == "ch": imgW = int((32 * max_wh_ratio)) ratio = w / float(h) if math.ceil(imgH * ratio) > imgW: resized_w = imgW else: resized_w = int(math.ceil(imgH * ratio)) try: resized_image = cv2.resize(img, (resized_w, imgH)) except: log("predict_rec.py resize_norm_img resize shape " + str((resized_w, imgH, imgW, h, w, ratio, max_wh_ratio)) + ' ' + str(self.rec_image_shape)) raise resized_image = resized_image.astype('float32') 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 return padding_im def resize_norm_img_srn(self, 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 srn_other_inputs(self, 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, 1, max_text_length, max_text_length]) gsrm_slf_attn_bias1 = np.tile( gsrm_slf_attn_bias1, [1, num_heads, 1, 1]).astype('float32') * [-1e9] gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape( [-1, 1, max_text_length, max_text_length]) gsrm_slf_attn_bias2 = np.tile( gsrm_slf_attn_bias2, [1, num_heads, 1, 1]).astype('float32') * [-1e9] encoder_word_pos = encoder_word_pos[np.newaxis, :] gsrm_word_pos = gsrm_word_pos[np.newaxis, :] return [ encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2 ] def process_image_srn(self, img, image_shape, num_heads, max_text_length): norm_img = self.resize_norm_img_srn(img, image_shape) norm_img = norm_img[np.newaxis, :] [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \ self.srn_other_inputs(image_shape, num_heads, max_text_length) gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32) gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32) encoder_word_pos = encoder_word_pos.astype(np.int64) gsrm_word_pos = gsrm_word_pos.astype(np.int64) return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2) def __call__(self, img_list): img_num = len(img_list) # Calculate the aspect ratio of all text bars width_list = [] for img in img_list: width_list.append(img.shape[1] / float(img.shape[0])) # Sorting can speed up the recognition process indices = np.argsort(np.array(width_list)) # rec_res = [] rec_res = [['', 0.0]] * img_num batch_num = self.rec_batch_num elapse = 0 for beg_img_no in range(0, img_num, batch_num): end_img_no = min(img_num, beg_img_no + batch_num) norm_img_batch = [] max_wh_ratio = 0 for ino in range(beg_img_no, end_img_no): # h, w = img_list[ino].shape[0:2] h, w = img_list[indices[ino]].shape[0:2] wh_ratio = w * 1.0 / h max_wh_ratio = max(max_wh_ratio, wh_ratio) for ino in range(beg_img_no, end_img_no): if self.rec_algorithm != "SRN": norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) else: norm_img = self.process_image_srn( img_list[indices[ino]], self.rec_image_shape, 8, 25) encoder_word_pos_list = [] gsrm_word_pos_list = [] gsrm_slf_attn_bias1_list = [] gsrm_slf_attn_bias2_list = [] encoder_word_pos_list.append(norm_img[1]) gsrm_word_pos_list.append(norm_img[2]) gsrm_slf_attn_bias1_list.append(norm_img[3]) gsrm_slf_attn_bias2_list.append(norm_img[4]) norm_img_batch.append(norm_img[0]) norm_img_batch = np.concatenate(norm_img_batch) norm_img_batch = norm_img_batch.copy() if self.rec_algorithm == "SRN": starttime = time.time() encoder_word_pos_list = np.concatenate(encoder_word_pos_list) gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list) gsrm_slf_attn_bias1_list = np.concatenate( gsrm_slf_attn_bias1_list) gsrm_slf_attn_bias2_list = np.concatenate( gsrm_slf_attn_bias2_list) inputs = [ norm_img_batch, encoder_word_pos_list, gsrm_word_pos_list, gsrm_slf_attn_bias1_list, gsrm_slf_attn_bias2_list, ] input_names = self.predictor.get_input_names() for i in range(len(input_names)): input_tensor = self.predictor.get_input_handle(input_names[ i]) input_tensor.copy_from_cpu(inputs[i]) self.predictor.run() outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) preds = {"predict": outputs[2]} else: starttime = time.time() self.input_tensor.copy_from_cpu(norm_img_batch) start_time = time.time() self.predictor.run() logging.info("ocr model predict time - rec" + str(time.time()-start_time)) outputs = [] for output_tensor in self.output_tensors: output = output_tensor.copy_to_cpu() outputs.append(output) preds = outputs[0] # print("tools/infer/predict_rec preds", preds) rec_result = self.postprocess_op(preds) for rno in range(len(rec_result)): # print("predict_rec", img_num, batch_num, beg_img_no, # indices[beg_img_no + rno], len(rec_res)) rec_res[indices[beg_img_no + rno]] = rec_result[rno] elapse += time.time() - starttime # 释放内存 self.predictor.clear_intermediate_tensor() self.predictor.try_shrink_memory() return rec_res, elapse class TextRecognizer2(object): shrink_memory_count = 0 def __init__(self, args): self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")] self.character_type = args.rec_char_type self.rec_batch_num = args.rec_batch_num self.rec_algorithm = args.rec_algorithm postprocess_params = { 'name': 'CTCLabelDecode', "character_type": args.rec_char_type, "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char } self.postprocess_op = build_post_process(postprocess_params) self.args = args # self.predictor, self.input_tensor, self.output_tensors = \ # utility.create_predictor(args, 'rec', logger) def resize_norm_img(self, img, max_wh_ratio): imgC, imgH, imgW = self.rec_image_shape assert imgC == img.shape[2] if self.character_type == "ch": imgW = int((32 * max_wh_ratio)) h, w = img.shape[:2] ratio = w / float(h) if math.ceil(imgH * ratio) > imgW: resized_w = imgW else: resized_w = int(math.ceil(imgH * ratio)) # print("predict_rec.py resize_norm_img resize shape", (resized_w, imgH)) resized_image = cv2.resize(img, (resized_w, imgH)) resized_image = resized_image.astype('float32') 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 return padding_im def __call__(self, img_list): from format_convert.convert_need_interface import from_gpu_interface_redis img_num = len(img_list) # Calculate the aspect ratio of all text bars width_list = [] for img in img_list: width_list.append(img.shape[1] / float(img.shape[0])) # Sorting can speed up the recognition process indices = np.argsort(np.array(width_list)) rec_res = [['', 0.0]] * img_num batch_num = self.rec_batch_num elapse = 0 all_gpu_time = 0 for beg_img_no in range(0, img_num, batch_num): # 预处理 end_img_no = min(img_num, beg_img_no + batch_num) norm_img_batch = [] max_wh_ratio = 0 for ino in range(beg_img_no, end_img_no): h, w = img_list[indices[ino]].shape[0:2] wh_ratio = w * 1.0 / h max_wh_ratio = max(max_wh_ratio, wh_ratio) for ino in range(beg_img_no, end_img_no): norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) norm_img_batch = np.concatenate(norm_img_batch) norm_img_batch = norm_img_batch.copy() starttime = time.time() # # 压缩numpy # compressed_array = io.BytesIO() # np.savez_compressed(compressed_array, norm_img_batch) # compressed_array.seek(0) # norm_img_batch = compressed_array.read() # 调用GPU接口 _dict = {"inputs": norm_img_batch, "args": str(namespace_to_dict(self.args)), "md5": _global.get("md5")} result = from_gpu_interface_redis(_dict, model_type="ocr", predictor_type="rec") if judge_error_code(result): logging.error("from_gpu_interface failed! " + str(result)) raise requests.exceptions.RequestException preds = result.get("preds") gpu_time = result.get("gpu_time") all_gpu_time += round(gpu_time, 2) # # 解压numpy # decompressed_array = io.BytesIO() # decompressed_array.write(preds) # decompressed_array.seek(0) # preds = np.load(decompressed_array, allow_pickle=True)['arr_0'] # log("inputs.shape" + str(preds.shape)) # 后处理 rec_result = self.postprocess_op(preds) for rno in range(len(rec_result)): rec_res[indices[beg_img_no + rno]] = rec_result[rno] elapse += time.time() - starttime log("ocr model predict time - rec - time " + str(all_gpu_time) + " - num " + str(img_num)) return rec_res, elapse def main(args): image_file_list = get_image_file_list(args.image_dir) text_recognizer = TextRecognizer(args) valid_image_file_list = [] img_list = [] for image_file in image_file_list: img, flag = check_and_read_gif(image_file) if not flag: img = cv2.imread(image_file) if img is None: logger.info("error in loading image:{}".format(image_file)) continue valid_image_file_list.append(image_file) img_list.append(img) try: rec_res, predict_time = text_recognizer(img_list) except: logger.info(traceback.format_exc()) logger.info( "ERROR!!!! \n" "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n" "If your model has tps module: " "TPS does not support variable shape.\n" "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ") exit() for ino in range(len(img_list)): logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[ino])) logger.info("Total predict time for {} images, cost: {:.3f}".format( len(img_list), predict_time)) if __name__ == "__main__": main(utility.parse_args())