# 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import os import sys import json __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.insert(0, os.path.abspath(os.path.join(__dir__, ".."))) os.environ["FLAGS_allocator_strategy"] = "auto_growth" import paddle from paddle.jit import to_static from ppocr.data import create_operators, transform from ppocr.modeling.architectures import build_model from ppocr.postprocess import build_post_process from ppocr.utils.save_load import load_model from ppocr.utils.utility import get_image_file_list from ppocr.utils.visual import draw_rectangle from tools.infer.utility import draw_boxes import tools.program as program import cv2 @paddle.no_grad() def main(config, device, logger, vdl_writer): global_config = config["Global"] # build post process post_process_class = build_post_process(config["PostProcess"], global_config) # build model if hasattr(post_process_class, "character"): config["Architecture"]["Head"]["out_channels"] = len( getattr(post_process_class, "character") ) model = build_model(config["Architecture"]) algorithm = config["Architecture"]["algorithm"] load_model(config, model) # create data ops transforms = [] for op in config["Eval"]["dataset"]["transforms"]: op_name = list(op)[0] if "Encode" in op_name: continue if op_name == "KeepKeys": op[op_name]["keep_keys"] = ["image", "shape"] transforms.append(op) global_config["infer_mode"] = True ops = create_operators(transforms, global_config) save_res_path = config["Global"]["save_res_path"] os.makedirs(save_res_path, exist_ok=True) model.eval() with open( os.path.join(save_res_path, "infer.txt"), mode="w", encoding="utf-8" ) as f_w: for file in get_image_file_list(config["Global"]["infer_img"]): logger.info("infer_img: {}".format(file)) with open(file, "rb") as f: img = f.read() data = {"image": img} batch = transform(data, ops) images = np.expand_dims(batch[0], axis=0) shape_list = np.expand_dims(batch[1], axis=0) images = paddle.to_tensor(images) preds = model(images) post_result = post_process_class(preds, [shape_list]) structure_str_list = post_result["structure_batch_list"][0] bbox_list = post_result["bbox_batch_list"][0] structure_str_list = structure_str_list[0] structure_str_list = ( ["", "", ""] + structure_str_list + ["

", "", ""] ) bbox_list_str = json.dumps(bbox_list.tolist()) logger.info("result: {}, {}".format(structure_str_list, bbox_list_str)) f_w.write("result: {}, {}\n".format(structure_str_list, bbox_list_str)) if len(bbox_list) > 0 and len(bbox_list[0]) == 4: img = draw_rectangle(file, bbox_list) else: img = draw_boxes(cv2.imread(file), bbox_list) cv2.imwrite(os.path.join(save_res_path, os.path.basename(file)), img) logger.info("save result to {}".format(save_res_path)) logger.info("success!") if __name__ == "__main__": config, device, logger, vdl_writer = program.preprocess() main(config, device, logger, vdl_writer)