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| 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"] |
|
|
| |
| post_process_class = build_post_process(config["PostProcess"], global_config) |
|
|
| |
| 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) |
|
|
| |
| 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 = ( |
| ["<html>", "<body>", "<table>"] |
| + structure_str_list |
| + ["</table>", "</body>", "</html>"] |
| ) |
| 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) |
|
|
