# -*- encoding: utf-8 -*- # @Author: SWHL # @Contact: liekkaskono@163.com import argparse import warnings from io import BytesIO from pathlib import Path from typing import Union import cv2 import numpy as np import yaml from onnxruntime import (GraphOptimizationLevel, InferenceSession, SessionOptions, get_available_providers, get_device) from PIL import Image, UnidentifiedImageError root_dir = Path(__file__).resolve().parent InputType = Union[str, np.ndarray, bytes, Path] class OrtInferSession(): def __init__(self, config): sess_opt = SessionOptions() sess_opt.log_severity_level = 4 sess_opt.enable_cpu_mem_arena = False sess_opt.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL cpu_ep = 'CPUExecutionProvider' cpu_provider_options = { 'arena_extend_strategy': 'kSameAsRequested', } cuda_ep = 'CUDAExecutionProvider' cuda_provider_options = { 'device_id': 0, 'arena_extend_strategy': 'kNextPowerOfTwo', 'cudnn_conv_algo_search': 'EXHAUSTIVE', 'do_copy_in_default_stream': True } EP_list = [] if config['use_cuda'] and get_device() == 'GPU' \ and cuda_ep in get_available_providers(): EP_list = [(cuda_ep, cuda_provider_options)] EP_list.append((cpu_ep, cpu_provider_options)) self._verify_model(config['model_path']) self.session = InferenceSession(config['model_path'], sess_options=sess_opt, providers=EP_list) if config['use_cuda'] and cuda_ep not in self.session.get_providers(): warnings.warn(f'{cuda_ep} is not avaiable for current env, the inference part is automatically shifted to be executed under {cpu_ep}.\n' 'Please ensure the installed onnxruntime-gpu version matches your cuda and cudnn version, ' 'you can check their relations from the offical web site: ' 'https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html', RuntimeWarning) def __call__(self, input_content: np.ndarray) -> np.ndarray: input_dict = dict(zip(self.get_input_names(), [input_content])) try: return self.session.run(self.get_output_names(), input_dict) except Exception as e: raise ONNXRuntimeError('ONNXRuntime inferece failed.') from e def get_input_names(self, ): return [v.name for v in self.session.get_inputs()] def get_output_names(self,): return [v.name for v in self.session.get_outputs()] def get_character_list(self, key: str = 'character'): return self.meta_dict[key].splitlines() def have_key(self, key: str = 'character') -> bool: self.meta_dict = self.session.get_modelmeta().custom_metadata_map if key in self.meta_dict.keys(): return True return False @staticmethod def _verify_model(model_path): model_path = Path(model_path) if not model_path.exists(): raise FileNotFoundError(f'{model_path} does not exists.') if not model_path.is_file(): raise FileExistsError(f'{model_path} is not a file.') class ONNXRuntimeError(Exception): pass class LoadImage(): def __init__(self, ): pass def __call__(self, img: InputType) -> np.ndarray: if not isinstance(img, InputType.__args__): raise LoadImageError( f'The img type {type(img)} does not in {InputType.__args__}') img = self.load_img(img) if img.ndim == 2: return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) if img.ndim == 3 and img.shape[2] == 4: return self.cvt_four_to_three(img) return img def load_img(self, img: InputType) -> np.ndarray: if isinstance(img, (str, Path)): self.verify_exist(img) try: img = np.array(Image.open(img)) except UnidentifiedImageError as e: raise LoadImageError( f'cannot identify image file {img}') from e return img if isinstance(img, bytes): return np.array(Image.open(BytesIO(img))) if isinstance(img, np.ndarray): return img raise LoadImageError(f'{type(img)} is not supported!') @staticmethod def cvt_four_to_three(img: np.ndarray) -> np.ndarray: '''RGBA → RGB ''' r, g, b, a = cv2.split(img) new_img = cv2.merge((b, g, r)) not_a = cv2.bitwise_not(a) not_a = cv2.cvtColor(not_a, cv2.COLOR_GRAY2BGR) new_img = cv2.bitwise_and(new_img, new_img, mask=a) new_img = cv2.add(new_img, not_a) return new_img @staticmethod def verify_exist(file_path: Union[str, Path]): if not Path(file_path).exists(): raise LoadImageError(f'{file_path} does not exist.') class LoadImageError(Exception): pass def read_yaml(yaml_path): with open(yaml_path, 'rb') as f: data = yaml.load(f, Loader=yaml.Loader) return data def concat_model_path(config): key = 'model_path' config['Det'][key] = str(root_dir / config['Det'][key]) config['Rec'][key] = str(root_dir / config['Rec'][key]) config['Cls'][key] = str(root_dir / config['Cls'][key]) return config def init_args(): parser = argparse.ArgumentParser() parser.add_argument('-img', '--img_path', type=str, default=None, required=True) parser.add_argument('-p', '--print_cost', action='store_true', default=False) global_group = parser.add_argument_group(title='Global') global_group.add_argument('--text_score', type=float, default=0.5) global_group.add_argument('--use_angle_cls', type=bool, default=True) global_group.add_argument('--use_text_det', type=bool, default=True) global_group.add_argument('--print_verbose', type=bool, default=False) global_group.add_argument('--min_height', type=int, default=30) global_group.add_argument('--width_height_ratio', type=int, default=8) det_group = parser.add_argument_group(title='Det') det_group.add_argument('--det_model_path', type=str, default=None) det_group.add_argument('--det_limit_side_len', type=float, default=736) det_group.add_argument('--det_limit_type', type=str, default='min', choices=['max', 'min']) det_group.add_argument('--det_thresh', type=float, default=0.3) det_group.add_argument('--det_box_thresh', type=float, default=0.5) det_group.add_argument('--det_unclip_ratio', type=float, default=1.6) det_group.add_argument('--det_use_dilation', type=bool, default=True) det_group.add_argument('--det_score_mode', type=str, default='fast', choices=['slow', 'fast']) cls_group = parser.add_argument_group(title='Cls') cls_group.add_argument('--cls_model_path', type=str, default=None) cls_group.add_argument('--cls_image_shape', type=list, default=[3, 48, 192]) cls_group.add_argument('--cls_label_list', type=list, default=['0', '180']) cls_group.add_argument('--cls_batch_num', type=int, default=6) cls_group.add_argument('--cls_thresh', type=float, default=0.9) rec_group = parser.add_argument_group(title='Rec') rec_group.add_argument('--rec_model_path', type=str, default=None) rec_group.add_argument('--rec_image_shape', type=list, default=[3, 48, 320]) rec_group.add_argument('--rec_batch_num', type=int, default=6) args = parser.parse_args() return args class UpdateParameters(): def __init__(self) -> None: pass def parse_kwargs(self, **kwargs): global_dict, det_dict, cls_dict, rec_dict = {}, {}, {}, {} for k, v in kwargs.items(): if k.startswith('det'): det_dict[k] = v elif k.startswith('cls'): cls_dict[k] = v elif k.startswith('rec'): rec_dict[k] = v else: global_dict[k] = v return global_dict, det_dict, cls_dict, rec_dict def __call__(self, config, **kwargs): global_dict, det_dict, cls_dict, rec_dict = self.parse_kwargs(**kwargs) new_config = { 'Global': self.update_global_params(config['Global'], global_dict), 'Det': self.update_det_params(config['Det'], det_dict), 'Cls': self.update_cls_params(config['Cls'], cls_dict), 'Rec': self.update_rec_params(config['Rec'], rec_dict) } return new_config def update_global_params(self, config, global_dict): if global_dict: config.update(global_dict) return config def update_det_params(self, config, det_dict): if det_dict: det_dict = {k.split('det_')[1]: v for k, v in det_dict.items()} if not det_dict['model_path']: det_dict['model_path'] = str(root_dir / config['model_path']) config.update(det_dict) return config def update_cls_params(self, config, cls_dict): if cls_dict: need_remove_prefix = ['cls_label_list', 'cls_model_path'] new_cls_dict = {} for k, v in cls_dict.items(): if k in need_remove_prefix: k = k.split('cls_')[1] new_cls_dict[k] = v if not new_cls_dict['model_path']: new_cls_dict['model_path'] = str( root_dir / config['model_path']) config.update(new_cls_dict) return config def update_rec_params(self, config, rec_dict): if rec_dict: need_remove_prefix = ['rec_model_path'] new_rec_dict = {} for k, v in rec_dict.items(): if k in need_remove_prefix: k = k.split('rec_')[1] new_rec_dict[k] = v if not new_rec_dict['model_path']: new_rec_dict['model_path'] = str( root_dir / config['model_path']) config.update(new_rec_dict) return config