786 lines
28 KiB
Python
786 lines
28 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import gc
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import logging
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import copy
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import time
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import os
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import sys
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from pathlib import Path
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from huggingface_hub import snapshot_download
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# 处理导入问题：支持直接运行和模块导入
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try:
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_package = __package__
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except NameError:
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_package = None
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if _package is None:
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# 直接运行时，添加父目录到路径并使用绝对导入
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parent_dir = Path(__file__).parent.parent
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if str(parent_dir) not in sys.path:
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sys.path.insert(0, str(parent_dir))
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from ocr.utils import get_project_base_directory
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from ocr.config import PARALLEL_DEVICES, MODEL_DIR
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from ocr.operators import * # noqa: F403
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import ocr.operators as operators
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from ocr.postprocess import build_post_process
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else:
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# 作为模块导入时使用相对导入
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from utils import get_project_base_directory
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from config import PARALLEL_DEVICES, MODEL_DIR
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from operators import * # noqa: F403
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import operators
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from postprocess import build_post_process
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import math
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import numpy as np
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import cv2
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import onnxruntime as ort
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loaded_models = {}
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def transform(data, ops=None):
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""" transform """
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if ops is None:
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ops = []
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for op in ops:
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data = op(data)
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if data is None:
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return None
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return data
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def create_operators(op_param_list, global_config=None):
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"""
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create operators based on the config
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Args:
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params(list): a dict list, used to create some operators
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"""
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assert isinstance(
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op_param_list, list), ('operator config should be a list')
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ops = []
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for operator in op_param_list:
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assert isinstance(operator,
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dict) and len(operator) == 1, "yaml format error"
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op_name = list(operator)[0]
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param = {} if operator[op_name] is None else operator[op_name]
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if global_config is not None:
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param.update(global_config)
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op = getattr(operators, op_name)(**param)
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ops.append(op)
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return ops
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def load_model(model_dir, nm, device_id: int | None = None):
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model_file_path = os.path.join(model_dir, nm + ".onnx")
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model_cached_tag = model_file_path + str(device_id) if device_id is not None else model_file_path
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global loaded_models
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loaded_model = loaded_models.get(model_cached_tag)
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if loaded_model:
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logging.info(f"load_model {model_file_path} reuses cached model")
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return loaded_model
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if not os.path.exists(model_file_path):
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raise ValueError("not find model file path {}".format(
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model_file_path))
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def cuda_is_available():
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try:
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import torch
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if torch.cuda.is_available() and torch.cuda.device_count() > device_id:
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return True
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except Exception:
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return False
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return False
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options = ort.SessionOptions()
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options.enable_cpu_mem_arena = False
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options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
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options.intra_op_num_threads = 2
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options.inter_op_num_threads = 2
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# https://github.com/microsoft/onnxruntime/issues/9509#issuecomment-951546580
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# Shrink GPU memory after execution
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run_options = ort.RunOptions()
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if cuda_is_available():
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cuda_provider_options = {
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"device_id": device_id, # Use specific GPU
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"gpu_mem_limit": 512 * 1024 * 1024, # Limit gpu memory
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"arena_extend_strategy": "kNextPowerOfTwo", # gpu memory allocation strategy
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}
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sess = ort.InferenceSession(
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model_file_path,
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options=options,
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providers=['CUDAExecutionProvider'],
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provider_options=[cuda_provider_options]
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)
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run_options.add_run_config_entry("memory.enable_memory_arena_shrinkage", "gpu:" + str(device_id))
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logging.info(f"load_model {model_file_path} uses GPU")
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else:
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sess = ort.InferenceSession(
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model_file_path,
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options=options,
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providers=['CPUExecutionProvider'])
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run_options.add_run_config_entry("memory.enable_memory_arena_shrinkage", "cpu")
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logging.info(f"load_model {model_file_path} uses CPU")
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loaded_model = (sess, run_options)
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loaded_models[model_cached_tag] = loaded_model
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return loaded_model
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class TextRecognizer:
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def __init__(self, model_dir, device_id: int | None = None):
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self.rec_image_shape = [int(v) for v in "3, 48, 320".split(",")]
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self.rec_batch_num = 16
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postprocess_params = {
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'name': 'CTCLabelDecode',
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"character_dict_path": os.path.join(model_dir, "ocr.res"),
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"use_space_char": True
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}
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self.postprocess_op = build_post_process(postprocess_params)
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self.predictor, self.run_options = load_model(model_dir, 'rec', device_id)
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self.input_tensor = self.predictor.get_inputs()[0]
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def resize_norm_img(self, img, max_wh_ratio):
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imgC, imgH, imgW = self.rec_image_shape
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assert imgC == img.shape[2]
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imgW = int((imgH * max_wh_ratio))
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w = self.input_tensor.shape[3:][0]
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if isinstance(w, str):
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pass
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elif w is not None and w > 0:
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imgW = w
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h, w = img.shape[:2]
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype('float32')
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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def resize_norm_img_vl(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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img = img[:, :, ::-1] # bgr2rgb
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resized_image = cv2.resize(
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img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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resized_image = resized_image.astype('float32')
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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return resized_image
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def resize_norm_img_srn(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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img_black = np.zeros((imgH, imgW))
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im_hei = img.shape[0]
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im_wid = img.shape[1]
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if im_wid <= im_hei * 1:
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img_new = cv2.resize(img, (imgH * 1, imgH))
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elif im_wid <= im_hei * 2:
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img_new = cv2.resize(img, (imgH * 2, imgH))
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elif im_wid <= im_hei * 3:
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img_new = cv2.resize(img, (imgH * 3, imgH))
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else:
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img_new = cv2.resize(img, (imgW, imgH))
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img_np = np.asarray(img_new)
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img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
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img_black[:, 0:img_np.shape[1]] = img_np
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img_black = img_black[:, :, np.newaxis]
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row, col, c = img_black.shape
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c = 1
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return np.reshape(img_black, (c, row, col)).astype(np.float32)
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def srn_other_inputs(self, image_shape, num_heads, max_text_length):
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imgC, imgH, imgW = image_shape
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feature_dim = int((imgH / 8) * (imgW / 8))
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encoder_word_pos = np.array(range(0, feature_dim)).reshape(
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(feature_dim, 1)).astype('int64')
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gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
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(max_text_length, 1)).astype('int64')
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gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
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gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
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[-1, 1, max_text_length, max_text_length])
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gsrm_slf_attn_bias1 = np.tile(
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gsrm_slf_attn_bias1,
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[1, num_heads, 1, 1]).astype('float32') * [-1e9]
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gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
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[-1, 1, max_text_length, max_text_length])
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gsrm_slf_attn_bias2 = np.tile(
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gsrm_slf_attn_bias2,
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[1, num_heads, 1, 1]).astype('float32') * [-1e9]
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encoder_word_pos = encoder_word_pos[np.newaxis, :]
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gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
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return [
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encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2
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]
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def process_image_srn(self, img, image_shape, num_heads, max_text_length):
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norm_img = self.resize_norm_img_srn(img, image_shape)
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norm_img = norm_img[np.newaxis, :]
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[encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
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self.srn_other_inputs(image_shape, num_heads, max_text_length)
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gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
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gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
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encoder_word_pos = encoder_word_pos.astype(np.int64)
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gsrm_word_pos = gsrm_word_pos.astype(np.int64)
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return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2)
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def resize_norm_img_sar(self, img, image_shape,
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width_downsample_ratio=0.25):
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imgC, imgH, imgW_min, imgW_max = image_shape
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h = img.shape[0]
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w = img.shape[1]
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valid_ratio = 1.0
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# make sure new_width is an integral multiple of width_divisor.
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width_divisor = int(1 / width_downsample_ratio)
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# resize
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ratio = w / float(h)
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resize_w = math.ceil(imgH * ratio)
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if resize_w % width_divisor != 0:
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resize_w = round(resize_w / width_divisor) * width_divisor
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if imgW_min is not None:
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resize_w = max(imgW_min, resize_w)
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if imgW_max is not None:
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valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
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resize_w = min(imgW_max, resize_w)
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resized_image = cv2.resize(img, (resize_w, imgH))
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resized_image = resized_image.astype('float32')
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# norm
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if image_shape[0] == 1:
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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else:
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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resize_shape = resized_image.shape
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padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
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padding_im[:, :, 0:resize_w] = resized_image
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pad_shape = padding_im.shape
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return padding_im, resize_shape, pad_shape, valid_ratio
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def resize_norm_img_spin(self, img):
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# return padding_im
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img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC)
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img = np.array(img, np.float32)
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img = np.expand_dims(img, -1)
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img = img.transpose((2, 0, 1))
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mean = [127.5]
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std = [127.5]
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mean = np.array(mean, dtype=np.float32)
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std = np.array(std, dtype=np.float32)
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mean = np.float32(mean.reshape(1, -1))
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stdinv = 1 / np.float32(std.reshape(1, -1))
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img -= mean
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img *= stdinv
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return img
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def resize_norm_img_svtr(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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resized_image = cv2.resize(
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img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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resized_image = resized_image.astype('float32')
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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return resized_image
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def resize_norm_img_abinet(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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resized_image = cv2.resize(
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img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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resized_image = resized_image.astype('float32')
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resized_image = resized_image / 255.
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mean = np.array([0.485, 0.456, 0.406])
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std = np.array([0.229, 0.224, 0.225])
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resized_image = (
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resized_image - mean[None, None, ...]) / std[None, None, ...]
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resized_image = resized_image.transpose((2, 0, 1))
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resized_image = resized_image.astype('float32')
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return resized_image
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def norm_img_can(self, img, image_shape):
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img = cv2.cvtColor(
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img, cv2.COLOR_BGR2GRAY) # CAN only predict gray scale image
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if self.rec_image_shape[0] == 1:
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h, w = img.shape
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_, imgH, imgW = self.rec_image_shape
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if h < imgH or w < imgW:
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padding_h = max(imgH - h, 0)
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padding_w = max(imgW - w, 0)
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img_padded = np.pad(img, ((0, padding_h), (0, padding_w)),
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'constant',
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constant_values=(255))
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img = img_padded
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img = np.expand_dims(img, 0) / 255.0 # h,w,c -> c,h,w
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img = img.astype('float32')
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return img
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def close(self):
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# close session and release manually
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logging.info('Close text recognizer.')
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if hasattr(self, "predictor"):
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del self.predictor
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gc.collect()
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def __call__(self, img_list):
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img_num = len(img_list)
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# Calculate the aspect ratio of all text bars
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width_list = []
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for img in img_list:
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width_list.append(img.shape[1] / float(img.shape[0]))
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# Sorting can speed up the recognition process
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indices = np.argsort(np.array(width_list))
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rec_res = [['', 0.0]] * img_num
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batch_num = self.rec_batch_num
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st = time.time()
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for beg_img_no in range(0, img_num, batch_num):
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end_img_no = min(img_num, beg_img_no + batch_num)
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norm_img_batch = []
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imgC, imgH, imgW = self.rec_image_shape[:3]
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max_wh_ratio = imgW / imgH
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# max_wh_ratio = 0
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for ino in range(beg_img_no, end_img_no):
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h, w = img_list[indices[ino]].shape[0:2]
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wh_ratio = w * 1.0 / h
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max_wh_ratio = max(max_wh_ratio, wh_ratio)
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for ino in range(beg_img_no, end_img_no):
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norm_img = self.resize_norm_img(img_list[indices[ino]],
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max_wh_ratio)
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norm_img = norm_img[np.newaxis, :]
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norm_img_batch.append(norm_img)
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norm_img_batch = np.concatenate(norm_img_batch)
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norm_img_batch = norm_img_batch.copy()
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input_dict = {}
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input_dict[self.input_tensor.name] = norm_img_batch
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for i in range(100000):
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try:
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outputs = self.predictor.run(None, input_dict, self.run_options)
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break
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except Exception as e:
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if i >= 3:
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raise e
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time.sleep(5)
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preds = outputs[0]
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rec_result = self.postprocess_op(preds)
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for rno in range(len(rec_result)):
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rec_res[indices[beg_img_no + rno]] = rec_result[rno]
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return rec_res, time.time() - st
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def __del__(self):
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self.close()
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class TextDetector:
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def __init__(self, model_dir, device_id: int | None = None):
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||
pre_process_list = [{
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||
'DetResizeForTest': {
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||
'limit_side_len': 960,
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||
'limit_type': "max",
|
||
}
|
||
}, {
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||
'NormalizeImage': {
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||
'std': [0.229, 0.224, 0.225],
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||
'mean': [0.485, 0.456, 0.406],
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||
'scale': '1./255.',
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||
'order': 'hwc'
|
||
}
|
||
}, {
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||
'ToCHWImage': None
|
||
}, {
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||
'KeepKeys': {
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||
'keep_keys': ['image', 'shape']
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||
}
|
||
}]
|
||
postprocess_params = {"name": "DBPostProcess", "thresh": 0.3, "box_thresh": 0.5, "max_candidates": 1000,
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||
"unclip_ratio": 1.5, "use_dilation": False, "score_mode": "fast", "box_type": "quad"}
|
||
|
||
self.postprocess_op = build_post_process(postprocess_params)
|
||
self.predictor, self.run_options = load_model(model_dir, 'det', device_id)
|
||
self.input_tensor = self.predictor.get_inputs()[0]
|
||
|
||
img_h, img_w = self.input_tensor.shape[2:]
|
||
if isinstance(img_h, str) or isinstance(img_w, str):
|
||
pass
|
||
elif img_h is not None and img_w is not None and img_h > 0 and img_w > 0:
|
||
pre_process_list[0] = {
|
||
'DetResizeForTest': {
|
||
'image_shape': [img_h, img_w]
|
||
}
|
||
}
|
||
self.preprocess_op = create_operators(pre_process_list)
|
||
|
||
def order_points_clockwise(self, pts):
|
||
rect = np.zeros((4, 2), dtype="float32")
|
||
s = pts.sum(axis=1)
|
||
rect[0] = pts[np.argmin(s)]
|
||
rect[2] = pts[np.argmax(s)]
|
||
tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0)
|
||
diff = np.diff(np.array(tmp), axis=1)
|
||
rect[1] = tmp[np.argmin(diff)]
|
||
rect[3] = tmp[np.argmax(diff)]
|
||
return rect
|
||
|
||
def clip_det_res(self, points, img_height, img_width):
|
||
for pno in range(points.shape[0]):
|
||
points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
|
||
points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
|
||
return points
|
||
|
||
def filter_tag_det_res(self, dt_boxes, image_shape):
|
||
img_height, img_width = image_shape[0:2]
|
||
dt_boxes_new = []
|
||
for box in dt_boxes:
|
||
if isinstance(box, list):
|
||
box = np.array(box)
|
||
box = self.order_points_clockwise(box)
|
||
box = self.clip_det_res(box, img_height, img_width)
|
||
rect_width = int(np.linalg.norm(box[0] - box[1]))
|
||
rect_height = int(np.linalg.norm(box[0] - box[3]))
|
||
if rect_width <= 3 or rect_height <= 3:
|
||
continue
|
||
dt_boxes_new.append(box)
|
||
dt_boxes = np.array(dt_boxes_new)
|
||
return dt_boxes
|
||
|
||
def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
|
||
img_height, img_width = image_shape[0:2]
|
||
dt_boxes_new = []
|
||
for box in dt_boxes:
|
||
if isinstance(box, list):
|
||
box = np.array(box)
|
||
box = self.clip_det_res(box, img_height, img_width)
|
||
dt_boxes_new.append(box)
|
||
dt_boxes = np.array(dt_boxes_new)
|
||
return dt_boxes
|
||
|
||
def close(self):
|
||
logging.info("Close text detector.")
|
||
if hasattr(self, "predictor"):
|
||
del self.predictor
|
||
gc.collect()
|
||
|
||
def __call__(self, img):
|
||
ori_im = img.copy()
|
||
data = {'image': img}
|
||
|
||
st = time.time()
|
||
data = transform(data, self.preprocess_op)
|
||
img, shape_list = data
|
||
if img is None:
|
||
return None, 0
|
||
img = np.expand_dims(img, axis=0)
|
||
shape_list = np.expand_dims(shape_list, axis=0)
|
||
img = img.copy()
|
||
input_dict = {}
|
||
input_dict[self.input_tensor.name] = img
|
||
for i in range(100000):
|
||
try:
|
||
outputs = self.predictor.run(None, input_dict, self.run_options)
|
||
break
|
||
except Exception as e:
|
||
if i >= 3:
|
||
raise e
|
||
time.sleep(5)
|
||
|
||
post_result = self.postprocess_op({"maps": outputs[0]}, shape_list)
|
||
dt_boxes = post_result[0]['points']
|
||
dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
|
||
|
||
return dt_boxes, time.time() - st
|
||
|
||
def __del__(self):
|
||
self.close()
|
||
|
||
|
||
class OCR:
|
||
def __init__(self, model_dir=None):
|
||
"""
|
||
If you have trouble downloading HuggingFace models, -_^ this might help!!
|
||
|
||
For Linux:
|
||
export HF_ENDPOINT=https://hf-mirror.com
|
||
|
||
For Windows:
|
||
Good luck
|
||
^_-
|
||
|
||
"""
|
||
if not model_dir:
|
||
try:
|
||
# 使用配置中的 MODEL_DIR，如果不存在则尝试默认路径
|
||
if MODEL_DIR and os.path.exists(MODEL_DIR):
|
||
model_dir = MODEL_DIR
|
||
else:
|
||
model_dir = os.path.join(
|
||
get_project_base_directory(),
|
||
"models", "deepdoc")
|
||
|
||
# Append muti-gpus task to the list
|
||
if PARALLEL_DEVICES > 0:
|
||
self.text_detector = []
|
||
self.text_recognizer = []
|
||
for device_id in range(PARALLEL_DEVICES):
|
||
self.text_detector.append(TextDetector(model_dir, device_id))
|
||
self.text_recognizer.append(TextRecognizer(model_dir, device_id))
|
||
else:
|
||
self.text_detector = [TextDetector(model_dir)]
|
||
self.text_recognizer = [TextRecognizer(model_dir)]
|
||
|
||
except Exception:
|
||
# 如果模型目录不存在，尝试从 HuggingFace 下载
|
||
default_model_dir = os.path.join(
|
||
get_project_base_directory(), "models", "deepdoc")
|
||
model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc",
|
||
local_dir=default_model_dir,
|
||
local_dir_use_symlinks=False)
|
||
|
||
if PARALLEL_DEVICES > 0:
|
||
self.text_detector = []
|
||
self.text_recognizer = []
|
||
for device_id in range(PARALLEL_DEVICES):
|
||
self.text_detector.append(TextDetector(model_dir, device_id))
|
||
self.text_recognizer.append(TextRecognizer(model_dir, device_id))
|
||
else:
|
||
self.text_detector = [TextDetector(model_dir)]
|
||
self.text_recognizer = [TextRecognizer(model_dir)]
|
||
else:
|
||
# 如果指定了 model_dir，直接使用
|
||
if PARALLEL_DEVICES > 0:
|
||
self.text_detector = []
|
||
self.text_recognizer = []
|
||
for device_id in range(PARALLEL_DEVICES):
|
||
self.text_detector.append(TextDetector(model_dir, device_id))
|
||
self.text_recognizer.append(TextRecognizer(model_dir, device_id))
|
||
else:
|
||
self.text_detector = [TextDetector(model_dir)]
|
||
self.text_recognizer = [TextRecognizer(model_dir)]
|
||
|
||
self.drop_score = 0.5
|
||
self.crop_image_res_index = 0
|
||
|
||
def get_rotate_crop_image(self, img, points):
|
||
'''
|
||
img_height, img_width = img.shape[0:2]
|
||
left = int(np.min(points[:, 0]))
|
||
right = int(np.max(points[:, 0]))
|
||
top = int(np.min(points[:, 1]))
|
||
bottom = int(np.max(points[:, 1]))
|
||
img_crop = img[top:bottom, left:right, :].copy()
|
||
points[:, 0] = points[:, 0] - left
|
||
points[:, 1] = points[:, 1] - top
|
||
'''
|
||
assert len(points) == 4, "shape of points must be 4*2"
|
||
img_crop_width = int(
|
||
max(
|
||
np.linalg.norm(points[0] - points[1]),
|
||
np.linalg.norm(points[2] - points[3])))
|
||
img_crop_height = int(
|
||
max(
|
||
np.linalg.norm(points[0] - points[3]),
|
||
np.linalg.norm(points[1] - points[2])))
|
||
pts_std = np.float32([[0, 0], [img_crop_width, 0],
|
||
[img_crop_width, img_crop_height],
|
||
[0, img_crop_height]])
|
||
M = cv2.getPerspectiveTransform(points, pts_std)
|
||
dst_img = cv2.warpPerspective(
|
||
img,
|
||
M, (img_crop_width, img_crop_height),
|
||
borderMode=cv2.BORDER_REPLICATE,
|
||
flags=cv2.INTER_CUBIC)
|
||
dst_img_height, dst_img_width = dst_img.shape[0:2]
|
||
if dst_img_height * 1.0 / dst_img_width >= 1.5:
|
||
# Try original orientation
|
||
rec_result = self.text_recognizer[0]([dst_img])
|
||
text, score = rec_result[0][0]
|
||
best_score = score
|
||
best_img = dst_img
|
||
|
||
# Try clockwise 90° rotation
|
||
rotated_cw = np.rot90(dst_img, k=3)
|
||
rec_result = self.text_recognizer[0]([rotated_cw])
|
||
rotated_cw_text, rotated_cw_score = rec_result[0][0]
|
||
if rotated_cw_score > best_score:
|
||
best_score = rotated_cw_score
|
||
best_img = rotated_cw
|
||
|
||
# Try counter-clockwise 90° rotation
|
||
rotated_ccw = np.rot90(dst_img, k=1)
|
||
rec_result = self.text_recognizer[0]([rotated_ccw])
|
||
rotated_ccw_text, rotated_ccw_score = rec_result[0][0]
|
||
if rotated_ccw_score > best_score:
|
||
best_img = rotated_ccw
|
||
|
||
# Use the best image
|
||
dst_img = best_img
|
||
return dst_img
|
||
|
||
def sorted_boxes(self, dt_boxes):
|
||
"""
|
||
Sort text boxes in order from top to bottom, left to right
|
||
args:
|
||
dt_boxes(array):detected text boxes with shape [4, 2]
|
||
return:
|
||
sorted boxes(array) with shape [4, 2]
|
||
"""
|
||
num_boxes = dt_boxes.shape[0]
|
||
sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
|
||
_boxes = list(sorted_boxes)
|
||
|
||
for i in range(num_boxes - 1):
|
||
for j in range(i, -1, -1):
|
||
if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \
|
||
(_boxes[j + 1][0][0] < _boxes[j][0][0]):
|
||
tmp = _boxes[j]
|
||
_boxes[j] = _boxes[j + 1]
|
||
_boxes[j + 1] = tmp
|
||
else:
|
||
break
|
||
return _boxes
|
||
|
||
def detect(self, img, device_id: int | None = None):
|
||
if device_id is None:
|
||
device_id = 0
|
||
|
||
time_dict = {'det': 0, 'rec': 0, 'cls': 0, 'all': 0}
|
||
|
||
if img is None:
|
||
return None, None, time_dict
|
||
|
||
start = time.time()
|
||
dt_boxes, elapse = self.text_detector[device_id](img)
|
||
time_dict['det'] = elapse
|
||
|
||
if dt_boxes is None:
|
||
end = time.time()
|
||
time_dict['all'] = end - start
|
||
return None, None, time_dict
|
||
|
||
return zip(self.sorted_boxes(dt_boxes), [
|
||
("", 0) for _ in range(len(dt_boxes))])
|
||
|
||
def recognize(self, ori_im, box, device_id: int | None = None):
|
||
if device_id is None:
|
||
device_id = 0
|
||
|
||
img_crop = self.get_rotate_crop_image(ori_im, box)
|
||
|
||
rec_res, elapse = self.text_recognizer[device_id]([img_crop])
|
||
text, score = rec_res[0]
|
||
if score < self.drop_score:
|
||
return ""
|
||
return text
|
||
|
||
def recognize_batch(self, img_list, device_id: int | None = None):
|
||
if device_id is None:
|
||
device_id = 0
|
||
rec_res, elapse = self.text_recognizer[device_id](img_list)
|
||
texts = []
|
||
for i in range(len(rec_res)):
|
||
text, score = rec_res[i]
|
||
if score < self.drop_score:
|
||
text = ""
|
||
texts.append(text)
|
||
return texts
|
||
|
||
def __call__(self, img, device_id = 0, cls=True):
|
||
time_dict = {'det': 0, 'rec': 0, 'cls': 0, 'all': 0}
|
||
if device_id is None:
|
||
device_id = 0
|
||
|
||
if img is None:
|
||
return None, None, time_dict
|
||
|
||
start = time.time()
|
||
ori_im = img.copy()
|
||
dt_boxes, elapse = self.text_detector[device_id](img)
|
||
time_dict['det'] = elapse
|
||
|
||
if dt_boxes is None:
|
||
end = time.time()
|
||
time_dict['all'] = end - start
|
||
return None, None, time_dict
|
||
|
||
img_crop_list = []
|
||
|
||
dt_boxes = self.sorted_boxes(dt_boxes)
|
||
|
||
for bno in range(len(dt_boxes)):
|
||
tmp_box = copy.deepcopy(dt_boxes[bno])
|
||
img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
|
||
img_crop_list.append(img_crop)
|
||
|
||
rec_res, elapse = self.text_recognizer[device_id](img_crop_list)
|
||
|
||
time_dict['rec'] = elapse
|
||
|
||
filter_boxes, filter_rec_res = [], []
|
||
for box, rec_result in zip(dt_boxes, rec_res):
|
||
text, score = rec_result
|
||
if score >= self.drop_score:
|
||
filter_boxes.append(box)
|
||
filter_rec_res.append(rec_result)
|
||
end = time.time()
|
||
time_dict['all'] = end - start
|
||
|
||
# for bno in range(len(img_crop_list)):
|
||
# print(f"{bno}, {rec_res[bno]}")
|
||
|
||
return list(zip([a.tolist() for a in filter_boxes], filter_rec_res))
|
||
|