文件一：test_ocr import cv2 import time from pp_onnx.onnx_paddleocr import ONNXPaddleOcr, draw_ocr model = ONNXPaddleOcr( use_angle_cls=True, use_gpu=False, providers=['RKNNExecutionProvider'], provider_options=[{'device_id': 0}] ) try: # 获取文本检测模型的ONNX会话 onnx_session = model.det_session # 获取实际使用的执行提供者 used_providers = onnx_session.get_providers() print(f"当前使用的执行提供者（计算设备）：{used_providers}") if 'RKNNExecutionProvider' in used_providers: print("✅ 成功使用RK3588 NPU加速推理") else: print("❌ 未使用NPU，当前设备：", used_providers) except AttributeError as e: print(f"获取会话失败：{e}，请检查 onnx_paddleocr.py 中会话属性名是否正确（如 det_session/rec_session）") def sav2Img(org_img, result, name="./result_img/draw_ocr_996_1.jpg"): from PIL import Image result = result[0] image = org_img[:, :, ::-1] boxes = [line[0] for line in result] txts = [line[1][0] for line in result] scores = [line[1][1] for line in result] im_show = draw_ocr(image, boxes, txts, scores) im_show = Image.fromarray(im_show) im_show.save(name) # 执行OCR推理 img = cv2.imread('./test_img/test1.jpg') if img is None: print(f"❌ 未找到图像文件：./test_img/test1.jpg") else: s = time.time() result = model.ocr(img) e = time.time() print(f"total time: {e - s:.3f} 秒") print("result:", result) for box in result[0]: print(box) sav2Img(img, result) 文件二：onnx_paddleocr import time from pp_onnx.predict_system import TextSystem from pp_onnx.utils import infer_args as init_args from pp_onnx.utils import str2bool, draw_ocr import argparse import sys class ONNXPaddleOcr(TextSystem): def __init__(self, **kwargs): # 默认参数 parser = init_args() # import IPython # IPython.embed(header='L-14') inference_args_dict = {} for action in parser._actions: inference_args_dict[action.dest] = action.default params = argparse.Namespace(**inference_args_dict) params.rec_image_shape = "3, 48, 320" # 根据传入的参数覆盖更新默认参数 params.__dict__.update(**kwargs) # 初始化模型 super().__init__(params) def ocr(self, img, det=True, rec=True, cls=True): if cls == True and self.use_angle_cls == False: print('Since the angle classifier is not initialized, the angle classifier will not be uesd during the forward process') if det and rec: ocr_res = [] dt_boxes, rec_res = self.__call__(img, cls) tmp_res = [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)] ocr_res.append(tmp_res) return ocr_res elif det and not rec: ocr_res = [] dt_boxes = self.text_detector(img) tmp_res = [box.tolist() for box in dt_boxes] ocr_res.append(tmp_res) return ocr_res else: ocr_res = [] cls_res = [] if not isinstance(img, list): img = [img] if self.use_angle_cls and cls: img, cls_res_tmp = self.text_classifier(img) if not rec: cls_res.append(cls_res_tmp) rec_res = self.text_recognizer(img) ocr_res.append(rec_res) if not rec: return cls_res return ocr_res def sav2Img(org_img, result, name="draw_ocr.jpg"): # 显示结果 from PIL import Image result = result[0] # image = Image.open(img_path).convert('RGB') # 图像转BGR2RGB image = org_img[:, :, ::-1] boxes = [line[0] for line in result] txts = [line[1][0] for line in result] scores = [line[1][1] for line in result] im_show = draw_ocr(image, boxes, txts, scores) im_show = Image.fromarray(im_show) im_show.save(name) if __name__ == '__main__': import cv2 model = ONNXPaddleOcr(use_angle_cls=True, use_gpu=False) img = cv2.imread('/data2/liujingsong3/fiber_box/test/img/20230531230052008263304.jpg') s = time.time() result = model.ocr(img) e = time.time() print("total time: {:.3f}".format(e - s)) print("result:", result) for box in result[0]: print(box) sav2Img(img, result) 文件三：predict_system import os import cv2 import copy import pp_onnx.predict_det as predict_det import pp_onnx.predict_cls as predict_cls import pp_onnx.predict_rec as predict_rec from pp_onnx.utils import get_rotate_crop_image, get_minarea_rect_crop class TextSystem(object): def __init__(self, args): self.text_detector = predict_det.TextDetector(args) self.text_recognizer = predict_rec.TextRecognizer(args) self.use_angle_cls = args.use_angle_cls self.drop_score = args.drop_score if self.use_angle_cls: self.text_classifier = predict_cls.TextClassifier(args) self.args = args self.crop_image_res_index = 0 def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res): os.makedirs(output_dir, exist_ok=True) bbox_num = len(img_crop_list) for bno in range(bbox_num): cv2.imwrite( os.path.join(output_dir, f"mg_crop_{bno+self.crop_image_res_index}.jpg"), img_crop_list[bno]) self.crop_image_res_index += bbox_num def __call__(self, img, cls=True): ori_im = img.copy() # 文字检测 dt_boxes = self.text_detector(img) if dt_boxes is None: return None, None img_crop_list = [] dt_boxes = sorted_boxes(dt_boxes) # 图片裁剪 for bno in range(len(dt_boxes)): tmp_box = copy.deepcopy(dt_boxes[bno]) if self.args.det_box_type == "quad": img_crop = get_rotate_crop_image(ori_im, tmp_box) else: img_crop = get_minarea_rect_crop(ori_im, tmp_box) img_crop_list.append(img_crop) # 方向分类 if self.use_angle_cls and cls: img_crop_list, angle_list = self.text_classifier(img_crop_list) # 图像识别 rec_res = self.text_recognizer(img_crop_list) if self.args.save_crop_res: self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list,rec_res) 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) # import IPython # IPython.embed(header='L-70') return filter_boxes, filter_rec_res def sorted_boxes(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 运行test_ocr报错：(box_ocr) root@ztl:/result/ocr/pp_onnx-main# python test_ocr.py 获取会话失败：'ONNXPaddleOcr' object has no attribute 'det_session'，请检查 onnx_paddleocr.py 中会话属性名是否正确（如 det_session/rec_session） total time: 11.161 秒 且检测一张图片耗时太差