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按照官方的部署方法搭建了环境,仅cpu运行推理,目前参数设置如下
import fastdeploy as fd import cv2 import os det_option = fd.RuntimeOption() cls_option = fd.RuntimeOption() rec_option = fd.RuntimeOption() print(det_option) det_option.use_openvino_backend() cls_option.use_openvino_backend() rec_option.use_openvino_backend() det_model_file = os.path.join("ch_PP-OCRv3_det_infer", "inference.pdmodel") det_params_file = os.path.join("ch_PP-OCRv3_det_infer", "inference.pdiparams") cls_model_file = os.path.join("ch_ppocr_mobile_v2.0_cls_infer", "inference.pdmodel") cls_params_file = os.path.join("ch_ppocr_mobile_v2.0_cls_infer", "inference.pdiparams") rec_model_file = os.path.join("ch_PP-OCRv3_rec_infer", "inference.pdmodel") rec_params_file = os.path.join("ch_PP-OCRv3_rec_infer", "inference.pdiparams") rec_label_file = "ppocr_keys_v1.txt" det_model = fd.vision.ocr.DBDetector( det_model_file, det_params_file, runtime_option=det_option) cls_model = fd.vision.ocr.Classifier( cls_model_file, cls_params_file, runtime_option=cls_option) rec_model = fd.vision.ocr.Recognizer( rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option) #设置输入图像的最大边长。 det_model.preprocessor.max_side_len = 960 #只有置信度高于这个值的文本框才会被认为是有效的文本框 det_model.postprocessor.det_db_thresh = 0.3 #只有置信度高于这个值的文本框才会被保留下来 det_model.postprocessor.det_db_box_thresh = 0.6 #检测到的文本框会按照这个比例进行扩展,以确保完整包含整个文本区域 det_model.postprocessor.det_db_unclip_ratio = 1.5 #设置文本框得分模式 det_model.postprocessor.det_db_score_mode = "slow" #设置是否在后处理中使用膨胀操作。膨胀操作可以扩大文本区域 det_model.postprocessor.use_dilation = False #设置文本方向分类的阈值。这个值用于控制分类结果的置信度阈值,只有置信度高于这个值的分类结果才会被认为是有效的 cls_model.postprocessor.cls_thresh = 0.9 print(det_option) ppocr_v3 = fd.vision.ocr.PPOCRv3( det_model=det_model, cls_model=cls_model, rec_model=rec_model) ppocr_v3.cls_batch_size = 1 ppocr_v3.rec_batch_size = 6 """ ppocr_v3.predict()返回结果 OCRResult代码定义在fastdeploy/vision/common/result.h中,用于表明图像检测和识别出来的文本框,文本框方向分类,以及文本框内的文本内容. API:fastdeploy.vision.OCRResult, 该结果返回: boxes(list of list(int)): 成员变量,表示单张图片检测出来的所有目标框坐标,boxes.size()表示单张图内检测出的框的个数,每个框以8个int数值依次表示框的4个坐标点,顺序为左下,右下,右上,左上. text(list of string): 成员变量,表示多个文本框内被识别出来的文本内容,其元素个数与boxes.size()一致. rec_scores(list of float): 成员变量,表示文本框内识别出来的文本的置信度,其元素个数与boxes.size()一致. cls_scores(list of float): 成员变量,表示文本框的分类结果的置信度,其元素个数与boxes.size()一致. cls_labels(list of int): 成员变量,表示文本框的方向分类类别,其元素个数与boxes.size()一致. """ def fd_ocr_predict(img): result = ppocr_v3.predict(img) return result.boxes,result.text,result.rec_scores if __name__ == "__main__": im = cv2.imread("picture/1.png") print(im) text_boxes, recognized_texts,confidence= fd_ocr_predict(im) print(text_boxes) print(recognized_texts) print(confidence) print(ppocr_v3.predict(im)) #vis_im = fd.vision.vis_ppocr(im, result) #cv2.imwrite("visualized_result.jpg", vis_im) #print("Visualized result save in ./visualized_result.jpg")
请问应该如何设置接口调用参数,或采取某些方法加快推理速度
The text was updated successfully, but these errors were encountered:
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按照官方的部署方法搭建了环境,仅cpu运行推理,目前参数设置如下
请问应该如何设置接口调用参数,或采取某些方法加快推理速度
The text was updated successfully, but these errors were encountered: