Correct PP-Series models' name

examples/vision/ocr/PP-OCRv2/python/infer.py

@@ -0,0 +1,128 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--det_model", required=True, help="Path of Detection model of PPOCR.")
+    parser.add_argument(
+        "--cls_model",
+        required=True,
+        help="Path of Classification model of PPOCR.")
+    parser.add_argument(
+        "--rec_model",
+        required=True,
+        help="Path of Recognization model of PPOCR.")
+    parser.add_argument(
+        "--rec_label_file",
+        required=True,
+        help="Path of Recognization model of PPOCR.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'cpu' or 'gpu'.")
+    parser.add_argument(
+        "--backend",
+        type=str,
+        default="default",
+        help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
+    )
+    parser.add_argument(
+        "--device_id",
+        type=int,
+        default=0,
+        help="Define which GPU card used to run model.")
+    parser.add_argument(
+        "--cpu_thread_num",
+        type=int,
+        default=9,
+        help="Number of threads while inference on CPU.")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+    if args.device.lower() == "gpu":
+        option.use_gpu(0)
+
+    option.set_cpu_thread_num(args.cpu_thread_num)
+
+    if args.backend.lower() == "trt":
+        assert args.device.lower(
+        ) == "gpu", "TensorRT backend require inference on device GPU."
+        option.use_trt_backend()
+    elif args.backend.lower() == "ort":
+        option.use_ort_backend()
+    elif args.backend.lower() == "paddle":
+        option.use_paddle_backend()
+    elif args.backend.lower() == "openvino":
+        assert args.device.lower(
+        ) == "cpu", "OpenVINO backend require inference on device CPU."
+        option.use_openvino_backend()
+    return option
+
+
+args = parse_arguments()
+
+# Detection模型, 检测文字框
+det_model_file = os.path.join(args.det_model, "inference.pdmodel")
+det_params_file = os.path.join(args.det_model, "inference.pdiparams")
+# Classification模型，方向分类，可选
+cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
+cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
+# Recognition模型，文字识别模型
+rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
+rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
+rec_label_file = args.rec_label_file
+
+# 对于三个模型，均采用同样的部署配置
+# 用户也可根据自行需求分别配置
+runtime_option = build_option(args)
+
+det_model = fd.vision.ocr.DBDetector(
+    det_model_file, det_params_file, runtime_option=runtime_option)
+cls_model = fd.vision.ocr.Classifier(
+    cls_model_file, cls_params_file, runtime_option=runtime_option)
+rec_model = fd.vision.ocr.Recognizer(
+    rec_model_file,
+    rec_params_file,
+    rec_label_file,
+    runtime_option=runtime_option)
+
+# 创建OCR系统，串联3个模型，其中cls_model可选，如无需求，可设置为None
+ocr_system = fd.vision.ocr.PPOCRSystemv2(
+    det_model=det_model, cls_model=cls_model, rec_model=rec_model)
+
+# 预测图片准备
+im = cv2.imread(args.image)
+
+#预测并打印结果
+result = ocr_system.predict(im)
+
+print(result)
+
+# 可视化结果
+vis_im = fd.vision.vis_ppocr(im, result)
+cv2.imwrite("visualized_result.jpg", vis_im)
+print("Visualized result save in ./visualized_result.jpg")