[Docs] Pick PPOCR fastdeploy docs from PaddleOCR (#1534)

* Pick PPOCR fastdeploy docs from PaddleOCR * improve ppocr * improve readme * remove old PP-OCRv2 and PP-OCRv3 folfers * rename kunlun to kunlunxin * improve readme * improve readme * improve readme --------- Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
2025-10-06 09:07:10 +08:00 · 2023-03-23 13:11:19 +08:00
parent c91e99b5f5
commit f36f9324de
281 changed files with 2272 additions and 14253 deletions
--- a/examples/vision/ocr/PP-OCR/cpu-gpu/python/infer_det.py
+++ b/examples/vision/ocr/PP-OCR/cpu-gpu/python/infer_det.py
@@ -0,0 +1,82 @@
+# 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(
+        "--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', 'kunlunxin' or 'gpu'.")
+    parser.add_argument(
+        "--device_id",
+        type=int,
+        default=0,
+        help="Define which GPU card used to run model.")
+    return parser.parse_args()
+
+
+def build_option(args):
+
+    det_option = fd.RuntimeOption()
+
+    if args.device.lower() == "gpu":
+        det_option.use_gpu(args.device_id)
+
+    return det_option
+
+
+args = parse_arguments()
+
+det_model_file = os.path.join(args.det_model, "inference.pdmodel")
+det_params_file = os.path.join(args.det_model, "inference.pdiparams")
+
+# Set the runtime option
+det_option = build_option(args)
+
+# Create the det_model
+det_model = fd.vision.ocr.DBDetector(
+    det_model_file, det_params_file, runtime_option=det_option)
+
+# Set the preporcessing parameters
+det_model.preprocessor.max_side_len = 960
+det_model.postprocessor.det_db_thresh = 0.3
+det_model.postprocessor.det_db_box_thresh = 0.6
+det_model.postprocessor.det_db_unclip_ratio = 1.5
+det_model.postprocessor.det_db_score_mode = "slow"
+det_model.postprocessor.use_dilation = False
+
+# Read the image
+im = cv2.imread(args.image)
+
+# Predict and return the results
+result = det_model.predict(im)
+
+print(result)
+
+# Visualize the results
+vis_im = fd.vision.vis_ppocr(im, result)
+cv2.imwrite("visualized_result.jpg", vis_im)
+print("Visualized result save in ./visualized_result.jpg")