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* 11-02/14:35 * 新增输入数据format错误判断 * 优化推理过程，减少内存分配次数 * 支持多输入rknn模型 * rknn模型输出shape为三维时，输出将被强制对齐为4纬。现在将直接抹除rknn补充的shape，方便部分对输出shape进行判断的模型进行正确的后处理。 * 11-03/17:25 * 支持导出多输入RKNN模型 * 更新各种文档 * ppseg改用Fastdeploy中的模型进行转换 * 11-03/17:25 * 新增开源头 * 11-03/21:48 * 删除无用debug代码，补充注释 * 11-04/01:00 * 新增rkpicodet代码 * 11-04/13:13 * 提交编译缺少的文件 * 11-04/14:03 * 更新安装文档 * 11-04/14:21 * 更新picodet_s配置文件 * 11-04/14:21 * 更新picodet自适应输出结果 * 11-04/14:21 * 更新文档 * * 更新配置文件 * * 修正配置文件 * * 添加缺失的python文件 * * 修正文档 * * 修正代码格式问题0 * * 按照要求修改 * * 按照要求修改 * * 按照要求修改 * * 按照要求修改 * * 按照要求修改 * test
60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
# Copyright (c) 2022 PaddlePaddle 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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import fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_file", required=True, help="Path of rknn model.")
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parser.add_argument("--config_file", required=True, help="Path of config.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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option.use_rknpu2()
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return option
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model_file = args.model_file
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params_file = ""
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config_file = args.config_file
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model = fd.vision.detection.RKPicoDet(
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model_file,
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params_file,
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config_file,
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runtime_option=runtime_option,
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model_format=fd.ModelFormat.RKNN)
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# 预测图片分割结果
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im = cv2.imread(args.image)
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result = model.predict(im.copy())
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print(result)
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# 可视化结果
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vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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