718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
# Tencent is pleased to support the open source community by making ncnn available.
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#
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# Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
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#
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# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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# in compliance with the License. You may obtain a copy of the License at
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#
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# https://opensource.org/licenses/BSD-3-Clause
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#
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# Unless required by applicable law or agreed to in writing, software distributed
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# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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# CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations under the License.
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import sys
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import cv2
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import numpy as np
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import ncnn
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from ncnn.model_zoo import get_model
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from ncnn.utils import draw_detection_objects
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if __name__ == "__main__":
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if len(sys.argv) != 2:
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print("Usage: %s [imagepath]\n" % (sys.argv[0]))
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sys.exit(0)
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imagepath = sys.argv[1]
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m = cv2.imread(imagepath)
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if m is None:
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print("cv2.imread %s failed\n" % (imagepath))
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sys.exit(0)
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net = get_model("mobilenetv2_yolov3", num_threads=4, use_gpu=True)
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objects = net(m)
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draw_detection_objects(m, net.class_names, objects)
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