mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
32047016d6
* Update README.md * Update README.md * Update README.md * Create README.md * Update README.md * Update README.md * Update README.md * Update README.md * Add evaluation calculate time and fix some bugs * Update classification __init__ * Move to ppseg Co-authored-by: Jason <jiangjiajun@baidu.com>
60 lines
2.2 KiB
C++
60 lines
2.2 KiB
C++
// 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.
|
|
|
|
#include "fastdeploy/vision/segmentation/ppseg/model.h"
|
|
|
|
namespace fastdeploy {
|
|
namespace vision {
|
|
namespace segmentation {
|
|
|
|
void FDTensor2FP32CVMat(cv::Mat& mat, FDTensor& infer_result,
|
|
bool contain_score_map) {
|
|
// output with argmax channel is 1
|
|
int channel = 1;
|
|
int height = infer_result.shape[1];
|
|
int width = infer_result.shape[2];
|
|
|
|
if (contain_score_map) {
|
|
// output without argmax and convent to NHWC
|
|
channel = infer_result.shape[3];
|
|
}
|
|
// create FP32 cvmat
|
|
if (infer_result.dtype == FDDataType::INT64) {
|
|
FDWARNING << "The PaddleSeg model is exported with argmax. Inference "
|
|
"result type is " +
|
|
Str(infer_result.dtype) +
|
|
". If you want the edge of segmentation image more "
|
|
"smoother. Please export model with --without_argmax "
|
|
"--with_softmax."
|
|
<< std::endl;
|
|
int64_t chw = channel * height * width;
|
|
int64_t* infer_result_buffer = static_cast<int64_t*>(infer_result.Data());
|
|
std::vector<float_t> float_result_buffer(chw);
|
|
mat = cv::Mat(height, width, CV_32FC(channel));
|
|
int index = 0;
|
|
for (int i = 0; i < height; i++) {
|
|
for (int j = 0; j < width; j++) {
|
|
mat.at<float_t>(i, j) =
|
|
static_cast<float_t>(infer_result_buffer[index++]);
|
|
}
|
|
}
|
|
} else if (infer_result.dtype == FDDataType::FP32) {
|
|
mat = cv::Mat(height, width, CV_32FC(channel), infer_result.Data());
|
|
}
|
|
}
|
|
|
|
} // namespace segmentation
|
|
} // namespace vision
|
|
} // namespace fastdeploy
|