Add new model PaddleSeg (#30)

* Support new model PaddleSeg

* Fix conflict

* PaddleSeg add visulization function

* fix bug

* Fix BindPPSeg wrong name

* Fix variable name

* Update by comments

* Add ppseg-unet example python version

Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
2022-07-21 15:38:21 +08:00
committed by GitHub
parent 8b0a0c6a10
commit a8458e6729
15 changed files with 453 additions and 8 deletions

View File

@@ -19,3 +19,8 @@ from ... import fastdeploy_main as C
def vis_detection(im_data, det_result, line_size=1, font_size=0.5):
C.vision.Visualize.vis_detection(im_data, det_result, line_size, font_size)
def vis_segmentation(im_data, seg_result, vis_im_data, num_classes=1000):
C.vision.Visualize.vis_segmentation(im_data, seg_result, vis_im_data,
num_classes)

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@@ -0,0 +1,46 @@
// 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.
#ifdef ENABLE_VISION_VISUALIZE
#include "fastdeploy/vision/visualize/visualize.h"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
namespace fastdeploy {
namespace vision {
void Visualize::VisSegmentation(const cv::Mat& im,
const SegmentationResult& result,
cv::Mat* vis_img, const int& num_classes) {
auto color_map = GetColorMap(num_classes);
int64_t height = result.masks.size();
int64_t width = result.masks[1].size();
*vis_img = cv::Mat::zeros(height, width, CV_8UC3);
int64_t index = 0;
for (int i = 0; i < height; i++) {
for (int j = 0; j < width; j++) {
int category_id = static_cast<int>(result.masks[i][j]);
vis_img->at<cv::Vec3b>(i, j)[0] = color_map[3 * category_id + 0];
vis_img->at<cv::Vec3b>(i, j)[1] = color_map[3 * category_id + 1];
vis_img->at<cv::Vec3b>(i, j)[2] = color_map[3 * category_id + 2];
}
}
cv::addWeighted(im, .5, *vis_img, .5, 0, *vis_img);
}
} // namespace vision
} // namespace fastdeploy
#endif

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@@ -27,8 +27,11 @@ class FASTDEPLOY_DECL Visualize {
static const std::vector<int>& GetColorMap(int num_classes = 1000);
static void VisDetection(cv::Mat* im, const DetectionResult& result,
int line_size = 2, float font_size = 0.5f);
static void VisSegmentation(const cv::Mat& im,
const SegmentationResult& result,
cv::Mat* vis_img, const int& num_classes = 1000);
};
} // namespace vision
} // namespace fastdeploy
} // namespace vision
} // namespace fastdeploy
#endif

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@@ -18,11 +18,20 @@ namespace fastdeploy {
void BindVisualize(pybind11::module& m) {
pybind11::class_<vision::Visualize>(m, "Visualize")
.def(pybind11::init<>())
.def_static("vis_detection", [](pybind11::array& im_data,
vision::DetectionResult& result,
int line_size, float font_size) {
auto im = PyArrayToCvMat(im_data);
vision::Visualize::VisDetection(&im, result, line_size, font_size);
.def_static("vis_detection",
[](pybind11::array& im_data, vision::DetectionResult& result,
int line_size, float font_size) {
auto im = PyArrayToCvMat(im_data);
vision::Visualize::VisDetection(&im, result, line_size,
font_size);
})
.def_static("vis_segmentation", [](pybind11::array& im_data,
vision::SegmentationResult& result,
pybind11::array& vis_im_data,
const int& num_classes) {
cv::Mat im = PyArrayToCvMat(im_data);
cv::Mat vis_im = PyArrayToCvMat(vis_im_data);
vision::Visualize::VisSegmentation(im, result, &vis_im, num_classes);
});
}
} // namespace fastdeploy
} // namespace fastdeploy