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a509dd8ec1
* add smoke model * add 3d vis * update code * update doc * mv paddle3d from detection to perception * update result for velocity * update code for CI * add set input data for TRT backend * add serving support for smoke model * update code * update code * update code --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
234 lines
13 KiB
C++
Executable File
234 lines
13 KiB
C++
Executable File
// 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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#pragma once
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#include "fastdeploy/vision/common/result.h"
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#include "fastdeploy/vision/tracking/pptracking/model.h"
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#include "opencv2/imgproc/imgproc.hpp"
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namespace fastdeploy {
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/** \brief All C++ FastDeploy Vision Models APIs are defined inside this namespace
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*
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*/
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namespace vision {
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class FASTDEPLOY_DECL Visualize {
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public:
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static int num_classes_;
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static std::vector<int> color_map_;
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static const std::vector<int>& GetColorMap(int num_classes = 1000);
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static cv::Mat VisDetection(const cv::Mat& im, const DetectionResult& result,
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float score_threshold = 0.0, int line_size = 1,
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float font_size = 0.5f);
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static cv::Mat VisPerception(const cv::Mat& im,
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const PerceptionResult& result,
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const std::string & config_file,
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float score_threshold = 0.0,
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int line_size = 1,
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float font_size = 0.5f);
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static cv::Mat VisFaceDetection(const cv::Mat& im,
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const FaceDetectionResult& result,
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int line_size = 1, float font_size = 0.5f);
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static cv::Mat VisSegmentation(const cv::Mat& im,
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const SegmentationResult& result);
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static cv::Mat VisMattingAlpha(const cv::Mat& im, const MattingResult& result,
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bool remove_small_connected_area = false);
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static cv::Mat RemoveSmallConnectedArea(const cv::Mat& alpha_pred,
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float threshold);
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static cv::Mat
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SwapBackgroundMatting(const cv::Mat& im, const cv::Mat& background,
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const MattingResult& result,
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bool remove_small_connected_area = false);
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static cv::Mat SwapBackgroundSegmentation(const cv::Mat& im,
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const cv::Mat& background,
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int background_label,
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const SegmentationResult& result);
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static cv::Mat VisOcr(const cv::Mat& srcimg, const OCRResult& ocr_result);
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};
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std::vector<int> GenerateColorMap(int num_classes = 1000);
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cv::Mat RemoveSmallConnectedArea(const cv::Mat& alpha_pred, float threshold);
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/** \brief Show the visualized results for detection models
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the result produced by model
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* \param[in] score_threshold threshold for result scores, the bounding box will not be shown if the score is less than score_threshold
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* \param[in] line_size line size for bounding boxes
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* \param[in] font_size font size for text
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat VisDetection(const cv::Mat& im,
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const DetectionResult& result,
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float score_threshold = 0.0,
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int line_size = 1, float font_size = 0.5f);
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/** \brief Show the visualized results with custom labels for detection models
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the result produced by model
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* \param[in] labels the visualized result will show the bounding box contain class label
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* \param[in] score_threshold threshold for result scores, the bounding box will not be shown if the score is less than score_threshold
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* \param[in] line_size line size for bounding boxes
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* \param[in] font_size font size for text
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat VisDetection(const cv::Mat& im,
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const DetectionResult& result,
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const std::vector<std::string>& labels,
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float score_threshold = 0.0,
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int line_size = 1, float font_size = 0.5f);
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/** \brief Show the visualized results with custom labels for detection models
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the result produced by model
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* \param[in] labels the visualized result will show the bounding box contain class label
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* \param[in] score_threshold threshold for result scores, the bounding box will not be shown if the score is less than score_threshold
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* \param[in] line_size line size for bounding boxes
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* \param[in] font_size font size for text
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat VisPerception(const cv::Mat& im,
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const PerceptionResult& result,
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const std::string & config_file,
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float score_threshold = 0.0,
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int line_size = 1 ,
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float font_size = 0.5f);
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/** \brief Show the visualized results for classification models
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the result produced by model
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* \param[in] top_k the length of return values, e.g., if topk==2, the result will include the 2 most possible class label for input image.
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* \param[in] score_threshold threshold for top_k scores, the class will not be shown if the score is less than score_threshold
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* \param[in] font_size font size
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat VisClassification(const cv::Mat& im,
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const ClassifyResult& result,
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int top_k = 5,
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float score_threshold = 0.0f,
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float font_size = 0.5f);
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/** \brief Show the visualized results with custom labels for classification models
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the result produced by model
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* \param[in] labels custom labels for user, the visualized result will show the corresponding custom labels
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* \param[in] top_k the length of return values, e.g., if topk==2, the result will include the 2 most possible class label for input image.
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* \param[in] score_threshold threshold for top_k scores, the class will not be shown if the score is less than score_threshold
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* \param[in] font_size font size
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat
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VisClassification(const cv::Mat& im, const ClassifyResult& result,
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const std::vector<std::string>& labels, int top_k = 5,
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float score_threshold = 0.0f, float font_size = 0.5f);
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/** \brief Show the visualized results for face detection models
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the result produced by model
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* \param[in] line_size line size for bounding boxes
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* \param[in] font_size font size for text
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat VisFaceDetection(const cv::Mat& im,
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const FaceDetectionResult& result,
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int line_size = 1,
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float font_size = 0.5f);
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/** \brief Show the visualized results for face alignment models
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the result produced by model
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* \param[in] line_size line size for circle point
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat VisFaceAlignment(const cv::Mat& im,
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const FaceAlignmentResult& result,
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int line_size = 1);
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/** \brief Show the visualized results for segmentation models
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the result produced by model
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* \param[in] weight transparent weight of visualized result image
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat VisSegmentation(const cv::Mat& im,
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const SegmentationResult& result,
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float weight = 0.5);
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/** \brief Show the visualized results for matting models
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the result produced by model
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* \param[in] transparent_background if transparent_background==true, the background will with transparent color
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* \param[in] transparent_threshold since the alpha value in MattringResult is a float between [0, 1], transparent_threshold is used to filter background pixel
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* \param[in] remove_small_connected_area if remove_small_connected_area==true, the visualized result will not include the small connected areas
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat VisMatting(const cv::Mat& im,
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const MattingResult& result,
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bool transparent_background = false,
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float transparent_threshold = 0.999,
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bool remove_small_connected_area = false);
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/** \brief Show the visualized results for Ocr models
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the result produced by model
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat VisOcr(const cv::Mat& im, const OCRResult& ocr_result,
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const float score_threshold = 0);
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FASTDEPLOY_DECL cv::Mat VisMOT(const cv::Mat& img, const MOTResult& results,
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float score_threshold = 0.0f,
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tracking::TrailRecorder* recorder = nullptr);
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/** \brief Swap the image background with MattingResult
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] background the background image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the MattingResult produced by model
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* \param[in] remove_small_connected_area if remove_small_connected_area==true, the visualized result will not include the small connected areas
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat
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SwapBackground(const cv::Mat& im, const cv::Mat& background,
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const MattingResult& result,
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bool remove_small_connected_area = false);
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/** \brief Swap the image background with SegmentationResult
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] background the background image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] result the SegmentationResult produced by model
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* \param[in] background_label the background label number in SegmentationResult
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat SwapBackground(const cv::Mat& im,
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const cv::Mat& background,
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const SegmentationResult& result,
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int background_label);
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/** \brief Show the visualized results for key point detection models
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*
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* \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
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* \param[in] results the result produced by model
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* \param[in] conf_threshold threshold for result scores, the result will not be shown if the score is less than conf_threshold
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* \return cv::Mat type stores the visualized results
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*/
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FASTDEPLOY_DECL cv::Mat
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VisKeypointDetection(const cv::Mat& im, const KeyPointDetectionResult& results,
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float conf_threshold = 0.5f);
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FASTDEPLOY_DECL cv::Mat VisHeadPose(const cv::Mat& im,
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const HeadPoseResult& result, int size = 50,
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int line_size = 1);
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} // namespace vision
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} // namespace fastdeploy
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