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Merge pull request #1253 from kvmanohar22:GSoC17_dnn_objdetect
GSoC'17 Learning compact models for object detection (#1253) * Final solver and model for SqueezeNet model * update README * update dependencies and CMakeLists * add global pooling * Add training scripts * fix typo * fix dependency of caffe * fix whitespace * Add squeezedet architecture * Pascal pre process script * Adding pre process scripts * Generate the graph of the model * more readable * fix some bugs in the graph * Post process class implementation * Complete minimal post processing and standalone running * Complete the base class * remove c++11 features and fix bugs * Complete example * fix bugs * Adding final scripts * Classification scripts * Update README.md * Add example code and results * Update README.md * Re-order and fix some bugs * fix build failure * Document classes and functions * Add instructions on how to use samples * update instructionos * fix docs failure * fix conversion types * fix type conversion warning * Change examples to sample directoryu * restructure directories * add more references * fix whitespace * retain aspect ratio * Add more examples * fix docs warnings * update with links to trained weights * threshold update * png -> jpg * fix tutorial * model files * precomp.hpp , fix readme links, module dependencies * copyrights - no copyright in samples - use new style OpenCV copyright header - precomp.hpp
This commit is contained in:
Kv Manohar
committed by
Alexander Alekhin
Alexander Alekhin
parent
c0b298c5f3
commit
41a5a5eaf5
149
modules/dnn_objdetect/include/opencv2/core_detect.hpp
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149
modules/dnn_objdetect/include/opencv2/core_detect.hpp
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef _OPENCV_DNN_OBJDETECT_CORE_DETECT_HPP_
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#define _OPENCV_DNN_OBJDETECT_CORE_DETECT_HPP_
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#include <vector>
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#include <memory>
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#include <opencv2/highgui.hpp>
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#include <opencv2/imgproc.hpp>
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/** @defgroup dnn_objdetect DNN used for object detection
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*/
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namespace cv
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{
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namespace dnn_objdetect
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{
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//! @addtogroup dnn_objdetect
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//! @{
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/** @brief Structure to hold the details pertaining to a single bounding box
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*/
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typedef struct
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{
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int xmin, xmax;
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int ymin, ymax;
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size_t class_idx;
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std::string label_name;
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double class_prob;
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} object;
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/** @brief A class to post process model predictions
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*/
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class CV_EXPORTS InferBbox
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{
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public:
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/** @brief Default constructer
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@param _delta_bbox Blob containing relative coordinates of bounding boxes
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@param _class_scores Blob containing the probability values of each class
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@param _conf_scores Blob containing the confidence scores
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*/
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InferBbox(Mat _delta_bbox, Mat _class_scores, Mat _conf_scores);
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/** @brief Filters the bounding boxes.
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*/
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void filter(double thresh = 0.8);
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/** @brief Vector which holds the final detections of the model
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*/
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std::vector<object> detections;
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protected:
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/** @brief Transform relative coordinates from ConvDet to bounding box coordinates
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@param bboxes Vector to hold the predicted bounding boxes
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*/
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void transform_bboxes(std::vector<std::vector<double> > *bboxes);
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/** @brief Computes final probability values of each bounding box
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@param final_probs Vector to hold the probability values
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*/
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void final_probability_dist(std::vector<std::vector<double> > *final_probs);
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/** @brief Transform bounding boxes from [x, y, h, w] to [xmin, ymin, xmax, ymax]
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@param pre Vector conatining initial co-ordinates
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@param post Vector containing the transformed co-ordinates
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*/
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void transform_bboxes_inv(std::vector<std::vector<double> > *pre,
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std::vector<std::vector<double> > *post);
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/** @brief Ensures that the bounding box values are within image boundaries
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@param min_max_boxes Vector containing bounding boxes of the form [xmin, ymin, xmax, ymax]
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*/
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void assert_predictions(std::vector<std::vector<double> > *min_max_boxes);
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/** @brief Filter top `n` predictions
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@param probs Final probability values of bounding boxes
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@param boxes Predicted bounding box co-ordinates
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@param top_n_boxes Contains bounding box co-ordinates of top `n` boxes
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@param top_n_idxs Containes class indices of top `n` bounding boxes
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@param top_n_probs Contains probability values of top `n` bounding boxes
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*/
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void filter_top_n(std::vector<std::vector<double> > *probs,
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std::vector<std::vector<double> > *boxes,
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std::vector<std::vector<double> > &top_n_boxes,
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std::vector<size_t> &top_n_idxs,
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std::vector<double> &top_n_probs);
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/** @brief Wrapper to apply Non-Maximal Supression
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@param top_n_boxes Contains bounding box co-ordinates of top `n` boxes
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@param top_n_idxs Containes class indices of top `n` bounding boxes
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@param top_n_probs Contains probability values of top `n` bounding boxes
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*/
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void nms_wrapper(std::vector<std::vector<double> > &top_n_boxes,
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std::vector<size_t> &top_n_idxs,
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std::vector<double> &top_n_probs);
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/** @brief Applies Non-Maximal Supression
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@param boxes Bounding box co-ordinates belonging to one class
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@param probs Probability values of boxes belonging to one class
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*/
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std::vector<bool> non_maximal_suppression(std::vector<std::vector<double> >
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*boxes, std::vector<double> *probs);
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/** @brief Computes intersection over union of bounding boxes
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@param boxes Vector of bounding box co-ordinates
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@param base_box Base box wrt which IOU is calculated
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@param iou Vector to store IOU values
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*/
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void intersection_over_union(std::vector<std::vector<double> > *boxes,
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std::vector<double> *base_box, std::vector<double> *iou);
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static inline bool comparator (std::pair<double, size_t> l1,
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std::pair<double, size_t> l2)
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{
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return l1.first > l2.first;
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}
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private:
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Mat delta_bbox;
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Mat class_scores;
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Mat conf_scores;
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unsigned int image_width;
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unsigned int image_height;
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unsigned int W, H;
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std::vector<std::vector<double> > anchors_values;
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std::vector<std::pair<double, double> > anchor_center;
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std::vector<std::pair<double, double> > anchor_shapes;
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std::vector<std::string> label_map;
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unsigned int num_classes;
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unsigned int anchors_per_grid;
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size_t anchors;
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double intersection_thresh;
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double nms_intersection_thresh;
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size_t n_top_detections;
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double epsilon;
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};
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//! @}
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} // namespace dnn_objdetect
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} // namespace cv
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#endif
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