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Merge pull request #481 from Nuzhny007/master
New video
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‎README.md

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# New videos!
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* 4-in-1 latest SOTA detectors in one video: YOLOv11, YOLOv12, RF-DETR, D-FINE
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[![4-in-1 latest SOTA detectors:](https://img.youtube.com/vi/Pb_HnejRpY4/0.jpg)](https://youtu.be/Pb_HnejRpY4)
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* YOLOv8-obb detection with rotated boxes (DOTA v1.0 trained)
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[![YOLOv8-obb detection:](https://img.youtube.com/vi/1e6ur57Fhzs/0.jpg)](https://youtu.be/1e6ur57Fhzs)

‎data/settings_dfine.ini

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#-----------------------------
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nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/dfine_m_obj2coco.onnx
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nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/dfine_m_obj2coco.onnx
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class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco_91.names
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class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
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#-----------------------------
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confidence_threshold = 0.5

‎data/settings_yolov11.ini

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ocv_dnn_backend = DNN_BACKEND_OPENCV
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#-----------------------------
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nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo11s.onnx
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nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo11s.onnx
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nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo11m.onnx
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nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolo11m.onnx
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class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
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#-----------------------------

‎data/settings_yolov11_seg.ini

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# opencv_dnn = 12
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# darknet_cudnn = 10
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# tensorrt = 11
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detector_backend = 12
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detector_backend = 11
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#-----------------------------
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# Target and backend for opencv_dnn detector

‎data/settings_yolov12.ini

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class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
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#-----------------------------
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confidence_threshold = 0.4
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confidence_threshold = 0.5
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max_crop_ratio = 0
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max_batch = 1

‎data/settings_yolov5.ini

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[detection]
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#-----------------------------
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# opencv_dnn = 12
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# darknet_cudnn = 10
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# tensorrt = 11
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detector_backend = 11
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#-----------------------------
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# Target and backend for opencv_dnn detector
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# DNN_TARGET_CPU
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# DNN_TARGET_OPENCL
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# DNN_TARGET_OPENCL_FP16
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# DNN_TARGET_MYRIAD
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# DNN_TARGET_CUDA
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# DNN_TARGET_CUDA_FP16
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ocv_dnn_target = DNN_TARGET_CPU
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# DNN_BACKEND_DEFAULT
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# DNN_BACKEND_HALIDE
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# DNN_BACKEND_INFERENCE_ENGINE
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# DNN_BACKEND_OPENCV
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# DNN_BACKEND_VKCOM
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# DNN_BACKEND_CUDA
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# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
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# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
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ocv_dnn_backend = DNN_BACKEND_OPENCV
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#-----------------------------
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nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov5m.onnx
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nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov5m.onnx
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class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
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#-----------------------------
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confidence_threshold = 0.5
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max_crop_ratio = 1.5
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max_batch = 1
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gpu_id = 0
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#-----------------------------
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# YOLOV3
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# YOLOV4
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# YOLOV5
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net_type = YOLOV5
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#-----------------------------
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# INT8
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# FP16
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# FP32
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inference_precision = FP16
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[tracking]
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#-----------------------------
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# DistCenters = 0 // Euclidean distance between centers, pixels
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# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
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# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
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# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
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distance_type = 0
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#-----------------------------
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# KalmanLinear = 0
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# KalmanUnscented = 1
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kalman_type = 0
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#-----------------------------
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# FilterCenter = 0
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# FilterRect = 1
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# FilterRRect = 2
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filter_goal = 0
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#-----------------------------
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# TrackNone = 0
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# TrackKCF = 1
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# TrackMIL = 2
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# TrackMedianFlow = 3
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# TrackGOTURN = 4
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# TrackMOSSE = 5
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# TrackCSRT = 6
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# TrackDAT = 7
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# TrackSTAPLE = 8
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# TrackLDES = 9
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# TrackDaSiamRPN = 10
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# Used if filter_goal == FilterRect
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lost_track_type = 0
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#-----------------------------
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# MatchHungrian = 0
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# MatchBipart = 1
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match_type = 0
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#-----------------------------
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# Use constant acceleration motion model:
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# 0 - unused (stable)
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# 1 - use acceleration in Kalman filter (experimental)
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use_aceleration = 0
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#-----------------------------
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# Delta time for Kalman filter
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delta_time = 0.4
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#-----------------------------
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# Accel noise magnitude for Kalman filter
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accel_noise = 0.2
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#-----------------------------
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# Distance threshold between region and object on two frames
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dist_thresh = 0.8
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#-----------------------------
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# If this value > 0 than will be used circle with this radius
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# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
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min_area_radius_pix = -1
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#-----------------------------
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# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
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min_area_radius_k = 0.8
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#-----------------------------
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# If the object do not assignment more than this frames then it will be removed
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max_skip_frames = 50
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#-----------------------------
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# The maximum trajectory length
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max_trace_len = 50
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#-----------------------------
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# Detection abandoned objects
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detect_abandoned = 0
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# After this time (in seconds) the object is considered abandoned
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min_static_time = 5
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# After this time (in seconds) the abandoned object will be removed
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max_static_time = 25
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# Speed in pixels. If speed of object is more that this value than object is non static
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max_speed_for_static = 10

‎data/settings_yolov6.ini

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[detection]
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#-----------------------------
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# opencv_dnn = 12
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# darknet_cudnn = 10
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# tensorrt = 11
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detector_backend = 11
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#-----------------------------
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# Target and backend for opencv_dnn detector
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# DNN_TARGET_CPU
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# DNN_TARGET_OPENCL
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# DNN_TARGET_OPENCL_FP16
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# DNN_TARGET_MYRIAD
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# DNN_TARGET_CUDA
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# DNN_TARGET_CUDA_FP16
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ocv_dnn_target = DNN_TARGET_CPU
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# DNN_BACKEND_DEFAULT
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# DNN_BACKEND_HALIDE
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# DNN_BACKEND_INFERENCE_ENGINE
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# DNN_BACKEND_OPENCV
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# DNN_BACKEND_VKCOM
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# DNN_BACKEND_CUDA
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# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
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# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
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ocv_dnn_backend = DNN_BACKEND_OPENCV
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#-----------------------------
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nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov6m.onnx
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nn_config = C:/work/home/mtracker/Multitarget-tracker/data/coco/yolov6m.onnx
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class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco/coco.names
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#-----------------------------
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confidence_threshold = 0.5
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max_crop_ratio = 1.5
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max_batch = 1
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gpu_id = 0
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#-----------------------------
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# YOLOV3
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# YOLOV4
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# YOLOV5
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net_type = YOLOV6
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#-----------------------------
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# INT8
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# FP16
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# FP32
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inference_precision = FP16
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[tracking]
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#-----------------------------
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# DistCenters = 0 // Euclidean distance between centers, pixels
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# DistRects = 1 // Euclidean distance between bounding rectangles, pixels
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# DistJaccard = 2 // Intersection over Union, IoU, [0, 1]
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# DistHist = 3 // Bhatacharia distance between histograms, [0, 1]
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distance_type = 0
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#-----------------------------
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# KalmanLinear = 0
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# KalmanUnscented = 1
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kalman_type = 0
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#-----------------------------
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# FilterCenter = 0
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# FilterRect = 1
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# FilterRRect = 2
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filter_goal = 0
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#-----------------------------
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# TrackNone = 0
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# TrackKCF = 1
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# TrackMIL = 2
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# TrackMedianFlow = 3
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# TrackGOTURN = 4
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# TrackMOSSE = 5
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# TrackCSRT = 6
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# TrackDAT = 7
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# TrackSTAPLE = 8
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# TrackLDES = 9
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# TrackDaSiamRPN = 10
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# Used if filter_goal == FilterRect
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lost_track_type = 0
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#-----------------------------
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# MatchHungrian = 0
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# MatchBipart = 1
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match_type = 0
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#-----------------------------
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# Use constant acceleration motion model:
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# 0 - unused (stable)
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# 1 - use acceleration in Kalman filter (experimental)
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use_aceleration = 0
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#-----------------------------
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# Delta time for Kalman filter
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delta_time = 0.4
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#-----------------------------
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# Accel noise magnitude for Kalman filter
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accel_noise = 0.2
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#-----------------------------
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# Distance threshold between region and object on two frames
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dist_thresh = 0.8
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#-----------------------------
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# If this value > 0 than will be used circle with this radius
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# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
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min_area_radius_pix = -1
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#-----------------------------
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# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
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min_area_radius_k = 0.8
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#-----------------------------
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# If the object do not assignment more than this frames then it will be removed
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max_skip_frames = 50
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#-----------------------------
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# The maximum trajectory length
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max_trace_len = 50
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#-----------------------------
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# Detection abandoned objects
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detect_abandoned = 0
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# After this time (in seconds) the object is considered abandoned
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min_static_time = 5
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# After this time (in seconds) the abandoned object will be removed
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max_static_time = 25
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# Speed in pixels. If speed of object is more that this value than object is non static
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max_speed_for_static = 10

‎example/examples.h

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averFps = 1000.f / currTime;
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else
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averFps = 0.9f * averFps + 0.1f * (1000.f / currTime);
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cv::putText(frame, std::to_string(cvRound(averFps)) + " fps", cv::Point(10, 20), cv::FONT_HERSHEY_TRIPLEX, 0.8, cv::Scalar(255, 0, 255));
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cv::putText(frame, std::to_string(cvRound(averFps)) + " fps", cv::Point(10, 40), cv::FONT_HERSHEY_TRIPLEX, 1.5, cv::Scalar(255, 0, 255));
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for (const auto& track : tracks)
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{
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if (track.IsRobust(2, // Minimal trajectory size
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0.5f, // Minimal ratio raw_trajectory_points / trajectory_lenght
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cv::Size2f(0.1f, 8.0f))) // Min and max ratio: width / height
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cv::Size2f(0.1f, 8.0f), 2)) // Min and max ratio: width / height
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{
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DrawTrack(frame, track, true, framesCounter);
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‎src/Detector/tensorrt_yolo/DFINE_bb.hpp

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auto boxes = outputs[1];
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auto scores = outputs[2];
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for (size_t i = 0; i < m_outpuDims[0].d[1]; ++i)
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for (size_t i = 0; i < static_cast<int>(m_outpuDims[0].d[1]); ++i)
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{
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float classConf = scores[i];
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int64_t classId = labels[i];

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