Merge pull request #436 from Nuzhny007/master

YOLOv8 instance segmentation

README.md

@@ -3,6 +3,8 @@
 
 # Last changes
 
+* YOLOv8 instance segmentation models worked with TensorRT! Export pretrained Pytorch models [here (ultralytics/ultralytics)](https://github.com/ultralytics/ultralytics) to onnx format and run Multitarget-tracker with -e=6 example
+
 * Re-identification model osnet_x0_25_msmt17 from [mikel-brostrom/yolo_tracking](https://github.com/mikel-brostrom/yolo_tracking)
 
 * YOLOv8 detector worked with TensorRT! Export pretrained Pytorch models [here (ultralytics/ultralytics)](https://github.com/ultralytics/ultralytics) to onnx format and run Multitarget-tracker with -e=6 example

combined/combined.cpp

@@ -326,7 +326,8 @@ bool CombinedDetector::InitDetector(cv::UMat frame)
 		YOLOv6,
 		YOLOv7,
 		YOLOv7Mask,
-		YOLOv8
+		YOLOv8,
+		YOLOv8Mask
 	};
 	YOLOModels usedModel = YOLOModels::YOLOv8;
 	switch (usedModel)
@@ -416,10 +417,8 @@ bool CombinedDetector::InitDetector(cv::UMat frame)
 		break;
 
 	case YOLOModels::YOLOv8:
-		//configDNN.emplace("modelConfiguration", pathToModel + "yolov8s.onnx");
-		//configDNN.emplace("modelBinary", pathToModel + "yolov8s.onnx");
-		configDNN.emplace("modelConfiguration", "C:/work/mtracking/Nuzhny007/Multitarget-tracker/data/yolov8x.onnx");
-		configDNN.emplace("modelBinary", "C:/work/mtracking/Nuzhny007/Multitarget-tracker/data/yolov8x.onnx");
+		configDNN.emplace("modelConfiguration", pathToModel + "yolov8s.onnx");
+		configDNN.emplace("modelBinary", pathToModel + "yolov8s.onnx");
 		configDNN.emplace("confidenceThreshold", "0.2");
 		configDNN.emplace("inference_precision", "FP16");
 		configDNN.emplace("net_type", "YOLOV8");
@@ -428,6 +427,18 @@ bool CombinedDetector::InitDetector(cv::UMat frame)
 		maxBatch = 1;
 		configDNN.emplace("maxCropRatio", "-1");
 		break;
+
+	case YOLOModels::YOLOv8Mask:
+		configDNN.emplace("modelConfiguration", pathToModel + "yolov8s-seg.onnx");
+		configDNN.emplace("modelBinary", pathToModel + "yolov8s-seg.onnx");
+		configDNN.emplace("confidenceThreshold", "0.2");
+		configDNN.emplace("inference_precision", "FP16");
+		configDNN.emplace("net_type", "YOLOV8Mask");
+		configDNN.emplace("inWidth", "640");
+		configDNN.emplace("inHeight", "640");
+		maxBatch = 1;
+		configDNN.emplace("maxCropRatio", "-1");
+		break;
 	}
 	configDNN.emplace("maxBatch", std::to_string(maxBatch));
 	configDNN.emplace("classNames", pathToModel + "coco.names");

data/coco/white_full.names

@@ -0,0 +1,9 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat

data/demo.py

@@ -0,0 +1,103 @@
+import sys
+import glob
+import getopt
+import numpy as np
+import cv2 as cv
+import pymtracking as mt
+
+print("OpenCV Version: {}".format(cv.__version__))
+
+
+def draw_regions(img, regions, color):
+    for reg in regions:
+        brect = reg.brect
+        cv.rectangle(img, (brect.x, brect.y, brect.width, brect.height), color, 2)
+
+
+def draw_tracks(img, tracks, fps):
+    for track in tracks:
+        brect = track.GetBoundingRect()
+        if track.isStatic:
+            cv.rectangle(img, (brect.x, brect.y, brect.width, brect.height), (255, 0, 255), 2)
+        elif track.IsRobust(int(fps / 4), 0.7, (0.1, 10.), 3):
+            cv.rectangle(img, (brect.x, brect.y, brect.width, brect.height), (0, 255, 0), 2)
+            trajectory = track.GetTrajectory()
+            for i in range(0, len(trajectory) - 1):
+                cv.line(img, trajectory[i], trajectory[i+1], (0, 255, 0), 1)
+
+
+def main():
+    args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
+    try:
+        video_src = video_src[0]
+    except:
+        video_src = 0
+    args = dict(args)
+
+    cam = cv.VideoCapture(video_src)
+
+    _ret, img = cam.read()
+    print("cam.read res = ", _ret, ", im size = ", img.shape)
+
+    fps = cam.get(cv.CAP_PROP_FPS)
+    print(video_src, " fps = ", fps)
+
+    configBGFG = mt.KeyVal()
+    configBGFG.Add('useRotatedRect', '20')
+    configBGFG.Add('history', '1000')
+    configBGFG.Add("nmixtures", "3")
+    configBGFG.Add("backgroundRatio", "0.7")
+    configBGFG.Add("noiseSigma", "0")
+    print("configBGFG = ", configBGFG)
+    mdetector = mt.BaseDetector(mt.BaseDetector.Detectors.MOG, configBGFG, img)
+    print("CanGrayProcessing: ", mdetector.CanGrayProcessing())
+    mdetector.SetMinObjectSize((1, 1))
+
+    tracker_settings = mt.TrackerSettings()
+
+    tracker_settings.SetDistance(mt.MTracker.DistRects)
+    tracker_settings.kalmanType = mt.MTracker.KalmanLinear
+    tracker_settings.filterGoal = mt.MTracker.FilterCenter
+    tracker_settings.lostTrackType = mt.MTracker.TrackNone
+    tracker_settings.matchType = mt.MTracker.MatchHungrian
+    tracker_settings.useAcceleration = False
+    tracker_settings.dt = 0.5
+    tracker_settings.accelNoiseMag = 0.1
+    tracker_settings.distThres = 0.95
+    tracker_settings.minAreaRadiusPix = img.shape[0] / 5.
+    tracker_settings.minAreaRadiusK = 0.8
+    tracker_settings.useAbandonedDetection = False
+    tracker_settings.maximumAllowedSkippedFrames = int(2 * fps)
+    tracker_settings.maxTraceLength = int(2 * fps)
+
+    mtracker = mt.MTracker(tracker_settings)
+
+    while True:
+        _ret, img = cam.read()
+        if _ret:
+            print("cam.read res = ", _ret, ", im size = ", img.shape, ", fps = ", fps)
+        else:
+            break
+
+        mdetector.Detect(img)
+        regions = mdetector.GetDetects()
+        print("mdetector.Detect:", len(regions))
+
+        mtracker.Update(regions, img, fps)
+        tracks = mtracker.GetTracks()
+        print("mtracker.Update:", len(tracks))
+
+        vis = img.copy()
+        # draw_regions(vis, regions, (255, 0, 255))
+        draw_tracks(vis, tracks, fps)
+        cv.imshow('detect', vis)
+
+        if cv.waitKey(int(1000 / fps)) == 27:
+            break
+
+    print('Done')
+
+
+if __name__ == '__main__':
+    main()
+    cv.destroyAllWindows()

data/settings_coco.ini

@@ -0,0 +1,155 @@
+[detection]
+
+#-----------------------------
+# opencv_dnn = 12
+# darknet_cudnn = 10
+# tensorrt = 11
+detector_backend = 10
+
+#-----------------------------
+# Target and backend for opencv_dnn detector
+# DNN_TARGET_CPU
+# DNN_TARGET_OPENCL
+# DNN_TARGET_OPENCL_FP16
+# DNN_TARGET_MYRIAD
+# DNN_TARGET_CUDA
+# DNN_TARGET_CUDA_FP16
+ocv_dnn_target = DNN_TARGET_CPU
+
+# DNN_BACKEND_DEFAULT
+# DNN_BACKEND_HALIDE
+# DNN_BACKEND_INFERENCE_ENGINE
+# DNN_BACKEND_OPENCV
+# DNN_BACKEND_VKCOM
+# DNN_BACKEND_CUDA
+# DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+# DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
+ocv_dnn_backend = DNN_BACKEND_OPENCV
+
+#-----------------------------
+# nn_weights = data/coco/yolov7.onnx
+# nn_config = data/coco/yolov7.onnx
+
+# nn_weights = data/coco/yolov6s.onnx
+# nn_config = data/coco/yolov6s.onnx
+
+nn_weights = C:/work/home/mtracker/Multitarget-tracker/data/yolov4.weights
+nn_config = C:/work/home/mtracker/Multitarget-tracker/data/yolov4.cfg
+
+class_names = C:/work/home/mtracker/Multitarget-tracker/data/coco.names
+
+#-----------------------------
+confidence_threshold = 0.2
+
+max_crop_ratio = -1
+max_batch = 1
+gpu_id = 0
+
+#-----------------------------
+# YOLOV3 
+# YOLOV4 
+# YOLOV5 
+net_type = YOLOV4
+
+#-----------------------------
+# INT8
+# FP16
+# FP32
+inference_precision = FP32
+
+#-----------------------------
+# Detect only set of types, ";"
+white_list = 
+
+#-----------------------------
+# For TensorRT optimization, bytes
+video_memory = 0;
+
+
+[tracking]
+
+#-----------------------------
+# DistCenters = 0   // Euclidean distance between centers, pixels
+# DistRects = 1     // Euclidean distance between bounding rectangles, pixels
+# DistJaccard = 2   // Intersection over Union, IoU, [0, 1]
+# DistHist = 3      // Bhatacharia distance between histograms, [0, 1]
+
+distance_type = 0
+
+#-----------------------------
+# KalmanLinear = 0
+# KalmanUnscented = 1
+
+kalman_type = 0
+
+#-----------------------------
+# FilterCenter = 0
+# FilterRect = 1
+
+filter_goal = 0
+
+#-----------------------------
+# TrackNone = 0
+# TrackKCF = 1
+# TrackMIL = 2
+# TrackMedianFlow = 3
+# TrackGOTURN = 4
+# TrackMOSSE = 5
+# TrackCSRT = 6
+# TrackDAT = 7
+# TrackSTAPLE = 8
+# TrackLDES = 9
+# Used if filter_goal == FilterRect
+
+lost_track_type = 0
+
+#-----------------------------
+# MatchHungrian = 0
+# MatchBipart = 1
+
+match_type = 0
+
+#-----------------------------
+# Use constant acceleration motion model:
+# 0 - unused (stable)
+# 1 - use acceleration in Kalman filter (experimental)
+use_aceleration = 0
+
+#-----------------------------
+# Delta time for Kalman filter
+delta_time = 0.4
+
+#-----------------------------
+# Accel noise magnitude for Kalman filter
+accel_noise = 0.2
+
+#-----------------------------
+# Distance threshold between region and object on two frames
+dist_thresh = 0.8 
+
+#-----------------------------
+# If this value > 0 than will be used circle with this radius
+# If this value <= 0 than will be used ellipse with size (3*vx, 3*vy), vx and vy - horizontal and vertical speed in pixelsa
+min_area_radius_pix = -1
+
+#-----------------------------
+# Minimal area radius in ration for object size. Used if min_area_radius_pix < 0
+min_area_radius_k = 0.8
+
+#-----------------------------
+# If the object do not assignment more than this frames then it will be removed
+max_skip_frames = 50
+
+#-----------------------------
+# The maximum trajectory length
+max_trace_len = 50
+
+#-----------------------------
+# Detection abandoned objects
+detect_abandoned = 0
+# After this time (in seconds) the object is considered abandoned
+min_static_time = 5
+# After this time (in seconds) the abandoned object will be removed
+max_static_time = 25
+# Speed in pixels. If speed of object is more that this value than object is non static
+max_speed_for_static = 10