Merge pull request #26153 from onurcankaraman:sample/trackerUpdate

sample: tracker parameters updated

samples/python/tracker.py

@@ -24,6 +24,10 @@ USAGE:
                     [--nanotrack_backbone NANOTRACK_BACKBONE]
                     [--nanotrack_headneck NANOTRACK_TARGET]
                     [--vittrack_net VITTRACK_MODEL]
+                    [--vittrack_net VITTRACK_MODEL]
+                    [--tracking_score_threshold TRACKING SCORE THRESHOLD FOR ONLY VITTRACK]
+                    [--backend CHOOSE ONE OF COMPUTATION BACKEND]
+                    [--target CHOOSE ONE OF COMPUTATION TARGET]
 '''
 
 # Python 2/3 compatibility
@@ -37,6 +41,11 @@ import argparse
 
 from video import create_capture, presets
 
+backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV,
+            cv.dnn.DNN_BACKEND_VKCOM, cv.dnn.DNN_BACKEND_CUDA)
+targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD,
+           cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)
+
 class App(object):
 
     def __init__(self, args):
@@ -57,15 +66,22 @@ class App(object):
             params.model = self.args.dasiamrpn_net
             params.kernel_cls1 = self.args.dasiamrpn_kernel_cls1
             params.kernel_r1 = self.args.dasiamrpn_kernel_r1
+            params.backend = args.backend
+            params.target = args.target
             tracker = cv.TrackerDaSiamRPN_create(params)
         elif self.trackerAlgorithm == 'nanotrack':
             params = cv.TrackerNano_Params()
             params.backbone = args.nanotrack_backbone
             params.neckhead = args.nanotrack_headneck
+            params.backend = args.backend
+            params.target = args.target
             tracker = cv.TrackerNano_create(params)
         elif self.trackerAlgorithm == 'vittrack':
             params = cv.TrackerVit_Params()
             params.net = args.vittrack_net
+            params.tracking_score_threshold = args.tracking_score_threshold
+            params.backend = args.backend
+            params.target = args.target
             tracker = cv.TrackerVit_create(params)
         else:
             sys.exit("Tracker {} is not recognized. Please use one of three available: mil, goturn, dasiamrpn, nanotrack.".format(self.trackerAlgorithm))
@@ -141,6 +157,24 @@ if __name__ == '__main__':
     parser.add_argument("--nanotrack_backbone", type=str, default="nanotrack_backbone_sim.onnx", help="Path to onnx model of NanoTrack backBone")
     parser.add_argument("--nanotrack_headneck", type=str, default="nanotrack_head_sim.onnx", help="Path to onnx model of NanoTrack headNeck")
     parser.add_argument("--vittrack_net", type=str, default="vitTracker.onnx", help="Path to onnx model of  vittrack")
+    parser.add_argument('--tracking_score_threshold', type=float,  help="Tracking score threshold. If a bbox of score >= 0.3, it is considered as found ")
+    parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
+                help="Choose one of computation backends: "
+                        "%d: automatically (by default), "
+                        "%d: Halide language (http://halide-lang.org/), "
+                        "%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
+                        "%d: OpenCV implementation, "
+                        "%d: VKCOM, "
+                        "%d: CUDA"% backends)
+    parser.add_argument("--target", choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
+                help="Choose one of target computation devices: "
+                        '%d: CPU target (by default), '
+                        '%d: OpenCL, '
+                        '%d: OpenCL fp16 (half-float precision), '
+                        '%d: VPU, '
+                        '%d: VULKAN, '
+                        '%d: CUDA, '
+                        '%d: CUDA fp16 (half-float preprocess)'% targets)
 
     args = parser.parse_args()
     App(args).run()