sample integration

integration_sample.py

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+from tracker.multitracker import JDETracker
+import cv2
+import matplotlib.pyplot as plt
+import torch
+from utils.parse_config import parse_model_cfg
+from utils import visualization as vis
+import numpy as np
+import argparse
+import time
+import collections
+import itertools
+
+parser = argparse.ArgumentParser()
+# parser.add_argument('arggg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
+parser.add_argument('--cfg', type=str, default='cfg/yolov3_576x320.cfg', help='cfg file path')
+parser.add_argument('--weights', type=str, default='jde_576x320_uncertainty.pt', help='path to weights file')
+parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
+parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
+parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
+parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
+parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
+parser.add_argument('--test-mot16', action='store_true', help='tracking buffer')
+parser.add_argument('--save-images', action='store_true', help='save tracking results (image)')
+parser.add_argument('--save-videos', action='store_true', help='save tracking results (video)')
+parser.add_argument('-f', action='store_true', help='Internal jupyter stuff')
+opt = parser.parse_args()
+opt
+
+cfg_dict = parse_model_cfg(opt.cfg)
+opt.img_size = (int(cfg_dict[0]['width']), int(cfg_dict[0]['height']))
+
+tracker = JDETracker(opt)
+
+
+
+class FPS:
+    def __init__(self,avarageof=50):
+        self.frametimestamps = collections.deque(maxlen=avarageof)
+    def __call__(self):
+        self.frametimestamps.append(time.time())
+        if(len(self.frametimestamps) > 1):
+            return len(self.frametimestamps)/(self.frametimestamps[-1]-self.frametimestamps[0])
+        else:
+            return 0.0
+
+
+
+
+def frames(path):
+    cap = cv2.VideoCapture(path)
+    while True:
+        success, frame = cap.read()
+        cv2.waitKey(1)
+        if success:
+            yield frame
+        else:
+            break
+
+fps = FPS()
+for frame in frames("../videos/11.mp4"):
+    frame = cv2.resize(frame,opt.img_size)
+    img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    blob = torch.from_numpy(img).cuda()
+    blob = blob.permute(2, 0, 1)[None]/255
+
+    online_targets = tracker.update(blob, frame)
+
+    online_tlwhs = [t.tlwh for t in online_targets]
+    online_ids = [t.track_id for t in online_targets]
+    online_im = vis.plot_tracking(frame, online_tlwhs, online_ids, frame_id=2,
+                                  fps=fps())
+    cv2.imshow("window", online_im)