deepsort画目标运动轨迹的代码
时间: 2023-10-04 17:07:06 浏览: 127
DeepSORT(Deep Learning-based Object Tracking)是一种基于深度学习的目标跟踪算法,它可以预测目标的运动轨迹。以下是使用DeepSORT进行目标跟踪并画出目标运动轨迹的代码示例:
首先,安装必要的库:
```python
pip install numpy
pip install opencv-python
pip install tensorflow
pip install keras
pip install filterpy
pip install scikit-learn
```
然后,导入必要的库:
```python
import numpy as np
import cv2
from deep_sort import preprocessing, nn_matching
from deep_sort.detection import Detection
from deep_sort.tracker import Tracker
from tools import generate_detections as gdet
```
接下来,定义一些变量:
```python
model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine", 0.2, None)
tracker = Tracker(metric)
```
其中,`model_filename`是预训练模型的文件路径,`encoder`是用于对检测框进行编码的函数,`metric`是用于计算距离的度量函数,`tracker`是目标跟踪器。
然后,读取视频并进行处理:
```python
video_path = 'path/to/video.mp4'
video_capture = cv2.VideoCapture(video_path)
while True:
ret, frame = video_capture.read()
if ret != True:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
detections = detect_objects(frame) # 检测物体
features = encoder(frame, detections) # 特征编码
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(detections, features)]
tracker.predict() # 预测目标位置
tracker.update(detections) # 更新目标位置
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
bbox = track.to_tlbr()
cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (0, 255, 0), 2)
cv2.putText(frame, str(track.track_id), (int(bbox[0]), int(bbox[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2, cv2.LINE_AA)
trace = np.array(track.trace).astype(np.int32)
cv2.polylines(frame, [trace], False, (0, 255, 0), 2)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
```
其中,`detect_objects`函数用于检测物体,`track.to_tlbr()`用于获取目标位置,`cv2.rectangle`和`cv2.putText`用于在图像上绘制框和ID,`track.trace`用于获取目标运动轨迹,`cv2.polylines`用于在图像上绘制运动轨迹。
完整的代码可在 https://github.com/nwojke/deep_sort 中找到。