帮我下一个yolov5双向计数的demo
时间: 2023-10-22 20:53:26 浏览: 50
当然,我可以帮你找到一个Yolov5双向计数的demo。你可以按照以下步骤进行操作:
1. 首先,你需要克隆Yolov5项目的代码库。打开终端并执行以下命令:
```
git clone https://github.com/ultralytics/yolov5.git
```
2. 进入克隆的代码库目录:
```
cd yolov5
```
3. 接下来,你需要安装所需的依赖项。执行以下命令:
```
pip install -r requirements.txt
```
4. 下载预训练的Yolov5权重文件。执行以下命令:
```
python models/export.py --weights yolov5s.pt
```
5. 接下来,你可以使用以下命令运行双向计数的demo:
```
python detect.py --source 0 --weights yolov5s.pt --count
```
这将使用摄像头作为输入源,并在图像中进行目标检测和计数。
请注意,这只是一个简单的示例,你可以根据自己的需求进行修改和定制。如果你想使用自己的数据集进行训练,可以参考Yolov5的文档和示例来进行操作。希望这对你有帮助!
相关问题
帮我写一个demo,实现yolov5双向追踪计数的功能。
以下是一个使用YOLOv5实现双向追踪计数的示例demo:
```python
import cv2
import torch
from torchvision.models import detection
from torchvision.transforms import functional as F
from deep_sort import build_tracker
from utils import draw_boxes, draw_counts
# 加载YOLOv5模型
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
model = model.autoshape() # 自动调整输入大小
model.eval()
# 初始化DeepSORT追踪器
tracker = build_tracker()
# 设定计数线坐标和计数器
count_line = [(0, 300), (800, 300)]
count_up = 0
count_down = 0
def detect_and_track(frame):
global count_up, count_down
# YOLOv5目标检测
results = model(frame)
# 解析检测结果并提取边界框和类别
boxes = results.xyxy[0][:, :4].tolist()
scores = results.xyxy[0][:, 4].tolist()
labels = results.xyxy[0][:, 5].tolist()
# 运行DeepSORT追踪器
tracker.update(boxes, labels)
# 获取追踪结果
tracked_objects = tracker.get_tracked_objects()
# 绘制边界框和计数线
frame = draw_boxes(frame, tracked_objects)
frame = draw_counts(frame, count_line, count_up, count_down)
# 计算车辆通过计数线的数量
for obj in tracked_objects:
cx, cy = obj['center']
if cy > count_line[0][1] and cy < count_line[1][1]:
if obj['direction'] == 'up':
count_up += 1
elif obj['direction'] == 'down':
count_down += 1
return frame
# 打开视频文件
cap = cv2.VideoCapture('path/to/video.mp4')
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# 调整帧大小和颜色通道顺序
frame = cv2.resize(frame, (800, 600))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 转换为PyTorch张量
frame_tensor = F.to_tensor(frame)
# 双向追踪计数
output_frame = detect_and_track(frame_tensor)
# 显示结果帧
cv2.imshow('Double Tracking and Counting', output_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```
请确保在运行代码之前已经安装了YOLOv5、DeepSORT和相关的依赖项,并将视频文件的路径替换为实际的视频文件路径。此外,你可能还需要根据你的项目需求进行适当的调整和修改。
写一个yolov5基于目标追踪的双向计数demo
当然,我可以为你提供一个基于目标追踪的Yolov5双向计数的demo。下面是一个简单的示例代码:
```python
import cv2
import torch
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords
from utils.torch_utils import select_device
# 加载Yolov5模型
weights = 'yolov5s.pt'
device = select_device('')
model = attempt_load(weights, map_location=device)
stride = int(model.stride.max())
# 设置视频输入
video_path = 'input.mp4'
cap = cv2.VideoCapture(video_path)
cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
ret, frame = cap.read()
# 设置目标追踪器
tracker = cv2.legacy.TrackerKCF_create()
# 初始化计数器
total_count = 0
enter_count = 0
exit_count = 0
while ret:
# 目标检测
img = torch.from_numpy(frame).to(device)
img = img.float() / 255.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
pred = model(img)[0]
pred = non_max_suppression(pred, 0.4, 0.5)
# 目标追踪和计数
for det in pred[0]:
if det is not None:
det = scale_coords(img.shape[2:], det.unsqueeze(0), frame.shape).round()
xmin, ymin, xmax, ymax = int(det[0][0]), int(det[0][1]), int(det[0][2]), int(det[0][3])
bbox = (xmin, ymin, xmax - xmin, ymax - ymin)
# 初始化目标追踪器
tracker.init(frame, bbox)
# 更新目标追踪
track_success, bbox = tracker.update(frame)
if track_success:
# 绘制追踪框
cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])),
(int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3])), (0, 255, 0), 2)
# 判断目标进出
center_x = int(bbox[0] + bbox[2] / 2)
if center_x < frame.shape[1] / 2:
enter_count += 1
else:
exit_count += 1
total_count = enter_count - exit_count
# 在图像上绘制计数信息
cv2.putText(frame, f"Total Count: {total_count}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
cv2.putText(frame, f"Enter Count: {enter_count}", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
cv2.putText(frame, f"Exit Count: {exit_count}", (10, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
# 显示图像
cv2.imshow('Demo', frame)
# 退出循环条件
if cv2.waitKey(1) == ord('q'):
break
ret, frame = cap.read()
cap.release()
cv2.destroyAllWindows()
```
请确保已经安装了必要的依赖项,并且已经下载了Yolov5的权重文件(yolov5s.pt)。将视频文件命名为"input.mp4"并与代码放在同一个目录下。运行代码后,它将显示一个窗口,显示每个目标的追踪框以及计数信息。你可以根据自己的需求进行修改和定制。希望对你有帮助!