YOLO V5单目小球测距
时间: 2025-01-05 22:30:18 浏览: 9
### 使用YOLO V5实现单目摄像头下小球的距离测量
#### 方法概述
对于基于单目摄像头的小球距离测量,可以采用相似三角形原理。该方法假设物体的真实尺寸已知,在此情况下可以通过测量物体在图像中的大小并与实际尺寸对比来估算其距离[^3]。
#### 关键步骤解析
- **模型准备**
需要预先训练好能够识别特定类别(本案例中小球)的YOLOv5模型。这涉及到数据集收集、标注以及调整网络参数以获得最佳性能[^4]。
- **获取真实世界尺寸**
测量待测物即小球的实际直径或其他特征长度作为后续计算的基础依据。
- **图像处理与对象检测**
利用已经训练完成的YOLOv5对输入帧执行推理操作,得到感兴趣区域(ROI),特别是目标中心坐标及其边界框尺寸信息。
- **应用几何关系求解距离**
基于上述提到的对象像素级尺度和预设物理单位下的真值构建比例方程从而推导出两者间相对位置关系——具体表现为直线视距上的远近程度变化规律表达式:
\[ D = \frac{f * W}{w} \]
其中\(D\)代表被摄体至摄像装置间的水平间距;\(W\)表示实物宽幅;而\(w\)则是对应投影映射后所呈现出来的表观宽度;最后\(f\)指的是焦距常数项,可通过实验测定或查阅设备说明书得知确切数值范围内的取值情况。
#### Python代码实例
下面给出一段简单的Python脚本来展示如何集成YOLOv5进行实时视频流分析并估计指定类别的三维空间位姿属性之一—径向偏离度量指标:
```python
import torch
from pathlib import Path
import cv2
def load_model():
model_path = "path/to/best.pt"
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = torch.hub.load('ultralytics/yolov5', 'custom', path=model_path, force_reload=True).to(device)
return model
def calculate_distance(focal_length, real_width, pixel_width):
distance = (real_width * focal_length) / pixel_width
return distance
model = load_model()
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
results = model(frame)
detections = results.pandas().xyxy[0]
for _, row in detections.iterrows():
label = int(row['class'])
# Assuming we are only interested in detecting balls and have their actual size.
if label == 0: # Replace with the correct class index for your ball type
bbox = [int(x) for x in row[['xmin', 'ymin', 'xmax', 'ymax']]]
width_in_pixels = abs(bbox[2]-bbox[0])
known_ball_diameter_mm = 70 # Example value; replace this with accurate measurement of a specific kind of ball you're using.
estimated_focal_length = 800 # This is an example value. You should calibrate it according to your camera setup or use intrinsic parameters from calibration process.
dist_to_camera_cm = round(calculate_distance(
estimated_focal_length,
known_ball_diameter_mm,
width_in_pixels), 2)
print(f"Detected Ball at Distance {dist_to_camera_cm} cm")
color = (0, 255, 0)
thickness = 2
font_scale = 1
text_position = tuple([bbox[0], bbox[1]])
display_text = f'Distance:{str(dist_to_camera_cm)}cm'
cv2.putText(frame, display_text ,text_position,cv2.FONT_HERSHEY_SIMPLEX,font_scale,color,thickness)
start_point = tuple([bbox[0], bbox[1]])
end_point = tuple([bbox[2], bbox[3]])
cv2.rectangle(img=frame, pt1=start_point, pt2=end_point, color=color, thickness=thickness)
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
cv2.destroyAllWindows()
```
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