yolo如何导出目标检测的时间和此时
时间: 2023-10-22 14:03:35 浏览: 40
的检测结果?
要导出目标检测的时间和检测结果,您可以使用以下步骤:
1. 导入必要的库和模块
```python
import cv2
import time
```
2. 读取视频文件或摄像头
```python
cap = cv2.VideoCapture('test.mp4') # 或者 cap = cv2.VideoCapture(0) 读取摄像头输入
```
3. 加载目标检测模型和配置文件
```python
model = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')
classes = []
with open('coco.names', 'r') as f:
classes = [line.strip() for line in f.readlines()]
```
4. 循环读取视频帧并进行目标检测
```python
while True:
ret, frame = cap.read() # 读取一帧
if not ret: # 如果读取失败,退出循环
break
h, w, _ = frame.shape # 获取图像的高度、宽度和通道数
# 将图像转换为 blob 格式,并将其输入到目标检测模型中
blob = cv2.dnn.blobFromImage(frame, 1 / 255, (416, 416), swapRB=True, crop=False)
model.setInput(blob)
start = time.time() # 记录开始时间
outs = model.forward(model.getUnconnectedOutLayersNames()) # 进行目标检测
end = time.time() # 记录结束时间
# 解析检测结果并绘制检测框
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x = int(detection[0] * w)
center_y = int(detection[1] * h)
width = int(detection[2] * w)
height = int(detection[3] * h)
left = int(center_x - width / 2)
top = int(center_y - height / 2)
class_ids.append(class_id)
confidences.append(float(confidence))
boxes.append([left, top, width, height])
# 非极大值抑制
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
for i in indices:
i = i[0]
box = boxes[i]
left, top, width, height = box
cv2.rectangle(frame, (left, top), (left + width, top + height), (0, 255, 0), 2)
cv2.putText(frame, classes[class_ids[i]], (left, top - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# 显示检测结果和时间
cv2.imshow('frame', frame)
print('Detection time: {:.2f}s'.format(end - start))
# 按下 q 键退出循环
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```
在上述代码中,我们使用 `time` 模块记录了目标检测的开始和结束时间,并使用 `print` 函数输出了检测时间。此外,我们还在视频帧上绘制了检测框和类别标签,并使用 `cv2.imshow` 函数显示了检测结果。如果需要将检测结果保存到文件中,可以使用 `cv2.imwrite` 函数将检测结果帧保存为图像文件。