写出用zed相机调用yolo v7模型的代码
时间: 2024-04-18 19:08:18 浏览: 10
以下是可以用zed相机调用yolo v7模型的Python代码示例:
首先,您需要安装zed相机SDK和yolo v7模型,并安装OpenCV和其他必要的库。然后,您可以使用以下代码:
```python
import cv2
import pyzed.sl as sl
import numpy as np
# Load the YOLOv7 object detection model
model = cv2.dnn.readNetFromDarknet("yolov7.cfg", "yolov7.weights")
# Set the input size
input_size = (416, 416)
# Set the confidence threshold
confidence_threshold = 0.5
# Open the ZED camera
zed = sl.Camera()
init = sl.InitParameters()
init.camera_resolution = sl.RESOLUTION.HD720
init.depth_mode = sl.DEPTH_MODE.NONE
init.camera_fps = 30
err = zed.open(init)
if err != sl.ERROR_CODE.SUCCESS:
print(repr(err))
zed.close()
exit()
# Capture frames from the ZED camera and resize them
while True:
# Grab a new image from the ZED camera
runtime_parameters = sl.RuntimeParameters()
left_image = sl.Mat()
zed.grab(runtime_parameters)
zed.retrieve_image(left_image, sl.VIEW.LEFT)
# Convert the image to a format that can be processed by YOLOv7
image = cv2.resize(left_image.get_data(), input_size)
blob = cv2.dnn.blobFromImage(image, 1/255.0, input_size, swapRB=True, crop=False)
# Set the inputs and run the YOLOv7 model
model.setInput(blob)
outputs = model.forward()
# Filter the outputs by confidence threshold and draw bounding boxes
for o in outputs:
for detection in o:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > confidence_threshold:
x, y, w, h = detection[:4] * np.array([left_image.get_width(), left_image.get_height()] * 2)
xmin, ymin, xmax, ymax = int(x - w/2), int(y - h/2), int(x + w/2), int(y + h/2)
cv2.rectangle(left_image.get_data(), (xmin, ymin), (xmax, ymax), (255,0,0), 2)
# Display the processed image
cv2.imshow("YOLOv7 object detection", left_image.get_data())
# Exit loop on ESCAPE
key = cv2.waitKey(1)
if key == 27:
break
# Release the ZED camera
zed.close()
# Close the OpenCV window
cv2.destroyAllWindows()
```
请注意,以上代码可能需要根据您的要求进行修改。