yolov8 结合d435的代码

以下是使用OpenCV和YOLOv4结合Intel D435相机进行目标检测的示例代码。请注意，代码的实现可能因不同的环境和库版本而有所不同。

import cv2
import numpy as np

# Load YOLOv4 model
net = cv2.dnn.readNet("yolov4.weights", "yolov4.cfg")

# Load Intel D435 camera
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
pipeline.start(config)

# Define classes
classes = ["person", "car", "bicycle"]

while True:
    # Get camera frame
    frames = pipeline.wait_for_frames()
    color_frame = frames.get_color_frame()
    depth_frame = frames.get_depth_frame()

    # Convert camera frame to OpenCV format
    color_image = np.asanyarray(color_frame.get_data())
    depth_image = np.asanyarray(depth_frame.get_data())

    # Preprocess input
    blob = cv2.dnn.blobFromImage(color_image, 1/255, (416, 416), swapRB=True, crop=False)

    # Set input to YOLOv4 model
    net.setInput(blob)

    # Run forward pass through YOLOv4 model
    outs = net.forward(net.getUnconnectedOutLayersNames())

    # Postprocess output
    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] * 416)
                center_y = int(detection[1] * 416)
                width = int(detection[2] * 416)
                height = int(detection[3] * 416)
                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])

    # Draw bounding boxes and labels on camera frame
    indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
    for i in indices:
        i = i[0]
        box = boxes[i]
        left = box[0]
        top = box[1]
        width = box[2]
        height = box[3]
        label = classes[class_ids[i]]
        color = (0, 255, 0)
        cv2.rectangle(color_image, (left, top), (left + width, top + height), color, 2)
        cv2.putText(color_image, label, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

    # Show camera frame
    cv2.imshow("Camera", color_image)

    # Break if 'q' is pressed
    if cv2.waitKey(1) == ord('q'):
        break

# Release resources
pipeline.stop()
cv2.destroyAllWindows()

该示例代码将从Intel D435相机读取彩色图像和深度图像，并将彩色图像传递给YOLOv4模型进行目标检测。检测到的目标将在彩色图像上用矩形框和标签进行标记，并在窗口中显示。