ZED相机物体识别代码

以下是使用ZED相机进行物体识别的示例代码（使用OpenCV和ZED SDK）：

import cv2
import numpy as np
import pyzed.sl as sl

# 初始化ZED相机
zed = sl.Camera()

init_params = sl.InitParameters()
init_params.camera_resolution = sl.RESOLUTION.HD720  # 设置相机分辨率
init_params.camera_fps = 30  # 设置相机帧率

err = zed.open(init_params)
if err != sl.ERROR_CODE.SUCCESS:
    print("相机打开失败")
    zed.close()
    exit()

# 设置对象检测器
net = cv2.dnn.readNetFromDarknet("yolov4.cfg", "yolov4.weights")  # 加载YOLOv4模型
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)  # 设置CUDA后端
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)  # 设置CUDA目标

# 获取相机参数
camera_params = zed.get_camera_information().calibration_parameters.left_cam

# 创建Mat对象以存储图像
image_size = (camera_params.width, camera_params.height)
image_zed = sl.Mat(image_size[1], image_size[0], sl.MAT_TYPE.U8_C4)

# 创建窗口
cv2.namedWindow("ZED", cv2.WINDOW_NORMAL)

# 循环读取图像并进行物体检测
while True:
    if zed.grab() == sl.ERROR_CODE.SUCCESS:
        # 从相机中获取图像
        zed.retrieve_image(image_zed, sl.VIEW.LEFT)

        # 将图像转换为OpenCV格式
        image_ocv = image_zed.get_data()

        # 运行物体检测器
        blob = cv2.dnn.blobFromImage(image_ocv, 1 / 255.0, (416, 416), swapRB=True, crop=False)
        net.setInput(blob)
        layer_names = net.getLayerNames()
        output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
        outputs = net.forward(output_layers)

        # 解析输出并绘制边界框
        class_ids = []
        confidences = []
        boxes = []
        for output in outputs:
            for detection in output:
                scores = detection[5:]
                class_id = np.argmax(scores)
                confidence = scores[class_id]
                if confidence > 0.5:
                    center_x = int(detection[0] * image_size[0])
                    center_y = int(detection[1] * image_size[1])
                    width = int(detection[2] * image_size[0])
                    height = int(detection[3] * image_size[1])
                    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])

        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 = str(class_ids[i])
            cv2.rectangle(image_ocv, (left, top), (left + width, top + height), (0, 255, 0), 2)
            cv2.putText(image_ocv, label, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

        # 显示图像
        cv2.imshow("ZED", image_ocv)

        # 检查是否退出
        key = cv2.waitKey(1)
        if key & 0xFF == ord('q'):
            break

# 关闭窗口和相机
cv2.destroyAllWindows()
zed.close()

这个示例代码使用YOLOv4模型进行物体检测，并且使用ZED SDK将图像从相机中读取到OpenCV中进行处理。请注意，这只是一个示例，你需要根据你的需求对代码进行适当的修改。