yolo-fastest 树莓派识别代码

以下是在树莓派上使用yolo-fastest进行目标检测的示例代码：

import cv2
import numpy as np

# 加载yolo-fastest的权重和配置文件
net = cv2.dnn.readNet("yolo-fastest.weights", "yolo-fastest.cfg")

# 加载目标类别
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# 设置输出层和颜色
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))

# 加载图像并进行预处理
img = cv2.imread("test.jpg")
height, width, channels = img.shape
blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB=True, crop=False)

# 运行前向传递
net.setInput(blob)
outs = net.forward(output_layers)

# 解析输出结果并画出边界框和类别
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] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)
            x = int(center_x - w / 2)
            y = int(center_y - h / 2)
            boxes.append([x, y, w, h])
            confidences.append(float(confidence))
            class_ids.append(class_id)

indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = str(classes[class_ids[i]])
        color = colors[class_ids[i]]
        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
        cv2.putText(img, label, (x, y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)

# 显示结果
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

请注意，此代码假定您已经将yolo-fastest的权重文件、配置文件和目标类别文件下载到了树莓派上，并将其放在了与代码相同的目录下。