PC端yolov3处理视频流并返回坐标给树莓派的代码

下面是一个使用 yolov3 在 PC 端处理视频流，并返回坐标给树莓派的代码示例：

import cv2
import numpy as np
import time
import socket

# Load YOLO model
net = cv2.dnn.readNetFromDarknet("yolov3.cfg", "yolov3.weights")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]
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))

# Start video capture
cap = cv2.VideoCapture(0)

# Connect to Raspberry Pi
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client.connect(("192.168.1.100", 8000))

while True:
    # Read a frame from the video
    ret, frame = cap.read()
    height, width, channels = frame.shape

    # Detect objects in the frame
    blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)

    # Show the detection results
    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:
                # Object detected
                center_x = int(detection[0] * width)
                center_y = int(detection[1] * height)
                w = int(detection[2] * width)
                h = int(detection[3] * height)
                x = center_x - w / 2
                y = center_y - h / 2
                class_ids.append(class_id)
                confidences.append(float(confidence))
                boxes.append([x, y, w, h])

    # Apply Non-Maxima Suppression
    indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

    # Send the coordinates to Raspberry Pi
    objects = []
    for i in indices:
        i = i[0]
        box = boxes[i]
        x = box[0]
        y = box[1]
        w = box[2]
        h = box[3]
        objects.append({"class_id": class_ids[i], "confidence": confidences[i], "x": x, "y": y, "w": w, "h": h})
    client.