yolo v3 代码实现

Yolov3的代码实现相对复杂，需要一定的深度学习和编程知识。以下是一个简单的Python代码示例，用于在图像中检测物体：

import cv2
import numpy as np

# 加载 YOLOv3 预训练模型和标签文件
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

# 加载图像
img = cv2.imread("object_detection.jpg")
height, width, _ = img.shape

# 将图像输入网络中进行前向计算
blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
output_layers_names = net.getUnconnectedOutLayersNames()
layerOutputs = net.forward(output_layers_names)

# 解析输出并画出边界框
boxes = []
confidences = []
class_ids = []
for output in layerOutputs:
    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] * 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]])
        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.putText(img, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)

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

这段代码使用OpenCV的DNN模块加载预训练的Yolov3模型，读取标签文件，并在输入图像中检测物体。它使用NMS算法过滤掉重叠的边界框，并在图像中绘制出检测结果。