yolo python代码

以下是一个使用YOLOv3模型检测图像的Python代码示例：

import cv2
import numpy as np

# 加载模型及其配置文件
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

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

# 随机颜色列表，用于绘制边框
colors = np.random.uniform(0, 255, size=(len(classes), 3))

# 加载图像
img = cv2.imread("test.jpg")

# 获取图像尺寸
height, width, _ = img.shape

# 将图像输入模型
blob = cv2.dnn.blobFromImage(img, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)

# 运行前向传递，获取检测结果
outs = net.forward(net.getUnconnectedOutLayersNames())

# 解析检测结果
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)
            class_ids.append(class_id)
            confidences.append(float(confidence))
            boxes.append([x, y, w, h])

# 非最大抑制，去除重叠的边框
indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# 绘制边框和类别标签
for i in indices:
    i = i[0]
    x, y, w, h = boxes[i]
    color = colors[class_ids[i]]
    cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
    cv2.putText(img, classes[class_ids[i]], (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

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

此代码使用OpenCV的dnn模块来加载YOLOv3模型和其配置文件，并使用Numpy库来生成随机颜色。代码还使用cv2.imread()函数来加载图像，然后使用cv2.dnn.blobFromImage()函数将图像转换为模型输入所需的格式。最后，使用cv2.rectangle()和cv2.putText()函数绘制边框和类别标签，并使用cv2.imshow()函数显示结果。