基于yolo目标检测的详细代码案例

最新推荐文章于 2025-05-21 13:56:20 发布

程序员奇奇

最新推荐文章于 2025-05-21 13:56:20 发布

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文章标签： YOLO 目标检测人工智能

本文链接：https://blog.youkuaiyun.com/pythonyanyan/article/details/135280597

本文详细介绍了如何使用OpenCV-python库加载YOLO模型，并对图像进行目标检测，包括模型加载、图像预处理、目标检测和结果展示等步骤。

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当涉及 YOLO（You Only Look Once）目标检测模型时，其实现涉及复杂的神经网络和预训练模型。因此，我提供一个示例代码，使用 opencv-python 库加载 YOLO 模型并对图像进行目标检测。

首先，确保你已经安装了 opencv-python 库。如果没有安装，可以使用以下命令安装：

import cv2
import numpy as np

# 加载YOLO模型及相关文件
net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
classes = []
with open('coco.names', 'r') as f:
    classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 加载测试图像
img = cv2.imread('test_image.jpg')
img = cv2.resize(img, None, fx=0.4, fy=0.4)
height, width, channels = img.shape

# 目标检测
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

# 显示检测结果
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:
            # 目标检测框坐标
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)

            # 边框的坐标
            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)

font = cv2.FONT_HERSHEY_PLAIN
colors = np.random.uniform(0, 255, size=(len(classes), 3))

for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = f"{classes[class_ids[i]]}: {confidences[i]:.2f}"
        color = colors[class_ids[i]]
        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
        cv2.putText(img, label, (x, y + 30), font, 2, color, 2)

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