yolov8 在检测图片中显示检测结果代码

以下是使用 YOLOv4 模型检测图片并显示检测结果的 Python 代码示例：

import cv2
import numpy as np

# 加载 YOLOv4 模型
net = cv2.dnn.readNetFromDarknet("yolov4.cfg", "yolov4.weights")

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

# 加载图片
img = cv2.imread("test.jpg")

# 调整尺寸并归一化
blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), swapRB=True, crop=False)

# 输入模型进行检测
net.setInput(blob)
output_layers = net.getUnconnectedOutLayersNames()
outs = net.forward(output_layers)

# 解析检测结果
conf_threshold = 0.5
nms_threshold = 0.4
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 > conf_threshold:
            center_x = int(detection[0] * img.shape[1])
            center_y = int(detection[1] * img.shape[0])
            width = int(detection[2] * img.shape[1])
            height = int(detection[3] * img.shape[0])
            left = int(center_x - width / 2)
            top = int(center_y - height / 2)
            class_ids.append(class_id)
            confidences.append(float(confidence))
            boxes.append([left, top, width, height])

# 非极大值抑制
indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)

# 显示检测结果
font_scale = 1
thickness = 1
color = (0, 255, 0)
for i in indices:
    i = i[0]
    box = boxes[i]
    left = box[0]
    top = box[1]
    width = box[2]
    height = box[3]
    class_id = class_ids[i]
    label = f"{classes[class_id]}: {confidences[i]:.2f}"
    cv2.rectangle(img, (left, top), (left + width, top + height), color, thickness)
    cv2.putText(img, label, (left, top - 5), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, thickness)

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

其中，`yolov4.cfg` 和 `yolov4.weights` 是 YOLOv4 模型的配置文件和权重文件，`coco.names` 是 COCO 数据集的类别标签文件。你需要根据自己的情况修改这些文件的路径。另外，`test.jpg` 是需要检测的图片文件，同样需要根据自己的情况修改路径。