用yolov8的模型进行分类
-
先上效果图
-
图片资源
-
模型下载地址
https://github.com/ultralytics/ultralytics
-
代码
import matplotlib.pyplot as plt
from ultralytics import YOLO
from PIL import Image
import cv2
model = YOLO('../ultralytics/yolov8n.pt')
# print(model.names)
img_path = '../ultralytics/ultralytics/assets/bus.jpg'
img = cv2.imread(img_path)
results = model(img_path)
preds = results[0].boxes.xyxy.cpu().numpy().astype('uint32')
colors = plt.cm.get_cmap('hsv', len(model.model.names))
for index, pred in enumerate(preds):
i = int(results[0].boxes.cls[index].item())
color = colors(i)
color = (color[0] * 200, color[1] * 255, color[2] * 200)
img = cv2.rectangle(img, (int(pred[0]), int(pred[1])), (int(pred[2]), int(pred[3])), color, 2)
img = cv2.putText(img, results[0].names[i], (int(pred[0]), int(pred[1])), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
cv2.imshow("1", img)
cv2.waitKey()
cv2.destroyAllWindows()
'''
print('..........')
print(len(results[0].names)) # 所有的分类名
print(len(results[0].boxes.cls)) # 结果分类
print(results[0].boxes.conf) # 置信度
print(results[0].boxes.cls) # 分类类别
print(type(int(results[0].boxes.cls[0].item()))) # 类别索引
print(results[0].names[0]) # 分类的名称（可以根据类别索引进行获取）
print(results[0].boxes.xyxy) # 左上角和右下角坐标
'''
