feat(yolo): 增强图像分类与姿态估计功能

- 图像分类中增加显示top1和top5的标签名及置信度
- 实例分割中引入OpenCV进行掩码处理并可视化抠图效果
- 姿态估计中添加关键点标注说明注释

03.图像分类.py

@@ -2,7 +2,16 @@ from ultralytics import YOLO
 
 # 加载预训练模型
 model = YOLO('models/yolov8n-cls.pt')
-results = model('./resources/图像分类.png',save=True)
-for result in results:
-    probs = result.probs
-    print(probs)
+model_names = model.names
+results = model('./resources/图像分类.png', save=True)
+for index in range(len(results)):
+    item = results[index]
+    probs = item.probs
+    top_1 = probs.top1
+    top_1_conf = probs.top1conf
+    top_5 = probs.top5
+    top_5_conf = probs.top5conf.numpy()
+    top_5_names = [model_names[i] for i in top_5]
+    print('第[{}]张图片分类结果:'.format(index))
+    print('top1:[{}],可置信度:[{}],名称:[{}]'.format(top_1, top_1_conf, model.names[probs.top1]))
+    print('top5:[{}],可置信度:[{}],名称:[{}]'.format(top_5, top_5_conf, top_5_names))

04.实例分割.py

@@ -1,8 +1,23 @@
+import cv2
 from ultralytics import YOLO
+import numpy as np
 
 # 加载预训练模型
 model = YOLO('models/yolov8n-seg.pt')
-results = model('./resources/图像分类.png',save= True)
+print(model.names)
+img_path = "./resources/图像分类.png"
+results = model(img_path, save=True)
+image = cv2.imread(img_path)
+# 创建掩码,如需单独分割,则在循环中创建多个mask
+mask = np.zeros(image.shape[:2], dtype=np.uint8)
 for result in results:
     masks = result.masks
-    print(masks)
+    points_array = masks.xy
+    for points in points_array:
+        points = points.astype(int)
+        cv2.fillPoly(mask, [points], 255)
+        green_bg = np.full_like(image, (0, 255, 0))
+        result = np.where(mask[:, :, np.newaxis] == 255, image, green_bg)
+        cv2.imshow('抠图', result)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()

05.姿态估计.py

@@ -3,6 +3,8 @@ from ultralytics import YOLO
 # 加载预训练模型
 model = YOLO('models/yolo11n-pose.pt')
 results = model('./resources/姿态估计',save= True)
+# 1.鼻子 2.左眼 3.右眼 4.左耳 5.右耳 6.左肩 7.右肩 8.左手肘 9.右肘 10.左手腕
+# 11.右腕 12.左髋 13.右髋 14.左膝 15.右膝 16. 左脚踝 17.右脚踝
 for result in results:
     keypoints = result.keypoints
     print(keypoints)