python手势识别的分类器
时间: 2024-01-19 09:18:22
浏览: 104
根据提供的引用内容,以下是一个基于Python的[手势](https://geek.csdn.net/educolumn/0cb002c0a73b1faac175056d746aa306?spm=1055.2569.3001.10083)识别分类器的示例代码:
```python
import mediapipe as mp
import cv2
# 初始化[手势](https://geek.csdn.net/educolumn/0cb002c0a73b1faac175056d746aa306?spm=1055.2569.3001.10083)识别模型
[han](https://geek.csdn.net/educolumn/0d22b54eaf6bcf967d9625e1679d00b4?spm=1055.2569.3001.10083)ds = mp.solutions.hands.Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5, min_tracking_confidence=0.5)
# 开启摄像头
cap = cv2.VideoCapture(0)
while True:
# 读取摄像头帧
ret, frame = cap.read()
# 将图像转换为RGB格式
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 进行[手势](https://geek.csdn.net/educolumn/0cb002c0a73b1faac175056d746aa306?spm=1055.2569.3001.10083)识别
results = hands.process(image)
# 绘制识别结果
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
# 在图像上绘制[手势](https://geek.csdn.net/educolumn/0cb002c0a73b1faac175056d746aa306?spm=1055.2569.3001.10083)关键点
mp.solutions.drawing_utils.draw_landmarks(image, hand_landmarks, mp.solutions.hands.HAND_CONNECTIONS)
```
相关推荐

















