运用Alnerext神经网络实现手势识别的Python代码
时间: 2023-11-19 10:29:45 浏览: 117
以下是使用Alnerext神经网络实现手势识别的Python代码示例:
```python
import numpy as np
import cv2
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
# 定义神经网络模型
model = keras.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 1)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10)
])
# 加载训练好的模型参数
model.load_weights('model.h5')
# 定义手势类别名称
gesture_names = ['Fist', 'L', 'Okay', 'Palm', 'Peace']
# 打开摄像头
cap = cv2.VideoCapture(0)
while True:
# 读取一帧图像
ret, frame = cap.read()
# 对图像进行预处理
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
ret, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 找到手势区域
max_area = 0
max_contour = None
for contour in contours:
area = cv2.contourArea(contour)
if area > max_area:
max_area = area
max_contour = contour
if max_contour is not None:
# 提取手势图像并进行预处理
x, y, w, h = cv2.boundingRect(max_contour)
gesture_img = gray[y:y+h, x:x+w]
gesture_img = cv2.resize(gesture_img, (64, 64))
gesture_img = np.reshape(gesture_img, (1, 64, 64, 1))
gesture_img = gesture_img.astype('float32') / 255.0
# 使用神经网络进行手势识别
prediction = model.predict(gesture_img)
gesture_id = np.argmax(prediction)
gesture_name = gesture_names[gesture_id]
# 在图像上显示手势类别
cv2.putText(frame, gesture_name, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
# 显示图像
cv2.imshow('frame', frame)
# 按下q键退出程序
if cv2.waitKey(1) == ord('q'):
break
# 关闭摄像头和窗口
cap.release()
cv2.destroyAllWindows()
```
注意,以上代码仅为示例代码,实际应用中需要根据具体情况进行修改和优化。
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