Python 实现「手势猜拳游戏」：好玩的实时机器学习项目

  # 安装所需库（Python 3.8+）  pip install numpy matplotlib opencv-python tensorflow-cpu

import tensorflow as tfimport tensorflow_datasets as tfds
# 自动下载数据集（首次运行需要等待）dataset, info = tfds.load('rock_paper_scissors',                          split=['train', 'test'],                         as_supervised=True,                         with_info=True,                         shuffle_files=True)
# 提取训练集和测试集train_ds, test_ds = dataset[0], dataset[1]
# 查看数据集信息print(f"训练集样本数: {info.splits['train'].num_examples}")print(f"测试集样本数: {info.splits['test'].num_examples}")

def preprocess(image, label):    # 统一尺寸 + 归一化    image = tf.image.resize(image, (150, 150))    image = tf.image.rgb_to_grayscale(image)  # 转为灰度图减少计算量    return image/255.0, label
# 配置数据管道BATCH_SIZE = 32train_ds = train_ds.map(preprocess).cache().shuffle(1000).batch(BATCH_SIZE)test_ds = test_ds.map(preprocess).batch(BATCH_SIZE)
# 数据增强层（仅在训练时启用）data_augmentation = tf.keras.Sequential([    tf.keras.layers.RandomRotation(0.1),    tf.keras.layers.RandomZoom(0.1),    tf.keras.layers.RandomContrast(0.1)])

model = tf.keras.Sequential([    # 输入层    tf.keras.layers.InputLayer(input_shape=(150, 150, 1)),        # 数据增强    data_augmentation,        # 特征提取    tf.keras.layers.Conv2D(16, (3,3), activation='relu'),    tf.keras.layers.MaxPooling2D(2,2),    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),    tf.keras.layers.MaxPooling2D(2,2),        # 分类器    tf.keras.layers.Flatten(),    tf.keras.layers.Dropout(0.5),    tf.keras.layers.Dense(64, activation='relu'),    tf.keras.layers.Dense(3, activation='softmax')])
# 编译模型model.compile(optimizer='adam',              loss='sparse_categorical_crossentropy',              metrics=['accuracy'])
model.summary()  # 查看模型结构（参数总量约5.1万）

# 自定义回调（防止过拟合）early_stop = tf.keras.callbacks.EarlyStopping(    monitor='val_loss',    patience=5,    restore_best_weights=True)
# 开始训练history = model.fit(    train_ds,    epochs=30,    validation_data=test_ds,    callbacks=[early_stop])
# 评估测试集loss, accuracy = model.evaluate(test_ds)print(f"测试集准确率: {accuracy*100:.2f}%")  # 约92-95%

import cv2import numpy as np
# 类别映射CLASS_MAP = {0: "Rock", 1: "Paper", 2: "Scissors"}
# 游戏逻辑判断def judge_game(user, computer):    if user == computer:        return "平局!"    elif (user - computer) % 3 == 1:        return "你赢了!"    else:        return "你输了!"
# 初始化摄像头cap = cv2.VideoCapture(0)while cap.isOpened():    ret, frame = cap.read()    if not ret:        break        # 预处理帧    roi = frame[100:400, 100:400]  # 捕获300x300区域    preprocessed = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)    preprocessed = cv2.resize(preprocessed, (150, 150))    input_tensor = np.expand_dims(preprocessed, axis=(0, -1))        # 预测手势    pred = model.predict(input_tensor)    user_choice = np.argmax(pred[0])    computer_choice = np.random.randint(0,3)  # 电脑随机出拳        # 显示结果    cv2.rectangle(frame, (100,100), (400,400), (0,255,0), 2)    cv2.putText(frame, f"You: {CLASS_MAP[user_choice]}",                (10,30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0), 2)    cv2.putText(frame, f"Computer: {CLASS_MAP[computer_choice]}",                (10,70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)    cv2.putText(frame, f"Result: {judge_game(user_choice, computer_choice)}",                (10,110), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)        cv2.imshow('Rock Paper Scissors', frame)        if cv2.waitKey(1) & 0xFF == ord('q'):        break
cap.release()cv2.destroyAllWindows()

   converter = tf.lite.TFLiteConverter.from_keras_model(model)   tflite_model = converter.convert()   with open('model.tflite', 'wb') as f:       f.write(tflite_model)