基于Tensorflow的手势识别.zip

import tensorflow as tf import os import numpy as np import cv2 as cv import csv from tensorflow import keras from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 获取图片，存放到对应的列表中，同时贴上标签，存放到label列表中 def get_files(file_dir): # 存放图片类别和标签的列表：第0类 list_0 = [] label_0 = [] # 存放图片类别和标签的列表：第1类 list_1 = [] label_1 = [] # 存放图片类别和标签的列表：第2类 list_2 = [] label_2 = [] # 存放图片类别和标签的列表：第3类 list_3 = [] label_3 = [] # 存放图片类别和标签的列表：第4类 list_4 = [] label_4 = [] # 存放图片类别和标签的列表：第5类 list_5 = [] label_5 = [] # 存放图片类别和标签的列表：第6类 list_6 = [] label_6 = [] # 存放图片类别和标签的列表：第6类 list_7 = [] label_7 = [] # 存放图片类别和标签的列表：第8类 list_8 = [] label_8 = [] # 存放图片类别和标签的列表：第9类 list_9 = [] label_9 = [] for file in os.listdir(file_dir): # 获得file_dir路径下的全部文件名 # 拼接出图片文件路径 image_file_path = os.path.join(file_dir, file) for image_name in os.listdir(image_file_path): # print('image_name',image_name) # 图片的完整路径 image_name_path = os.path.join(image_file_path, image_name) # print('image_name_path',image_name_path) # 将图片存放入对应的列表 if image_file_path[-1:] == '0': list_0.append(image_name_path) label_0.append(0) elif image_file_path[-1:] == '1': list_1.append(image_name_path) label_1.append(1) elif image_file_path[-1:] == '2': list_2.append(image_name_path) label_2.append(2) elif image_file_path[-1:] == '3': list_3.append(image_name_path) label_3.append(3) elif image_file_path[-1:] == '4': list_3.append(image_name_path) label_3.append(4) elif image_file_path[-1:] == '5': list_3.append(image_name_path) label_3.append(5) elif image_file_path[-1:] == '6': list_3.append(image_name_path) label_3.append(6) elif image_file_path[-1:] == '7': list_3.append(image_name_path) label_3.append(7) elif image_file_path[-1:] == '8': list_3.append(image_name_path) label_3.append(8) else: list_4.append(image_name_path) label_4.append(9) # 合并数据 image_list = np.hstack((list_0, list_1, list_2, list_3, list_4, list_5, list_6, list_7, list_8, list_9)) label_list = np.hstack((label_0, label_1, label_2, label_3, label_4, label_5, label_6, label_7, label_8, label_9)) # 利用shuffle打乱数据 temp = np.array([image_list, label_list]) temp = temp.transpose() # 转置 np.random.shuffle(temp) # 将所有的image和label转换成list image_list = list(temp[:, 0]) image_list = [i for i in image_list] label_list = list(temp[:, 1]) label_list = [int(float(i)) for i in label_list] # print(image_list) # print(label_list) return image_list, label_list def get_tensor(image_list, label_list): ims = [] for image in image_list: # 读取路径下的图片 x = tf.io.read_file(image) # 将路径映射为照片,3通道 x = tf.image.decode_jpeg(x, channels=3) # 修改图像大小 x = tf.image.resize(x, [32, 32]) # 将图像压入列表中 ims.append(x) # 将列表转换成tensor类型 img = tf.convert_to_tensor(ims) y = tf.convert_to_tensor(label_list) return img, y def train_model(train_data): # 构建模型 network = keras.Sequential([ keras.layers.Conv2D(32, kernel_size=[5, 5], padding="same", activation=tf.nn.relu), keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), keras.layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'), keras.layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu), keras.layers.Flatten(), keras.layers.Dense(512, activation='relu'), keras.layers.Dropout(0.5), keras.layers.Dense(128, activation='relu'), keras.layers.Dense(10)]) network.build(input_shape=(None, 100, 100, channels)) network.summary() network.compile(optimizer=optimizers.SGD(lr=0.001), loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'] ) checkpoint_filepath = "./gesture_recognition_model/gestureModel" callbacks = [ # 保存模型的回调函数 tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filepath, # 保存路径 save_weights_only=True, verbose=0, save_freq='epoch'), # 保存频次以周期频次来计算 # 中止训练的回调函数 tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5) # 防止过拟合,如果过拟合了之后就终止1_val验证集上loss变高终止观察3个周期 ] # 只要在model.fit训练模型里面加上 callbacks=callbacks 这个参数,那在训练模型的时候就会按 照我们设计的回调函数来保存模型 # 模型训练 network.fit(train_data, epochs=5, callbacks=callbacks) # 因为是dataset数据集是个元组自带标签所以不用分x和y了 # network.evaluate(test_data) network.save_weights('./gesture_recognition_model/gestureModel_one.h5') print("保存模型权重成功") tf.saved_model.save(network, './gesture_recognition_model/gestureModel_one') print("保存模型成功") return network def preprocess_image(image): image = tf.image.decode_jpeg(image, channels=channels) image = tf.image.resize(image, [100, 100]) image /= 255.0 # normalize to [0,1] range return image def load_and_preprocess_image(path, label): image = tf.io.read_file(path) return preprocess_image(image), label if __name__ == "__main__": # 训练图片的路径 global channels channels = 3 train_dir = './train_gesture_data/' test_dir = './test_gesture_data/' AUTOTUNE = tf.data.experimental.AUTOTUNE # 训练图片与标签 image_list, label_list = get_files(train_dir) # 测试图片与标签 test_image_list, test_label_list = get_files(test_dir) # for i in range(len(image_list)): # print('图片路径 [{}] : 类型 [{}]'.format(image_list[i], label_list[i])) x_train, y_train = get_tensor(image_list, label_list) x_test, y_test = get_tensor(test_image_list, test_label_list) # 载入训练数据集 db_train = tf.data.Dataset.from_tensor_slices((image_list, y_train)) # # shuffle:打乱数据,map:数据预处理，batch:一次取喂入10样本训练 db_train = db_train.shuffle(1000).map(load_and_preprocess_image).batch(10) print("dataset", db_train) network = train_model(db_train)