基于CNN卷积神经网络 猫狗图像识别

目录

一：数据集准备

二：读取自己的数据集

三：搭建网络 训练模型

四：猫狗图像识别

一：数据集准备

从官网下载比较麻烦，可根据以下链接，从百度网盘获取数据集

https://pan.baidu.com/s/13hw4LK8ihR6-6-8mpjLKDA

密码：dmp4

猫狗图像识别数据集 如下

二：读取自己的数据集

import osimport cv2import numpy as npfrom tqdm import tqdm# 进度条from random import shuffle# 随机打乱IMAGE_SIZE = 50def label_img(img_name):label_name = img_name.split('.')[0]# print(label_name)if label_name == 'cat':return [1, 0]elif label_name == 'dog':return [0, 1]return []def create_train_data(dir_path):train_data = []# 遍历目录获取每一张图片的路径for img_path in tqdm(os.listdir(dir_path)):# print(img_path)label = label_img(img_path)if len(label) != 0:img_path = os.path.join(dir_path, img_path)# 单通道img_mat = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)# print(img_mat, type(img_mat), img_mat.shape)if img_mat is not None:img = cv2.resize(img_mat, (IMAGE_SIZE, IMAGE_SIZE))# cv2.imshow("resize", img)# cv2.waitKey(0)# cv2.destoryAllWindows()train_data.append([np.array(img), np.array(label)])shuffle(train_data)np.save('mark_catDog_TrainData.npy', train_data)create_train_data("./train/train")

等待加载……..

直至加载完成100%

三：搭建网络 训练模型

1 导入库

import tflearnimport numpy as npfrom tflearn.layers.estimator import regressionfrom tflearn.layers.conv import conv_2d, max_pool_2dfrom tflearn.layers.core import input_data, fully_connected, dropout

2 搭建网络

# 搭建网络 卷积 激活 池化 全连# 输入层conv_input = input_data([None, IMAGE_SIZE, IMAGE_SIZE, 1], name='input')# 第一层卷积conv1 = conv_2d(conv_input, 32, 5, activation='relu')conv1 = max_pool_2d(conv1, 2)# 第二层网络conv2 = conv_2d(conv1, 64, 5, activation='relu')conv2 = max_pool_2d(conv2, 2)# 第三层网络conv3 = conv_2d(conv2, 1024, 5, activation='relu')conv3 = max_pool_2d(conv3, 2)# 全连层1fully_layer1 = fully_connected(conv3, 1024, activation='relu')# 防止过拟合fully_layer1 = dropout(fully_layer1, 0.6)# 全连层2fully_layer2 = fully_connected(fully_layer1, 2, activation='softmax')# 设置损失函数和优化器model_net = regression(fully_layer2, optimizer='adam', learning_rate=0.0005, loss='categorical_crossentropy', name='model_net')

3 数据集划分

# 创建模型model = tflearn.DNN(model_net, tensorboard_dir='log')# 加载数据npy文件train_data = np.load('mark_catDog_TrainData.npy', allow_pickle=True)print(train_data, len(train_data), type(train_data))# 数据划分train = train_data[:-30]test = train_data[-30:]X = np.array([i[0] for i in train]).reshape((-1, IMAGE_SIZE, IMAGE_SIZE, 1))Y = [i[1] for i in train]x_test = np.array([i[0] for i in test]).reshape((-1, IMAGE_SIZE, IMAGE_SIZE, 1))y_test = [i[1] for i in test]

4 模型训练及保存

# 模型训练model.fit({'input': X},{'model_net': Y},n_epoch=2,validation_set=({'input': x_test}, {'model_net': y_test}),snapshot_step=10,show_metric=True,run_id="model_class")# 模型保存model.save("model/cat-dog.model")

训练时间较长，需要耐心等待

直至加载完毕，将模型保存

训练模型 完整源码：

import tflearnimport numpy as npfrom tflearn.layers.estimator import regressionfrom tflearn.layers.conv import conv_2d, max_pool_2dfrom tflearn.layers.core import input_data, fully_connected, dropoutIMAGE_SIZE = 50# 搭建网络 卷积 激活 池化 全连# 输入层conv_input = input_data([None, IMAGE_SIZE, IMAGE_SIZE, 1], name='input')# 第一层卷积conv1 = conv_2d(conv_input, 32, 5, activation='relu')conv1 = max_pool_2d(conv1, 2)# 第二层网络conv2 = conv_2d(conv1, 64, 5, activation='relu')conv2 = max_pool_2d(conv2, 2)# 第三层网络conv3 = conv_2d(conv2, 1024, 5, activation='relu')conv3 = max_pool_2d(conv3, 2)# 全连层1fully_layer1 = fully_connected(conv3, 1024, activation='relu')# 防止过拟合fully_layer1 = dropout(fully_layer1, 0.6)# 全连层2fully_layer2 = fully_connected(fully_layer1, 2, activation='softmax')# 设置损失函数和优化器model_net = regression(fully_layer2, optimizer='adam', learning_rate=0.0001, loss='categorical_crossentropy', name='model_net')# 创建模型model = tflearn.DNN(model_net, tensorboard_dir='log')# 加载数据npy文件train_data = np.load('mark_catDog_TrainData.npy', allow_pickle=True)print(train_data, len(train_data), type(train_data))# 数据划分train = train_data[:-30]test = train_data[-30:]X = np.array([i[0] for i in train]).reshape((-1, IMAGE_SIZE, IMAGE_SIZE, 1))Y = [i[1] for i in train]x_test = np.array([i[0] for i in test]).reshape((-1, IMAGE_SIZE, IMAGE_SIZE, 1))y_test = [i[1] for i in test]# 模型训练model.fit({'input': X},{'model_net': Y},n_epoch=2,validation_set=({'input': x_test}, {'model_net': y_test}),snapshot_step=10,show_metric=True,run_id="model_class")# 模型保存model.save("model/cat-dog.model")

四：猫狗图像识别

模型保存后，可以使用模型测试，看看猫狗识别的准确率如何

可以从数据集中下载图片至工程进行测试：

测试1

# 将检测的图片变成np arraydef classify(img_path):# 灰度读取img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)if img is not None:img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE))data = img.reshape(-1, IMAGE_SIZE, IMAGE_SIZE, 1)return datapredict = model.predict(classify('dog.11392.jpg'))[0]print(predict)max_index = np.argmax(predict)if max_index == 0:print("猫")elif max_index == 1:print("狗")

测试结果如下，识别为狗，识别正确

测试2

predict = model.predict(classify('cat.299.jpg'))[0]print(predict)max_index = np.argmax(predict)if max_index == 0:print("猫")elif max_index == 1:print("狗")

测试结果如下，错将猫识别为狗，识别错误

经过多次测试，识别的准确率并不高，模型训练的不够好，需要调参优化，再多花时间训练

# 设置损失函数和优化器model_net = regression(fully_layer2, optimizer='adam', learning_rate=0.0005, loss='categorical_crossentropy', name='model_net')

深度学习，一般学习率learning_rate是设置为0.0001，

若是对这个猫狗识别比较感兴趣的，可以自己调参优化，多搭建几层网络，如果训练出比较好的模型可以告诉我一下，嘻嘻(可以理解为博主很懒)

如果对于使用模型不够熟悉的，可以参考以下源码(图片自行准备，图片名称记得修改)

import tflearnimport numpy as npimport cv2from tflearn.layers.estimator import regressionfrom tflearn.layers.conv import conv_2d, max_pool_2dfrom tflearn.layers.core import input_data, fully_connected, dropoutIMAGE_SIZE = 50# 搭建网络 卷积 激活 池化 全连# 输入层conv_input = input_data([None, IMAGE_SIZE, IMAGE_SIZE, 1], name='input')# 第一层卷积conv1 = conv_2d(conv_input, 32, 5, activation='relu')conv1 = max_pool_2d(conv1, 2)# 第二层网络conv2 = conv_2d(conv1, 64, 5, activation='relu')conv2 = max_pool_2d(conv2, 2)# 第三层网络conv3 = conv_2d(conv2, 1024, 5, activation='relu')conv3 = max_pool_2d(conv3, 2)# 全连层1fully_layer1 = fully_connected(conv3, 1024, activation='relu')# 防止过拟合fully_layer1 = dropout(fully_layer1, 0.6)# 全连层2fully_layer2 = fully_connected(fully_layer1, 2, activation='softmax')# 设置损失函数和优化器model_net = regression(fully_layer2, optimizer='adam', learning_rate=0.0005, loss='categorical_crossentropy', name='model_net')# 加载数据npy文件train_data = np.load('mark_catDog_TrainData.npy', allow_pickle=True)print(train_data, len(train_data), type(train_data))# 数据划分train = train_data[:-30]test = train_data[-30:]X = np.array([i[0] for i in train]).reshape((-1, IMAGE_SIZE, IMAGE_SIZE, 1))Y = [i[1] for i in train]x_test = np.array([i[0] for i in test]).reshape((-1, IMAGE_SIZE, IMAGE_SIZE, 1))y_test = [i[1] for i in test]# 创建模型model = tflearn.DNN(model_net, tensorboard_dir='log')# 加载模型model.load("./model/cat-dog.model")# 将检测的图片变成np arraydef classify(img_path):# 灰度读取img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)if img is not None:img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE))data = img.reshape(-1, IMAGE_SIZE, IMAGE_SIZE, 1)return datapredict = model.predict(classify('cat.299.jpg'))[0]print(predict)max_index = np.argmax(predict)if max_index == 0:print("猫")elif max_index == 1:print("狗")