CNN实现图片分类.zip

import numpy as np from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix, classification_report import os import cv2 import matplotlib.pyplot as plt import pandas as pd from tensorflow import keras import glob from keras.layers import Dropout from keras.layers.normalization import BatchNormalization plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False # 定义图片路径 path = 'photo/' # 读取图像 def read_img(path): cate = [path + x for x in os.listdir(path) if os.path.isdir(path + x)] imgs = [] labels = [] fpath = [] for idx, folder in enumerate(cate): # 遍历整个目录判断每个文件是不是符合 for im in glob.glob(folder + '/*.jpg'): # print('reading the images:%s' % (im)) img = cv2.imread(im) # 调用opencv库读取像素点 img = cv2.resize(img, (32, 32)) # 图像像素大小一致 imgs.append(img) # 图像数据 labels.append(idx) # 图像类标 fpath.append(path + im) # 图像路径名 # print(path+im, idx) return np.asarray(fpath, np.string_), np.asarray(imgs, np.float32), np.asarray(labels, np.int32) # 读取图像 fpaths, data, label = read_img(path) # print(data.shape) # (1000, 256, 256, 3) # 计算有多少类图片 num_classes = len(set(label)) # 定义随机种子，将数据进行打乱处理 np.random.seed(116) np.random.shuffle(data) np.random.seed(116) np.random.shuffle(label) x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=1,stratify=label) x_train,x_valid,y_train,y_valid= train_test_split(x_train, y_train, test_size=0.1, random_state=1,stratify=y_train) #定义0-9所对应的实际类别 target_names=['people','beaches','buildings','trucks','dinosaurs', 'elephants','flowers','horses','mountains','food'] #数据归一化处理 from sklearn.preprocessing import StandardScaler scaler=StandardScaler() x_train_scaler=scaler.fit_transform( x_train.astype(np.float32).reshape(-1,1)).reshape(-1,32,32,3) x_valid_scaler=scaler.fit_transform( x_valid.astype(np.float32).reshape(-1,1)).reshape(-1,32,32,3) x_test_scaler=scaler.fit_transform( x_test.astype(np.float32).reshape(-1,1)).reshape(-1,32,32,3) #模型构建 model=keras.models.Sequential() model.add(keras.layers.Conv2D(filters=32,kernel_size=3,padding='same',activation='relu',input_shape=(32,32,3))) model.add(keras.layers.Conv2D(filters=32,kernel_size=3,padding='same',activation='relu')) model.add(keras.layers.MaxPool2D(pool_size=2)) # model.add(keras.layers.Conv2D(filters=256,kernel_size=3,padding='same',activation='relu')) # model.add(keras.layers.Conv2D(filters=256,kernel_size=3,padding='same',activation='relu')) # model.add(keras.layers.MaxPool2D(pool_size=2)) # model.add(keras.layers.Conv2D(filters=128,kernel_size=3,padding='same',activation='relu')) # model.add(keras.layers.Conv2D(filters=128,kernel_size=3,padding='same',activation='relu')) # model.add(keras.layers.MaxPool2D(pool_size=2)) model.add(keras.layers.Flatten()) model.add(keras.layers.Dense(32,activation='relu')) # model.add(Dropout(0.25)) #随机退出率 model.add(keras.layers.Dense(10,activation="softmax")) model.compile(loss="sparse_categorical_crossentropy", optimizer="Adam",metrics=["accuracy"], dropout=0.5) model.summary() #模型训练 history=model.fit(x_train_scaler,y_train,epochs=8, validation_data=(x_valid_scaler,y_valid)) #画出模型在训练过程中的损失函数和准确率的变化 def plot_learning(history): pd.DataFrame(history.history).plot(figsize=(8,5)) plt.grid(True) plt.gca().set_ylim(0,1) plt.title('训练过程损失函数和准确率的变化图') plt.show() plot_learning(history) #模型预测 predict=model.predict(x_test_scaler) # predict=model.evaluate(x_val_scaler,y_val) predict=np.argmax(predict,axis=1) #输出测试集的精确率、召回率和F1测度等指标 print (classification_report(y_test, predict,target_names=target_names)) #输出混淆矩阵 def plot_confusion_matrix(cm,title='混淆矩阵热力图',cmap=plt.cm.binary): # plt.figure(figsize=(11, 11)) plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() xlocations = np.array(range(len(target_names))) plt.xticks(xlocations, target_names, rotation=0) plt.yticks(xlocations, target_names) plt.ylabel('正确标签') plt.xlabel('预测标签') plt.show() conmatrix=confusion_matrix(y_true=y_test, y_pred=predict) plot_confusion_matrix(conmatrix) print(conmatrix) #绘制混淆矩阵 import seaborn as sn df_cm=pd.DataFrame(conmatrix, index=target_names, columns=target_names) plt.figure(figsize=(11,11)) # plt.yticks(rotation=90) plt.title('混淆矩阵对应正误个数图') sn.heatmap(df_cm,annot=True,cmap="BuPu") plt.show()