153 lines
3.7 KiB
Python
153 lines
3.7 KiB
Python
#%%
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from tensorflow.keras.layers import Input, Lambda, Dense, Flatten,Dropout
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from tensorflow.keras.models import Model
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from tensorflow.keras.applications.vgg19 import VGG19
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from tensorflow.keras.applications.vgg19 import preprocess_input
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from tensorflow.keras.preprocessing import image
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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from tensorflow.keras.models import Sequential
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import numpy as np
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import pandas as pd
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import os
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import cv2
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import matplotlib.pyplot as plt
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# %%
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os.chdir(r"C:\Users\pittb\Documents\biedronkapp_2.0\BiedronkApp\biedronki_dataset")
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train_path="train"
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test_path="test"
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# %%
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x_train=[]
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for folder in os.listdir(train_path):
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sub_path=train_path+"/"+folder
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for img in os.listdir(sub_path):
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image_path=sub_path+"/"+img
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img_arr=cv2.imread(image_path)
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img_arr=cv2.resize(img_arr,(224,224))
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x_train.append(img_arr)
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x_test=[]
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for folder in os.listdir(test_path):
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sub_path=test_path+"/"+folder
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for img in os.listdir(sub_path):
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image_path=sub_path+"/"+img
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img_arr=cv2.imread(image_path)
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img_arr=cv2.resize(img_arr,(224,224))
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x_test.append(img_arr)
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# %%
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train_x=np.array(x_train)
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test_x=np.array(x_test)
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train_x=train_x/255.0
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test_x=test_x/255.0
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# %%
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train_datagen = ImageDataGenerator(rescale = 1./255)
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test_datagen = ImageDataGenerator(rescale = 1./255)
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# %%
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training_set = train_datagen.flow_from_directory(train_path,
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target_size = (224, 224),
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batch_size = 32,
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class_mode = 'sparse')
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test_set = test_datagen.flow_from_directory(test_path,
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target_size = (224, 224),
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batch_size = 32,
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class_mode = 'sparse')
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# %%
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train_y=training_set.classes
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test_y=test_set.classes
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training_set.class_indices
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train_y.shape,test_y.shape
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# %%
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IMAGE_SIZE = [224, 224]
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vgg = VGG19(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
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for layer in vgg.layers:
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layer.trainable = False
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# %%
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# our layers - you can add more if you want
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x = Flatten()(vgg.output)
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prediction = Dense(2, activation='softmax')(x)
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# %%
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# create a model object
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model = Model(inputs=vgg.input, outputs=prediction)
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# view the structure of the model
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model.summary()
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# %%
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model.compile(
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loss='sparse_categorical_crossentropy',
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optimizer="adam",
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metrics=['accuracy']
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)
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# %%
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from tensorflow.keras.callbacks import EarlyStopping
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early_stop=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=5)
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# %%
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history = model.fit(
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train_x,
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train_y,
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epochs=10,
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callbacks=[early_stop],
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batch_size=32,shuffle=True)
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# %%
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plt.plot(history.history['loss'], label='train loss')
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plt.legend()
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plt.savefig('vgg-loss-rps-1.png')
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plt.show()
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# %%
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# accuracies
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plt.plot(history.history['accuracy'], label='train acc')
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plt.legend()
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plt.savefig('vgg-acc-rps-1.png')
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plt.show()
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# %%
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model.evaluate(test_x,test_y,batch_size=6)
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# %%
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from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
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import numpy as np
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y_pred=model.predict(test_x)
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y_pred=np.argmax(y_pred,axis=1)
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accuracy_score(y_pred,test_y)
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print(classification_report(y_pred,test_y))
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# %%
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img_path = r"C:\Users\pittb\Documents\biedronkapp_2.0\BiedronkApp\biedronki_dataset\test\azjatyckie\78687-Biedronki-azjatyckie-Czy-sa-grozne-fot.-cocoparisienne---Pixabay.jpg"
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img_nump = np.array(cv2.imread(img_path))
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img_nump = cv2.resize(img_nump,(224,224))
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img_nump = img_nump/255.0
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img_nump = img_nump[np.newaxis,...]
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pr = model.predict(img_nump)
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print(pr)
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print(np.argmax(pr,axis=1))
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# %%
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# %%
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model.save('saved_model/my_model')
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# %%
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# %%
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