118 lines
3.5 KiB
Python
118 lines
3.5 KiB
Python
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import os
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import numpy as np
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import random
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import shutil
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from keras.models import Sequential
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from keras.layers import Conv2D, Flatten, MaxPooling2D, Dense
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from keras.preprocessing import image
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from keras.preprocessing.image import ImageDataGenerator
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import matplotlib.pyplot as plt
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import random
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#dataset from https://www.kaggle.com/asdasdasasdas/garbage-classification
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'''#sepperating the file into training and testing data, creation of folders by hand removal of 75 images from papers for a more even distribution
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def sepperate(type):
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for i in type:
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folder = "Garbage classification\\Garbage classification\\" + i
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destination = "Garbage classification\\testset\\" + i
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howmany = len(os.listdir(folder))
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for j in range(int(howmany*0.2)):
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move1 = random.choice(os.listdir(folder))
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source = "Garbage classification\\Garbage classification\\" + i + "\\" + move1
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d = shutil.move(source, destination, copy_function = shutil.copytree)
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types = ["cardboard", "glass", "metal", "paper", "plastic"]
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sepperate(types)
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os.rename("Garbage classification\\Garbage classification", "Garbage classification\\trainset")
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'''
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classifier = Sequential()
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classifier.add(Conv2D(32, (3, 3), input_shape=(110, 110, 3), activation = "relu"))
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classifier.add(MaxPooling2D(pool_size = (2, 2)))
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classifier.add(Conv2D(64, (3, 3), activation = "relu"))
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classifier.add(MaxPooling2D(pool_size=(2, 2)))
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# this layer in ver 4
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classifier.add(Conv2D(32, (3, 3), activation = "relu"))
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classifier.add(MaxPooling2D(pool_size=(2, 2)))
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# -----------------
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classifier.add(Flatten())
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classifier.add(Dense(activation = "relu", units = 64 ))
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classifier.add(Dense(activation = "softmax", units = 5))
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classifier.compile(optimizer = "adam", loss = "categorical_crossentropy", metrics = ["accuracy"])
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train_datagen = ImageDataGenerator(
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rescale=1./255,
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shear_range=0.1,
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zoom_range=0.1,
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width_shift_range=0.1,
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height_shift_range=0.1,
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horizontal_flip=True,
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vertical_flip=True,
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)
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test_datagen = ImageDataGenerator(
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rescale=1./255,
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validation_split=0.1
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)
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train_generator = train_datagen.flow_from_directory(
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"Garbage classification\\trainset",
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target_size=(110, 110),
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batch_size=16,
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class_mode='categorical',
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#seed=0
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)
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test_generator = test_datagen.flow_from_directory(
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"Garbage classification\\testset",
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target_size=(110, 110),
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batch_size=16,
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class_mode='categorical',
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)
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#Teaching the classifier
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'''classifier.fit_generator( train_generator, steps_per_epoch = 165, epochs = 32, validation_data = test_generator )
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classifier.save_weights('model_ver_6.h5')'''
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labels = (train_generator.class_indices)
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labels = dict((value,key) for key,value in labels.items())
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classifier.load_weights("model_ver_6.h5")
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def getTrashPhoto(x, type):
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for i in range(x):
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kind = random.choice(type)
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path = "Garbage classification\\testset\\" + kind
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file = random.choice(os.listdir(path))
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path = "Garbage classification\\testset\\" + kind + "\\" + file
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var = image.load_img(path, target_size = (110,110))
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ti = image.img_to_array(var)
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ti=np.array(ti)/255.0
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ti = np.expand_dims(ti, axis = 0)
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prediction = classifier.predict(ti)
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plt.subplot(1, 3, i+1)
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plt.imshow(var)
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plt.title("AI thinks:%s \nReality:\n %s" % (labels[np.argmax(prediction)], file))
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plt.show()
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types = ["cardboard", "glass", "metal", "paper", "plastic"]
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type = ["metal"]
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getTrashPhoto(3, types)
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plt.show()
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