implementation for 2nd individual project

checkingTrash.py

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+
+import os
+import numpy as np
+import random
+import shutil
+from keras.models import Sequential
+from keras.layers import Conv2D, Flatten, MaxPooling2D, Dense
+from keras.preprocessing import image
+
+#dataset from https://www.kaggle.com/asdasdasasdas/garbage-classification
+
+'''#sepperating the file into training and testing data, creation of folders by hand removal of 75 images from papers for a more even distribution
+def sepperate(type):
+    for i in type:
+        folder = "Garbage classification\\Garbage classification\\" + i
+        destination = "Garbage classification\\testset\\" + i
+        howmany = len(os.listdir(folder))
+        for j in range(int(howmany*0.2)):
+            move1 = random.choice(os.listdir(folder))
+            source = "Garbage classification\\Garbage classification\\" + i + "\\" + move1
+            d = shutil.move(source, destination, copy_function = shutil.copytree)
+
+
+types = ["cardboard", "glass", "metal", "paper", "plastic"]
+sepperate(types)
+os.rename("Garbage classification\\Garbage classification", "Garbage classification\\trainset")
+'''
+
+
+classifier = Sequential()
+classifier.add(Conv2D(32, (3, 3), input_shape=(110, 110, 3), activation = "relu"))
+classifier.add(MaxPooling2D(pool_size = (2, 2)))
+classifier.add(Conv2D(64, (3, 3), activation = "relu"))
+classifier.add(MaxPooling2D(pool_size=(2, 2)))
+# this layer in ver 4
+classifier.add(Conv2D(32, (3, 3), activation = "relu"))
+classifier.add(MaxPooling2D(pool_size=(2, 2)))
+# -----------------
+classifier.add(Flatten())
+classifier.add(Dense(activation = "relu", units = 64 ))
+classifier.add(Dense(activation = "softmax", units = 5))
+classifier.compile(optimizer = "adam", loss = "binary_crossentropy", metrics = ["accuracy"])
+
+
+from keras.preprocessing.image import ImageDataGenerator
+
+train_datagen = ImageDataGenerator(
+    rescale=1./255,
+    shear_range=0.1,
+    zoom_range=0.1,
+    width_shift_range=0.1,
+    height_shift_range=0.1,
+    horizontal_flip=True,
+    vertical_flip=True,
+)
+
+test_datagen = ImageDataGenerator(
+    rescale=1./255,
+    validation_split=0.1
+)
+
+train_generator = train_datagen.flow_from_directory(
+    "Garbage classification\\trainset",
+    target_size=(110, 110),
+    batch_size=16,
+    class_mode='categorical',
+    #seed=0
+)
+
+test_generator = test_datagen.flow_from_directory(
+    "Garbage classification\\testset",
+    target_size=(110, 110),
+    batch_size=16,
+    class_mode='categorical',
+)
+
+
+#Teaching the classifier
+'''classifier.fit_generator( train_generator, steps_per_epoch = 150, epochs = 25, validation_data = test_generator )
+classifier.save_weights('model_ver_4.h5')'''
+
+import matplotlib.pyplot as plt
+labels = (train_generator.class_indices)
+labels = dict((value,key) for key,value in labels.items())
+
+
+classifier.load_weights("model_ver_4.h5")
+import random
+
+
+def getTrashPhoto(x, type):
+    for i in range(x):
+        kind = random.choice(type)
+        path = "Garbage classification\\testset\\" + kind
+        file = random.choice(os.listdir(path))
+        path = "Garbage classification\\testset\\" + kind + "\\" + file
+        gz = image.load_img(path, target_size = (110,110))
+        ti = image.img_to_array(gz)
+        ti=np.array(ti)/255.0
+        ti = np.expand_dims(ti, axis = 0)
+        prediction = classifier.predict(ti)
+        plt.subplot(1, 3, i+1)
+        plt.imshow(gz)
+        plt.title("AI thinks:%s \nReality:\n %s" % (labels[np.argmax(prediction)], file))
+    plt.show()
+
+
+types = ["cardboard", "glass", "metal", "paper", "plastic"]
+type = ["metal"]
+
+getTrashPhoto(3, types)
+
+
+plt.show()