88 lines
4.7 KiB
Markdown
88 lines
4.7 KiB
Markdown
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# Report - Individual Project Kamila Bobkowska
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## General information
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Whenever our agent (the garbage truck) visits a dumpster before it takes in garbage it needs to recognize what kind of garbage it is. Even though there are dumpsters of different kinds we assume that people make mistakes.
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Per dumpster we assume that:
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* there is from 1 to 3 trash correctly sorted
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* there is from 0 to 2 incorrectly sorted trash
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The assessment of correctness is performed by a Convolutional Neural network. From the initial idea the types of trash changed. Now the Garbage truck will recognize between the following five kinds of debris:
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* paper
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* plastic
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* glass
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* cardboard
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* metal
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## Implementation
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As mentioned above to solve the problem of sorting I used CNNs. I implemented it using mostly Keras and Tensorflow. I used a fairly basic way of doing so, I decided to have 3 2D convolution layers. As I will have more than 2 output nodes I used a softmax as the output activation function. I decided to decrease the size of the images to (110, 110). I decided to use (2,2) Pooling as I have read that it is the most widely used and works best with most cases.
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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 = "binary_crossentropy", metrics = ["accuracy"])
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```
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After that I did some work with preprocessing the images I was working with. Made sure that the size is still (110, 110). set a batch size, allowed for flips and such.
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Later I trained my classifier and saved it's weights for future use in the project. I attempted this about 5 times with different number of epochs and the size per epoch.
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```
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classifier.fit_generator( train_generator, steps_per_epoch = 150, epochs = 25, validation_data = test_generator )
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classifier.save_weights('model_ver_4.h5')
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```
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Training:
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## Changes in the common part
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I mostly worked with the classes: `Garbagetruck.py` and `dumpster.py`.
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In the class `dumpster.py` I added a short function that chooses random images of trash and adds them to a list.
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In the class `Garbagetruck.py` I added a function that uses the saved model and gets a list of what trash actually is in the dumpster according to the CCN. Then I enhanced the function that collects garbage and if it was incorrectly sorted it says so. Beyond that it is also possible to run the program in such a way that in each step we can see what iamges is the CNN looking at. At the endo fo examinging each dumpster a general report of what is in the garbage truck is displayed.
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## Data set
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I got my data set from Kaggle: https://www.kaggle.com/asdasdasasdas/garbage-classification .
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Before starting I removed the category trash completely and created folders for the test set. Later I used a function:
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```
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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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To randomly pick something for the test and train set. Later after running about two trials I also removed about 75 pictures from paper as I noticed there was some inbalance in the number of images per category.
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## Additional information
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The weights are saved and later used as it would be too time conspumping to run it every time. For this project I used the following libraries:
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```
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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 # optional
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```
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