First CNN model version

.gitignore

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+# project data
+data

README.md

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+# Noise detection project
+
+## Requirements
+
+```
+pip install -r requirements.txt
+```
+
+## How to use
+
+Generate dataset (run only once time) :
+```
+python generate_dataset.py
+```
+
+It will split scenes and generate all data you need for your neural network.
+You can specify the number of sub images you want in the script by modifying NUMBER_SUB_IMAGES variables.
+
+
+After your built your neural network in classification_cnn_keras.py, you just have to run it :
+```
+python classification_cnn_keras.py
+```

TODO.md

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+# 1. Create database 
+    - 6 scenes for train
+    - 3 scenes for validation
+    - Equilibrer noise / final classes
+
+# 2. Test CNN (check if size is correct)
+
+# 3. Results 
+    - noise_classification_32_16_16_32.h5 : 81.15%
+    - noise_classification_64_32_32_64.h5 : loss: 0.4416 - acc: 0.7993 - val_loss: 0.9338 - val_acc: 0.6943
+

classification_cnn_keras.py

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+'''This script goes along the blog post
+"Building powerful image classification models using very little data"
+from blog.keras.io.
+```
+data/
+    train/
+        final/
+            final001.png
+            final002.png
+            ...
+        noisy/
+            noisy001.png
+            noisy002.png
+            ...
+    validation/
+        final/
+            final001.png
+            final002.png
+            ...
+        noisy/
+            noisy001.png
+            noisy002.png
+            ...
+```
+'''
+
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers import Activation, Dropout, Flatten, Dense
+from keras import backend as K
+
+
+# dimensions of our images.
+img_width, img_height = 20, 20
+
+train_data_dir = 'data/train'
+validation_data_dir = 'data/validation'
+nb_train_samples = 115200
+nb_validation_samples = 57600
+epochs = 50
+batch_size = 16
+
+if K.image_data_format() == 'channels_first':
+    input_shape = (3, img_width, img_height)
+else:
+    input_shape = (img_width, img_height, 3)
+
+model = Sequential()
+model.add(Conv2D(40, (3, 3), input_shape=input_shape))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Conv2D(20, (3, 3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Conv2D(40, (2, 2)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+
+model.add(Flatten())
+model.add(Dense(40))
+model.add(Activation('relu'))
+model.add(Dropout(0.5))
+model.add(Dense(1))
+model.add(Activation('sigmoid'))
+
+model.compile(loss='binary_crossentropy',
+              optimizer='rmsprop',
+              metrics=['accuracy'])
+
+# this is the augmentation configuration we will use for training
+train_datagen = ImageDataGenerator(
+    rescale=1. / 255,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True)
+
+# this is the augmentation configuration we will use for testing:
+# only rescaling
+test_datagen = ImageDataGenerator(rescale=1. / 255)
+
+train_generator = train_datagen.flow_from_directory(
+    train_data_dir,
+    target_size=(img_width, img_height),
+    batch_size=batch_size,
+    class_mode='binary')
+
+validation_generator = test_datagen.flow_from_directory(
+    validation_data_dir,
+    target_size=(img_width, img_height),
+    batch_size=batch_size,
+    class_mode='binary')
+
+model.fit_generator(
+    train_generator,
+    steps_per_epoch=nb_train_samples // batch_size,
+    epochs=epochs,
+    validation_data=validation_generator,
+    validation_steps=nb_validation_samples // batch_size)
+
+model.save_weights('noise_classification_32_16_16_32_07_img20.h5')

generate_dataset.py

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+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Sep 14 21:02:42 2018
+
+@author: jbuisine
+"""
+
+from __future__ import print_function
+import keras
+from keras.datasets import cifar10
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+import os, glob, image_slicer
+from PIL import Image
+
+# show to create own dataset https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d
+
+NUMBER_SUB_IMAGES = 1600
+
+def create_images(folder, output_folder):
+    images_path = glob.glob(folder + "/*.png")
+
+    for img in images_path:
+        image_name = img.replace(folder, '').replace('/', '')
+        tiles = image_slicer.slice(img, NUMBER_SUB_IMAGES, save = False)
+        image_slicer.save_tiles(tiles, directory=output_folder, prefix='part_'+image_name)
+
+def generate_dataset():
+    create_images('img_train/final', 'data/train/final')
+    create_images('img_train/noisy', 'data/train/noisy')
+    create_images('img_validation/final', 'data/validation/final')
+    create_images('img_validation/noisy', 'data/validation/noisy')
+
+def main():
+    # create database using img folder (generate first time only)
+    generate_dataset()
+
+if __name__== "__main__":
+    main()