Syntesis images noise detection using CNN approach

jbuisine 21d32fcc38 Merge tag 'v0.0.5' into develop 6 лет назад
img_train a22b32b71a First CNN model version 6 лет назад
img_validation a22b32b71a First CNN model version 6 лет назад
modules 67c2a0d3a5 Creation of modules for models 6 лет назад
.gitignore 67c2a0d3a5 Creation of modules for models 6 лет назад
README.md a900315b27 Update of SVD model 6 лет назад
RESULTS.md 2c8ad26c6b Add of model using SVD of images 6 лет назад
classification_cnn_keras.py 67c2a0d3a5 Creation of modules for models 6 лет назад
classification_cnn_keras_cross_validation.py 67c2a0d3a5 Creation of modules for models 6 лет назад
classification_cnn_keras_svd.py a900315b27 Update of SVD model 6 лет назад
generate_dataset.py 2c8ad26c6b Add of model using SVD of images 6 лет назад
requirements.txt 2c8ad26c6b Add of model using SVD of images 6 лет назад

README.md

Noise detection project

Requirements

pip install -r requirements.txt

How to use

Generate dataset (run only once time or clean data folder before) :

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_SUBIMAGES variables.

There are 3 kinds of Neural Networks :

  • classification_cnn_keras.py : based on cropped images and do convolution
  • classification_cnn_keras_cross_validation.py : based on cropped images and do convolution. Data are randomly split for training
  • classification_cnn_keras_svd.py : based on svd metrics of image

Note that the image input size need to change in you used specific size for your croped images.

After your built your neural network in classification_cnn_keras.py, you just have to run it :

python classification_cnn_keras.py

Modules

This project contains modules :

  • modules/image_metrics : where all computed metrics function are developed
  • modules/model_helper : contains helpful function to save or display model information and performance

All these modules will be enhanced during development of the project

How to contribute

This git project uses git-flow implementation. You are free to contribute to it.