Colorization of Grey Images by applying a Convolutional Autoencoder on the Jetson Nano

by Dennis Konkol and Tim Niklas Witte

This repository contains an pretrained convolutional autoencoder on the imagenet2012 dataset for colorization of grey images. The live camera stream will be colorizatized in real time. The architecture of the ANN is optimized to run on the Jetson Nano. It has 300.000 parameters. In total, 10 FPS can be archived on this embedded GPU.

The ./Paper_GPU_colorization.pdf file represents the entire documentation of this project in form of a paper. All corresponding files including the .tex file and figures are stored in ./Paper.

This project was created as a part of the GPU programming course of Mario Porrmann in the winter term 2021/22 at the Osnabrück University.

Requirements

• TensorFlow 2
• OpenCV 3.3.1
• CSI camera must be plugged in (see code of live_recolor[_plot].py)

Model

 Model: "autoencoder"
_______________________________________________________________
 Layer (type)                Output Shape            Param #   
===============================================================
 encoder (Encoder)           multiple                148155    
                                                               
 decoder (Decoder)           multiple                150145    
                                                                 
===============================================================
Total params: 298,302
Trainable params: 297,210
Non-trainable params: 1,092
_______________________________________________________________

Hyperparameters

optimizer = Adam
learning rate = 0.0001
loss function = mean squared error
batch size = 32

Usage

Training

Run Training.py to start the training of the model on the imagenet2012 dataset. Each epoch the weights are stored into ./saved_models. Besides, in ./test_logs are the corresponding trainings statistics (train and test loss and also a batch of colorized test images) logged. Note that, the imagenet2012 dataset must be stored in ./imagenet as described in this blog article. This includes a change of the variable data_dir at line 38.

python3 Training.py

Live colorization

The launch of live_recolor_plot.py opens a window as shown in the GIF at the start of this README. Note that, the CSI camera must be plugged in.

python3 live_recolor.py

It has the following structure:

(1) | (2) | (3)

(1) = live RGB camera image
(2) = live grey camera image
(3) = live colorized image

In order to get additionally displayed a loss plot (mean squared error between (1) and (3)), run live_recolor_plot.py instead. The loss plot is presented right from (3).

python3 live_recolor_plot.py

Pretrained Model

The model was runned for 13 epochs on the imagnet2012 dataset and its weights are stored in ./saved_models.

Note that, grey images must have a shape of (256,256,1). The following code will load the pretrained model and colorized an image:

autoencoder = Autoencoder()
autoencoder.build((1, 256, 256, 1)) # need a batch size
autoencoder.load_weights("./saved_models/trainied_weights_epoch_12")
autoencoder.summary()

grey_img = ... # grey_img.shape = (256,256,1)
grey_img = np.expand_dims(grey_img, axis=0) # add batch dim
ab_img = autoencoder(grey_img) # get ab values

rbg_img = getRGB(grey_img, ab_img) # contained in Training.py