2.7 KiB
Colorization of Grey Images by applying a Convolutional Autoencoder on the Jetson Nano
by Dennis Konkol and Tim Niklas Witte
This repository contains an pretrainied convolutional autoencoder 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.
Requirements
- TensorFlow 2
- OpenCV 3.3.1
- CSI camera plugged it (see code of
live_recolor[_plot].py)
Model
Model: "autoencoder"
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Layer (type) Output Shape Param #
===============================================================
encoder (Encoder) multiple 148155
decoder (Decoder) multiple 150145
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Total params: 298,302
Trainable params: 297,210
Non-trainable params: 1,092
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Usage
Training
Run Training.py to start the training of the model.
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.
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) | (4)
(1) = live RGB camera image
(2) = live grey camera image
(3) = live colorized image
To get also 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
Pretrainied Model
The model was runned for 13 epochs and its weights are stored in ./saved_models.
Note that, the grey images must have a shape of (256,256,1).
The following code will load the 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
colorized_img = autoencoder(grey_img)