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70 lines
1.9 KiB
Markdown
70 lines
1.9 KiB
Markdown
# Colorization of Grey Images by applying a Convolutional Autoencoder on the Jetson Nano
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## by Dennis Konkol and Tim Niklas Witte
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This repository contains an pretrainied convolutional autoencoder for colorization of grey images.
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The live camera stream will be colorizatized in real time.
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The architecture of the ANN is optimized to run on the Jetson Nano.
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In total, 10 FPS can be archived on this embedded GPU.
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## Requirements
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- TensorFlow 2
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- OpenCV 3.3.1
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## Usage
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### Training
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Run `Training.py` to start the training of the model.
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Each epoch the weights are stored into `./saved_models`.
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Besides, in `./test_logs` are the corresponding trainings statistics (train and test loss and also a batch of colorized test images) logged.
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```bash
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python3 Training.py
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```
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### Live colorization
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The launch of `live_recolor_plot.py` opens a window as shown in the GIF at the start of this README.
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```bash
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python3 live_recolor.py
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```
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It has the following structure:
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```bash
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(1) | (2) | (3) | (4)
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(1) = live RGB camera image
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(2) = live grey camera image
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(3) = live colorized image
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```
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To get also displayed a loss plot (mean squared error between `(1)` and `(3)`),
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run `live_recolor_plot.py` instead.
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The loss plot is presented right from `(3)`.
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```bash
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python3 live_recolor_plot.py
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```
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### Pretrainied Model
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The model was runned for 13 epochs and its weights are stored in `./saved_models`.
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Note that, the grey images must have a shape of `(256,256,1)`.
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The following code will load the model and colorized an image:
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```python3
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autoencoder = Autoencoder()
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autoencoder.build((1, 256, 256, 1)) # need a batch size
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autoencoder.load_weights("./saved_models/trainied_weights_epoch_12")
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autoencoder.summary()
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grey_img = ... # grey_img.shape = (256,256,1)
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grey_img = np.expand_dims(grey_img, axis=0) # add batch dim
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colorized_img = autoencoder(grey_img)
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```
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