This is the implementation of Google's Inception ResNet v2 for the task of automatic colorization of gray-scale images. The task-at-hand is treated as a classification problem and uses CIELAB color space for assigning colors to the pixels.
The dataset consists of two compressed zip files:
- ab.zip : This contains 25 .npy files consisting of a and b dimensions of LAB color space images, of the MIRFLICKR25k randomly sized colored image dataset. The LAB color space generally takes up large disk spaces, hence is a lot slower to load. That is the reason, I divided this into 25 files, so that it can be loaded at the time of requirement.
- l.zip : This consists of a gray_scale.npy file which is the grayscale version of the MIRFLICKR25k dataset.
The image dataset which I used was taken from the MIRFLICKR25k.
The following steps should be followed to run the 'DeepColor' system:
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Download the dataset from the link in the 'DeepColor/Data.txt'.
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In order to successfully run the system locally:
- Install Python3
- Set the correct path for the dataset by altering the file path to be the location of the dataset in the file explorer/finder.
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To run the the system in google colab:
- Import the downloaded dataset in your drive.
- Run the cells in the iPython notebook and mount the drive by entering your credentials.
- set the correct path to the dataset in the drive by altering the file path.