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https://github.com/itsjavi/mediatidy-python

Image Classifier and Photo Library Organizer using Python and Tensorflow
https://github.com/itsjavi/mediatidy-python

cnn-classification convolutional-neural-networks deep-learning image-classification tensorflow2

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Image Classifier and Photo Library Organizer using Python and Tensorflow

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README 

        
# tidymypics

Command-line app written in Python to classify your photo library by year,

separating different image classes (of your choice) using a trained Tensorflow model.



This CLI application consists of 2 parts:

- A command to generate and train a new model

- A command to classify your photo library and organize it by class and year



## Requirements



- UNIX-compatible OS (only tested on macOS 12)

- Python 3.8+ with PIP 22.0+

- Tensorflow 2.9+

    - For macOS, follow [this guide](https://developer.apple.com/metal/tensorflow-plugin/)



Dataset required to train the model:



- Images correctly classified manually under the `data/train` directory of this project. One folder for every different class, for example: `data/train/photo` and `data/train/screenshot`.

- At least 1000 images (samples) for every class class.

- The more and the more diverse, the better.

- All classes should contain the same number of images (to avoid bias), or aproximately.

- Avoid duplicated images. Try not to add images that are too similar (e.g. photo bursts).



## Usage



### Setup



- Clone or download this repository

- Open your terminal and change the directory to this one

- Run `pip install -r requirements.txt` to install the dependencies



### Creating a new model



To create a new model with your own data (pictures), use the `python -m tidymypics --train TRAIN_PATH TEST_PATH`.



A new model will be generated using `TRAIN_PATH` as the path where your train samples are stored (by class like mentioned before).



It also requires a `TEST_PATH` argument, which should be a directory similarly structured as `TRAIN_PATH`, 

to test your model with new and "unseen" images, and see how good are the predictions.



### Using the Command-Line tool

Once you have a model, you can use again the command-line tool to organize your images.

From the source code folder, run something like this:



```bash

# Usage: python -m tidymypics SOURCE_DIR OUTPUT_DIR

#   Example:

python -m tidymypics --year ~/Pictures/MyMessyAlbums ~/Pictures/OrganizedAlbums

``` 



All the images will be organized by class and year of when the picture was taken.

Files will be also renamed using the creation timestamp, and the first 7 characters of the image's MD5 hash

to avoid duplicate file names.



If you don't add the `--year` option, pictures won't be grouped by year, but they will all live in the same

directory, separated only by class.



If you add the `--move` option, pictures will be moved instead of copied.



> To know more about all the options and the usage, just run `python -m tidymypics` without any parameter.



## Implementation Considerations



You can use anything as your classification class names  (e.g. people, pet, landscape, artwork, text, nudity, etc.), 

but take in consideration the model architecture and layers:



- Adds random noise

- Flips images randomly (either vertically or horizontally)

- Reads the images as grayscale. If color information is important for your classification, you should modify the code and use `rgb` with 3 channels.

- Reads the images resizing them to 800x480px

- Uses a Drop Out layer



These transformations are only done in-memory, to compute the model, and not to the original images.



> Check `tidymypics/core.py`, `build_model()` function to know more details about the implementation.