Skin-tone-classifier

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1. 👋 **BYE**: We have removed the function to pop up a resulting window when processing a **single** image.

* It can raise an error when running the app in a **web browser** environment, e.g., Jupyter Notebook or Google
Colab.
* If you want to see the processed image, please use the `-d` option to store the report image in the `./debug`
folder.

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🎉**We have officially released the 1.0.0 version of the library!** In this version, we have made the following changes:

1. ✨ **NEW!**: We add the `threshold` parameter to control the minimum percentage of required face areas (Defaults to
0.15).
* In previous versions, the library could incorrectly identify non-face areas as faces, such as shirts, collars,
necks, etc.
In order to improve its accuracy, the new version will further calculate the proportion of skin in the recognized
area
after recognizing the facial area. If it is less than the `threshold` value, the recognition area will be ignored.
(While it's still not perfect, it's an improvement over what it was before.)
2. ✨ **NEW!**: Now, we will back up the previous results if it already exists.
The backup file will be named as `result_bak_<current_timestamp>.csv`.
3. 🐛 **FIX!**: We fix the bug that the `image_type` option does not work in the previous version.
4. 🐛 **FIX!**: We fix the bug that the library will create an empty `log` folder when checking the help information by
running `stone -h`.

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In this version, we have made the following changes:

1. ✨ **NEW!**: Now we support skin tone classification for **black and white** images.
* In this case, the app will use different skin tone palettes for color images and black/white images.
* We use a new parameter `-t` or `--image_type` to specify the type of the input image.
It can be `color`, `bw` or `auto`(default).
`auto` will let the app automatically detect whether the input is color or black/white image.
* We use a new parameter `-bw` or `--black_white` to specify whether to convert the input to black/white image.
If so, the app will convert the input to black/white image and then classify the skin tones based on the
black/white palette.

For example:
<div style="display: flex; align-items: center;">
<img src="https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/demo-1.png" alt="Processing color image" style="display: block; margin: 20px">
<img src="https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/demo_bw-1.png" alt="Processing black/white image" style="display: block; margin: 20px">
</div>

2. ✨ **NEW!**: Now we support **multiprocessing** for processing the images. It will largely speed up the processing.
* The number of processes is set to the number of CPU cores by default.
* You can specify the number of processes by `--n_workers` parameter.
3. 🧬 **CHANGE!**: We add more details in the report image to facilitate the debugging, as shown above.
* We add the face id in the report image.
* We add the effective face or skin area in the report image. In this case, the other areas are blurred.
4. 🧬 **CHANGE!**: Now, we save the report images into different folders based on their `image_type` (color or
black/white) and the number of detected faces.
* For example, if the input image is **color** and there are **2 faces** detected, the report image will be saved
in `./debug/color/faces_2/` folder.
* If the input image is **black/white** and no face has been detected, the report image will be saved
in `./debug/bw/faces_0/` folder.
* You can easily to tune the parameters and rerun the app based on the report images in the corresponding folder.
5. 🐛 **FIX!**: We fix the bug that the app will crash when the input image has dimensionality errors.
* Now, the app won't crash and will report the error message in `./result.csv`.

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