An easy-to-use library for skin tone classification
Project description
Skin Tone Classifier (stone)
An easy-to-use library for skin tone classification.
This can be used to detect face or skin area in the specified images. The detected skin tones are then classified into the specified color categories. The library finally generates results to report the detected faces (if any), dominant skin tones and color category.
Changelog (v0.2.0)
In this version, we have made the following changes:
-
✨ 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 becolor
,bw
orauto
(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:
-
-
✨ 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.
-
🧬 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.
-
🧬 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.
- For example, if the input image is color and there are 2 faces detected, the report image will be saved
in
-
🐛 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
.
- Now, the app won't crash and will report the error message in
Citation
If you are interested in our work, please cite:
@article{https://doi.org/10.1111/ssqu.13242,
author = {Rej\'{o}n Pi\tilde{n}a, Ren\'{e} Alejandro and Ma, Chenglong},
title = {Classification Algorithm for Skin Color (CASCo): A new tool to measure skin color in social science research},
journal = {Social Science Quarterly},
volume = {n/a},
number = {n/a},
pages = {},
keywords = {colorism, measurement, photo elicitation, racism, skin color, spectrometers},
doi = {https://doi.org/10.1111/ssqu.13242},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ssqu.13242},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/ssqu.13242},
abstract = {Abstract Objective A growing body of literature reveals that skin color has significant effects on people's income, health, education, and employment. However, the ways in which skin color has been measured in empirical research have been criticized for being inaccurate, if not subjective and biased. Objective Introduce an objective, automatic, accessible and customizable Classification Algorithm for Skin Color (CASCo). Methods We review the methods traditionally used to measure skin color (verbal scales, visual aids or color palettes, photo elicitation, spectrometers and image-based algorithms), noting their shortcomings. We highlight the need for a different tool to measure skin color Results We present CASCo, a (social researcher-friendly) Python library that uses face detection, skin segmentation and k-means clustering algorithms to determine the skin tone category of portraits. Conclusion After assessing the merits and shortcomings of all the methods available, we argue CASCo is well equipped to overcome most challenges and objections posed against its alternatives. While acknowledging its limitations, we contend that CASCo should complement researchers. toolkit in this area.}
}
Installation
To install SkinToneClassifier:
pip install skin-tone-classifier --upgrade
HOW TO USE
Quick Start
Given the famous photo of Lenna, to detect her skin tone,
stone -i /path/to/lenna.jpg --debug
Then, you can find the processed image in ./debug/color/faces_1
folder, e.g.,
In this image, from left to right you can find the following information:
- detected face with a label (Face 1) enclosed by a rectangle.
- dominant colors.
- The number of colors depends on settings (default is 2) and their sizes depend on their proportion.
- specified color palette and the target label is enclosed by a rectangle.
- you can find a summary text at the bottom.
Furthermore, there will be a report file named result.csv
which contains more detailed information, e.g.,
file | image type | face id | dominant 1 | props 1 | dominant 2 | props 2 | skin tone | PERLA | accuracy(0-100) |
---|---|---|---|---|---|---|---|---|---|
lena_std-1 | color | 1 | #CF7371 | 0.52 | #E4A89F | 0.48 | #E7C1B8 | CI | 85.39 |
Interpretation of the table
file
: the filename of the processed image.image type
: the type of the processed image, i.e.,color
orbw
(black/white).face id
: the id of the detected face, which matches the reported image.NA
means no face has been detected.dominant n
: then
-th dominant color of the detected face.props n
: the proportion of then
-th dominant color, (0~1.0).skin tone
: the skin tone category of the detected face.PERLA
: the label of skin tone category of the detected face.accuracy
: the accuracy of the skin tone category of the detected face, (0~100). The larger the better.
Detailed Usage
To see the usage and parameters, run:
stone -h
Output in console:
usage: stone [-h] [-i IMAGE FILENAME [IMAGE FILENAME ...]] [-p COLOR [COLOR ...]] [-l LABEL [LABEL ...]]
[-t IMAGE TYPE] [-d] [-bw] [-o DIRECTORY] [--n_workers N_WORKERS] [--n_colors N]
[--new_width WIDTH] [--scale SCALE] [--min_nbrs NEIGHBORS] [--min_size WIDTH [HEIGHT ...]]
Skin Tone Classifier
optional arguments:
-h, --help show this help message and exit
-i IMAGE FILENAME [IMAGE FILENAME ...], --images IMAGE FILENAME [IMAGE FILENAME ...]
Image filename(s) to process;
Supports multiple values separated by space, e.g., "a.jpg b.png";
Supports directory or file name(s), e.g., "./path/to/images/ a.jpg";
The app will search all images in current directory in default.
-p COLOR [COLOR ...], --palette COLOR [COLOR ...]
Skin tone palette;
Supports RGB hex value leading by "#" or RGB values separated by comma(,),
E.g., "-p #373028 #422811" or "-p 255,255,255 100,100,100"
-l LABEL [LABEL ...], --labels LABEL [LABEL ...]
Skin tone labels; default values are the uppercase alphabet list.
-t IMAGE TYPE, --image_type IMAGE TYPE
Specify whether the inputs image(s) is/are colored or black/white.
Valid choices are: "auto", "color" or "bw",
Defaults to "auto", which will be detected automatically.
-d, --debug Whether to output processed images, used for debugging and verification.
-bw, --black_white Whether to convert the input to black/white image(s).
Then the app will use the black/white palette to classify the image.
-o DIRECTORY, --output DIRECTORY
The path of output file, defaults to current directory.
--n_workers N_WORKERS
The number of workers to process the images, defaults to the number of CPUs in the system.
--n_colors N CONFIG: the number of dominant colors to be extracted, defaults to 2.
--new_width WIDTH CONFIG: resize the images with the specified width. Negative value will be ignored, defaults to 250.
--scale SCALE CONFIG: how much the image size is reduced at each image scale, defaults to 1.1
--min_nbrs NEIGHBORS CONFIG: how many neighbors each candidate rectangle should have to retain it.
Higher value results in less detections but with higher quality, defaults to 5
--min_size WIDTH [HEIGHT ...]
CONFIG: minimum possible face size. Faces smaller than that are ignored, defaults to "90 90".
Use Cases
1. To process multiple images
1.1 Multiple filenames
stone -i (or --images) a.jpg b.png
1.2 Images in some folder(s)
stone -i ./path/to/images/
NB: Supported image formats: .jpg, .gif, .png, .jpeg, .webp, .tif
.
In default (i.e., stone
without -i
option), the app will search images in current folder.
2. To specify color categories
2.1 Use hex values
stone -p (or --palette) #373028 #422811 #fbf2f3
NB: Values start with '#' and are separated by space.
2.2 Use RGB tuple values
stone -c 55,48,40 66,40,17 251,242,243
NB: Values split by comma ',', multiple values are still separated by space.
3. Specify output folder
The app puts the final report (result.csv
) in current folder in default.
To change the output folder:
stone -o (or --output) ./path/to/output/
The output folder will be created if it does not exist.
In result.csv
, each row is showing the color information of each detected face.
If more than one faces are detected, there will be multiple rows for that image.
4. Store report images for debugging
stone -d (or --debug)
This option will store the report image (like the Lenna example above) in
./path/to/output/debug/<image type>/faces_<n>
folder,
where <image type>
indicates if the image is color
or bw
(black/white);
<n>
is the number of faces detected in the image.
By default, to save space, the app does not store report images.
Like in the result.csv
file, there will be more than one report images if 2 or more faces were detected.
5. Specify the types of the input image(s)
5.1 The input are color images
stone -t (or --image_type) color
5.2 The input are black/white images
stone -t (or --image_type) bw
5.3 In default, the app will detect the image type automatically, i.e.,
stone -t (or --image_type) auto
For color
images, we use the color
palette to detect faces:
#373028 #422811 #513b2e #6f503c #81654f #9d7a54 #bea07e #e5c8a6 #e7c1b8 #f3dad6 #fbf2f3
(Please refer to our paper above for more details.)
For bw
images, we use the bw
palette to detect faces:
#FFFFFF #F0F0F0 #E0E0E0 #D0D0D0 #C0C0C0 #B0B0B0 #A0A0A0 #909090 #808080 #707070 #606060 #505050 #404040 #303030 #202020 #101010 #000000
(Please refer to Leigh, A., & Susilo, T. (2009). Is voting skin-deep? Estimating the effect of candidate ballot photographs on election outcomes. Journal of Economic Psychology, 30(1), 61-70. for more details.)
6. Convert the color
images to black/white
images and then do the classification using bw
palette
stone -bw (or --black_white)
For example:
Input
Convert to black/white image
The final report image
NB: we did not do the opposite, i.e., convert black/white
images to color
images
because the current AI models cannot accurately "guess" the color of the skin from a black/white
image.
It can further bias the analysis results.
7. Tune parameters of face detection
The rest parameters of CONFIG
are used to detect face.
Please refer to https://stackoverflow.com/a/20805153/8860079 for detailed information.
8. Multiprocessing settings
stone --n_workers <Any Positive Integer>
Use --n_workers
to specify the number of workers to process images in parallel, defaults to the number of CPUs in your system.
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