v0.2.0

Changelog (v0.2.0)

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:

     

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.