usls/examples/face-parsing

Using YOLOv8-seg model trained on CelebAMask-HQ for face-parsing.

Quick Start

cargo run -r --example face-parsing

Pretrained Model

• face-parsing-dyn
• face-parsing-dyn-f16

Datasets

• CelebAMask-HQ

YOLO Labels

• Download Processed YOLO labels

• Or you can run Python script

  import cv2
  import numpy as np
  from pathlib import Path
  from tqdm import tqdm
  
  
  mapping = {
      'background': 0,
      'skin': 1,
      'nose': 2,
      'eye_g': 3,
      'l_eye': 4,
      'r_eye': 5,
      'l_brow': 6,
      'r_brow': 7,
      'l_ear': 8,
      'r_ear': 9,
      'mouth': 10,
      'u_lip': 11,
      'l_lip': 12,
      'hair': 13,
      'hat': 14,
      'ear_r': 15,
      'neck_l': 16,
      'neck': 17,
      'cloth': 18
  }
  
  
  
  def main():
  	saveout_dir = Path("labels")
  	if not saveout_dir.exists():
  		saveout_dir.mkdir()
  	else:
  		import shutil
  		shutil.rmtree(saveout_dir)
  		saveout_dir.mkdir()
  
  
  	image_list = [x for x in Path("CelebAMask-HQ-mask-anno/").rglob("*.png")]
  	for image_path in tqdm(image_list, total=len(image_list)):
  		image_gray = cv2.imread(str(image_path), cv2.IMREAD_GRAYSCALE)
  		stem = image_path.stem
  		name, cls_ = stem.split("_", 1)
  		segments = cv2.findContours(image_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] 
  
  		saveout = saveout_dir / f"{int(name)}.txt"
  		with open(saveout, 'a+') as f:
  			for segment in segments:
  				line = f"{mapping[cls_]}"
  				segment = segment / 512
  				for seg in segment:
  					xn, yn = seg[0]
  					line += f" {xn} {yn}"
  				f.write(line + "\n")
  
  
  
  
  if __name__ == "__main__":
      main()
  
  

Results