preprocess.py

import os
import glob
import sys
import cv2
import argparse
import numpy as np
import matplotlib.pyplot as plt

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
import rembg

if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument('--path', type=str, help="path to image (png, jpeg, etc.)")
    parser.add_argument('--model', default='u2net', type=str, help="rembg model, see https://github.com/danielgatis/rembg#models")
    parser.add_argument('--size', default=512, type=int, help="output resolution")
    parser.add_argument('--border_ratio', default=0.2, type=float, help="output border ratio")
    parser.add_argument('--recenter', type=bool, default=False, help="recenter, potentially not helpful for multiview zero123")    
    opt = parser.parse_args()

    session = rembg.new_session(model_name=opt.model)

    if os.path.isdir(opt.path):
        print(f'[INFO] processing directory {opt.path}...')
        files = glob.glob(f'{opt.path}/*')
        out_dir = opt.path
    else: # isfile
        files = [opt.path]
        out_dir = os.path.dirname(opt.path)
    
    savedir=opt.path+'_pose0/'
    os.makedirs(savedir,exist_ok=True)
    for file in files:
        if file.endswith('jpg') or  file.endswith('png'):
            out_base = os.path.basename(file).split('.')[0]

            out_rgba = os.path.join(savedir,out_base + '.png')

            # load image
            print(f'[INFO] loading image {file}...')
            image = cv2.imread(file, cv2.IMREAD_UNCHANGED)
            
            # carve background
            print(f'[INFO] background removal...')
            carved_image = rembg.remove(image, session=session) # [H, W, 4]
            mask = carved_image[..., -1] > 0

            # recenter
            if opt.recenter:
                # pass
                # print('???')
                print(f'[INFO] recenter...')
                final_rgba = np.zeros((opt.size, opt.size, 4), dtype=np.uint8)
                
                coords = np.nonzero(mask)
                x_min, x_max = coords[0].min(), coords[0].max()
                y_min, y_max = coords[1].min(), coords[1].max()
                h = x_max - x_min
                w = y_max - y_min
                desired_size = int(opt.size * (1 - opt.border_ratio))
                scale = desired_size / max(h, w)
                h2 = int(h * scale)
                w2 = int(w * scale)
                x2_min = (opt.size - h2) // 2
                x2_max = x2_min + h2
                y2_min = (opt.size - w2) // 2
                y2_max = y2_min + w2
                print(x_min, x_max, x2_min, x2_max)
                final_rgba[x2_min:x2_max, y2_min:y2_max] = cv2.resize(carved_image[x_min:x_max, y_min:y_max], (w2, h2), interpolation=cv2.INTER_AREA)
                
            else:
                final_rgba = carved_image
            
            # write image
            cv2.imwrite(out_rgba, final_rgba)
            print('out path:',out_rgba)