import os default_n_threads = 8 os.environ['OPENBLAS_NUM_THREADS'] = f"{default_n_threads}" os.environ['MKL_NUM_THREADS'] = f"{default_n_threads}" os.environ['OMP_NUM_THREADS'] = f"{default_n_threads}" from utils.cv import * from utils.io_utils import * import shutil lst = ['photo_2026-01-03_01-03-02', 'Generated Image January 03, 2026 - 12_47AM', 'Generated Image January 03, 2026 - 12_00AM'] dirs = ['tmp/cmp_part_extr/ours', 'workspace/datasets/testcaseall_output_woattn'] heads = ['ours', 'woattn'] save_dir = 'tmp/woattn_cmp' for imgn in lst: for ii, d in enumerate(dirs): src_dir = osp.join(d, imgn) from talking_head.preprocess import further_extr further_extr(src_dir, rotate=False) saved = save_dir os.makedirs(saved, exist_ok=True) if ii == 0: src_img = np.array(Image.open(osp.join(src_dir, 'src_img.png'))) sz = src_img.shape[:2] xyxy = np.array(cv2.boundingRect(cv2.findNonZero(src_img[..., -1]))) xyxy[[2, 3]] += xyxy[[0, 1]] img = src_img[xyxy[1]: xyxy[3], xyxy[0]: xyxy[2]].copy() save_tmp_img(img, osp.join(saved, imgn + '_input.png')) img_list = [] src_infop = osp.join(osp.join(src_dir, 'optimized'), 'info.json') infos = json2dict(src_infop) load_img_depth(osp.join(src_dir, 'optimized'), infos, pad=0) flist = [] for k, v in infos['parts'].items(): # depth_median = v['depth'][v['img'][..., -1] > 127] # dm = np.median(depth_median) # v['depth_median'] = dm if k =='armlf': v['depth_median'] = -1. if k == 'bottomwear': continue v.pop('depth') save_tmp_img(v['img']) flist.append(v) flist.sort(key = lambda x: x['depth_median'], reverse=True) # for p in img = img_alpha_blending( flist, final_size=sz, premultiplied=False) img = img save_tmp_img(img, osp.join(saved, imgn + '_' + heads[ii]) + '.png')