import cv2 import json import numpy as np import torch from basicsr.utils import FileClient, imfrombytes from collections import OrderedDict print('Load JSON metadata...') # use the json file in FFHQ dataset with open('ffhq-dataset-v2.json', 'rb') as f: json_data = json.load(f, object_pairs_hook=OrderedDict) print('Open LMDB file...') # read ffhq images file_client = FileClient('lmdb', db_paths='datasets/ffhq/ffhq_512.lmdb') with open('datasets/ffhq/ffhq_512.lmdb/meta_info.txt') as fin: paths = [line.split('.')[0] for line in fin] save_img = False scale = 0.5 # 0.5 for official FFHQ (512x512), 1 for others enlarge_ratio = 1.4 # only for eyes save_dict = {} for item_idx, item in enumerate(json_data.values()): print(f'\r{item_idx} / {len(json_data)}, {item["image"]["file_path"]} ', end='', flush=True) # parse landmarks lm = np.array(item['image']['face_landmarks']) lm = lm * scale item_dict = {} # get image if save_img: img_bytes = file_client.get(paths[item_idx]) img = imfrombytes(img_bytes, float32=True) map_left_eye = list(range(36, 42)) map_right_eye = list(range(42, 48)) map_mouth = list(range(48, 68)) # eye_left mean_left_eye = np.mean(lm[map_left_eye], 0) # (x, y) half_len_left_eye = np.max((np.max(np.max(lm[map_left_eye], 0) - np.min(lm[map_left_eye], 0)) / 2, 16)) item_dict['left_eye'] = [mean_left_eye[0], mean_left_eye[1], half_len_left_eye] # mean_left_eye[0] = 512 - mean_left_eye[0] # for testing flip half_len_left_eye *= enlarge_ratio loc_left_eye = np.hstack((mean_left_eye - half_len_left_eye + 1, mean_left_eye + half_len_left_eye)).astype(int) if save_img: eye_left_img = img[loc_left_eye[1]:loc_left_eye[3], loc_left_eye[0]:loc_left_eye[2], :] cv2.imwrite(f'tmp/{item_idx:08d}_eye_left.png', eye_left_img * 255) # eye_right mean_right_eye = np.mean(lm[map_right_eye], 0) half_len_right_eye = np.max((np.max(np.max(lm[map_right_eye], 0) - np.min(lm[map_right_eye], 0)) / 2, 16)) item_dict['right_eye'] = [mean_right_eye[0], mean_right_eye[1], half_len_right_eye] # mean_right_eye[0] = 512 - mean_right_eye[0] # # for testing flip half_len_right_eye *= enlarge_ratio loc_right_eye = np.hstack( (mean_right_eye - half_len_right_eye + 1, mean_right_eye + half_len_right_eye)).astype(int) if save_img: eye_right_img = img[loc_right_eye[1]:loc_right_eye[3], loc_right_eye[0]:loc_right_eye[2], :] cv2.imwrite(f'tmp/{item_idx:08d}_eye_right.png', eye_right_img * 255) # mouth mean_mouth = np.mean(lm[map_mouth], 0) half_len_mouth = np.max((np.max(np.max(lm[map_mouth], 0) - np.min(lm[map_mouth], 0)) / 2, 16)) item_dict['mouth'] = [mean_mouth[0], mean_mouth[1], half_len_mouth] # mean_mouth[0] = 512 - mean_mouth[0] # for testing flip loc_mouth = np.hstack((mean_mouth - half_len_mouth + 1, mean_mouth + half_len_mouth)).astype(int) if save_img: mouth_img = img[loc_mouth[1]:loc_mouth[3], loc_mouth[0]:loc_mouth[2], :] cv2.imwrite(f'tmp/{item_idx:08d}_mouth.png', mouth_img * 255) save_dict[f'{item_idx:08d}'] = item_dict print('Save...') torch.save(save_dict, './FFHQ_eye_mouth_landmarks_512.pth')