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GFPGAN / scripts /parse_landmark.py

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	import cv2
	import json
	import numpy as np
	import os
	import torch
	from basicsr.utils import FileClient, imfrombytes
	from collections import OrderedDict

	# ---------------------------- This script is used to parse facial landmarks ------------------------------------- #
	# Configurations
	save_img = False
	scale = 0.5 # 0.5 for official FFHQ (512x512), 1 for others
	enlarge_ratio = 1.4 # only for eyes
	json_path = 'ffhq-dataset-v2.json'
	face_path = 'datasets/ffhq/ffhq_512.lmdb'
	save_path = './FFHQ_eye_mouth_landmarks_512.pth'

	print('Load JSON metadata...')
	# use the official json file in FFHQ dataset
	with open(json_path, '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=face_path)
	with open(os.path.join(face_path, 'meta_info.txt')) as fin:
	paths = [line.split('.')[0] for line in fin]

	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)

	# get landmarks for each component
	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, save_path)