File size: 7,157 Bytes
e34aada |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import os
os.environ["OMP_NUM_THREADS"] = "1"
import sys
import glob
import cv2
import tqdm
import numpy as np
from data_gen.utils.mp_feature_extractors.face_landmarker import MediapipeLandmarker
from utils.commons.multiprocess_utils import multiprocess_run_tqdm
import warnings
warnings.filterwarnings('ignore')
import random
random.seed(42)
import pickle
import json
import gzip
from typing import Any
def load_file(filename, is_gzip: bool = False, is_json: bool = False) -> Any:
if is_json:
if is_gzip:
with gzip.open(filename, "r", encoding="utf-8") as f:
loaded_object = json.load(f)
return loaded_object
else:
with open(filename, "r", encoding="utf-8") as f:
loaded_object = json.load(f)
return loaded_object
else:
if is_gzip:
with gzip.open(filename, "rb") as f:
loaded_object = pickle.load(f)
return loaded_object
else:
with open(filename, "rb") as f:
loaded_object = pickle.load(f)
return loaded_object
def save_file(filename, content, is_gzip: bool = False, is_json: bool = False) -> None:
if is_json:
if is_gzip:
with gzip.open(filename, "w", encoding="utf-8") as f:
json.dump(content, f)
else:
with open(filename, "w", encoding="utf-8") as f:
json.dump(content, f)
else:
if is_gzip:
with gzip.open(filename, "wb") as f:
pickle.dump(content, f)
else:
with open(filename, "wb") as f:
pickle.dump(content, f)
face_landmarker = None
def extract_lms_mediapipe_job(img):
if img is None:
return None
global face_landmarker
if face_landmarker is None:
face_landmarker = MediapipeLandmarker()
lm478 = face_landmarker.extract_lm478_from_img(img)
return lm478
def extract_landmark_job(img_name):
try:
# if img_name == 'datasets/PanoHeadGen/raw/images/multi_view/chunk_0/seed0000002.png':
# print(1)
# input()
out_name = img_name.replace("/images_512/", "/lms_2d/").replace(".png","_lms.npy")
if os.path.exists(out_name):
print("out exists, skip...")
return
try:
os.makedirs(os.path.dirname(out_name), exist_ok=True)
except:
pass
img = cv2.imread(img_name)[:,:,::-1]
if img is not None:
lm468 = extract_lms_mediapipe_job(img)
if lm468 is not None:
np.save(out_name, lm468)
# print("Hahaha, solve one item!!!")
except Exception as e:
print(e)
pass
def out_exist_job(img_name):
out_name = img_name.replace("/images_512/", "/lms_2d/").replace(".png","_lms.npy")
if os.path.exists(out_name):
return None
else:
return img_name
# def get_todo_img_names(img_names):
# todo_img_names = []
# for i, res in multiprocess_run_tqdm(out_exist_job, img_names, num_workers=64):
# if res is not None:
# todo_img_names.append(res)
# return todo_img_names
if __name__ == '__main__':
import argparse, glob, tqdm, random
parser = argparse.ArgumentParser()
parser.add_argument("--img_dir", default='/home/tiger/datasets/raw/FFHQ/images_512/')
parser.add_argument("--ds_name", default='FFHQ')
parser.add_argument("--num_workers", default=64, type=int)
parser.add_argument("--process_id", default=0, type=int)
parser.add_argument("--total_process", default=1, type=int)
parser.add_argument("--reset", action='store_true')
parser.add_argument("--img_names_file", default="img_names.pkl", type=str)
parser.add_argument("--load_img_names", action="store_true")
args = parser.parse_args()
print(f"args {args}")
img_dir = args.img_dir
img_names_file = os.path.join(img_dir, args.img_names_file)
if args.load_img_names:
img_names = load_file(img_names_file)
print(f"load image names from {img_names_file}")
else:
if args.ds_name == 'FFHQ_MV':
img_name_pattern1 = os.path.join(img_dir, "ref_imgs/*.png")
img_names1 = glob.glob(img_name_pattern1)
img_name_pattern2 = os.path.join(img_dir, "mv_imgs/*.png")
img_names2 = glob.glob(img_name_pattern2)
img_names = img_names1 + img_names2
img_names = sorted(img_names)
elif args.ds_name == 'FFHQ':
img_name_pattern = os.path.join(img_dir, "*.png")
img_names = glob.glob(img_name_pattern)
img_names = sorted(img_names)
elif args.ds_name == "PanoHeadGen":
# img_name_patterns = ["ref/*/*.png", "multi_view/*/*.png", "reverse/*/*.png"]
img_name_patterns = ["ref/*/*.png"]
img_names = []
for img_name_pattern in img_name_patterns:
img_name_pattern_full = os.path.join(img_dir, img_name_pattern)
img_names_part = glob.glob(img_name_pattern_full)
img_names.extend(img_names_part)
img_names = sorted(img_names)
# save image names
if not args.load_img_names:
save_file(img_names_file, img_names)
print(f"save image names in {img_names_file}")
print(f"total images number: {len(img_names)}")
process_id = args.process_id
total_process = args.total_process
if total_process > 1:
assert process_id <= total_process -1
num_samples_per_process = len(img_names) // total_process
if process_id == total_process:
img_names = img_names[process_id * num_samples_per_process : ]
else:
img_names = img_names[process_id * num_samples_per_process : (process_id+1) * num_samples_per_process]
# if not args.reset:
# img_names = get_todo_img_names(img_names)
print(f"todo_image {img_names[:10]}")
print(f"processing images number in this process: {len(img_names)}")
# print(f"todo images number: {len(img_names)}")
# input()
# exit()
if args.num_workers == 1:
index = 0
for img_name in tqdm.tqdm(img_names, desc=f"Root process {args.process_id}: extracting MP-based landmark2d"):
try:
extract_landmark_job(img_name)
except Exception as e:
print(e)
pass
if index % max(1, int(len(img_names) * 0.003)) == 0:
print(f"processed {index} / {len(img_names)}")
sys.stdout.flush()
index += 1
else:
for i, res in multiprocess_run_tqdm(
extract_landmark_job, img_names,
num_workers=args.num_workers,
desc=f"Root {args.process_id}: extracing MP-based landmark2d"):
# if index % max(1, int(len(img_names) * 0.003)) == 0:
print(f"processed {i+1} / {len(img_names)}")
sys.stdout.flush()
print(f"Root {args.process_id}: Finished extracting.") |