mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-03-12 20:42:45 -07:00
503 lines
17 KiB
Python
503 lines
17 KiB
Python
import traceback
|
|
import json
|
|
import multiprocessing
|
|
import shutil
|
|
from pathlib import Path
|
|
import cv2
|
|
import numpy as np
|
|
|
|
from core import imagelib, pathex
|
|
from core.cv2ex import *
|
|
from core.interact import interact as io
|
|
from core.joblib import Subprocessor
|
|
from core.leras import nn
|
|
from DFLIMG import *
|
|
from facelib import FaceType, LandmarksProcessor
|
|
|
|
from . import Extractor, Sorter
|
|
from .Extractor import ExtractSubprocessor
|
|
|
|
|
|
def extract_vggface2_dataset(input_dir, device_args={} ):
|
|
multi_gpu = device_args.get('multi_gpu', False)
|
|
cpu_only = device_args.get('cpu_only', False)
|
|
|
|
input_path = Path(input_dir)
|
|
if not input_path.exists():
|
|
raise ValueError('Input directory not found. Please ensure it exists.')
|
|
|
|
bb_csv = input_path / 'loose_bb_train.csv'
|
|
if not bb_csv.exists():
|
|
raise ValueError('loose_bb_train.csv found. Please ensure it exists.')
|
|
|
|
bb_lines = bb_csv.read_text().split('\n')
|
|
bb_lines.pop(0)
|
|
|
|
bb_dict = {}
|
|
for line in bb_lines:
|
|
name, l, t, w, h = line.split(',')
|
|
name = name[1:-1]
|
|
l, t, w, h = [ int(x) for x in (l, t, w, h) ]
|
|
bb_dict[name] = (l,t,w, h)
|
|
|
|
|
|
output_path = input_path.parent / (input_path.name + '_out')
|
|
|
|
dir_names = pathex.get_all_dir_names(input_path)
|
|
|
|
if not output_path.exists():
|
|
output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
data = []
|
|
for dir_name in io.progress_bar_generator(dir_names, "Collecting"):
|
|
cur_input_path = input_path / dir_name
|
|
cur_output_path = output_path / dir_name
|
|
|
|
if not cur_output_path.exists():
|
|
cur_output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
input_path_image_paths = pathex.get_image_paths(cur_input_path)
|
|
|
|
for filename in input_path_image_paths:
|
|
filename_path = Path(filename)
|
|
|
|
name = filename_path.parent.name + '/' + filename_path.stem
|
|
if name not in bb_dict:
|
|
continue
|
|
|
|
l,t,w,h = bb_dict[name]
|
|
if min(w,h) < 128:
|
|
continue
|
|
|
|
data += [ ExtractSubprocessor.Data(filename=filename,rects=[ (l,t,l+w,t+h) ], landmarks_accurate=False, force_output_path=cur_output_path ) ]
|
|
|
|
face_type = FaceType.fromString('full_face')
|
|
|
|
io.log_info ('Performing 2nd pass...')
|
|
data = ExtractSubprocessor (data, 'landmarks', 256, face_type, debug_dir=None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False).run()
|
|
|
|
io.log_info ('Performing 3rd pass...')
|
|
ExtractSubprocessor (data, 'final', 256, face_type, debug_dir=None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=None).run()
|
|
|
|
|
|
"""
|
|
import code
|
|
code.interact(local=dict(globals(), **locals()))
|
|
|
|
data_len = len(data)
|
|
i = 0
|
|
while i < data_len-1:
|
|
i_name = Path(data[i].filename).parent.name
|
|
|
|
sub_data = []
|
|
|
|
for j in range (i, data_len):
|
|
j_name = Path(data[j].filename).parent.name
|
|
if i_name == j_name:
|
|
sub_data += [ data[j] ]
|
|
else:
|
|
break
|
|
i = j
|
|
|
|
cur_output_path = output_path / i_name
|
|
|
|
io.log_info (f"Processing: {str(cur_output_path)}, {i}/{data_len} ")
|
|
|
|
if not cur_output_path.exists():
|
|
cur_output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
for dir_name in dir_names:
|
|
|
|
cur_input_path = input_path / dir_name
|
|
cur_output_path = output_path / dir_name
|
|
|
|
input_path_image_paths = pathex.get_image_paths(cur_input_path)
|
|
l = len(input_path_image_paths)
|
|
#if l < 250 or l > 350:
|
|
# continue
|
|
|
|
io.log_info (f"Processing: {str(cur_input_path)} ")
|
|
|
|
if not cur_output_path.exists():
|
|
cur_output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
|
|
data = []
|
|
for filename in input_path_image_paths:
|
|
filename_path = Path(filename)
|
|
|
|
name = filename_path.parent.name + '/' + filename_path.stem
|
|
if name not in bb_dict:
|
|
continue
|
|
|
|
bb = bb_dict[name]
|
|
l,t,w,h = bb
|
|
if min(w,h) < 128:
|
|
continue
|
|
|
|
data += [ ExtractSubprocessor.Data(filename=filename,rects=[ (l,t,l+w,t+h) ], landmarks_accurate=False ) ]
|
|
|
|
|
|
|
|
io.log_info ('Performing 2nd pass...')
|
|
data = ExtractSubprocessor (data, 'landmarks', 256, face_type, debug_dir=None, multi_gpu=False, cpu_only=False, manual=False).run()
|
|
|
|
io.log_info ('Performing 3rd pass...')
|
|
data = ExtractSubprocessor (data, 'final', 256, face_type, debug_dir=None, multi_gpu=False, cpu_only=False, manual=False, final_output_path=cur_output_path).run()
|
|
|
|
|
|
io.log_info (f"Sorting: {str(cur_output_path)} ")
|
|
Sorter.main (input_path=str(cur_output_path), sort_by_method='hist')
|
|
|
|
import code
|
|
code.interact(local=dict(globals(), **locals()))
|
|
|
|
#try:
|
|
# io.log_info (f"Removing: {str(cur_input_path)} ")
|
|
# shutil.rmtree(cur_input_path)
|
|
#except:
|
|
# io.log_info (f"unable to remove: {str(cur_input_path)} ")
|
|
|
|
|
|
|
|
|
|
def extract_vggface2_dataset(input_dir, device_args={} ):
|
|
multi_gpu = device_args.get('multi_gpu', False)
|
|
cpu_only = device_args.get('cpu_only', False)
|
|
|
|
input_path = Path(input_dir)
|
|
if not input_path.exists():
|
|
raise ValueError('Input directory not found. Please ensure it exists.')
|
|
|
|
output_path = input_path.parent / (input_path.name + '_out')
|
|
|
|
dir_names = pathex.get_all_dir_names(input_path)
|
|
|
|
if not output_path.exists():
|
|
output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
|
|
|
|
for dir_name in dir_names:
|
|
|
|
cur_input_path = input_path / dir_name
|
|
cur_output_path = output_path / dir_name
|
|
|
|
l = len(pathex.get_image_paths(cur_input_path))
|
|
if l < 250 or l > 350:
|
|
continue
|
|
|
|
io.log_info (f"Processing: {str(cur_input_path)} ")
|
|
|
|
if not cur_output_path.exists():
|
|
cur_output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
Extractor.main( str(cur_input_path),
|
|
str(cur_output_path),
|
|
detector='s3fd',
|
|
image_size=256,
|
|
face_type='full_face',
|
|
max_faces_from_image=1,
|
|
device_args=device_args )
|
|
|
|
io.log_info (f"Sorting: {str(cur_input_path)} ")
|
|
Sorter.main (input_path=str(cur_output_path), sort_by_method='hist')
|
|
|
|
try:
|
|
io.log_info (f"Removing: {str(cur_input_path)} ")
|
|
shutil.rmtree(cur_input_path)
|
|
except:
|
|
io.log_info (f"unable to remove: {str(cur_input_path)} ")
|
|
|
|
"""
|
|
|
|
#unused in end user workflow
|
|
def dev_test_68(input_dir ):
|
|
# process 68 landmarks dataset with .pts files
|
|
input_path = Path(input_dir)
|
|
if not input_path.exists():
|
|
raise ValueError('input_dir not found. Please ensure it exists.')
|
|
|
|
output_path = input_path.parent / (input_path.name+'_aligned')
|
|
|
|
io.log_info(f'Output dir is % {output_path}')
|
|
|
|
if output_path.exists():
|
|
output_images_paths = pathex.get_image_paths(output_path)
|
|
if len(output_images_paths) > 0:
|
|
io.input_bool("WARNING !!! \n %s contains files! \n They will be deleted. \n Press enter to continue." % (str(output_path)), False )
|
|
for filename in output_images_paths:
|
|
Path(filename).unlink()
|
|
else:
|
|
output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
images_paths = pathex.get_image_paths(input_path)
|
|
|
|
for filepath in io.progress_bar_generator(images_paths, "Processing"):
|
|
filepath = Path(filepath)
|
|
|
|
|
|
pts_filepath = filepath.parent / (filepath.stem+'.pts')
|
|
if pts_filepath.exists():
|
|
pts = pts_filepath.read_text()
|
|
pts_lines = pts.split('\n')
|
|
|
|
lmrk_lines = None
|
|
for pts_line in pts_lines:
|
|
if pts_line == '{':
|
|
lmrk_lines = []
|
|
elif pts_line == '}':
|
|
break
|
|
else:
|
|
if lmrk_lines is not None:
|
|
lmrk_lines.append (pts_line)
|
|
|
|
if lmrk_lines is not None and len(lmrk_lines) == 68:
|
|
try:
|
|
lmrks = [ np.array ( lmrk_line.strip().split(' ') ).astype(np.float32).tolist() for lmrk_line in lmrk_lines]
|
|
except Exception as e:
|
|
print(e)
|
|
print(filepath)
|
|
continue
|
|
|
|
rect = LandmarksProcessor.get_rect_from_landmarks(lmrks)
|
|
|
|
output_filepath = output_path / (filepath.stem+'.jpg')
|
|
|
|
img = cv2_imread(filepath)
|
|
img = imagelib.normalize_channels(img, 3)
|
|
cv2_imwrite(output_filepath, img, [int(cv2.IMWRITE_JPEG_QUALITY), 95] )
|
|
|
|
raise Exception("unimplemented")
|
|
#DFLJPG.x(output_filepath, face_type=FaceType.toString(FaceType.MARK_ONLY),
|
|
# landmarks=lmrks,
|
|
# source_filename=filepath.name,
|
|
# source_rect=rect,
|
|
# source_landmarks=lmrks
|
|
# )
|
|
|
|
io.log_info("Done.")
|
|
|
|
#unused in end user workflow
|
|
def extract_umd_csv(input_file_csv,
|
|
face_type='full_face',
|
|
device_args={} ):
|
|
|
|
#extract faces from umdfaces.io dataset csv file with pitch,yaw,roll info.
|
|
multi_gpu = device_args.get('multi_gpu', False)
|
|
cpu_only = device_args.get('cpu_only', False)
|
|
face_type = FaceType.fromString(face_type)
|
|
|
|
input_file_csv_path = Path(input_file_csv)
|
|
if not input_file_csv_path.exists():
|
|
raise ValueError('input_file_csv not found. Please ensure it exists.')
|
|
|
|
input_file_csv_root_path = input_file_csv_path.parent
|
|
output_path = input_file_csv_path.parent / ('aligned_' + input_file_csv_path.name)
|
|
|
|
io.log_info("Output dir is %s." % (str(output_path)) )
|
|
|
|
if output_path.exists():
|
|
output_images_paths = pathex.get_image_paths(output_path)
|
|
if len(output_images_paths) > 0:
|
|
io.input_bool("WARNING !!! \n %s contains files! \n They will be deleted. \n Press enter to continue." % (str(output_path)), False )
|
|
for filename in output_images_paths:
|
|
Path(filename).unlink()
|
|
else:
|
|
output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
try:
|
|
with open( str(input_file_csv_path), 'r') as f:
|
|
csv_file = f.read()
|
|
except Exception as e:
|
|
io.log_err("Unable to open or read file " + str(input_file_csv_path) + ": " + str(e) )
|
|
return
|
|
|
|
strings = csv_file.split('\n')
|
|
keys = strings[0].split(',')
|
|
keys_len = len(keys)
|
|
csv_data = []
|
|
for i in range(1, len(strings)):
|
|
values = strings[i].split(',')
|
|
if keys_len != len(values):
|
|
io.log_err("Wrong string in csv file, skipping.")
|
|
continue
|
|
|
|
csv_data += [ { keys[n] : values[n] for n in range(keys_len) } ]
|
|
|
|
data = []
|
|
for d in csv_data:
|
|
filename = input_file_csv_root_path / d['FILE']
|
|
|
|
|
|
x,y,w,h = float(d['FACE_X']), float(d['FACE_Y']), float(d['FACE_WIDTH']), float(d['FACE_HEIGHT'])
|
|
|
|
data += [ ExtractSubprocessor.Data(filename=filename, rects=[ [x,y,x+w,y+h] ]) ]
|
|
|
|
images_found = len(data)
|
|
faces_detected = 0
|
|
if len(data) > 0:
|
|
io.log_info ("Performing 2nd pass from csv file...")
|
|
data = ExtractSubprocessor (data, 'landmarks', multi_gpu=multi_gpu, cpu_only=cpu_only).run()
|
|
|
|
io.log_info ('Performing 3rd pass...')
|
|
data = ExtractSubprocessor (data, 'final', face_type, None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=output_path).run()
|
|
faces_detected += sum([d.faces_detected for d in data])
|
|
|
|
|
|
io.log_info ('-------------------------')
|
|
io.log_info ('Images found: %d' % (images_found) )
|
|
io.log_info ('Faces detected: %d' % (faces_detected) )
|
|
io.log_info ('-------------------------')
|
|
|
|
|
|
|
|
def dev_test(input_dir):
|
|
# LaPa dataset
|
|
|
|
image_size = 1024
|
|
face_type = FaceType.HEAD
|
|
|
|
input_path = Path(input_dir)
|
|
images_path = input_path / 'images'
|
|
if not images_path.exists:
|
|
raise ValueError('LaPa dataset: images folder not found.')
|
|
labels_path = input_path / 'labels'
|
|
if not labels_path.exists:
|
|
raise ValueError('LaPa dataset: labels folder not found.')
|
|
landmarks_path = input_path / 'landmarks'
|
|
if not landmarks_path.exists:
|
|
raise ValueError('LaPa dataset: landmarks folder not found.')
|
|
|
|
output_path = input_path / 'out'
|
|
if output_path.exists():
|
|
output_images_paths = pathex.get_image_paths(output_path)
|
|
if len(output_images_paths) != 0:
|
|
io.input(f"\n WARNING !!! \n {output_path} contains files! \n They will be deleted. \n Press enter to continue.\n")
|
|
for filename in output_images_paths:
|
|
Path(filename).unlink()
|
|
output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
data = []
|
|
|
|
img_paths = pathex.get_image_paths (images_path)
|
|
for filename in img_paths:
|
|
filepath = Path(filename)
|
|
|
|
landmark_filepath = landmarks_path / (filepath.stem + '.txt')
|
|
if not landmark_filepath.exists():
|
|
raise ValueError(f'no landmarks for {filepath}')
|
|
|
|
#img = cv2_imread(filepath)
|
|
|
|
lm = landmark_filepath.read_text()
|
|
lm = lm.split('\n')
|
|
if int(lm[0]) != 106:
|
|
raise ValueError(f'wrong landmarks format in {landmark_filepath}')
|
|
|
|
lmrks = []
|
|
for i in range(106):
|
|
x,y = lm[i+1].split(' ')
|
|
x,y = float(x), float(y)
|
|
lmrks.append ( (x,y) )
|
|
|
|
lmrks = np.array(lmrks)
|
|
|
|
l,t = np.min(lmrks, 0)
|
|
r,b = np.max(lmrks, 0)
|
|
|
|
l,t,r,b = ( int(x) for x in (l,t,r,b) )
|
|
|
|
#for x, y in lmrks:
|
|
# x,y = int(x), int(y)
|
|
# cv2.circle(img, (x, y), 1, (0,255,0) , 1, lineType=cv2.LINE_AA)
|
|
|
|
#imagelib.draw_rect(img, (l,t,r,b), (0,255,0) )
|
|
|
|
|
|
data += [ ExtractSubprocessor.Data(filepath=filepath, rects=[ (l,t,r,b) ]) ]
|
|
|
|
#cv2.imshow("", img)
|
|
#cv2.waitKey(0)
|
|
|
|
if len(data) > 0:
|
|
device_config = nn.DeviceConfig.BestGPU()
|
|
|
|
io.log_info ("Performing 2nd pass...")
|
|
data = ExtractSubprocessor (data, 'landmarks', image_size, 95, face_type, device_config=device_config).run()
|
|
io.log_info ("Performing 3rd pass...")
|
|
data = ExtractSubprocessor (data, 'final', image_size, 95, face_type, final_output_path=output_path, device_config=device_config).run()
|
|
|
|
|
|
for filename in pathex.get_image_paths (output_path):
|
|
filepath = Path(filename)
|
|
|
|
|
|
dflimg = DFLJPG.load(filepath)
|
|
|
|
src_filename = dflimg.get_source_filename()
|
|
image_to_face_mat = dflimg.get_image_to_face_mat()
|
|
|
|
label_filepath = labels_path / ( Path(src_filename).stem + '.png')
|
|
if not label_filepath.exists():
|
|
raise ValueError(f'{label_filepath} does not exist')
|
|
|
|
mask = cv2_imread(label_filepath)
|
|
#mask[mask == 10] = 0 # remove hair
|
|
mask[mask > 0] = 1
|
|
mask = cv2.warpAffine(mask, image_to_face_mat, (image_size, image_size), cv2.INTER_LINEAR)
|
|
mask = cv2.blur(mask, (3,3) )
|
|
|
|
#cv2.imshow("", (mask*255).astype(np.uint8) )
|
|
#cv2.waitKey(0)
|
|
|
|
dflimg.set_xseg_mask(mask)
|
|
dflimg.save()
|
|
|
|
|
|
import code
|
|
code.interact(local=dict(globals(), **locals()))
|
|
|
|
|
|
def dev_resave_pngs(input_dir):
|
|
input_path = Path(input_dir)
|
|
if not input_path.exists():
|
|
raise ValueError('input_dir not found. Please ensure it exists.')
|
|
|
|
images_paths = pathex.get_image_paths(input_path, image_extensions=['.png'], subdirs=True, return_Path_class=True)
|
|
|
|
for filepath in io.progress_bar_generator(images_paths,"Processing"):
|
|
cv2_imwrite(filepath, cv2_imread(filepath))
|
|
|
|
|
|
def dev_segmented_trash(input_dir):
|
|
input_path = Path(input_dir)
|
|
if not input_path.exists():
|
|
raise ValueError('input_dir not found. Please ensure it exists.')
|
|
|
|
output_path = input_path.parent / (input_path.name+'_trash')
|
|
output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
images_paths = pathex.get_image_paths(input_path, return_Path_class=True)
|
|
|
|
trash_paths = []
|
|
for filepath in images_paths:
|
|
json_file = filepath.parent / (filepath.stem +'.json')
|
|
if not json_file.exists():
|
|
trash_paths.append(filepath)
|
|
|
|
for filepath in trash_paths:
|
|
|
|
try:
|
|
filepath.rename ( output_path / filepath.name )
|
|
except:
|
|
io.log_info ('fail to trashing %s' % (src.name) )
|
|
|