mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2024-11-23 00:00:09 -08:00
594 lines
20 KiB
Python
594 lines
20 KiB
Python
import traceback
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import json
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import multiprocessing
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import shutil
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from pathlib import Path
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import cv2
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import numpy as np
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from core import imagelib, pathex
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from core.cv2ex import *
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from core.interact import interact as io
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from core.joblib import Subprocessor
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from core.leras import nn
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from DFLIMG import *
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from facelib import FaceType, LandmarksProcessor
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from . import Extractor, Sorter
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from .Extractor import ExtractSubprocessor
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def extract_vggface2_dataset(input_dir, device_args={} ):
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multi_gpu = device_args.get('multi_gpu', False)
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cpu_only = device_args.get('cpu_only', False)
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input_path = Path(input_dir)
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if not input_path.exists():
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raise ValueError('Input directory not found. Please ensure it exists.')
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bb_csv = input_path / 'loose_bb_train.csv'
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if not bb_csv.exists():
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raise ValueError('loose_bb_train.csv found. Please ensure it exists.')
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bb_lines = bb_csv.read_text().split('\n')
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bb_lines.pop(0)
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bb_dict = {}
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for line in bb_lines:
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name, l, t, w, h = line.split(',')
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name = name[1:-1]
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l, t, w, h = [ int(x) for x in (l, t, w, h) ]
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bb_dict[name] = (l,t,w, h)
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output_path = input_path.parent / (input_path.name + '_out')
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dir_names = pathex.get_all_dir_names(input_path)
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if not output_path.exists():
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output_path.mkdir(parents=True, exist_ok=True)
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data = []
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for dir_name in io.progress_bar_generator(dir_names, "Collecting"):
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cur_input_path = input_path / dir_name
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cur_output_path = output_path / dir_name
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if not cur_output_path.exists():
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cur_output_path.mkdir(parents=True, exist_ok=True)
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input_path_image_paths = pathex.get_image_paths(cur_input_path)
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for filename in input_path_image_paths:
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filename_path = Path(filename)
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name = filename_path.parent.name + '/' + filename_path.stem
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if name not in bb_dict:
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continue
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l,t,w,h = bb_dict[name]
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if min(w,h) < 128:
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continue
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data += [ ExtractSubprocessor.Data(filename=filename,rects=[ (l,t,l+w,t+h) ], landmarks_accurate=False, force_output_path=cur_output_path ) ]
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face_type = FaceType.fromString('full_face')
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io.log_info ('Performing 2nd pass...')
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data = ExtractSubprocessor (data, 'landmarks', 256, face_type, debug_dir=None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False).run()
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io.log_info ('Performing 3rd pass...')
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ExtractSubprocessor (data, 'final', 256, face_type, debug_dir=None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=None).run()
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"""
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import code
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code.interact(local=dict(globals(), **locals()))
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data_len = len(data)
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i = 0
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while i < data_len-1:
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i_name = Path(data[i].filename).parent.name
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sub_data = []
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for j in range (i, data_len):
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j_name = Path(data[j].filename).parent.name
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if i_name == j_name:
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sub_data += [ data[j] ]
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else:
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break
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i = j
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cur_output_path = output_path / i_name
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io.log_info (f"Processing: {str(cur_output_path)}, {i}/{data_len} ")
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if not cur_output_path.exists():
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cur_output_path.mkdir(parents=True, exist_ok=True)
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for dir_name in dir_names:
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cur_input_path = input_path / dir_name
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cur_output_path = output_path / dir_name
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input_path_image_paths = pathex.get_image_paths(cur_input_path)
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l = len(input_path_image_paths)
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#if l < 250 or l > 350:
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# continue
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io.log_info (f"Processing: {str(cur_input_path)} ")
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if not cur_output_path.exists():
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cur_output_path.mkdir(parents=True, exist_ok=True)
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data = []
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for filename in input_path_image_paths:
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filename_path = Path(filename)
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name = filename_path.parent.name + '/' + filename_path.stem
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if name not in bb_dict:
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continue
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bb = bb_dict[name]
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l,t,w,h = bb
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if min(w,h) < 128:
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continue
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data += [ ExtractSubprocessor.Data(filename=filename,rects=[ (l,t,l+w,t+h) ], landmarks_accurate=False ) ]
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io.log_info ('Performing 2nd pass...')
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data = ExtractSubprocessor (data, 'landmarks', 256, face_type, debug_dir=None, multi_gpu=False, cpu_only=False, manual=False).run()
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io.log_info ('Performing 3rd pass...')
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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()
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io.log_info (f"Sorting: {str(cur_output_path)} ")
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Sorter.main (input_path=str(cur_output_path), sort_by_method='hist')
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import code
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code.interact(local=dict(globals(), **locals()))
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#try:
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# io.log_info (f"Removing: {str(cur_input_path)} ")
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# shutil.rmtree(cur_input_path)
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#except:
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# io.log_info (f"unable to remove: {str(cur_input_path)} ")
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def extract_vggface2_dataset(input_dir, device_args={} ):
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multi_gpu = device_args.get('multi_gpu', False)
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cpu_only = device_args.get('cpu_only', False)
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input_path = Path(input_dir)
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if not input_path.exists():
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raise ValueError('Input directory not found. Please ensure it exists.')
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output_path = input_path.parent / (input_path.name + '_out')
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dir_names = pathex.get_all_dir_names(input_path)
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if not output_path.exists():
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output_path.mkdir(parents=True, exist_ok=True)
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for dir_name in dir_names:
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cur_input_path = input_path / dir_name
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cur_output_path = output_path / dir_name
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l = len(pathex.get_image_paths(cur_input_path))
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if l < 250 or l > 350:
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continue
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io.log_info (f"Processing: {str(cur_input_path)} ")
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if not cur_output_path.exists():
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cur_output_path.mkdir(parents=True, exist_ok=True)
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Extractor.main( str(cur_input_path),
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str(cur_output_path),
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detector='s3fd',
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image_size=256,
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face_type='full_face',
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max_faces_from_image=1,
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device_args=device_args )
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io.log_info (f"Sorting: {str(cur_input_path)} ")
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Sorter.main (input_path=str(cur_output_path), sort_by_method='hist')
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try:
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io.log_info (f"Removing: {str(cur_input_path)} ")
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shutil.rmtree(cur_input_path)
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except:
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io.log_info (f"unable to remove: {str(cur_input_path)} ")
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"""
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#unused in end user workflow
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def dev_test_68(input_dir ):
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# process 68 landmarks dataset with .pts files
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input_path = Path(input_dir)
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if not input_path.exists():
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raise ValueError('input_dir not found. Please ensure it exists.')
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output_path = input_path.parent / (input_path.name+'_aligned')
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io.log_info(f'Output dir is % {output_path}')
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if output_path.exists():
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output_images_paths = pathex.get_image_paths(output_path)
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if len(output_images_paths) > 0:
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io.input_bool("WARNING !!! \n %s contains files! \n They will be deleted. \n Press enter to continue." % (str(output_path)), False )
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for filename in output_images_paths:
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Path(filename).unlink()
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else:
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output_path.mkdir(parents=True, exist_ok=True)
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images_paths = pathex.get_image_paths(input_path)
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for filepath in io.progress_bar_generator(images_paths, "Processing"):
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filepath = Path(filepath)
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pts_filepath = filepath.parent / (filepath.stem+'.pts')
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if pts_filepath.exists():
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pts = pts_filepath.read_text()
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pts_lines = pts.split('\n')
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lmrk_lines = None
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for pts_line in pts_lines:
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if pts_line == '{':
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lmrk_lines = []
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elif pts_line == '}':
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break
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else:
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if lmrk_lines is not None:
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lmrk_lines.append (pts_line)
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if lmrk_lines is not None and len(lmrk_lines) == 68:
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try:
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lmrks = [ np.array ( lmrk_line.strip().split(' ') ).astype(np.float32).tolist() for lmrk_line in lmrk_lines]
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except Exception as e:
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print(e)
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print(filepath)
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continue
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rect = LandmarksProcessor.get_rect_from_landmarks(lmrks)
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output_filepath = output_path / (filepath.stem+'.jpg')
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img = cv2_imread(filepath)
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img = imagelib.normalize_channels(img, 3)
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cv2_imwrite(output_filepath, img, [int(cv2.IMWRITE_JPEG_QUALITY), 95] )
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raise Exception("unimplemented")
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#DFLJPG.x(output_filepath, face_type=FaceType.toString(FaceType.MARK_ONLY),
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# landmarks=lmrks,
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# source_filename=filepath.name,
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# source_rect=rect,
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# source_landmarks=lmrks
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# )
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io.log_info("Done.")
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#unused in end user workflow
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def extract_umd_csv(input_file_csv,
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face_type='full_face',
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device_args={} ):
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#extract faces from umdfaces.io dataset csv file with pitch,yaw,roll info.
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multi_gpu = device_args.get('multi_gpu', False)
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cpu_only = device_args.get('cpu_only', False)
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face_type = FaceType.fromString(face_type)
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input_file_csv_path = Path(input_file_csv)
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if not input_file_csv_path.exists():
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raise ValueError('input_file_csv not found. Please ensure it exists.')
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input_file_csv_root_path = input_file_csv_path.parent
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output_path = input_file_csv_path.parent / ('aligned_' + input_file_csv_path.name)
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io.log_info("Output dir is %s." % (str(output_path)) )
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if output_path.exists():
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output_images_paths = pathex.get_image_paths(output_path)
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if len(output_images_paths) > 0:
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io.input_bool("WARNING !!! \n %s contains files! \n They will be deleted. \n Press enter to continue." % (str(output_path)), False )
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for filename in output_images_paths:
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Path(filename).unlink()
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else:
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output_path.mkdir(parents=True, exist_ok=True)
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try:
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with open( str(input_file_csv_path), 'r') as f:
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csv_file = f.read()
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except Exception as e:
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io.log_err("Unable to open or read file " + str(input_file_csv_path) + ": " + str(e) )
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return
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strings = csv_file.split('\n')
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keys = strings[0].split(',')
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keys_len = len(keys)
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csv_data = []
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for i in range(1, len(strings)):
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values = strings[i].split(',')
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if keys_len != len(values):
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io.log_err("Wrong string in csv file, skipping.")
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continue
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csv_data += [ { keys[n] : values[n] for n in range(keys_len) } ]
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data = []
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for d in csv_data:
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filename = input_file_csv_root_path / d['FILE']
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x,y,w,h = float(d['FACE_X']), float(d['FACE_Y']), float(d['FACE_WIDTH']), float(d['FACE_HEIGHT'])
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data += [ ExtractSubprocessor.Data(filename=filename, rects=[ [x,y,x+w,y+h] ]) ]
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images_found = len(data)
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faces_detected = 0
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if len(data) > 0:
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io.log_info ("Performing 2nd pass from csv file...")
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data = ExtractSubprocessor (data, 'landmarks', multi_gpu=multi_gpu, cpu_only=cpu_only).run()
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io.log_info ('Performing 3rd pass...')
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data = ExtractSubprocessor (data, 'final', face_type, None, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, final_output_path=output_path).run()
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faces_detected += sum([d.faces_detected for d in data])
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io.log_info ('-------------------------')
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io.log_info ('Images found: %d' % (images_found) )
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io.log_info ('Faces detected: %d' % (faces_detected) )
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io.log_info ('-------------------------')
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def dev_test1(input_dir):
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# LaPa dataset
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image_size = 1024
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face_type = FaceType.HEAD
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input_path = Path(input_dir)
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images_path = input_path / 'images'
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if not images_path.exists:
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raise ValueError('LaPa dataset: images folder not found.')
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labels_path = input_path / 'labels'
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if not labels_path.exists:
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raise ValueError('LaPa dataset: labels folder not found.')
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landmarks_path = input_path / 'landmarks'
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if not landmarks_path.exists:
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raise ValueError('LaPa dataset: landmarks folder not found.')
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output_path = input_path / 'out'
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if output_path.exists():
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output_images_paths = pathex.get_image_paths(output_path)
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if len(output_images_paths) != 0:
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io.input(f"\n WARNING !!! \n {output_path} contains files! \n They will be deleted. \n Press enter to continue.\n")
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for filename in output_images_paths:
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Path(filename).unlink()
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output_path.mkdir(parents=True, exist_ok=True)
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data = []
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img_paths = pathex.get_image_paths (images_path)
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for filename in img_paths:
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filepath = Path(filename)
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landmark_filepath = landmarks_path / (filepath.stem + '.txt')
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if not landmark_filepath.exists():
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raise ValueError(f'no landmarks for {filepath}')
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#img = cv2_imread(filepath)
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lm = landmark_filepath.read_text()
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lm = lm.split('\n')
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if int(lm[0]) != 106:
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raise ValueError(f'wrong landmarks format in {landmark_filepath}')
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lmrks = []
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for i in range(106):
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x,y = lm[i+1].split(' ')
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x,y = float(x), float(y)
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lmrks.append ( (x,y) )
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lmrks = np.array(lmrks)
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l,t = np.min(lmrks, 0)
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r,b = np.max(lmrks, 0)
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l,t,r,b = ( int(x) for x in (l,t,r,b) )
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#for x, y in lmrks:
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# x,y = int(x), int(y)
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# cv2.circle(img, (x, y), 1, (0,255,0) , 1, lineType=cv2.LINE_AA)
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#imagelib.draw_rect(img, (l,t,r,b), (0,255,0) )
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data += [ ExtractSubprocessor.Data(filepath=filepath, rects=[ (l,t,r,b) ]) ]
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#cv2.imshow("", img)
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#cv2.waitKey(0)
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if len(data) > 0:
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device_config = nn.DeviceConfig.BestGPU()
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io.log_info ("Performing 2nd pass...")
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data = ExtractSubprocessor (data, 'landmarks', image_size, 95, face_type, device_config=device_config).run()
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io.log_info ("Performing 3rd pass...")
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data = ExtractSubprocessor (data, 'final', image_size, 95, face_type, final_output_path=output_path, device_config=device_config).run()
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for filename in pathex.get_image_paths (output_path):
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filepath = Path(filename)
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dflimg = DFLJPG.load(filepath)
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src_filename = dflimg.get_source_filename()
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image_to_face_mat = dflimg.get_image_to_face_mat()
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label_filepath = labels_path / ( Path(src_filename).stem + '.png')
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if not label_filepath.exists():
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raise ValueError(f'{label_filepath} does not exist')
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mask = cv2_imread(label_filepath)
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#mask[mask == 10] = 0 # remove hair
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mask[mask > 0] = 1
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mask = cv2.warpAffine(mask, image_to_face_mat, (image_size, image_size), cv2.INTER_LINEAR)
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mask = cv2.blur(mask, (3,3) )
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#cv2.imshow("", (mask*255).astype(np.uint8) )
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#cv2.waitKey(0)
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dflimg.set_xseg_mask(mask)
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dflimg.save()
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import code
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code.interact(local=dict(globals(), **locals()))
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def dev_resave_pngs(input_dir):
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input_path = Path(input_dir)
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if not input_path.exists():
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raise ValueError('input_dir not found. Please ensure it exists.')
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images_paths = pathex.get_image_paths(input_path, image_extensions=['.png'], subdirs=True, return_Path_class=True)
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for filepath in io.progress_bar_generator(images_paths,"Processing"):
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cv2_imwrite(filepath, cv2_imread(filepath))
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def dev_segmented_trash(input_dir):
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input_path = Path(input_dir)
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if not input_path.exists():
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raise ValueError('input_dir not found. Please ensure it exists.')
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output_path = input_path.parent / (input_path.name+'_trash')
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output_path.mkdir(parents=True, exist_ok=True)
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|
|
|
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) )
|
|
|
|
|
|
|
|
def dev_test(input_dir):
|
|
"""
|
|
extract FaceSynthetics dataset https://github.com/microsoft/FaceSynthetics
|
|
|
|
BACKGROUND = 0
|
|
SKIN = 1
|
|
NOSE = 2
|
|
RIGHT_EYE = 3
|
|
LEFT_EYE = 4
|
|
RIGHT_BROW = 5
|
|
LEFT_BROW = 6
|
|
RIGHT_EAR = 7
|
|
LEFT_EAR = 8
|
|
MOUTH_INTERIOR = 9
|
|
TOP_LIP = 10
|
|
BOTTOM_LIP = 11
|
|
NECK = 12
|
|
HAIR = 13
|
|
BEARD = 14
|
|
CLOTHING = 15
|
|
GLASSES = 16
|
|
HEADWEAR = 17
|
|
FACEWEAR = 18
|
|
IGNORE = 255
|
|
"""
|
|
|
|
|
|
image_size = 1024
|
|
face_type = FaceType.WHOLE_FACE
|
|
|
|
input_path = Path(input_dir)
|
|
|
|
|
|
|
|
output_path = input_path.parent / f'{input_path.name}_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 = []
|
|
|
|
for filepath in io.progress_bar_generator(pathex.get_paths(input_path), "Processing"):
|
|
if filepath.suffix == '.txt':
|
|
|
|
image_filepath = filepath.parent / f'{filepath.name.split("_")[0]}.png'
|
|
if not image_filepath.exists():
|
|
print(f'{image_filepath} does not exist, skipping')
|
|
|
|
lmrks = []
|
|
for lmrk_line in filepath.read_text().split('\n'):
|
|
if len(lmrk_line) == 0:
|
|
continue
|
|
|
|
x, y = lmrk_line.split(' ')
|
|
x, y = float(x), float(y)
|
|
|
|
lmrks.append( (x,y) )
|
|
|
|
lmrks = np.array(lmrks[:68], np.float32)
|
|
rect = LandmarksProcessor.get_rect_from_landmarks(lmrks)
|
|
data += [ ExtractSubprocessor.Data(filepath=image_filepath, rects=[rect], landmarks=[ lmrks ] ) ]
|
|
|
|
if len(data) > 0:
|
|
io.log_info ("Performing 3rd pass...")
|
|
data = ExtractSubprocessor (data, 'final', image_size, 95, face_type, final_output_path=output_path, device_config=nn.DeviceConfig.CPU()).run()
|
|
|
|
for filename in io.progress_bar_generator(pathex.get_image_paths (output_path), "Processing"):
|
|
filepath = Path(filename)
|
|
|
|
dflimg = DFLJPG.load(filepath)
|
|
|
|
src_filename = dflimg.get_source_filename()
|
|
image_to_face_mat = dflimg.get_image_to_face_mat()
|
|
|
|
seg_filepath = input_path / ( Path(src_filename).stem + '_seg.png')
|
|
if not seg_filepath.exists():
|
|
raise ValueError(f'{seg_filepath} does not exist')
|
|
|
|
seg = cv2_imread(seg_filepath)
|
|
seg_inds = np.isin(seg, [1,2,3,4,5,6,9,10,11])
|
|
seg[~seg_inds] = 0
|
|
seg[seg_inds] = 1
|
|
seg = seg.astype(np.float32)
|
|
seg = cv2.warpAffine(seg, image_to_face_mat, (image_size, image_size), cv2.INTER_LANCZOS4)
|
|
dflimg.set_xseg_mask(seg)
|
|
dflimg.save()
|
|
|