mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
670 lines
No EOL
31 KiB
Python
670 lines
No EOL
31 KiB
Python
import traceback
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import os
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import sys
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import time
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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 numpy as np
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import mathlib
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import cv2
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from utils import Path_utils
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from utils.DFLJPG import DFLJPG
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from utils.cv2_utils import *
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from utils import image_utils
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import facelib
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from facelib import FaceType
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from facelib import LandmarksProcessor
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from nnlib import nnlib
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from joblib import Subprocessor
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from interact import interact as io
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class ExtractSubprocessor(Subprocessor):
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class Cli(Subprocessor.Cli):
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#override
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def on_initialize(self, client_dict):
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self.log_info ('Running on %s.' % (client_dict['device_name']) )
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self.type = client_dict['type']
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self.image_size = client_dict['image_size']
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self.face_type = client_dict['face_type']
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self.device_idx = client_dict['device_idx']
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self.cpu_only = client_dict['device_type'] == 'CPU'
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self.output_path = Path(client_dict['output_dir']) if 'output_dir' in client_dict.keys() else None
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self.debug_dir = client_dict['debug_dir']
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self.detector = client_dict['detector']
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self.accurate_landmarks_extractor = client_dict['accurate_landmarks_extractor']
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self.cached_image = (None, None)
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self.e = None
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device_config = nnlib.DeviceConfig ( cpu_only=self.cpu_only, force_gpu_idx=self.device_idx, allow_growth=True)
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if self.type == 'rects':
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if self.detector is not None:
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if self.detector == 'mt':
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nnlib.import_all (device_config)
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self.e = facelib.MTCExtractor()
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elif self.detector == 'dlib':
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nnlib.import_dlib (device_config)
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self.e = facelib.DLIBExtractor(nnlib.dlib)
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elif self.detector == 's3fd':
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nnlib.import_all (device_config)
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self.e = facelib.S3FDExtractor()
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else:
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raise ValueError ("Wrong detector type.")
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if self.e is not None:
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self.e.__enter__()
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elif self.type == 'landmarks':
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nnlib.import_all (device_config)
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self.e = facelib.LandmarksExtractor(nnlib.keras)
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self.e.__enter__()
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if self.accurate_landmarks_extractor and device_config.gpu_vram_gb[0] >= 2:
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self.second_pass_e = facelib.S3FDExtractor()
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self.second_pass_e.__enter__()
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else:
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self.second_pass_e = None
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elif self.type == 'final':
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pass
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#override
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def on_finalize(self):
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if self.e is not None:
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self.e.__exit__()
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#override
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def process_data(self, data):
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filename_path = Path( data[0] )
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filename_path_str = str(filename_path)
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if self.cached_image[0] == filename_path_str:
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image = self.cached_image[1] #cached image for manual extractor
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else:
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image = cv2_imread( filename_path_str )
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if image is None:
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self.log_err ( 'Failed to extract %s, reason: cv2_imread() fail.' % ( str(filename_path) ) )
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return None
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image_shape = image.shape
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if len(image_shape) == 2:
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h, w = image.shape
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ch = 1
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else:
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h, w, ch = image.shape
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if ch == 1:
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image = np.repeat ( image [:,:,np.newaxis], 3, -1 )
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elif ch == 4:
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image = image[:,:,0:3]
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wm = w % 2
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hm = h % 2
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if wm + hm != 0: #fix odd image
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image = image[0:h-hm,0:w-wm,:]
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self.cached_image = ( filename_path_str, image )
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src_dflimg = None
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h, w, ch = image.shape
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if h == w:
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#extracting from already extracted jpg image?
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if filename_path.suffix == '.jpg':
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src_dflimg = DFLJPG.load ( str(filename_path) )
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if self.type == 'rects':
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if min(w,h) < 128:
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self.log_err ( 'Image is too small %s : [%d, %d]' % ( str(filename_path), w, h ) )
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rects = []
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else:
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rects = self.e.extract_from_bgr (image)
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return [str(filename_path), rects]
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elif self.type == 'landmarks':
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rects = data[1]
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if rects is None:
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landmarks = None
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else:
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landmarks = self.e.extract_from_bgr (image, rects, self.second_pass_e if src_dflimg is None else None)
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return [str(filename_path), landmarks]
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elif self.type == 'final':
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result = []
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faces = data[1]
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if self.debug_dir is not None:
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debug_output_file = str( Path(self.debug_dir) / (filename_path.stem+'.jpg') )
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debug_image = image.copy()
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if src_dflimg is not None and len(faces) != 1:
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#if re-extracting from dflimg and more than 1 or zero faces detected - dont process and just copy it
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print("src_dflimg is not None and len(faces) != 1", str(filename_path) )
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output_file = str(self.output_path / filename_path.name)
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if str(filename_path) != str(output_file):
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shutil.copy ( str(filename_path), str(output_file) )
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result.append (output_file)
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else:
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face_idx = 0
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for face in faces:
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rect = np.array(face[0])
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image_landmarks = face[1]
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if image_landmarks is None:
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continue
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image_landmarks = np.array(image_landmarks)
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if self.face_type == FaceType.MARK_ONLY:
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face_image = image
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face_image_landmarks = image_landmarks
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else:
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image_to_face_mat = LandmarksProcessor.get_transform_mat (image_landmarks, self.image_size, self.face_type)
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face_image = cv2.warpAffine(image, image_to_face_mat, (self.image_size, self.image_size), cv2.INTER_LANCZOS4)
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face_image_landmarks = LandmarksProcessor.transform_points (image_landmarks, image_to_face_mat)
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landmarks_bbox = LandmarksProcessor.transform_points ( [ (0,0), (0,self.image_size-1), (self.image_size-1, self.image_size-1), (self.image_size-1,0) ], image_to_face_mat, True)
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rect_area = mathlib.polygon_area(np.array(rect[[0,2,2,0]]), np.array(rect[[1,1,3,3]]))
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landmarks_area = mathlib.polygon_area(landmarks_bbox[:,0], landmarks_bbox[:,1] )
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if landmarks_area > 4*rect_area: #get rid of faces which umeyama-landmark-area > 4*detector-rect-area
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continue
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if self.debug_dir is not None:
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LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type, transparent_mask=True)
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if src_dflimg is not None:
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#if extracting from dflimg copy it in order not to lose quality
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output_file = str(self.output_path / filename_path.name)
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if str(filename_path) != str(output_file):
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shutil.copy ( str(filename_path), str(output_file) )
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else:
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output_file = '{}_{}{}'.format(str(self.output_path / filename_path.stem), str(face_idx), '.jpg')
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cv2_imwrite(output_file, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), 85] )
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DFLJPG.embed_data(output_file, face_type=FaceType.toString(self.face_type),
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landmarks=face_image_landmarks.tolist(),
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source_filename=filename_path.name,
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source_rect=rect,
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source_landmarks=image_landmarks.tolist(),
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image_to_face_mat=image_to_face_mat
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)
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result.append (output_file)
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face_idx += 1
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if self.debug_dir is not None:
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cv2_imwrite(debug_output_file, debug_image, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
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return result
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#overridable
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def get_data_name (self, data):
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#return string identificator of your data
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return data[0]
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#override
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def __init__(self, input_data, type, image_size, face_type, debug_dir, multi_gpu=False, cpu_only=False, manual=False, manual_window_size=0, detector=None, output_path=None, accurate_landmarks_extractor=False ):
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self.input_data = input_data
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self.type = type
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self.image_size = image_size
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self.face_type = face_type
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self.debug_dir = debug_dir
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self.multi_gpu = multi_gpu
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self.cpu_only = cpu_only
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self.detector = detector
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self.output_path = output_path
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self.manual = manual
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self.manual_window_size = manual_window_size
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self.accurate_landmarks_extractor = accurate_landmarks_extractor
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self.result = []
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no_response_time_sec = 60 if not self.manual else 999999
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super().__init__('Extractor', ExtractSubprocessor.Cli, no_response_time_sec)
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#override
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def on_clients_initialized(self):
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if self.manual == True:
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self.wnd_name = 'Manual pass'
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io.named_window(self.wnd_name)
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io.capture_mouse(self.wnd_name)
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io.capture_keys(self.wnd_name)
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self.cache_original_image = (None, None)
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self.cache_image = (None, None)
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self.cache_text_lines_img = (None, None)
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self.hide_help = False
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self.landmarks = None
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self.x = 0
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self.y = 0
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self.rect_size = 100
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self.rect_locked = False
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self.extract_needed = True
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io.progress_bar (None, len (self.input_data))
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#override
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def on_clients_finalized(self):
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if self.manual == True:
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io.destroy_all_windows()
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io.progress_bar_close()
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def get_devices_for_type (self, type, multi_gpu, cpu_only):
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if 'cpu' in nnlib.device.backend:
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cpu_only = True
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if not cpu_only and (type == 'rects' or type == 'landmarks'):
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if type == 'rects' and (self.detector == 'mt') and nnlib.device.backend == "plaidML":
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cpu_only = True
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else:
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if multi_gpu:
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devices = nnlib.device.getValidDevicesWithAtLeastTotalMemoryGB(2)
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if not multi_gpu or len(devices) == 0:
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devices = [nnlib.device.getBestValidDeviceIdx()]
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if len(devices) == 0:
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devices = [0]
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for idx in devices:
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dev_name = nnlib.device.getDeviceName(idx)
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dev_vram = nnlib.device.getDeviceVRAMTotalGb(idx)
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if not self.manual and ( self.type == 'rects' and self.detector != 's3fd' ):
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for i in range ( int (max (1, dev_vram / 2) ) ):
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yield (idx, 'GPU', '%s #%d' % (dev_name,i) , dev_vram)
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else:
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yield (idx, 'GPU', dev_name, dev_vram)
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if cpu_only and (type == 'rects' or type == 'landmarks'):
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if self.manual:
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yield (0, 'CPU', 'CPU', 0 )
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else:
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for i in range( min(8, multiprocessing.cpu_count() // 2) ):
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yield (i, 'CPU', 'CPU%d' % (i), 0 )
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if type == 'final':
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for i in range( min(8, multiprocessing.cpu_count()) ):
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yield (i, 'CPU', 'CPU%d' % (i), 0 )
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#override
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def process_info_generator(self):
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base_dict = {'type' : self.type,
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'image_size': self.image_size,
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'face_type': self.face_type,
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'debug_dir': self.debug_dir,
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'output_dir': str(self.output_path),
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'detector': self.detector}
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for (device_idx, device_type, device_name, device_total_vram_gb) in self.get_devices_for_type(self.type, self.multi_gpu, self.cpu_only):
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client_dict = base_dict.copy()
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client_dict['device_idx'] = device_idx
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client_dict['device_name'] = device_name
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client_dict['device_type'] = device_type
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client_dict['accurate_landmarks_extractor'] = self.accurate_landmarks_extractor
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yield client_dict['device_name'], {}, client_dict
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#override
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def get_data(self, host_dict):
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if not self.manual:
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if len (self.input_data) > 0:
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return self.input_data.pop(0)
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else:
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need_remark_face = False
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redraw_needed = False
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while len (self.input_data) > 0:
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data = self.input_data[0]
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filename, faces = data
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is_frame_done = False
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if need_remark_face: # need remark image from input data that already has a marked face?
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need_remark_face = False
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if len(faces) != 0: # If there was already a face then lock the rectangle to it until the mouse is clicked
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self.rect, self.landmarks = faces.pop()
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faces.clear()
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redraw_needed = True
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self.rect_locked = True
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self.rect_size = ( self.rect[2] - self.rect[0] ) / 2
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self.x = ( self.rect[0] + self.rect[2] ) / 2
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self.y = ( self.rect[1] + self.rect[3] ) / 2
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if len(faces) == 0:
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if self.cache_original_image[0] == filename:
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self.original_image = self.cache_original_image[1]
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else:
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self.original_image = cv2_imread( filename )
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self.cache_original_image = (filename, self.original_image )
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(h,w,c) = self.original_image.shape
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self.view_scale = 1.0 if self.manual_window_size == 0 else self.manual_window_size / ( h * (16.0/9.0) )
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if self.cache_image[0] == (h,w,c) + (self.view_scale,filename):
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self.image = self.cache_image[1]
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else:
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self.image = cv2.resize (self.original_image, ( int(w*self.view_scale), int(h*self.view_scale) ), interpolation=cv2.INTER_LINEAR)
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self.cache_image = ( (h,w,c) + (self.view_scale,filename), self.image )
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(h,w,c) = self.image.shape
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sh = (0,0, w, min(100, h) )
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if self.cache_text_lines_img[0] == sh:
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self.text_lines_img = self.cache_text_lines_img[1]
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else:
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self.text_lines_img = (image_utils.get_draw_text_lines ( self.image, sh,
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[ 'Match landmarks with face exactly. Click to confirm/unconfirm selection',
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'[Enter] - confirm face landmarks and continue',
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'[Space] - confirm as unmarked frame and continue',
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'[Mouse wheel] - change rect',
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'[,] [.]- prev frame, next frame. [Q] - skip remaining frames',
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'[h] - hide this help'
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], (1, 1, 1) )*255).astype(np.uint8)
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self.cache_text_lines_img = (sh, self.text_lines_img)
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while True:
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io.process_messages(0.0001)
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new_x = self.x
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new_y = self.y
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new_rect_size = self.rect_size
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mouse_events = io.get_mouse_events(self.wnd_name)
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for ev in mouse_events:
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(x, y, ev, flags) = ev
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if ev == io.EVENT_MOUSEWHEEL and not self.rect_locked:
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mod = 1 if flags > 0 else -1
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diff = 1 if new_rect_size <= 40 else np.clip(new_rect_size / 10, 1, 10)
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new_rect_size = max (5, new_rect_size + diff*mod)
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elif ev == io.EVENT_LBUTTONDOWN:
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self.rect_locked = not self.rect_locked
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self.extract_needed = True
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elif not self.rect_locked:
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new_x = np.clip (x, 0, w-1) / self.view_scale
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new_y = np.clip (y, 0, h-1) / self.view_scale
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key_events = io.get_key_events(self.wnd_name)
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key, = key_events[-1] if len(key_events) > 0 else (0,)
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if key == ord('\r') or key == ord('\n'):
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#confirm frame
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is_frame_done = True
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faces.append ( [(self.rect), self.landmarks] )
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break
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elif key == ord(' '):
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#confirm skip frame
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is_frame_done = True
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break
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elif key == ord(',') and len(self.result) > 0:
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#go prev frame
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if self.rect_locked:
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# Only save the face if the rect is still locked
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faces.append ( [(self.rect), self.landmarks] )
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self.input_data.insert(0, self.result.pop() )
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io.progress_bar_inc(-1)
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need_remark_face = True
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break
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elif key == ord('.'):
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#go next frame
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if self.rect_locked:
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# Only save the face if the rect is still locked
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faces.append ( [(self.rect), self.landmarks] )
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need_remark_face = True
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is_frame_done = True
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break
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elif key == ord('q'):
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#skip remaining
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if self.rect_locked:
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faces.append ( [(self.rect), self.landmarks] )
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while len(self.input_data) > 0:
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self.result.append( self.input_data.pop(0) )
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io.progress_bar_inc(1)
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break
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elif key == ord('h'):
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self.hide_help = not self.hide_help
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break
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if self.x != new_x or \
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self.y != new_y or \
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self.rect_size != new_rect_size or \
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self.extract_needed or \
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redraw_needed:
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self.x = new_x
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self.y = new_y
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self.rect_size = new_rect_size
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self.rect = ( int(self.x-self.rect_size),
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int(self.y-self.rect_size),
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int(self.x+self.rect_size),
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int(self.y+self.rect_size) )
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if redraw_needed:
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redraw_needed = False
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return [filename, None]
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else:
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return [filename, [self.rect]]
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else:
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is_frame_done = True
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if is_frame_done:
|
|
self.result.append ( data )
|
|
self.input_data.pop(0)
|
|
io.progress_bar_inc(1)
|
|
self.extract_needed = True
|
|
self.rect_locked = False
|
|
|
|
return None
|
|
|
|
#override
|
|
def on_data_return (self, host_dict, data):
|
|
if not self.manual:
|
|
self.input_data.insert(0, data)
|
|
|
|
#override
|
|
def on_result (self, host_dict, data, result):
|
|
if self.manual == True:
|
|
filename, landmarks = result
|
|
if landmarks is not None:
|
|
self.landmarks = landmarks[0][1]
|
|
|
|
(h,w,c) = self.image.shape
|
|
|
|
if not self.hide_help:
|
|
image = cv2.addWeighted (self.image,1.0,self.text_lines_img,1.0,0)
|
|
else:
|
|
image = self.image.copy()
|
|
|
|
view_rect = (np.array(self.rect) * self.view_scale).astype(np.int).tolist()
|
|
view_landmarks = (np.array(self.landmarks) * self.view_scale).astype(np.int).tolist()
|
|
|
|
if self.rect_size <= 40:
|
|
scaled_rect_size = h // 3 if w > h else w // 3
|
|
|
|
p1 = (self.x - self.rect_size, self.y - self.rect_size)
|
|
p2 = (self.x + self.rect_size, self.y - self.rect_size)
|
|
p3 = (self.x - self.rect_size, self.y + self.rect_size)
|
|
|
|
wh = h if h < w else w
|
|
np1 = (w / 2 - wh / 4, h / 2 - wh / 4)
|
|
np2 = (w / 2 + wh / 4, h / 2 - wh / 4)
|
|
np3 = (w / 2 - wh / 4, h / 2 + wh / 4)
|
|
|
|
mat = cv2.getAffineTransform( np.float32([p1,p2,p3])*self.view_scale, np.float32([np1,np2,np3]) )
|
|
image = cv2.warpAffine(image, mat,(w,h) )
|
|
view_landmarks = LandmarksProcessor.transform_points (view_landmarks, mat)
|
|
|
|
landmarks_color = (255,255,0) if self.rect_locked else (0,255,0)
|
|
LandmarksProcessor.draw_rect_landmarks (image, view_rect, view_landmarks, self.image_size, self.face_type, landmarks_color=landmarks_color)
|
|
self.extract_needed = False
|
|
|
|
io.show_image (self.wnd_name, image)
|
|
else:
|
|
if self.type == 'rects':
|
|
self.result.append ( result )
|
|
elif self.type == 'landmarks':
|
|
self.result.append ( result )
|
|
elif self.type == 'final':
|
|
self.result += result
|
|
|
|
io.progress_bar_inc(1)
|
|
|
|
#override
|
|
def get_result(self):
|
|
return self.result
|
|
|
|
|
|
class DeletedFilesSearcherSubprocessor(Subprocessor):
|
|
class Cli(Subprocessor.Cli):
|
|
#override
|
|
def on_initialize(self, client_dict):
|
|
self.debug_paths_stems = client_dict['debug_paths_stems']
|
|
return None
|
|
|
|
#override
|
|
def process_data(self, data):
|
|
input_path_stem = Path(data[0]).stem
|
|
return any ( [ input_path_stem == d_stem for d_stem in self.debug_paths_stems] )
|
|
|
|
#override
|
|
def get_data_name (self, data):
|
|
#return string identificator of your data
|
|
return data[0]
|
|
|
|
#override
|
|
def __init__(self, input_paths, debug_paths ):
|
|
self.input_paths = input_paths
|
|
self.debug_paths_stems = [ Path(d).stem for d in debug_paths]
|
|
self.result = []
|
|
super().__init__('DeletedFilesSearcherSubprocessor', DeletedFilesSearcherSubprocessor.Cli, 60)
|
|
|
|
#override
|
|
def process_info_generator(self):
|
|
for i in range(min(multiprocessing.cpu_count(), 8)):
|
|
yield 'CPU%d' % (i), {}, {'debug_paths_stems' : self.debug_paths_stems}
|
|
|
|
#override
|
|
def on_clients_initialized(self):
|
|
io.progress_bar ("Searching deleted files", len (self.input_paths))
|
|
|
|
#override
|
|
def on_clients_finalized(self):
|
|
io.progress_bar_close()
|
|
|
|
#override
|
|
def get_data(self, host_dict):
|
|
if len (self.input_paths) > 0:
|
|
return [self.input_paths.pop(0)]
|
|
return None
|
|
|
|
#override
|
|
def on_data_return (self, host_dict, data):
|
|
self.input_paths.insert(0, data[0])
|
|
|
|
#override
|
|
def on_result (self, host_dict, data, result):
|
|
if result == False:
|
|
self.result.append( data[0] )
|
|
io.progress_bar_inc(1)
|
|
|
|
#override
|
|
def get_result(self):
|
|
return self.result
|
|
|
|
def main(input_dir,
|
|
output_dir,
|
|
debug_dir=None,
|
|
detector='mt',
|
|
manual_fix=False,
|
|
manual_output_debug_fix=False,
|
|
manual_window_size=1368,
|
|
image_size=256,
|
|
face_type='full_face',
|
|
device_args={}):
|
|
|
|
input_path = Path(input_dir)
|
|
output_path = Path(output_dir)
|
|
face_type = FaceType.fromString(face_type)
|
|
|
|
multi_gpu = device_args.get('multi_gpu', False)
|
|
cpu_only = device_args.get('cpu_only', False)
|
|
|
|
accurate_landmarks_extractor = io.input_bool("Use more accurate landmarks extraction? (y/n ?:help skip:n) : ", False, help_message="More accurate landmarks extraction, but processing is slower." )
|
|
|
|
|
|
if not input_path.exists():
|
|
raise ValueError('Input directory not found. Please ensure it exists.')
|
|
|
|
if output_path.exists():
|
|
if not manual_output_debug_fix and input_path != output_path:
|
|
for filename in Path_utils.get_image_paths(output_path):
|
|
Path(filename).unlink()
|
|
else:
|
|
output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
if manual_output_debug_fix:
|
|
if debug_dir is None:
|
|
raise ValueError('debug-dir must be specified')
|
|
detector = 'manual'
|
|
io.log_info('Performing re-extract frames which were deleted from _debug directory.')
|
|
|
|
input_path_image_paths = Path_utils.get_image_unique_filestem_paths(input_path, verbose_print_func=io.log_info)
|
|
if debug_dir is not None:
|
|
debug_output_path = Path(debug_dir)
|
|
|
|
if manual_output_debug_fix:
|
|
if not debug_output_path.exists():
|
|
raise ValueError("%s not found " % ( str(debug_output_path) ))
|
|
|
|
input_path_image_paths = DeletedFilesSearcherSubprocessor (input_path_image_paths, Path_utils.get_image_paths(debug_output_path) ).run()
|
|
input_path_image_paths = sorted (input_path_image_paths)
|
|
else:
|
|
if debug_output_path.exists():
|
|
for filename in Path_utils.get_image_paths(debug_output_path):
|
|
Path(filename).unlink()
|
|
else:
|
|
debug_output_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
images_found = len(input_path_image_paths)
|
|
faces_detected = 0
|
|
if images_found != 0:
|
|
if detector == 'manual':
|
|
io.log_info ('Performing manual extract...')
|
|
extracted_faces = ExtractSubprocessor ([ (filename,[]) for filename in input_path_image_paths ], 'landmarks', image_size, face_type, debug_dir, cpu_only=cpu_only, manual=True, manual_window_size=manual_window_size, accurate_landmarks_extractor=accurate_landmarks_extractor).run()
|
|
else:
|
|
io.log_info ('Performing 1st pass...')
|
|
extracted_rects = ExtractSubprocessor ([ (x,) for x in input_path_image_paths ], 'rects', image_size, face_type, debug_dir, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, detector=detector).run()
|
|
|
|
io.log_info ('Performing 2nd pass...')
|
|
extracted_faces = ExtractSubprocessor (extracted_rects, 'landmarks', image_size, face_type, debug_dir, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, accurate_landmarks_extractor=accurate_landmarks_extractor).run()
|
|
|
|
if manual_fix:
|
|
io.log_info ('Performing manual fix...')
|
|
|
|
if all ( np.array ( [ len(data[1]) > 0 for data in extracted_faces] ) == True ):
|
|
io.log_info ('All faces are detected, manual fix not needed.')
|
|
else:
|
|
extracted_faces = ExtractSubprocessor (extracted_faces, 'landmarks', image_size, face_type, debug_dir, manual=True, manual_window_size=manual_window_size, accurate_landmarks_extractor=accurate_landmarks_extractor).run()
|
|
|
|
if len(extracted_faces) > 0:
|
|
io.log_info ('Performing 3rd pass...')
|
|
final_imgs_paths = ExtractSubprocessor (extracted_faces, 'final', image_size, face_type, debug_dir, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False, output_path=output_path).run()
|
|
faces_detected = len(final_imgs_paths)
|
|
|
|
io.log_info ('-------------------------')
|
|
io.log_info ('Images found: %d' % (images_found) )
|
|
io.log_info ('Faces detected: %d' % (faces_detected) )
|
|
io.log_info ('-------------------------') |