mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
583 lines
No EOL
26 KiB
Python
583 lines
No EOL
26 KiB
Python
import traceback
|
|
import os
|
|
import sys
|
|
import time
|
|
import multiprocessing
|
|
import shutil
|
|
from pathlib import Path
|
|
import numpy as np
|
|
import cv2
|
|
from utils import Path_utils
|
|
from utils.DFLJPG import DFLJPG
|
|
from utils.cv2_utils import *
|
|
from utils import image_utils
|
|
import facelib
|
|
from facelib import FaceType
|
|
from facelib import LandmarksProcessor
|
|
from nnlib import nnlib
|
|
from joblib import Subprocessor
|
|
from interact import interact as io
|
|
|
|
class ExtractSubprocessor(Subprocessor):
|
|
|
|
class Cli(Subprocessor.Cli):
|
|
|
|
#override
|
|
def on_initialize(self, client_dict):
|
|
self.log_info ('Running on %s.' % (client_dict['device_name']) )
|
|
self.type = client_dict['type']
|
|
self.image_size = client_dict['image_size']
|
|
self.face_type = client_dict['face_type']
|
|
self.device_idx = client_dict['device_idx']
|
|
self.cpu_only = client_dict['device_type'] == 'CPU'
|
|
self.output_path = Path(client_dict['output_dir']) if 'output_dir' in client_dict.keys() else None
|
|
self.debug = client_dict['debug']
|
|
self.detector = client_dict['detector']
|
|
|
|
self.cached_image = (None, None)
|
|
|
|
self.e = None
|
|
device_config = nnlib.DeviceConfig ( cpu_only=self.cpu_only, force_gpu_idx=self.device_idx, allow_growth=True)
|
|
if self.type == 'rects':
|
|
if self.detector is not None:
|
|
if self.detector == 'mt':
|
|
nnlib.import_all (device_config)
|
|
self.e = facelib.MTCExtractor()
|
|
elif self.detector == 'dlib':
|
|
nnlib.import_dlib (device_config)
|
|
self.e = facelib.DLIBExtractor(nnlib.dlib)
|
|
else:
|
|
raise ValueError ("Wrond detector type.")
|
|
|
|
if self.e is not None:
|
|
self.e.__enter__()
|
|
|
|
elif self.type == 'landmarks':
|
|
nnlib.import_all (device_config)
|
|
self.e = facelib.LandmarksExtractor(nnlib.keras)
|
|
self.e.__enter__()
|
|
|
|
elif self.type == 'final':
|
|
pass
|
|
|
|
#override
|
|
def on_finalize(self):
|
|
if self.e is not None:
|
|
self.e.__exit__()
|
|
|
|
#override
|
|
def process_data(self, data):
|
|
filename_path = Path( data[0] )
|
|
|
|
filename_path_str = str(filename_path)
|
|
if self.cached_image[0] == filename_path_str:
|
|
image = self.cached_image[1]
|
|
else:
|
|
image = cv2_imread( filename_path_str )
|
|
self.cached_image = ( filename_path_str, image )
|
|
|
|
if image is None:
|
|
self.log_err ( 'Failed to extract %s, reason: cv2_imread() fail.' % ( str(filename_path) ) )
|
|
else:
|
|
if self.type == 'rects':
|
|
rects = self.e.extract_from_bgr (image)
|
|
return [str(filename_path), rects]
|
|
|
|
elif self.type == 'landmarks':
|
|
rects = data[1]
|
|
landmarks = self.e.extract_from_bgr (image, rects)
|
|
return [str(filename_path), landmarks]
|
|
|
|
elif self.type == 'final':
|
|
src_dflimg = None
|
|
(h,w,c) = image.shape
|
|
if h == w:
|
|
#extracting from already extracted jpg image?
|
|
if filename_path.suffix == '.jpg':
|
|
src_dflimg = DFLJPG.load ( str(filename_path) )
|
|
|
|
result = []
|
|
faces = data[1]
|
|
|
|
if self.debug:
|
|
debug_output_file = '{}{}'.format( str(Path(str(self.output_path) + '_debug') / filename_path.stem), '.jpg')
|
|
debug_image = image.copy()
|
|
|
|
for (face_idx, face) in enumerate(faces):
|
|
output_file = '{}_{}{}'.format(str(self.output_path / filename_path.stem), str(face_idx), '.jpg')
|
|
|
|
rect = face[0]
|
|
image_landmarks = np.array(face[1])
|
|
|
|
if self.debug:
|
|
LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type)
|
|
|
|
if self.face_type == FaceType.MARK_ONLY:
|
|
face_image = image
|
|
face_image_landmarks = image_landmarks
|
|
else:
|
|
image_to_face_mat = LandmarksProcessor.get_transform_mat (image_landmarks, self.image_size, self.face_type)
|
|
face_image = cv2.warpAffine(image, image_to_face_mat, (self.image_size, self.image_size), cv2.INTER_LANCZOS4)
|
|
face_image_landmarks = LandmarksProcessor.transform_points (image_landmarks, image_to_face_mat)
|
|
|
|
if src_dflimg is not None:
|
|
#if extracting from dflimg just copy it in order not to lose quality
|
|
shutil.copy ( str(filename_path), str(output_file) )
|
|
else:
|
|
cv2_imwrite(output_file, face_image, [int(cv2.IMWRITE_JPEG_QUALITY), 85] )
|
|
|
|
DFLJPG.embed_data(output_file, face_type = FaceType.toString(self.face_type),
|
|
landmarks = face_image_landmarks.tolist(),
|
|
source_filename = filename_path.name,
|
|
source_rect= rect,
|
|
source_landmarks = image_landmarks.tolist()
|
|
)
|
|
|
|
result.append (output_file)
|
|
|
|
if self.debug:
|
|
cv2_imwrite(debug_output_file, debug_image, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
|
|
|
|
return result
|
|
return None
|
|
|
|
#overridable
|
|
def get_data_name (self, data):
|
|
#return string identificator of your data
|
|
return data[0]
|
|
|
|
#override
|
|
def __init__(self, input_data, type, image_size, face_type, debug, multi_gpu=False, cpu_only=False, manual=False, manual_window_size=0, detector=None, output_path=None ):
|
|
self.input_data = input_data
|
|
self.type = type
|
|
self.image_size = image_size
|
|
self.face_type = face_type
|
|
self.debug = debug
|
|
self.multi_gpu = multi_gpu
|
|
self.cpu_only = cpu_only
|
|
self.detector = detector
|
|
self.output_path = output_path
|
|
self.manual = manual
|
|
self.manual_window_size = manual_window_size
|
|
self.result = []
|
|
|
|
no_response_time_sec = 60 if not self.manual else 999999
|
|
super().__init__('Extractor', ExtractSubprocessor.Cli, no_response_time_sec)
|
|
|
|
#override
|
|
def on_clients_initialized(self):
|
|
if self.manual == True:
|
|
self.wnd_name = 'Manual pass'
|
|
io.named_window(self.wnd_name)
|
|
io.capture_mouse(self.wnd_name)
|
|
io.capture_keys(self.wnd_name)
|
|
|
|
self.cache_original_image = (None, None)
|
|
self.cache_image = (None, None)
|
|
self.cache_text_lines_img = (None, None)
|
|
|
|
self.landmarks = None
|
|
self.x = 0
|
|
self.y = 0
|
|
self.rect_size = 100
|
|
self.rect_locked = False
|
|
self.redraw_needed = True
|
|
|
|
io.progress_bar (None, len (self.input_data))
|
|
|
|
#override
|
|
def on_clients_finalized(self):
|
|
if self.manual == True:
|
|
io.destroy_all_windows()
|
|
|
|
io.progress_bar_close()
|
|
|
|
def get_devices_for_type (self, type, multi_gpu, cpu_only):
|
|
if 'cpu' in nnlib.device.backend:
|
|
cpu_only = True
|
|
|
|
if not cpu_only and (type == 'rects' or type == 'landmarks'):
|
|
if type == 'rects' and self.detector == 'mt' and nnlib.device.backend == "plaidML":
|
|
cpu_only = True
|
|
else:
|
|
if multi_gpu:
|
|
devices = nnlib.device.getValidDevicesWithAtLeastTotalMemoryGB(2)
|
|
if not multi_gpu or len(devices) == 0:
|
|
devices = [nnlib.device.getBestValidDeviceIdx()]
|
|
if len(devices) == 0:
|
|
devices = [0]
|
|
|
|
for idx in devices:
|
|
dev_name = nnlib.device.getDeviceName(idx)
|
|
dev_vram = nnlib.device.getDeviceVRAMTotalGb(idx)
|
|
|
|
if not self.manual and self.type == 'rects' and self.detector == 'mt':
|
|
for i in range ( int (max (1, dev_vram / 2) ) ):
|
|
yield (idx, 'GPU', '%s #%d' % (dev_name,i) , dev_vram)
|
|
else:
|
|
yield (idx, 'GPU', dev_name, dev_vram)
|
|
|
|
if cpu_only and (type == 'rects' or type == 'landmarks'):
|
|
if self.manual:
|
|
yield (0, 'CPU', 'CPU', 0 )
|
|
else:
|
|
for i in range( min(8, multiprocessing.cpu_count() // 2) ):
|
|
yield (i, 'CPU', 'CPU%d' % (i), 0 )
|
|
|
|
if type == 'final':
|
|
for i in range( min(8, multiprocessing.cpu_count()) ):
|
|
yield (i, 'CPU', 'CPU%d' % (i), 0 )
|
|
|
|
#override
|
|
def process_info_generator(self):
|
|
base_dict = {'type' : self.type,
|
|
'image_size': self.image_size,
|
|
'face_type': self.face_type,
|
|
'debug': self.debug,
|
|
'output_dir': str(self.output_path),
|
|
'detector': self.detector}
|
|
|
|
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):
|
|
client_dict = base_dict.copy()
|
|
client_dict['device_idx'] = device_idx
|
|
client_dict['device_name'] = device_name
|
|
client_dict['device_type'] = device_type
|
|
yield client_dict['device_name'], {}, client_dict
|
|
|
|
#override
|
|
def get_data(self, host_dict):
|
|
if not self.manual:
|
|
if len (self.input_data) > 0:
|
|
return self.input_data.pop(0)
|
|
else:
|
|
skip_remaining = False
|
|
allow_remark_faces = False
|
|
while len (self.input_data) > 0:
|
|
data = self.input_data[0]
|
|
filename, faces = data
|
|
is_frame_done = False
|
|
go_to_prev_frame = False
|
|
|
|
# Can we mark an image that already has a marked face?
|
|
if allow_remark_faces:
|
|
allow_remark_faces = False
|
|
# If there was already a face then lock the rectangle to it until the mouse is clicked
|
|
if len(faces) > 0:
|
|
self.rect, self.landmarks = faces.pop()
|
|
|
|
self.rect_locked = True
|
|
self.redraw_needed = True
|
|
faces.clear()
|
|
self.rect_size = ( self.rect[2] - self.rect[0] ) / 2
|
|
self.x = ( self.rect[0] + self.rect[2] ) / 2
|
|
self.y = ( self.rect[1] + self.rect[3] ) / 2
|
|
|
|
if len(faces) == 0:
|
|
if self.cache_original_image[0] == filename:
|
|
self.original_image = self.cache_original_image[1]
|
|
else:
|
|
self.original_image = cv2_imread( filename )
|
|
self.cache_original_image = (filename, self.original_image )
|
|
|
|
(h,w,c) = self.original_image.shape
|
|
self.view_scale = 1.0 if self.manual_window_size == 0 else self.manual_window_size / ( h * (16.0/9.0) )
|
|
|
|
if self.cache_image[0] == (h,w,c) + (self.view_scale,filename):
|
|
self.image = self.cache_image[1]
|
|
else:
|
|
self.image = cv2.resize (self.original_image, ( int(w*self.view_scale), int(h*self.view_scale) ), interpolation=cv2.INTER_LINEAR)
|
|
self.cache_image = ( (h,w,c) + (self.view_scale,filename), self.image )
|
|
|
|
(h,w,c) = self.image.shape
|
|
|
|
sh = (0,0, w, min(100, h) )
|
|
if self.cache_text_lines_img[0] == sh:
|
|
self.text_lines_img = self.cache_text_lines_img[1]
|
|
else:
|
|
self.text_lines_img = (image_utils.get_draw_text_lines ( self.image, sh,
|
|
[ 'Match landmarks with face exactly. Click to confirm/unconfirm selection',
|
|
'[Enter] - confirm face landmarks and continue',
|
|
'[Space] - confirm as unmarked frame and continue',
|
|
'[Mouse wheel] - change rect',
|
|
'[,] [.]- prev frame, next frame',
|
|
'[Q] - skip remaining frames'
|
|
], (1, 1, 1) )*255).astype(np.uint8)
|
|
|
|
self.cache_text_lines_img = (sh, self.text_lines_img)
|
|
|
|
while True:
|
|
new_x = self.x
|
|
new_y = self.y
|
|
new_rect_size = self.rect_size
|
|
|
|
mouse_events = io.get_mouse_events(self.wnd_name)
|
|
for ev in mouse_events:
|
|
(x, y, ev, flags) = ev
|
|
if ev == io.EVENT_MOUSEWHEEL and not self.rect_locked:
|
|
mod = 1 if flags > 0 else -1
|
|
diff = 1 if new_rect_size <= 40 else np.clip(new_rect_size / 10, 1, 10)
|
|
new_rect_size = max (5, new_rect_size + diff*mod)
|
|
elif ev == io.EVENT_LBUTTONDOWN:
|
|
self.rect_locked = not self.rect_locked
|
|
self.redraw_needed = True
|
|
elif not self.rect_locked:
|
|
new_x = np.clip (x, 0, w-1) / self.view_scale
|
|
new_y = np.clip (y, 0, h-1) / self.view_scale
|
|
|
|
key_events = io.get_key_events(self.wnd_name)
|
|
key, = key_events[-1] if len(key_events) > 0 else (0,)
|
|
|
|
if key == ord('\r') or key == ord('\n'):
|
|
faces.append ( [(self.rect), self.landmarks] )
|
|
is_frame_done = True
|
|
break
|
|
elif key == ord(' '):
|
|
is_frame_done = True
|
|
break
|
|
elif key == ord('.'):
|
|
allow_remark_faces = True
|
|
# Only save the face if the rect is still locked
|
|
if self.rect_locked:
|
|
faces.append ( [(self.rect), self.landmarks] )
|
|
is_frame_done = True
|
|
break
|
|
elif key == ord(',') and len(self.result) > 0:
|
|
# Only save the face if the rect is still locked
|
|
if self.rect_locked:
|
|
faces.append ( [(self.rect), self.landmarks] )
|
|
go_to_prev_frame = True
|
|
break
|
|
elif key == ord('q'):
|
|
skip_remaining = True
|
|
break
|
|
|
|
if self.x != new_x or \
|
|
self.y != new_y or \
|
|
self.rect_size != new_rect_size or \
|
|
self.redraw_needed:
|
|
self.x = new_x
|
|
self.y = new_y
|
|
self.rect_size = new_rect_size
|
|
|
|
self.rect = ( int(self.x-self.rect_size),
|
|
int(self.y-self.rect_size),
|
|
int(self.x+self.rect_size),
|
|
int(self.y+self.rect_size) )
|
|
|
|
return [filename, [self.rect]]
|
|
|
|
io.process_messages(0.0001)
|
|
else:
|
|
is_frame_done = True
|
|
|
|
if is_frame_done:
|
|
self.result.append ( data )
|
|
self.input_data.pop(0)
|
|
io.progress_bar_inc(1)
|
|
self.redraw_needed = True
|
|
self.rect_locked = False
|
|
elif go_to_prev_frame:
|
|
self.input_data.insert(0, self.result.pop() )
|
|
io.progress_bar_inc(-1)
|
|
allow_remark_faces = True
|
|
self.redraw_needed = True
|
|
self.rect_locked = False
|
|
elif skip_remaining:
|
|
if self.rect_locked:
|
|
faces.append ( [(self.rect), self.landmarks] )
|
|
while len(self.input_data) > 0:
|
|
self.result.append( self.input_data.pop(0) )
|
|
io.progress_bar_inc(1)
|
|
|
|
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:
|
|
self.landmarks = result[1][0][1]
|
|
|
|
(h,w,c) = self.image.shape
|
|
image = cv2.addWeighted (self.image,1.0,self.text_lines_img,1.0,0)
|
|
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)
|
|
|
|
LandmarksProcessor.draw_rect_landmarks (image, view_rect, view_landmarks, self.image_size, self.face_type)
|
|
|
|
if self.rect_locked:
|
|
LandmarksProcessor.draw_landmarks(image, view_landmarks, (255,255,0) )
|
|
self.redraw_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=False,
|
|
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)
|
|
|
|
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:
|
|
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:
|
|
debug = True
|
|
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:
|
|
debug_output_path = Path(str(output_path) + '_debug')
|
|
|
|
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, cpu_only=cpu_only, manual=True, manual_window_size=manual_window_size).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, 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, multi_gpu=multi_gpu, cpu_only=cpu_only, manual=False).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, manual=True, manual_window_size=manual_window_size).run()
|
|
|
|
if len(extracted_faces) > 0:
|
|
io.log_info ('Performing 3rd pass...')
|
|
final_imgs_paths = ExtractSubprocessor (extracted_faces, 'final', image_size, face_type, debug, 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 ('-------------------------') |