mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-07 05:22:06 -07:00
FacesetRelighter fixes and improvements:
now you have 3 ways: 1) define light directions manually (not for google colab) watch demo https://youtu.be/79xz7yEO5Jw 2) relight faceset with one random direction 3) relight faceset with predefined 8 directions
This commit is contained in:
Colombo
parent
fe58459f36
commit
05153d9ba5
3 changed files with 235 additions and 36 deletions
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@ -1,6 +1,7 @@
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import traceback
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from pathlib import Path
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import imagelib
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from interact import interact as io
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from nnlib import DeepPortraitRelighting
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from utils import Path_utils
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@ -8,20 +9,195 @@ from utils.cv2_utils import *
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from utils.DFLJPG import DFLJPG
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from utils.DFLPNG import DFLPNG
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class RelightEditor:
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def __init__(self, image_paths, dpr, lighten):
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self.image_paths = image_paths
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self.dpr = dpr
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self.lighten = lighten
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self.current_img_path = None
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self.current_img = None
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self.current_img_shape = None
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self.pick_new_face()
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self.alt_azi_ar = [ [0,0] ]
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self.alt_azi_cur = 0
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self.mouse_x = self.mouse_y = 9999
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self.screen_status_block = None
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self.screen_status_block_dirty = True
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self.screen_changed = True
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def pick_new_face(self):
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self.current_img_path = self.image_paths[ np.random.randint(len(self.image_paths)) ]
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self.current_img = cv2_imread (str(self.current_img_path))
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self.current_img_shape = self.current_img.shape
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self.set_screen_changed()
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def set_screen_changed(self):
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self.screen_changed = True
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def switch_screen_changed(self):
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result = self.screen_changed
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self.screen_changed = False
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return result
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def make_screen(self):
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alt,azi=self.alt_azi_ar[self.alt_azi_cur]
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img = self.dpr.relight (self.current_img, alt, azi, self.lighten)
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h,w,c = img.shape
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lines = ['Pick light directions for whole faceset.',
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'[q]-new test face',
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'[w][e]-navigate',
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'[r]-new [t]-delete [enter]-process',
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'']
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for i, (alt,azi) in enumerate(self.alt_azi_ar):
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s = '>:' if self.alt_azi_cur == i else ' :'
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s += f'alt=[{ int(alt):03}] azi=[{ int(azi):03}]'
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lines += [ s ]
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lines_count = len(lines)
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h_line = 16
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sh = lines_count * h_line
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sw = 400
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sc = c
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status_img = np.ones ( (sh,sw,sc) ) * 0.1
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for i in range(lines_count):
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status_img[ i*h_line:(i+1)*h_line, 0:sw] += \
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imagelib.get_text_image ( (h_line,sw,c), lines[i], color=[0.8]*c )
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status_img = np.clip(status_img*255, 0, 255).astype(np.uint8)
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#combine screens
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if sh > h:
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img = np.concatenate ([img, np.zeros( (sh-h,w,c), dtype=img.dtype ) ], axis=0)
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elif h > sh:
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status_img = np.concatenate ([status_img, np.zeros( (h-sh,sw,sc), dtype=img.dtype ) ], axis=0)
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img = np.concatenate ([img, status_img], axis=1)
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return img
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def run(self):
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wnd_name = "Relighter"
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io.named_window(wnd_name)
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io.capture_keys(wnd_name)
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io.capture_mouse(wnd_name)
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zoom_factor = 1.0
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is_angle_editing = False
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is_exit = False
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while not is_exit:
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io.process_messages(0.0001)
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mouse_events = io.get_mouse_events(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_LBUTTONDOWN:
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is_angle_editing = True
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if ev == io.EVENT_LBUTTONUP:
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is_angle_editing = False
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if is_angle_editing:
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h,w,c = self.current_img_shape
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self.alt_azi_ar[self.alt_azi_cur] = \
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[np.clip ( ( 0.5-y/w )*2.0, -1, 1)*90, \
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np.clip ( (x / h - 0.5)*2.0, -1, 1)*90 ]
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self.set_screen_changed()
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key_events = io.get_key_events(wnd_name)
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key, chr_key, ctrl_pressed, alt_pressed, shift_pressed = key_events[-1] if len(key_events) > 0 else (0,0,False,False,False)
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if key != 0:
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if chr_key == 'q':
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self.pick_new_face()
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elif chr_key == 'w':
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self.alt_azi_cur = np.clip (self.alt_azi_cur-1, 0, len(self.alt_azi_ar)-1)
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self.set_screen_changed()
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elif chr_key == 'e':
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self.alt_azi_cur = np.clip (self.alt_azi_cur+1, 0, len(self.alt_azi_ar)-1)
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self.set_screen_changed()
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elif chr_key == 'r':
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#add direction
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self.alt_azi_ar += [ [0,0] ]
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self.alt_azi_cur +=1
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self.set_screen_changed()
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elif chr_key == 't':
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if len(self.alt_azi_ar) > 1:
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self.alt_azi_ar.pop(self.alt_azi_cur)
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self.alt_azi_cur = np.clip (self.alt_azi_cur, 0, len(self.alt_azi_ar)-1)
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self.set_screen_changed()
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elif key == 27 or chr_key == '\r' or chr_key == '\n': #esc
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is_exit = True
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if self.switch_screen_changed():
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screen = self.make_screen()
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if zoom_factor != 1.0:
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h,w,c = screen.shape
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screen = cv2.resize ( screen, ( int(w*zoom_factor), int(h*zoom_factor) ) )
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io.show_image (wnd_name, screen )
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io.destroy_window(wnd_name)
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return self.alt_azi_ar
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def relight(input_dir, lighten=None, random_one=None):
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if lighten is None:
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lighten = io.input_bool ("Lighten the faces? ( y/n default:n ) : ", False)
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lighten = io.input_bool ("Lighten the faces? ( y/n default:n ?:help ) : ", False, help_message="Lighten the faces instead of shadow. May produce artifacts." )
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if io.is_colab():
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io.log_info("In colab version you cannot choose light directions manually.")
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manual = False
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else:
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manual = io.input_bool ("Choose light directions manually? ( y/n default:y ) : ", True)
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if not manual:
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if random_one is None:
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random_one = io.input_bool ("Relight the faces only with one random direction? ( y/n default:y ) : ", True)
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random_one = io.input_bool ("Relight the faces only with one random direction? ( y/n default:y ?:help) : ", True, help_message="Otherwise faceset will be relighted with predefined 7 light directions.")
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input_path = Path(input_dir)
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image_paths = [Path(x) for x in Path_utils.get_image_paths(input_dir)]
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filtered_image_paths = []
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for filepath in io.progress_bar_generator(image_paths, "Collecting fileinfo"):
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try:
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath) )
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else:
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dflimg = None
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image_paths = [Path(x) for x in Path_utils.get_image_paths(input_path)]
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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else:
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if not dflimg.get_relighted():
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filtered_image_paths += [filepath]
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except:
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io.log_err (f"Exception occured while processing file {filepath.name}. Error: {traceback.format_exc()}")
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image_paths = filtered_image_paths
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if len(image_paths) == 0:
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io.log_info("No files to process.")
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return
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dpr = DeepPortraitRelighting()
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if manual:
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alt_azi_ar = RelightEditor(image_paths, dpr, lighten).run()
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else:
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if not random_one:
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alt_azi_ar = [(60,0), (60,60), (0,60), (-60,60), (-60,0), (-60,-60), (0,-60), (60,-60)]
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for filepath in io.progress_bar_generator(image_paths, "Relighting"):
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try:
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if filepath.suffix == '.png':
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@ -36,24 +212,30 @@ def relight(input_dir, lighten=None, random_one=None):
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continue
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else:
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if dflimg.get_relighted():
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io.log_info (f"Skipping already relighted face [{filepath.name}]")
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continue
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img = cv2_imread (str(filepath))
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if random_one:
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relighted_imgs = dpr.relight_random(img,lighten=lighten)
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alt = np.random.randint(-90,91)
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azi = np.random.randint(-90,91)
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relighted_imgs = [dpr.relight(img,alt=alt,azi=azi,lighten=lighten)]
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else:
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relighted_imgs = dpr.relight_all(img,lighten=lighten)
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relighted_imgs = [dpr.relight(img,alt=alt,azi=azi,lighten=lighten) for (alt,azi) in alt_azi_ar ]
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i = 0
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for i,relighted_img in enumerate(relighted_imgs):
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im_flags = []
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if filepath.suffix == '.jpg':
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im_flags += [int(cv2.IMWRITE_JPEG_QUALITY), 100]
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relighted_filename = filepath.parent / (filepath.stem+f'_relighted_{i}'+filepath.suffix)
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while True:
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relighted_filepath = filepath.parent / (filepath.stem+f'_relighted_{i}'+filepath.suffix)
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if not relighted_filepath.exists():
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break
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i += 1
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cv2_imwrite (relighted_filename, relighted_img )
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dflimg.embed_and_set (relighted_filename, source_filename="_", relighted=True )
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cv2_imwrite (relighted_filepath, relighted_img )
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dflimg.embed_and_set (relighted_filepath, source_filename="_", relighted=True )
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except:
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io.log_err (f"Exception occured while processing file {filepath.name}. Error: {traceback.format_exc()}")
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@ -1,33 +1,55 @@
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import math
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from pathlib import Path
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import numpy as np
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import cv2
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import numpy as np
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import numpy.linalg as npla
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class DeepPortraitRelighting(object):
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def __init__(self):
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from nnlib import nnlib
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nnlib.import_torch()
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self.torch = nnlib.torch
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self.torch_device = nnlib.torch_device
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self.model = DeepPortraitRelighting.build_model(self.torch, self.torch_device)
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self.shs = [
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[1.084125496282453138e+00,-4.642676300617166185e-01,2.837846795150648915e-02,6.765292733937575687e-01,-3.594067725393816914e-01,4.790996460111427574e-02,-2.280054643781863066e-01,-8.125983081159608712e-02,2.881082012687687932e-01],
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[1.084125496282453138e+00,-4.642676300617170626e-01,5.466255701105990905e-01,3.996219229512094628e-01,-2.615439760463462715e-01,-2.511241554473071513e-01,6.495694866016435420e-02,3.510322039081858470e-01,1.189662732386344152e-01],
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[1.084125496282453138e+00,-4.642676300617179508e-01,6.532524688468428486e-01,-1.782088862752457814e-01,3.326676893441832261e-02,-3.610566644446819295e-01,3.647561777790956361e-01,-7.496419691318900735e-02,-5.412289239602386531e-02],
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[1.084125496282453138e+00,-4.642676300617186724e-01,2.679669346194941126e-01,-6.218447693376460972e-01,3.030269583891490037e-01,-1.991061409014726058e-01,-6.162944418511027977e-02,-3.176699976873690878e-01,1.920509612235956343e-01],
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[1.084125496282453138e+00,-4.642676300617186724e-01,-3.191031669056417219e-01,-5.972188577671910803e-01,3.446016675533919993e-01,1.127753677656503223e-01,-1.716692196540034188e-01,2.163406460637767315e-01,2.555824552121269688e-01],
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[1.084125496282453138e+00,-4.642676300617178398e-01,-6.658820752324799974e-01,-1.228749652534838893e-01,1.266842924569576145e-01,3.397347243069742673e-01,3.036887095295650041e-01,2.213893524577207617e-01,-1.886557316342868038e-02],
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[1.084125496282453138e+00,-4.642676300617169516e-01,-5.112381993903207800e-01,4.439962822886048266e-01,-1.866289387481862572e-01,3.108669041197227867e-01,2.021743042675238355e-01,-3.148681770175290051e-01,3.974379604123656762e-02]
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]
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def SH_basis(self, alt, azi):
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alt = alt * math.pi / 180.0
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azi = azi * math.pi / 180.0
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x = math.cos(alt)*math.sin(azi)
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y = -math.cos(alt)*math.cos(azi)
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z = math.sin(alt)
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normal = np.array([x,y,z])
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norm_X = normal[0]
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norm_Y = normal[1]
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norm_Z = normal[2]
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sh_basis = np.zeros((9))
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att= np.pi*np.array([1, 2.0/3.0, 1/4.0])
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sh_basis[0] = 0.5/np.sqrt(np.pi)*att[0]
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sh_basis[1] = np.sqrt(3)/2/np.sqrt(np.pi)*norm_Y*att[1]
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sh_basis[2] = np.sqrt(3)/2/np.sqrt(np.pi)*norm_Z*att[1]
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sh_basis[3] = np.sqrt(3)/2/np.sqrt(np.pi)*norm_X*att[1]
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sh_basis[4] = np.sqrt(15)/2/np.sqrt(np.pi)*norm_Y*norm_X*att[2]
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sh_basis[5] = np.sqrt(15)/2/np.sqrt(np.pi)*norm_Y*norm_Z*att[2]
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sh_basis[6] = np.sqrt(5)/4/np.sqrt(np.pi)*(3*norm_Z**2-1)*att[2]
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sh_basis[7] = np.sqrt(15)/2/np.sqrt(np.pi)*norm_X*norm_Z*att[2]
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sh_basis[8] = np.sqrt(15)/4/np.sqrt(np.pi)*(norm_X**2-norm_Y**2)*att[2]
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return sh_basis
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#n = [0..8]
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def relight(self, img, n, lighten=False):
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def relight(self, img, alt, azi, lighten=False):
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torch = self.torch
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sh = (np.array (self.shs[np.clip(n, 0,8)]).reshape( (1,9,1,1) )*0.7).astype(np.float32)
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sh = self.SH_basis (alt, azi)
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sh = (sh.reshape( (1,9,1,1) ) ).astype(np.float32)
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sh = torch.autograd.Variable(torch.from_numpy(sh).to(self.torch_device))
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row, col, _ = img.shape
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@ -55,12 +77,6 @@ class DeepPortraitRelighting(object):
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result = cv2.resize(result, (col, row))
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return result
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def relight_all(self, img, lighten=False):
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return [ self.relight(img, n, lighten=lighten) for n in range( len(self.shs) ) ]
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def relight_random(self, img, lighten=False):
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return [ self.relight(img, np.random.randint(len(self.shs)), lighten=lighten ) ]
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@staticmethod
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def build_model(torch, torch_device):
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nn = torch.nn
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@ -144,6 +144,7 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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if 'CUDA_VISIBLE_DEVICES' in os.environ.keys():
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os.environ.pop('CUDA_VISIBLE_DEVICES')
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io.log_info ("Using PyTorch backend.")
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import torch
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nnlib.torch = torch
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