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from types import ModuleType
from typing import List
import cv2
import numpy
import facefusion.jobs.job_manager
import facefusion.jobs.job_store
from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, logger, state_manager, translator, video_manager
from facefusion.common_helper import get_middle
from facefusion.face_creator import scale_face
from facefusion.face_helper import warp_face_by_face_landmark_5
from facefusion.face_masker import create_area_mask, create_box_mask, create_occlusion_mask, create_region_mask
from facefusion.face_selector import select_faces
from facefusion.filesystem import in_directory, is_image, is_video, same_file_extension
from facefusion.processors.modules.face_debugger import choices as face_debugger_choices
from facefusion.processors.modules.face_debugger.types import FaceDebuggerInputs
from facefusion.processors.types import ProcessorOutputs
from facefusion.program_helper import find_argument_group
from facefusion.types import ApplyStateItem, Args, Face, InferencePool, ProcessMode, VisionFrame
from facefusion.vision import read_static_image, read_static_video_frame
def get_inference_pool() -> InferencePool:
pass
def clear_inference_pool() -> None:
pass
def register_args(program : ArgumentParser) -> None:
group_processors = find_argument_group(program, 'processors')
if group_processors:
group_processors.add_argument('--face-debugger-items', help = translator.get('help.items', __package__).format(choices = ', '.join(face_debugger_choices.face_debugger_items)), default = config.get_str_list('processors', 'face_debugger_items', 'face-landmark-5/68 face-mask'), choices = face_debugger_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS')
facefusion.jobs.job_store.register_step_keys([ 'face_debugger_items' ])
def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None:
apply_state_item('face_debugger_items', args.get('face_debugger_items'))
def get_common_modules() -> List[ModuleType]:
return [ content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer ]
def pre_check() -> bool:
for common_module in get_common_modules():
if not common_module.pre_check():
return False
return True
def pre_process(mode : ProcessMode) -> bool:
if mode in [ 'output', 'preview' ] and not is_image(state_manager.get_item('target_path')) and not is_video(state_manager.get_item('target_path')):
logger.error(translator.get('choose_image_or_video_target') + translator.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not in_directory(state_manager.get_item('output_path')):
logger.error(translator.get('specify_image_or_video_output') + translator.get('exclamation_mark'), __name__)
return False
if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')):
logger.error(translator.get('match_target_and_output_extension') + translator.get('exclamation_mark'), __name__)
return False
return True
def post_process() -> None:
read_static_image.cache_clear()
read_static_video_frame.cache_clear()
video_manager.clear_video_pool()
if state_manager.get_item('video_memory_strategy') == 'strict':
for common_module in get_common_modules():
common_module.clear_inference_pool()
def debug_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
face_debugger_items = state_manager.get_item('face_debugger_items')
if 'bounding-box' in face_debugger_items:
temp_vision_frame = draw_bounding_box(target_face, temp_vision_frame)
if 'face-mask' in face_debugger_items:
temp_vision_frame = draw_face_mask(target_face, temp_vision_frame)
if 'face-landmark-5' in face_debugger_items:
temp_vision_frame = draw_face_landmark_5(target_face, temp_vision_frame)
if 'face-landmark-5/68' in face_debugger_items:
temp_vision_frame = draw_face_landmark_5_68(target_face, temp_vision_frame)
if 'face-landmark-68' in face_debugger_items:
temp_vision_frame = draw_face_landmark_68(target_face, temp_vision_frame)
if 'face-landmark-68/5' in face_debugger_items:
temp_vision_frame = draw_face_landmark_68_5(target_face, temp_vision_frame)
return temp_vision_frame
def draw_bounding_box(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame)
bounding_box = target_face.bounding_box.astype(numpy.int32)
x1, y1, x2, y2 = bounding_box
box_color = 0, 0, 255
border_scale = calculate_scale(temp_vision_frame)
border_color = 100, 100, 255
cv2.rectangle(temp_vision_frame, (x1, y1), (x2, y2), box_color, border_scale)
if target_face.angle == 0:
cv2.line(temp_vision_frame, (x1, y1), (x2, y1), border_color, border_scale + 1)
if target_face.angle == 180:
cv2.line(temp_vision_frame, (x1, y2), (x2, y2), border_color, border_scale + 1)
if target_face.angle == 90:
cv2.line(temp_vision_frame, (x2, y1), (x2, y2), border_color, border_scale + 1)
if target_face.angle == 270:
cv2.line(temp_vision_frame, (x1, y1), (x1, y2), border_color, border_scale + 1)
return temp_vision_frame
def draw_face_mask(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
crop_masks = []
temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame)
face_landmark_5 = target_face.landmark_set.get('5')
face_landmark_68 = target_face.landmark_set.get('68')
face_landmark_5_68 = target_face.landmark_set.get('5/68')
crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5_68, 'arcface_128', (512, 512))
inverse_matrix = cv2.invertAffineTransform(affine_matrix)
temp_size = temp_vision_frame.shape[:2][::-1]
mask_scale = calculate_scale(temp_vision_frame)
mask_color = 0, 255, 0
if numpy.array_equal(face_landmark_5, face_landmark_5_68):
mask_color = 255, 255, 0
if target_face.origin == 'refill':
mask_color = 0, 165, 255
if 'box' in state_manager.get_item('face_mask_types'):
box_mask = create_box_mask(crop_vision_frame, 0, state_manager.get_item('face_mask_padding'))
crop_masks.append(box_mask)
if 'occlusion' in state_manager.get_item('face_mask_types'):
occlusion_mask = create_occlusion_mask(crop_vision_frame)
crop_masks.append(occlusion_mask)
if 'area' in state_manager.get_item('face_mask_types'):
face_landmark_68 = cv2.transform(face_landmark_68.reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
area_mask = create_area_mask(crop_vision_frame, face_landmark_68, state_manager.get_item('face_mask_areas'))
crop_masks.append(area_mask)
if 'region' in state_manager.get_item('face_mask_types'):
region_mask = create_region_mask(crop_vision_frame, state_manager.get_item('face_mask_regions'))
crop_masks.append(region_mask)
crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1)
crop_mask = (crop_mask * 255).astype(numpy.uint8)
inverse_vision_frame = cv2.warpAffine(crop_mask, inverse_matrix, temp_size)
inverse_vision_frame = cv2.threshold(inverse_vision_frame, 100, 255, cv2.THRESH_BINARY)[1]
inverse_contours, _ = cv2.findContours(inverse_vision_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
cv2.drawContours(temp_vision_frame, inverse_contours, -1, mask_color, mask_scale)
return temp_vision_frame
def draw_face_landmark_5(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame)
face_landmark_5 = target_face.landmark_set.get('5')
point_scale = calculate_scale(temp_vision_frame)
point_color = 0, 0, 255
if target_face.origin == 'refill':
point_color = 0, 165, 255
if numpy.any(face_landmark_5):
face_landmark_5 = face_landmark_5.astype(numpy.int32)
for point in face_landmark_5:
cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1)
return temp_vision_frame
def draw_face_landmark_5_68(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame)
face_landmark_5 = target_face.landmark_set.get('5')
face_landmark_5_68 = target_face.landmark_set.get('5/68')
point_scale = calculate_scale(temp_vision_frame)
point_color = 0, 255, 0
if numpy.array_equal(face_landmark_5, face_landmark_5_68):
point_color = 255, 255, 0
if target_face.origin == 'refill':
point_color = 0, 165, 255
if numpy.any(face_landmark_5_68):
face_landmark_5_68 = face_landmark_5_68.astype(numpy.int32)
for point in face_landmark_5_68:
cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1)
return temp_vision_frame
def draw_face_landmark_68(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame)
face_landmark_68 = target_face.landmark_set.get('68')
face_landmark_68_5 = target_face.landmark_set.get('68/5')
point_scale = calculate_scale(temp_vision_frame)
point_color = 0, 255, 0
if numpy.array_equal(face_landmark_68, face_landmark_68_5):
point_color = 255, 255, 0
if target_face.origin == 'refill':
point_color = 0, 165, 255
if numpy.any(face_landmark_68):
face_landmark_68 = face_landmark_68.astype(numpy.int32)
for point in face_landmark_68:
cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1)
return temp_vision_frame
def draw_face_landmark_68_5(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
temp_vision_frame = numpy.ascontiguousarray(temp_vision_frame)
face_landmark_68_5 = target_face.landmark_set.get('68/5')
point_scale = calculate_scale(temp_vision_frame)
point_color = 255, 255, 0
if target_face.origin == 'refill':
point_color = 0, 165, 255
if numpy.any(face_landmark_68_5):
face_landmark_68_5 = face_landmark_68_5.astype(numpy.int32)
for point in face_landmark_68_5:
cv2.circle(temp_vision_frame, tuple(point), point_scale, point_color, -1)
return temp_vision_frame
def calculate_scale(temp_vision_frame : VisionFrame) -> int:
frame_height, _ = temp_vision_frame.shape[:2]
frame_scale = round(frame_height / 270)
return max(1, min(10, frame_scale))
def process_frame(inputs : FaceDebuggerInputs) -> ProcessorOutputs:
reference_vision_frame = inputs.get('reference_vision_frame')
source_vision_frames = inputs.get('source_vision_frames')
target_vision_frames = inputs.get('target_vision_frames')
temp_vision_frame = inputs.get('temp_vision_frame')
temp_vision_mask = inputs.get('temp_vision_mask')
target_vision_frame = get_middle(target_vision_frames)
target_faces = select_faces(reference_vision_frame, source_vision_frames, target_vision_frames)
if target_faces:
for target_face in target_faces:
target_face = scale_face(target_face, target_vision_frame, temp_vision_frame)
temp_vision_frame = debug_face(target_face, temp_vision_frame)
return temp_vision_frame, temp_vision_mask
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