mirror of
https://github.com/hacksider/Deep-Live-Cam.git
synced 2025-12-06 00:20:02 +01:00
Version 2.0c Release!
Sharpness and some other improvements added!
This commit is contained in:
Kenneth Estanislao
parent
f9270c5d1c
commit
ae2d21456d
7
modules/custom_types.py
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7
modules/custom_types.py
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@ -0,0 +1,7 @@
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from typing import Any
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from insightface.app.common import Face
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import numpy
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Face = Face
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Frame = numpy.ndarray[Any, Any]
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@ -1,3 +1,5 @@
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# --- START OF FILE globals.py ---
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import os
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from typing import List, Dict, Any
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@ -9,35 +11,61 @@ file_types = [
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("Video", ("*.mp4", "*.mkv")),
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]
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source_target_map = []
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simple_map = {}
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# Face Mapping Data
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souce_target_map: List[Dict[str, Any]] = [] # Stores detailed map for image/video processing
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simple_map: Dict[str, Any] = {} # Stores simplified map (embeddings/faces) for live/simple mode
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source_path = None
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target_path = None
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output_path = None
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# Paths
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source_path: str | None = None
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target_path: str | None = None
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output_path: str | None = None
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# Processing Options
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frame_processors: List[str] = []
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keep_fps = True
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keep_audio = True
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keep_frames = False
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many_faces = False
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map_faces = False
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color_correction = False # New global variable for color correction toggle
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nsfw_filter = False
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video_encoder = None
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video_quality = None
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live_mirror = False
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live_resizable = True
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max_memory = None
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execution_providers: List[str] = []
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execution_threads = None
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headless = None
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log_level = "error"
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keep_fps: bool = True
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keep_audio: bool = True
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keep_frames: bool = False
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many_faces: bool = False # Process all detected faces with default source
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map_faces: bool = False # Use souce_target_map or simple_map for specific swaps
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color_correction: bool = False # Enable color correction (implementation specific)
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nsfw_filter: bool = False
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# Video Output Options
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video_encoder: str | None = None
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video_quality: int | None = None # Typically a CRF value or bitrate
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# Live Mode Options
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live_mirror: bool = False
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live_resizable: bool = True
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camera_input_combobox: Any | None = None # Placeholder for UI element if needed
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webcam_preview_running: bool = False
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show_fps: bool = False
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# System Configuration
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max_memory: int | None = None # Memory limit in GB? (Needs clarification)
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execution_providers: List[str] = [] # e.g., ['CUDAExecutionProvider', 'CPUExecutionProvider']
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execution_threads: int | None = None # Number of threads for CPU execution
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headless: bool | None = None # Run without UI?
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log_level: str = "error" # Logging level (e.g., 'debug', 'info', 'warning', 'error')
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# Face Processor UI Toggles (Example)
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fp_ui: Dict[str, bool] = {"face_enhancer": False}
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camera_input_combobox = None
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webcam_preview_running = False
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show_fps = False
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mouth_mask = False
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show_mouth_mask_box = False
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mask_feather_ratio = 8
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mask_down_size = 0.50
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mask_size = 1
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# Face Swapper Specific Options
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face_swapper_enabled: bool = True # General toggle for the swapper processor
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opacity: float = 1.0 # Blend factor for the swapped face (0.0-1.0)
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sharpness: float = 0.0 # Sharpness enhancement for swapped face (0.0-1.0+)
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# Mouth Mask Options
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mouth_mask: bool = False # Enable mouth area masking/pasting
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show_mouth_mask_box: bool = False # Visualize the mouth mask area (for debugging)
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mask_feather_ratio: int = 12 # Denominator for feathering calculation (higher = smaller feather)
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mask_down_size: float = 0.1 # Expansion factor for lower lip mask (relative)
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mask_size: float = 1.0 # Expansion factor for upper lip mask (relative)
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# --- START: Added for Frame Interpolation ---
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enable_interpolation: bool = True # Toggle temporal smoothing
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interpolation_weight: float = 0 # Blend weight for current frame (0.0-1.0). Lower=smoother.
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# --- END: Added for Frame Interpolation ---
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# --- END OF FILE globals.py ---
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@ -1,3 +1,3 @@
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name = 'Deep-Live-Cam'
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version = '1.8.1'
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version = '2.0c'
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edition = 'GitHub Edition'
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@ -1,16 +1,18 @@
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# --- START OF FILE face_enhancer.py ---
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from typing import Any, List
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import cv2
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import threading
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import gfpgan
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import os
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import platform
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import torch # Make sure torch is imported
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import modules.globals
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import modules.processors.frame.core
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from modules.core import update_status
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from modules.face_analyser import get_one_face
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from modules.typing import Frame, Face
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import platform
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import torch
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from modules.utilities import (
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conditional_download,
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is_image,
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@ -48,83 +50,157 @@ def pre_start() -> bool:
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return True
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TENSORRT_AVAILABLE = False
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try:
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import torch_tensorrt
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TENSORRT_AVAILABLE = True
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except ImportError as im:
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print(f"TensorRT is not available: {im}")
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pass
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except Exception as e:
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print(f"TensorRT is not available: {e}")
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pass
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def get_face_enhancer() -> Any:
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"""
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Initializes and returns the GFPGAN face enhancer instance,
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prioritizing CUDA, then MPS (Mac), then CPU.
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"""
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global FACE_ENHANCER
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with THREAD_LOCK:
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if FACE_ENHANCER is None:
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model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
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device = None
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try:
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# Priority 1: CUDA
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if torch.cuda.is_available():
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device = torch.device("cuda")
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print(f"{NAME}: Using CUDA device.")
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# Priority 2: MPS (Mac Silicon)
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elif platform.system() == "Darwin" and torch.backends.mps.is_available():
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device = torch.device("mps")
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print(f"{NAME}: Using MPS device.")
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# Priority 3: CPU
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else:
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device = torch.device("cpu")
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print(f"{NAME}: Using CPU device.")
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selected_device = None
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device_priority = []
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FACE_ENHANCER = gfpgan.GFPGANer(
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model_path=model_path,
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upscale=1, # upscale=1 means enhancement only, no resizing
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arch='clean',
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channel_multiplier=2,
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bg_upsampler=None,
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device=device
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)
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print(f"{NAME}: GFPGANer initialized successfully on {device}.")
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if TENSORRT_AVAILABLE and torch.cuda.is_available():
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selected_device = torch.device("cuda")
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device_priority.append("TensorRT+CUDA")
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elif torch.cuda.is_available():
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selected_device = torch.device("cuda")
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device_priority.append("CUDA")
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elif torch.backends.mps.is_available() and platform.system() == "Darwin":
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selected_device = torch.device("mps")
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device_priority.append("MPS")
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elif not torch.cuda.is_available():
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selected_device = torch.device("cpu")
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device_priority.append("CPU")
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except Exception as e:
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print(f"{NAME}: Error initializing GFPGANer: {e}")
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# Fallback to CPU if initialization with GPU fails for some reason
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if device is not None and device.type != 'cpu':
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print(f"{NAME}: Falling back to CPU due to error.")
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try:
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device = torch.device("cpu")
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FACE_ENHANCER = gfpgan.GFPGANer(
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model_path=model_path,
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upscale=1,
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arch='clean',
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channel_multiplier=2,
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bg_upsampler=None,
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device=device
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)
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print(f"{NAME}: GFPGANer initialized successfully on CPU after fallback.")
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except Exception as fallback_e:
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print(f"{NAME}: FATAL: Could not initialize GFPGANer even on CPU: {fallback_e}")
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FACE_ENHANCER = None # Ensure it's None if totally failed
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else:
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# If it failed even on the first CPU attempt or device was already CPU
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print(f"{NAME}: FATAL: Could not initialize GFPGANer on CPU: {e}")
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FACE_ENHANCER = None # Ensure it's None if totally failed
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FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=selected_device)
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# for debug:
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print(f"Selected device: {selected_device} and device priority: {device_priority}")
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# Check if enhancer is still None after attempting initialization
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if FACE_ENHANCER is None:
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raise RuntimeError(f"{NAME}: Failed to initialize GFPGANer. Check logs for errors.")
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return FACE_ENHANCER
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def enhance_face(temp_frame: Frame) -> Frame:
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"""Enhances faces in a single frame using the global GFPGANer instance."""
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# Ensure enhancer is ready
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enhancer = get_face_enhancer()
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try:
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with THREAD_SEMAPHORE:
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_, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True)
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# The enhance method returns: _, restored_faces, restored_img
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_, _, restored_img = enhancer.enhance(
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temp_frame,
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has_aligned=False, # Assume faces are not pre-aligned
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only_center_face=False, # Enhance all detected faces
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paste_back=True # Paste enhanced faces back onto the original image
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)
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# GFPGAN might return None if no face is detected or an error occurs
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if restored_img is None:
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# print(f"{NAME}: Warning: GFPGAN enhancement returned None. Returning original frame.")
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return temp_frame
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return restored_img
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except Exception as e:
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print(f"{NAME}: Error during face enhancement: {e}")
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# Return the original frame in case of error during enhancement
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return temp_frame
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def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
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target_face = get_one_face(temp_frame)
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if target_face:
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def process_frame(source_face: Face | None, temp_frame: Frame) -> Frame:
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"""Processes a frame: enhances face if detected."""
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# We don't strictly need source_face for enhancement only
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# Check if any face exists to potentially save processing time, though GFPGAN also does detection.
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# For simplicity and ensuring enhancement is attempted if possible, we can rely on enhance_face.
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# target_face = get_one_face(temp_frame) # This gets only ONE face
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# If you want to enhance ONLY if a face is detected by your *own* analyser first:
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# has_face = get_one_face(temp_frame) is not None # Or use get_many_faces
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# if has_face:
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# temp_frame = enhance_face(temp_frame)
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# else: # Enhance regardless, let GFPGAN handle detection
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temp_frame = enhance_face(temp_frame)
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return temp_frame
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def process_frames(
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source_path: str, temp_frame_paths: List[str], progress: Any = None
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source_path: str | None, temp_frame_paths: List[str], progress: Any = None
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) -> None:
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"""Processes multiple frames from file paths."""
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for temp_frame_path in temp_frame_paths:
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if not os.path.exists(temp_frame_path):
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print(f"{NAME}: Warning: Frame path not found {temp_frame_path}, skipping.")
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if progress:
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progress.update(1)
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continue
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temp_frame = cv2.imread(temp_frame_path)
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result = process_frame(None, temp_frame)
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cv2.imwrite(temp_frame_path, result)
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if temp_frame is None:
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print(f"{NAME}: Warning: Failed to read frame {temp_frame_path}, skipping.")
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if progress:
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progress.update(1)
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continue
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result_frame = process_frame(None, temp_frame)
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cv2.imwrite(temp_frame_path, result_frame)
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if progress:
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progress.update(1)
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def process_image(source_path: str, target_path: str, output_path: str) -> None:
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def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
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"""Processes a single image file."""
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target_frame = cv2.imread(target_path)
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result = process_frame(None, target_frame)
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cv2.imwrite(output_path, result)
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if target_frame is None:
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print(f"{NAME}: Error: Failed to read target image {target_path}")
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return
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result_frame = process_frame(None, target_frame)
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cv2.imwrite(output_path, result_frame)
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print(f"{NAME}: Enhanced image saved to {output_path}")
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def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
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modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
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def process_video(source_path: str | None, temp_frame_paths: List[str]) -> None:
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"""Processes video frames using the frame processor core."""
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# source_path might be optional depending on how process_video is called
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modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)
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# Optional: Keep process_frame_v2 if it's used elsewhere, otherwise it's redundant
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# def process_frame_v2(temp_frame: Frame) -> Frame:
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# target_face = get_one_face(temp_frame)
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# if target_face:
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# temp_frame = enhance_face(temp_frame)
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# return temp_frame
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def process_frame_v2(temp_frame: Frame) -> Frame:
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target_face = get_one_face(temp_frame)
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if target_face:
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temp_frame = enhance_face(temp_frame)
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return temp_frame
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# --- END OF FILE face_enhancer.py ---
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609
modules/processors/frame/face_masking.py
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609
modules/processors/frame/face_masking.py
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@ -0,0 +1,609 @@
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import cv2
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import numpy as np
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from modules.typing import Face, Frame
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import modules.globals
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def apply_color_transfer(source, target):
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"""
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Apply color transfer from target to source image
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"""
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source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
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target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32")
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source_mean, source_std = cv2.meanStdDev(source)
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target_mean, target_std = cv2.meanStdDev(target)
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# Reshape mean and std to be broadcastable
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source_mean = source_mean.reshape(1, 1, 3)
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source_std = source_std.reshape(1, 1, 3)
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target_mean = target_mean.reshape(1, 1, 3)
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target_std = target_std.reshape(1, 1, 3)
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# Perform the color transfer
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source = (source - source_mean) * (target_std / source_std) + target_mean
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return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
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def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
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mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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landmarks = face.landmark_2d_106
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if landmarks is not None:
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# Convert landmarks to int32
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landmarks = landmarks.astype(np.int32)
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# Extract facial features
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right_side_face = landmarks[0:16]
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left_side_face = landmarks[17:32]
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right_eye = landmarks[33:42]
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right_eye_brow = landmarks[43:51]
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left_eye = landmarks[87:96]
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left_eye_brow = landmarks[97:105]
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# Calculate padding
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padding = int(
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np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05
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) # 5% of face width
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# Create a slightly larger convex hull for padding
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hull = cv2.convexHull(face_outline)
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hull_padded = []
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for point in hull:
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x, y = point[0]
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center = np.mean(face_outline, axis=0)
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direction = np.array([x, y]) - center
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direction = direction / np.linalg.norm(direction)
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padded_point = np.array([x, y]) + direction * padding
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hull_padded.append(padded_point)
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hull_padded = np.array(hull_padded, dtype=np.int32)
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# Fill the padded convex hull
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cv2.fillConvexPoly(mask, hull_padded, 255)
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# Smooth the mask edges
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mask = cv2.GaussianBlur(mask, (5, 5), 3)
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return mask
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def create_lower_mouth_mask(
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face: Face, frame: Frame
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) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
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mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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mouth_cutout = None
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landmarks = face.landmark_2d_106
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if landmarks is not None:
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# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
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lower_lip_order = [
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65,
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66,
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62,
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70,
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69,
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18,
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19,
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20,
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21,
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22,
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23,
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24,
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0,
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8,
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7,
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6,
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5,
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4,
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3,
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2,
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65,
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]
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lower_lip_landmarks = landmarks[lower_lip_order].astype(
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np.float32
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) # Use float for precise calculations
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# Calculate the center of the landmarks
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center = np.mean(lower_lip_landmarks, axis=0)
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# Expand the landmarks outward using the mouth_mask_size
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expansion_factor = (
|
||||
1 + modules.globals.mask_down_size * modules.globals.mouth_mask_size
|
||||
) # Adjust expansion based on slider
|
||||
expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
|
||||
|
||||
# Extend the top lip part
|
||||
toplip_indices = [
|
||||
20,
|
||||
0,
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
4,
|
||||
5,
|
||||
] # Indices for landmarks 2, 65, 66, 62, 70, 69, 18
|
||||
toplip_extension = (
|
||||
modules.globals.mask_size * modules.globals.mouth_mask_size * 0.5
|
||||
) # Adjust extension based on slider
|
||||
for idx in toplip_indices:
|
||||
direction = expanded_landmarks[idx] - center
|
||||
direction = direction / np.linalg.norm(direction)
|
||||
expanded_landmarks[idx] += direction * toplip_extension
|
||||
|
||||
# Extend the bottom part (chin area)
|
||||
chin_indices = [
|
||||
11,
|
||||
12,
|
||||
13,
|
||||
14,
|
||||
15,
|
||||
16,
|
||||
] # Indices for landmarks 21, 22, 23, 24, 0, 8
|
||||
chin_extension = 2 * 0.2 # Adjust this factor to control the extension
|
||||
for idx in chin_indices:
|
||||
expanded_landmarks[idx][1] += (
|
||||
expanded_landmarks[idx][1] - center[1]
|
||||
) * chin_extension
|
||||
|
||||
# Convert back to integer coordinates
|
||||
expanded_landmarks = expanded_landmarks.astype(np.int32)
|
||||
|
||||
# Calculate bounding box for the expanded lower mouth
|
||||
min_x, min_y = np.min(expanded_landmarks, axis=0)
|
||||
max_x, max_y = np.max(expanded_landmarks, axis=0)
|
||||
|
||||
# Add some padding to the bounding box
|
||||
padding = int((max_x - min_x) * 0.1) # 10% padding
|
||||
min_x = max(0, min_x - padding)
|
||||
min_y = max(0, min_y - padding)
|
||||
max_x = min(frame.shape[1], max_x + padding)
|
||||
max_y = min(frame.shape[0], max_y + padding)
|
||||
|
||||
# Ensure the bounding box dimensions are valid
|
||||
if max_x <= min_x or max_y <= min_y:
|
||||
if (max_x - min_x) <= 1:
|
||||
max_x = min_x + 1
|
||||
if (max_y - min_y) <= 1:
|
||||
max_y = min_y + 1
|
||||
|
||||
# Create the mask
|
||||
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
|
||||
cv2.fillPoly(mask_roi, [expanded_landmarks - [min_x, min_y]], 255)
|
||||
|
||||
# Apply Gaussian blur to soften the mask edges
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
|
||||
|
||||
# Place the mask ROI in the full-sized mask
|
||||
mask[min_y:max_y, min_x:max_x] = mask_roi
|
||||
|
||||
# Extract the masked area from the frame
|
||||
mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
|
||||
|
||||
# Return the expanded lower lip polygon in original frame coordinates
|
||||
lower_lip_polygon = expanded_landmarks
|
||||
|
||||
return mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon
|
||||
|
||||
def create_eyes_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
|
||||
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
|
||||
eyes_cutout = None
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None:
|
||||
# Left eye landmarks (87-96) and right eye landmarks (33-42)
|
||||
left_eye = landmarks[87:96]
|
||||
right_eye = landmarks[33:42]
|
||||
|
||||
# Calculate centers and dimensions for each eye
|
||||
left_eye_center = np.mean(left_eye, axis=0).astype(np.int32)
|
||||
right_eye_center = np.mean(right_eye, axis=0).astype(np.int32)
|
||||
|
||||
# Calculate eye dimensions with size adjustment
|
||||
def get_eye_dimensions(eye_points):
|
||||
x_coords = eye_points[:, 0]
|
||||
y_coords = eye_points[:, 1]
|
||||
width = int((np.max(x_coords) - np.min(x_coords)) * (1 + modules.globals.mask_down_size * modules.globals.eyes_mask_size))
|
||||
height = int((np.max(y_coords) - np.min(y_coords)) * (1 + modules.globals.mask_down_size * modules.globals.eyes_mask_size))
|
||||
return width, height
|
||||
|
||||
left_width, left_height = get_eye_dimensions(left_eye)
|
||||
right_width, right_height = get_eye_dimensions(right_eye)
|
||||
|
||||
# Add extra padding
|
||||
padding = int(max(left_width, right_width) * 0.2)
|
||||
|
||||
# Calculate bounding box for both eyes
|
||||
min_x = min(left_eye_center[0] - left_width//2, right_eye_center[0] - right_width//2) - padding
|
||||
max_x = max(left_eye_center[0] + left_width//2, right_eye_center[0] + right_width//2) + padding
|
||||
min_y = min(left_eye_center[1] - left_height//2, right_eye_center[1] - right_height//2) - padding
|
||||
max_y = max(left_eye_center[1] + left_height//2, right_eye_center[1] + right_height//2) + padding
|
||||
|
||||
# Ensure coordinates are within frame bounds
|
||||
min_x = max(0, min_x)
|
||||
min_y = max(0, min_y)
|
||||
max_x = min(frame.shape[1], max_x)
|
||||
max_y = min(frame.shape[0], max_y)
|
||||
|
||||
# Create mask for the eyes region
|
||||
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
|
||||
|
||||
# Draw ellipses for both eyes
|
||||
left_center = (left_eye_center[0] - min_x, left_eye_center[1] - min_y)
|
||||
right_center = (right_eye_center[0] - min_x, right_eye_center[1] - min_y)
|
||||
|
||||
# Calculate axes lengths (half of width and height)
|
||||
left_axes = (left_width//2, left_height//2)
|
||||
right_axes = (right_width//2, right_height//2)
|
||||
|
||||
# Draw filled ellipses
|
||||
cv2.ellipse(mask_roi, left_center, left_axes, 0, 0, 360, 255, -1)
|
||||
cv2.ellipse(mask_roi, right_center, right_axes, 0, 0, 360, 255, -1)
|
||||
|
||||
# Apply Gaussian blur to soften mask edges
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
|
||||
|
||||
# Place the mask ROI in the full-sized mask
|
||||
mask[min_y:max_y, min_x:max_x] = mask_roi
|
||||
|
||||
# Extract the masked area from the frame
|
||||
eyes_cutout = frame[min_y:max_y, min_x:max_x].copy()
|
||||
|
||||
# Create polygon points for visualization
|
||||
def create_ellipse_points(center, axes):
|
||||
t = np.linspace(0, 2*np.pi, 32)
|
||||
x = center[0] + axes[0] * np.cos(t)
|
||||
y = center[1] + axes[1] * np.sin(t)
|
||||
return np.column_stack((x, y)).astype(np.int32)
|
||||
|
||||
# Generate points for both ellipses
|
||||
left_points = create_ellipse_points((left_eye_center[0], left_eye_center[1]), (left_width//2, left_height//2))
|
||||
right_points = create_ellipse_points((right_eye_center[0], right_eye_center[1]), (right_width//2, right_height//2))
|
||||
|
||||
# Combine points for both eyes
|
||||
eyes_polygon = np.vstack([left_points, right_points])
|
||||
|
||||
return mask, eyes_cutout, (min_x, min_y, max_x, max_y), eyes_polygon
|
||||
|
||||
def create_curved_eyebrow(points):
|
||||
if len(points) >= 5:
|
||||
# Sort points by x-coordinate
|
||||
sorted_idx = np.argsort(points[:, 0])
|
||||
sorted_points = points[sorted_idx]
|
||||
|
||||
# Calculate dimensions
|
||||
x_min, y_min = np.min(sorted_points, axis=0)
|
||||
x_max, y_max = np.max(sorted_points, axis=0)
|
||||
width = x_max - x_min
|
||||
height = y_max - y_min
|
||||
|
||||
# Create more points for smoother curve
|
||||
num_points = 50
|
||||
x = np.linspace(x_min, x_max, num_points)
|
||||
|
||||
# Fit quadratic curve through points for more natural arch
|
||||
coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 2)
|
||||
y = np.polyval(coeffs, x)
|
||||
|
||||
# Increased offsets to create more separation
|
||||
top_offset = height * 0.5 # Increased from 0.3 to shift up more
|
||||
bottom_offset = height * 0.2 # Increased from 0.1 to shift down more
|
||||
|
||||
# Create smooth curves
|
||||
top_curve = y - top_offset
|
||||
bottom_curve = y + bottom_offset
|
||||
|
||||
# Create curved endpoints with more pronounced taper
|
||||
end_points = 5
|
||||
start_x = np.linspace(x[0] - width * 0.15, x[0], end_points) # Increased taper
|
||||
end_x = np.linspace(x[-1], x[-1] + width * 0.15, end_points) # Increased taper
|
||||
|
||||
# Create tapered ends
|
||||
start_curve = np.column_stack((
|
||||
start_x,
|
||||
np.linspace(bottom_curve[0], top_curve[0], end_points)
|
||||
))
|
||||
end_curve = np.column_stack((
|
||||
end_x,
|
||||
np.linspace(bottom_curve[-1], top_curve[-1], end_points)
|
||||
))
|
||||
|
||||
# Combine all points to form a smooth contour
|
||||
contour_points = np.vstack([
|
||||
start_curve,
|
||||
np.column_stack((x, top_curve)),
|
||||
end_curve,
|
||||
np.column_stack((x[::-1], bottom_curve[::-1]))
|
||||
])
|
||||
|
||||
# Add slight padding for better coverage
|
||||
center = np.mean(contour_points, axis=0)
|
||||
vectors = contour_points - center
|
||||
padded_points = center + vectors * 1.2 # Increased padding slightly
|
||||
|
||||
return padded_points
|
||||
return points
|
||||
|
||||
def create_eyebrows_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
|
||||
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
|
||||
eyebrows_cutout = None
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None:
|
||||
# Left eyebrow landmarks (97-105) and right eyebrow landmarks (43-51)
|
||||
left_eyebrow = landmarks[97:105].astype(np.float32)
|
||||
right_eyebrow = landmarks[43:51].astype(np.float32)
|
||||
|
||||
# Calculate centers and dimensions for each eyebrow
|
||||
left_center = np.mean(left_eyebrow, axis=0)
|
||||
right_center = np.mean(right_eyebrow, axis=0)
|
||||
|
||||
# Calculate bounding box with padding adjusted by size
|
||||
all_points = np.vstack([left_eyebrow, right_eyebrow])
|
||||
padding_factor = modules.globals.eyebrows_mask_size
|
||||
min_x = np.min(all_points[:, 0]) - 25 * padding_factor
|
||||
max_x = np.max(all_points[:, 0]) + 25 * padding_factor
|
||||
min_y = np.min(all_points[:, 1]) - 20 * padding_factor
|
||||
max_y = np.max(all_points[:, 1]) + 15 * padding_factor
|
||||
|
||||
# Ensure coordinates are within frame bounds
|
||||
min_x = max(0, int(min_x))
|
||||
min_y = max(0, int(min_y))
|
||||
max_x = min(frame.shape[1], int(max_x))
|
||||
max_y = min(frame.shape[0], int(max_y))
|
||||
|
||||
# Create mask for the eyebrows region
|
||||
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
|
||||
|
||||
try:
|
||||
# Convert points to local coordinates
|
||||
left_local = left_eyebrow - [min_x, min_y]
|
||||
right_local = right_eyebrow - [min_x, min_y]
|
||||
|
||||
def create_curved_eyebrow(points):
|
||||
if len(points) >= 5:
|
||||
# Sort points by x-coordinate
|
||||
sorted_idx = np.argsort(points[:, 0])
|
||||
sorted_points = points[sorted_idx]
|
||||
|
||||
# Calculate dimensions
|
||||
x_min, y_min = np.min(sorted_points, axis=0)
|
||||
x_max, y_max = np.max(sorted_points, axis=0)
|
||||
width = x_max - x_min
|
||||
height = y_max - y_min
|
||||
|
||||
# Create more points for smoother curve
|
||||
num_points = 50
|
||||
x = np.linspace(x_min, x_max, num_points)
|
||||
|
||||
# Fit quadratic curve through points for more natural arch
|
||||
coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 2)
|
||||
y = np.polyval(coeffs, x)
|
||||
|
||||
# Increased offsets to create more separation
|
||||
top_offset = height * 0.5 # Increased from 0.3 to shift up more
|
||||
bottom_offset = height * 0.2 # Increased from 0.1 to shift down more
|
||||
|
||||
# Create smooth curves
|
||||
top_curve = y - top_offset
|
||||
bottom_curve = y + bottom_offset
|
||||
|
||||
# Create curved endpoints with more pronounced taper
|
||||
end_points = 5
|
||||
start_x = np.linspace(x[0] - width * 0.15, x[0], end_points) # Increased taper
|
||||
end_x = np.linspace(x[-1], x[-1] + width * 0.15, end_points) # Increased taper
|
||||
|
||||
# Create tapered ends
|
||||
start_curve = np.column_stack((
|
||||
start_x,
|
||||
np.linspace(bottom_curve[0], top_curve[0], end_points)
|
||||
))
|
||||
end_curve = np.column_stack((
|
||||
end_x,
|
||||
np.linspace(bottom_curve[-1], top_curve[-1], end_points)
|
||||
))
|
||||
|
||||
# Combine all points to form a smooth contour
|
||||
contour_points = np.vstack([
|
||||
start_curve,
|
||||
np.column_stack((x, top_curve)),
|
||||
end_curve,
|
||||
np.column_stack((x[::-1], bottom_curve[::-1]))
|
||||
])
|
||||
|
||||
# Add slight padding for better coverage
|
||||
center = np.mean(contour_points, axis=0)
|
||||
vectors = contour_points - center
|
||||
padded_points = center + vectors * 1.2 # Increased padding slightly
|
||||
|
||||
return padded_points
|
||||
return points
|
||||
|
||||
# Generate and draw eyebrow shapes
|
||||
left_shape = create_curved_eyebrow(left_local)
|
||||
right_shape = create_curved_eyebrow(right_local)
|
||||
|
||||
# Apply multi-stage blurring for natural feathering
|
||||
# First, strong Gaussian blur for initial softening
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
|
||||
|
||||
# Second, medium blur for transition areas
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (11, 11), 3)
|
||||
|
||||
# Finally, light blur for fine details
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (5, 5), 1)
|
||||
|
||||
# Normalize mask values
|
||||
mask_roi = cv2.normalize(mask_roi, None, 0, 255, cv2.NORM_MINMAX)
|
||||
|
||||
# Place the mask ROI in the full-sized mask
|
||||
mask[min_y:max_y, min_x:max_x] = mask_roi
|
||||
|
||||
# Extract the masked area from the frame
|
||||
eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
|
||||
|
||||
# Combine points for visualization
|
||||
eyebrows_polygon = np.vstack([
|
||||
left_shape + [min_x, min_y],
|
||||
right_shape + [min_x, min_y]
|
||||
]).astype(np.int32)
|
||||
|
||||
except Exception as e:
|
||||
# Fallback to simple polygons if curve fitting fails
|
||||
left_local = left_eyebrow - [min_x, min_y]
|
||||
right_local = right_eyebrow - [min_x, min_y]
|
||||
cv2.fillPoly(mask_roi, [left_local.astype(np.int32)], 255)
|
||||
cv2.fillPoly(mask_roi, [right_local.astype(np.int32)], 255)
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
|
||||
mask[min_y:max_y, min_x:max_x] = mask_roi
|
||||
eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
|
||||
eyebrows_polygon = np.vstack([left_eyebrow, right_eyebrow]).astype(np.int32)
|
||||
|
||||
return mask, eyebrows_cutout, (min_x, min_y, max_x, max_y), eyebrows_polygon
|
||||
|
||||
def apply_mask_area(
|
||||
frame: np.ndarray,
|
||||
cutout: np.ndarray,
|
||||
box: tuple,
|
||||
face_mask: np.ndarray,
|
||||
polygon: np.ndarray,
|
||||
) -> np.ndarray:
|
||||
min_x, min_y, max_x, max_y = box
|
||||
box_width = max_x - min_x
|
||||
box_height = max_y - min_y
|
||||
|
||||
if (
|
||||
cutout is None
|
||||
or box_width is None
|
||||
or box_height is None
|
||||
or face_mask is None
|
||||
or polygon is None
|
||||
):
|
||||
return frame
|
||||
|
||||
try:
|
||||
resized_cutout = cv2.resize(cutout, (box_width, box_height))
|
||||
roi = frame[min_y:max_y, min_x:max_x]
|
||||
|
||||
if roi.shape != resized_cutout.shape:
|
||||
resized_cutout = cv2.resize(
|
||||
resized_cutout, (roi.shape[1], roi.shape[0])
|
||||
)
|
||||
|
||||
color_corrected_area = apply_color_transfer(resized_cutout, roi)
|
||||
|
||||
# Create mask for the area
|
||||
polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
|
||||
|
||||
# Split points for left and right parts if needed
|
||||
if len(polygon) > 50: # Arbitrary threshold to detect if we have multiple parts
|
||||
mid_point = len(polygon) // 2
|
||||
left_points = polygon[:mid_point] - [min_x, min_y]
|
||||
right_points = polygon[mid_point:] - [min_x, min_y]
|
||||
cv2.fillPoly(polygon_mask, [left_points], 255)
|
||||
cv2.fillPoly(polygon_mask, [right_points], 255)
|
||||
else:
|
||||
adjusted_polygon = polygon - [min_x, min_y]
|
||||
cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
|
||||
|
||||
# Apply strong initial feathering
|
||||
polygon_mask = cv2.GaussianBlur(polygon_mask, (21, 21), 7)
|
||||
|
||||
# Apply additional feathering
|
||||
feather_amount = min(
|
||||
30,
|
||||
box_width // modules.globals.mask_feather_ratio,
|
||||
box_height // modules.globals.mask_feather_ratio,
|
||||
)
|
||||
feathered_mask = cv2.GaussianBlur(
|
||||
polygon_mask.astype(float), (0, 0), feather_amount
|
||||
)
|
||||
feathered_mask = feathered_mask / feathered_mask.max()
|
||||
|
||||
# Apply additional smoothing to the mask edges
|
||||
feathered_mask = cv2.GaussianBlur(feathered_mask, (5, 5), 1)
|
||||
|
||||
face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
|
||||
combined_mask = feathered_mask * (face_mask_roi / 255.0)
|
||||
|
||||
combined_mask = combined_mask[:, :, np.newaxis]
|
||||
blended = (
|
||||
color_corrected_area * combined_mask + roi * (1 - combined_mask)
|
||||
).astype(np.uint8)
|
||||
|
||||
# Apply face mask to blended result
|
||||
face_mask_3channel = (
|
||||
np.repeat(face_mask_roi[:, :, np.newaxis], 3, axis=2) / 255.0
|
||||
)
|
||||
final_blend = blended * face_mask_3channel + roi * (1 - face_mask_3channel)
|
||||
|
||||
frame[min_y:max_y, min_x:max_x] = final_blend.astype(np.uint8)
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
return frame
|
||||
|
||||
def draw_mask_visualization(
|
||||
frame: Frame,
|
||||
mask_data: tuple,
|
||||
label: str,
|
||||
draw_method: str = "polygon"
|
||||
) -> Frame:
|
||||
mask, cutout, (min_x, min_y, max_x, max_y), polygon = mask_data
|
||||
|
||||
vis_frame = frame.copy()
|
||||
|
||||
# Ensure coordinates are within frame bounds
|
||||
height, width = vis_frame.shape[:2]
|
||||
min_x, min_y = max(0, min_x), max(0, min_y)
|
||||
max_x, max_y = min(width, max_x), min(height, max_y)
|
||||
|
||||
if draw_method == "ellipse" and len(polygon) > 50: # For eyes
|
||||
# Split points for left and right parts
|
||||
mid_point = len(polygon) // 2
|
||||
left_points = polygon[:mid_point]
|
||||
right_points = polygon[mid_point:]
|
||||
|
||||
try:
|
||||
# Fit ellipses to points - need at least 5 points
|
||||
if len(left_points) >= 5 and len(right_points) >= 5:
|
||||
# Convert points to the correct format for ellipse fitting
|
||||
left_points = left_points.astype(np.float32)
|
||||
right_points = right_points.astype(np.float32)
|
||||
|
||||
# Fit ellipses
|
||||
left_ellipse = cv2.fitEllipse(left_points)
|
||||
right_ellipse = cv2.fitEllipse(right_points)
|
||||
|
||||
# Draw the ellipses
|
||||
cv2.ellipse(vis_frame, left_ellipse, (0, 255, 0), 2)
|
||||
cv2.ellipse(vis_frame, right_ellipse, (0, 255, 0), 2)
|
||||
except Exception as e:
|
||||
# If ellipse fitting fails, draw simple rectangles as fallback
|
||||
left_rect = cv2.boundingRect(left_points)
|
||||
right_rect = cv2.boundingRect(right_points)
|
||||
cv2.rectangle(vis_frame,
|
||||
(left_rect[0], left_rect[1]),
|
||||
(left_rect[0] + left_rect[2], left_rect[1] + left_rect[3]),
|
||||
(0, 255, 0), 2)
|
||||
cv2.rectangle(vis_frame,
|
||||
(right_rect[0], right_rect[1]),
|
||||
(right_rect[0] + right_rect[2], right_rect[1] + right_rect[3]),
|
||||
(0, 255, 0), 2)
|
||||
else: # For mouth and eyebrows
|
||||
# Draw the polygon
|
||||
if len(polygon) > 50: # If we have multiple parts
|
||||
mid_point = len(polygon) // 2
|
||||
left_points = polygon[:mid_point]
|
||||
right_points = polygon[mid_point:]
|
||||
cv2.polylines(vis_frame, [left_points], True, (0, 255, 0), 2, cv2.LINE_AA)
|
||||
cv2.polylines(vis_frame, [right_points], True, (0, 255, 0), 2, cv2.LINE_AA)
|
||||
else:
|
||||
cv2.polylines(vis_frame, [polygon], True, (0, 255, 0), 2, cv2.LINE_AA)
|
||||
|
||||
# Add label
|
||||
cv2.putText(
|
||||
vis_frame,
|
||||
label,
|
||||
(min_x, min_y - 10),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.5,
|
||||
(255, 255, 255),
|
||||
1,
|
||||
)
|
||||
|
||||
return vis_frame
|
||||
File diff suppressed because it is too large
Load Diff
9
modules/run.py
Normal file
9
modules/run.py
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
# Import the tkinter fix to patch the ScreenChanged error
|
||||
import tkinter_fix
|
||||
|
||||
import core
|
||||
|
||||
if __name__ == '__main__':
|
||||
core.run()
|
||||
26
modules/tkinter_fix.py
Normal file
26
modules/tkinter_fix.py
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
import tkinter
|
||||
|
||||
# Only needs to be imported once at the beginning of the application
|
||||
def apply_patch():
|
||||
# Create a monkey patch for the internal _tkinter module
|
||||
original_init = tkinter.Tk.__init__
|
||||
|
||||
def patched_init(self, *args, **kwargs):
|
||||
# Call the original init
|
||||
original_init(self, *args, **kwargs)
|
||||
|
||||
# Define the missing ::tk::ScreenChanged procedure
|
||||
self.tk.eval("""
|
||||
if {[info commands ::tk::ScreenChanged] == ""} {
|
||||
proc ::tk::ScreenChanged {args} {
|
||||
# Do nothing
|
||||
return
|
||||
}
|
||||
}
|
||||
""")
|
||||
|
||||
# Apply the monkey patch
|
||||
tkinter.Tk.__init__ = patched_init
|
||||
|
||||
# Apply the patch automatically when this module is imported
|
||||
apply_patch()
|
||||
138
modules/ui.py
138
modules/ui.py
|
|
@ -27,6 +27,7 @@ from modules.utilities import (
|
|||
)
|
||||
from modules.video_capture import VideoCapturer
|
||||
from modules.gettext import LanguageManager
|
||||
from modules import globals
|
||||
import platform
|
||||
|
||||
if platform.system() == "Windows":
|
||||
|
|
@ -35,7 +36,7 @@ if platform.system() == "Windows":
|
|||
ROOT = None
|
||||
POPUP = None
|
||||
POPUP_LIVE = None
|
||||
ROOT_HEIGHT = 700
|
||||
ROOT_HEIGHT = 750
|
||||
ROOT_WIDTH = 600
|
||||
|
||||
PREVIEW = None
|
||||
|
|
@ -152,20 +153,20 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
root.protocol("WM_DELETE_WINDOW", lambda: destroy())
|
||||
|
||||
source_label = ctk.CTkLabel(root, text=None)
|
||||
source_label.place(relx=0.1, rely=0.1, relwidth=0.3, relheight=0.25)
|
||||
source_label.place(relx=0.1, rely=0.05, relwidth=0.275, relheight=0.225)
|
||||
|
||||
target_label = ctk.CTkLabel(root, text=None)
|
||||
target_label.place(relx=0.6, rely=0.1, relwidth=0.3, relheight=0.25)
|
||||
target_label.place(relx=0.6, rely=0.05, relwidth=0.275, relheight=0.225)
|
||||
|
||||
select_face_button = ctk.CTkButton(
|
||||
root, text=_("Select a face"), cursor="hand2", command=lambda: select_source_path()
|
||||
)
|
||||
select_face_button.place(relx=0.1, rely=0.4, relwidth=0.3, relheight=0.1)
|
||||
select_face_button.place(relx=0.1, rely=0.30, relwidth=0.3, relheight=0.1)
|
||||
|
||||
swap_faces_button = ctk.CTkButton(
|
||||
root, text="↔", cursor="hand2", command=lambda: swap_faces_paths()
|
||||
)
|
||||
swap_faces_button.place(relx=0.45, rely=0.4, relwidth=0.1, relheight=0.1)
|
||||
swap_faces_button.place(relx=0.45, rely=0.30, relwidth=0.1, relheight=0.1)
|
||||
|
||||
select_target_button = ctk.CTkButton(
|
||||
root,
|
||||
|
|
@ -173,7 +174,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
cursor="hand2",
|
||||
command=lambda: select_target_path(),
|
||||
)
|
||||
select_target_button.place(relx=0.6, rely=0.4, relwidth=0.3, relheight=0.1)
|
||||
select_target_button.place(relx=0.6, rely=0.30, relwidth=0.3, relheight=0.1)
|
||||
|
||||
keep_fps_value = ctk.BooleanVar(value=modules.globals.keep_fps)
|
||||
keep_fps_checkbox = ctk.CTkSwitch(
|
||||
|
|
@ -186,7 +187,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
keep_fps_checkbox.place(relx=0.1, rely=0.6)
|
||||
keep_fps_checkbox.place(relx=0.1, rely=0.5)
|
||||
|
||||
keep_frames_value = ctk.BooleanVar(value=modules.globals.keep_frames)
|
||||
keep_frames_switch = ctk.CTkSwitch(
|
||||
|
|
@ -199,7 +200,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
keep_frames_switch.place(relx=0.1, rely=0.65)
|
||||
keep_frames_switch.place(relx=0.1, rely=0.55)
|
||||
|
||||
enhancer_value = ctk.BooleanVar(value=modules.globals.fp_ui["face_enhancer"])
|
||||
enhancer_switch = ctk.CTkSwitch(
|
||||
|
|
@ -212,7 +213,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
enhancer_switch.place(relx=0.1, rely=0.7)
|
||||
enhancer_switch.place(relx=0.1, rely=0.6)
|
||||
|
||||
keep_audio_value = ctk.BooleanVar(value=modules.globals.keep_audio)
|
||||
keep_audio_switch = ctk.CTkSwitch(
|
||||
|
|
@ -225,7 +226,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
keep_audio_switch.place(relx=0.6, rely=0.6)
|
||||
keep_audio_switch.place(relx=0.6, rely=0.5)
|
||||
|
||||
many_faces_value = ctk.BooleanVar(value=modules.globals.many_faces)
|
||||
many_faces_switch = ctk.CTkSwitch(
|
||||
|
|
@ -238,7 +239,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
many_faces_switch.place(relx=0.6, rely=0.65)
|
||||
many_faces_switch.place(relx=0.6, rely=0.55)
|
||||
|
||||
color_correction_value = ctk.BooleanVar(value=modules.globals.color_correction)
|
||||
color_correction_switch = ctk.CTkSwitch(
|
||||
|
|
@ -251,7 +252,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
color_correction_switch.place(relx=0.6, rely=0.70)
|
||||
color_correction_switch.place(relx=0.6, rely=0.6)
|
||||
|
||||
# nsfw_value = ctk.BooleanVar(value=modules.globals.nsfw_filter)
|
||||
# nsfw_switch = ctk.CTkSwitch(root, text='NSFW filter', variable=nsfw_value, cursor='hand2', command=lambda: setattr(modules.globals, 'nsfw_filter', nsfw_value.get()))
|
||||
|
|
@ -269,7 +270,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
close_mapper_window() if not map_faces.get() else None
|
||||
),
|
||||
)
|
||||
map_faces_switch.place(relx=0.1, rely=0.75)
|
||||
map_faces_switch.place(relx=0.1, rely=0.65)
|
||||
|
||||
show_fps_value = ctk.BooleanVar(value=modules.globals.show_fps)
|
||||
show_fps_switch = ctk.CTkSwitch(
|
||||
|
|
@ -282,7 +283,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
show_fps_switch.place(relx=0.6, rely=0.75)
|
||||
show_fps_switch.place(relx=0.6, rely=0.65)
|
||||
|
||||
mouth_mask_var = ctk.BooleanVar(value=modules.globals.mouth_mask)
|
||||
mouth_mask_switch = ctk.CTkSwitch(
|
||||
|
|
@ -292,7 +293,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
cursor="hand2",
|
||||
command=lambda: setattr(modules.globals, "mouth_mask", mouth_mask_var.get()),
|
||||
)
|
||||
mouth_mask_switch.place(relx=0.1, rely=0.55)
|
||||
mouth_mask_switch.place(relx=0.1, rely=0.45)
|
||||
|
||||
show_mouth_mask_box_var = ctk.BooleanVar(value=modules.globals.show_mouth_mask_box)
|
||||
show_mouth_mask_box_switch = ctk.CTkSwitch(
|
||||
|
|
@ -304,7 +305,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
modules.globals, "show_mouth_mask_box", show_mouth_mask_box_var.get()
|
||||
),
|
||||
)
|
||||
show_mouth_mask_box_switch.place(relx=0.6, rely=0.55)
|
||||
show_mouth_mask_box_switch.place(relx=0.6, rely=0.45)
|
||||
|
||||
start_button = ctk.CTkButton(
|
||||
root, text=_("Start"), cursor="hand2", command=lambda: analyze_target(start, root)
|
||||
|
|
@ -365,6 +366,72 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
live_button.place(relx=0.65, rely=0.86, relwidth=0.2, relheight=0.05)
|
||||
# --- End Camera Selection ---
|
||||
|
||||
# 1) Define a DoubleVar for transparency (0 = fully transparent, 1 = fully opaque)
|
||||
transparency_var = ctk.DoubleVar(value=1.0)
|
||||
|
||||
def on_transparency_change(value: float):
|
||||
# Convert slider value to float
|
||||
val = float(value)
|
||||
modules.globals.opacity = val # Set global opacity
|
||||
percentage = int(val * 100)
|
||||
|
||||
if percentage == 0:
|
||||
modules.globals.fp_ui["face_enhancer"] = False
|
||||
update_status("Transparency set to 0% - Face swapping disabled.")
|
||||
elif percentage == 100:
|
||||
modules.globals.face_swapper_enabled = True
|
||||
update_status("Transparency set to 100%.")
|
||||
else:
|
||||
modules.globals.face_swapper_enabled = True
|
||||
update_status(f"Transparency set to {percentage}%")
|
||||
|
||||
# 2) Transparency label and slider (placed ABOVE sharpness)
|
||||
transparency_label = ctk.CTkLabel(root, text="Transparency:")
|
||||
transparency_label.place(relx=0.15, rely=0.69, relwidth=0.2, relheight=0.05)
|
||||
|
||||
transparency_slider = ctk.CTkSlider(
|
||||
root,
|
||||
from_=0.0,
|
||||
to=1.0,
|
||||
variable=transparency_var,
|
||||
command=on_transparency_change,
|
||||
fg_color="#E0E0E0",
|
||||
progress_color="#007BFF",
|
||||
button_color="#FFFFFF",
|
||||
button_hover_color="#CCCCCC",
|
||||
height=5,
|
||||
border_width=1,
|
||||
corner_radius=3,
|
||||
)
|
||||
transparency_slider.place(relx=0.35, rely=0.71, relwidth=0.5, relheight=0.02)
|
||||
|
||||
# 3) Sharpness label & slider
|
||||
sharpness_var = ctk.DoubleVar(value=0.0) # start at 0.0
|
||||
def on_sharpness_change(value: float):
|
||||
modules.globals.sharpness = float(value)
|
||||
update_status(f"Sharpness set to {value:.1f}")
|
||||
|
||||
sharpness_label = ctk.CTkLabel(root, text="Sharpness:")
|
||||
sharpness_label.place(relx=0.15, rely=0.74, relwidth=0.2, relheight=0.05)
|
||||
|
||||
sharpness_slider = ctk.CTkSlider(
|
||||
root,
|
||||
from_=0.0,
|
||||
to=5.0,
|
||||
variable=sharpness_var,
|
||||
command=on_sharpness_change,
|
||||
fg_color="#E0E0E0",
|
||||
progress_color="#007BFF",
|
||||
button_color="#FFFFFF",
|
||||
button_hover_color="#CCCCCC",
|
||||
height=5,
|
||||
border_width=1,
|
||||
corner_radius=3,
|
||||
)
|
||||
sharpness_slider.place(relx=0.35, rely=0.76, relwidth=0.5, relheight=0.02)
|
||||
|
||||
# Status and link at the bottom
|
||||
global status_label
|
||||
status_label = ctk.CTkLabel(root, text=None, justify="center")
|
||||
status_label.place(relx=0.1, rely=0.9, relwidth=0.8)
|
||||
|
||||
|
|
@ -381,6 +448,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
|||
|
||||
return root
|
||||
|
||||
|
||||
def close_mapper_window():
|
||||
global POPUP, POPUP_LIVE
|
||||
if POPUP and POPUP.winfo_exists():
|
||||
|
|
@ -397,7 +465,7 @@ def analyze_target(start: Callable[[], None], root: ctk.CTk):
|
|||
return
|
||||
|
||||
if modules.globals.map_faces:
|
||||
modules.globals.source_target_map = []
|
||||
modules.globals.souce_target_map = []
|
||||
|
||||
if is_image(modules.globals.target_path):
|
||||
update_status("Getting unique faces")
|
||||
|
|
@ -406,8 +474,8 @@ def analyze_target(start: Callable[[], None], root: ctk.CTk):
|
|||
update_status("Getting unique faces")
|
||||
get_unique_faces_from_target_video()
|
||||
|
||||
if len(modules.globals.source_target_map) > 0:
|
||||
create_source_target_popup(start, root, modules.globals.source_target_map)
|
||||
if len(modules.globals.souce_target_map) > 0:
|
||||
create_source_target_popup(start, root, modules.globals.souce_target_map)
|
||||
else:
|
||||
update_status("No faces found in target")
|
||||
else:
|
||||
|
|
@ -429,7 +497,7 @@ def create_source_target_popup(
|
|||
POPUP.destroy()
|
||||
select_output_path(start)
|
||||
else:
|
||||
update_pop_status("At least 1 source with target is required!")
|
||||
update_pop_status("Atleast 1 source with target is required!")
|
||||
|
||||
scrollable_frame = ctk.CTkScrollableFrame(
|
||||
POPUP, width=POPUP_SCROLL_WIDTH, height=POPUP_SCROLL_HEIGHT
|
||||
|
|
@ -489,7 +557,7 @@ def update_popup_source(
|
|||
global source_label_dict
|
||||
|
||||
source_path = ctk.filedialog.askopenfilename(
|
||||
title=_("select a source image"),
|
||||
title=_("select an source image"),
|
||||
initialdir=RECENT_DIRECTORY_SOURCE,
|
||||
filetypes=[img_ft],
|
||||
)
|
||||
|
|
@ -584,7 +652,7 @@ def select_source_path() -> None:
|
|||
|
||||
PREVIEW.withdraw()
|
||||
source_path = ctk.filedialog.askopenfilename(
|
||||
title=_("select a source image"),
|
||||
title=_("select an source image"),
|
||||
initialdir=RECENT_DIRECTORY_SOURCE,
|
||||
filetypes=[img_ft],
|
||||
)
|
||||
|
|
@ -627,7 +695,7 @@ def select_target_path() -> None:
|
|||
|
||||
PREVIEW.withdraw()
|
||||
target_path = ctk.filedialog.askopenfilename(
|
||||
title=_("select a target image or video"),
|
||||
title=_("select an target image or video"),
|
||||
initialdir=RECENT_DIRECTORY_TARGET,
|
||||
filetypes=[img_ft, vid_ft],
|
||||
)
|
||||
|
|
@ -696,21 +764,17 @@ def check_and_ignore_nsfw(target, destroy: Callable = None) -> bool:
|
|||
|
||||
|
||||
def fit_image_to_size(image, width: int, height: int):
|
||||
if width is None or height is None or width <= 0 or height <= 0:
|
||||
if width is None and height is None:
|
||||
return image
|
||||
h, w, _ = image.shape
|
||||
ratio_h = 0.0
|
||||
ratio_w = 0.0
|
||||
ratio_w = width / w
|
||||
if width > height:
|
||||
ratio_h = height / h
|
||||
# Use the smaller ratio to ensure the image fits within the given dimensions
|
||||
ratio = min(ratio_w, ratio_h)
|
||||
|
||||
# Compute new dimensions, ensuring they're at least 1 pixel
|
||||
new_width = max(1, int(ratio * w))
|
||||
new_height = max(1, int(ratio * h))
|
||||
new_size = (new_width, new_height)
|
||||
|
||||
else:
|
||||
ratio_w = width / w
|
||||
ratio = max(ratio_w, ratio_h)
|
||||
new_size = (int(ratio * w), int(ratio * h))
|
||||
return cv2.resize(image, dsize=new_size)
|
||||
|
||||
|
||||
|
|
@ -791,9 +855,9 @@ def webcam_preview(root: ctk.CTk, camera_index: int):
|
|||
return
|
||||
create_webcam_preview(camera_index)
|
||||
else:
|
||||
modules.globals.source_target_map = []
|
||||
modules.globals.souce_target_map = []
|
||||
create_source_target_popup_for_webcam(
|
||||
root, modules.globals.source_target_map, camera_index
|
||||
root, modules.globals.souce_target_map, camera_index
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -1108,7 +1172,7 @@ def update_webcam_source(
|
|||
global source_label_dict_live
|
||||
|
||||
source_path = ctk.filedialog.askopenfilename(
|
||||
title=_("select a source image"),
|
||||
title=_("select an source image"),
|
||||
initialdir=RECENT_DIRECTORY_SOURCE,
|
||||
filetypes=[img_ft],
|
||||
)
|
||||
|
|
@ -1160,7 +1224,7 @@ def update_webcam_target(
|
|||
global target_label_dict_live
|
||||
|
||||
target_path = ctk.filedialog.askopenfilename(
|
||||
title=_("select a target image"),
|
||||
title=_("select an target image"),
|
||||
initialdir=RECENT_DIRECTORY_SOURCE,
|
||||
filetypes=[img_ft],
|
||||
)
|
||||
|
|
|
|||
|
|
@ -19,3 +19,5 @@ onnxruntime-gpu==1.22.0; sys_platform != 'darwin'
|
|||
tensorflow; sys_platform != 'darwin'
|
||||
opennsfw2==0.10.2
|
||||
protobuf==4.25.1
|
||||
git+https://github.com/xinntao/BasicSR.git@master
|
||||
git+https://github.com/TencentARC/GFPGAN.git@master
|
||||
3
run.py
3
run.py
|
|
@ -1,5 +1,8 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
# Import the tkinter fix to patch the ScreenChanged error
|
||||
import tkinter_fix
|
||||
|
||||
from modules import core
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
|||
26
tkinter_fix.py
Normal file
26
tkinter_fix.py
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
import tkinter
|
||||
|
||||
# Only needs to be imported once at the beginning of the application
|
||||
def apply_patch():
|
||||
# Create a monkey patch for the internal _tkinter module
|
||||
original_init = tkinter.Tk.__init__
|
||||
|
||||
def patched_init(self, *args, **kwargs):
|
||||
# Call the original init
|
||||
original_init(self, *args, **kwargs)
|
||||
|
||||
# Define the missing ::tk::ScreenChanged procedure
|
||||
self.tk.eval("""
|
||||
if {[info commands ::tk::ScreenChanged] == ""} {
|
||||
proc ::tk::ScreenChanged {args} {
|
||||
# Do nothing
|
||||
return
|
||||
}
|
||||
}
|
||||
""")
|
||||
|
||||
# Apply the monkey patch
|
||||
tkinter.Tk.__init__ = patched_init
|
||||
|
||||
# Apply the patch automatically when this module is imported
|
||||
apply_patch()
|
||||
Loading…
Reference in New Issue
Block a user