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b3c4ed9250
Deep-Live-Cam/modules/processors/frame/face_swapper.py
Kenneth Estanislao b3c4ed9250 optimization with mac 
Hoping this would solve the mac issues, if you're a mac user, please report if there is an improvement
2025-11-16 20:09:12 +08:00

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Python
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from typing import Any, List, Optional
import cv2
import insightface
import threading
import numpy as np
import platform
import modules.globals
import modules.processors.frame.core
from modules.core import update_status
from modules.face_analyser import get_one_face, get_many_faces, default_source_face
from modules.typing import Face, Frame
from modules.utilities import (
conditional_download,
is_image,
is_video,
)
from modules.cluster_analysis import find_closest_centroid
import os
from collections import deque
import time
FACE_SWAPPER = None
THREAD_LOCK = threading.Lock()
NAME = "DLC.FACE-SWAPPER"
# --- START: Added for Interpolation ---
PREVIOUS_FRAME_RESULT = None # Stores the final processed frame from the previous step
# --- END: Added for Interpolation ---
# --- START: Mac M1-M5 Optimizations ---
IS_APPLE_SILICON = platform.system() == 'Darwin' and platform.machine() == 'arm64'
FRAME_CACHE = deque(maxlen=3) # Cache for frame reuse
FACE_DETECTION_CACHE = {} # Cache face detections
LAST_DETECTION_TIME = 0
DETECTION_INTERVAL = 0.033 # ~30 FPS detection rate for live mode
FRAME_SKIP_COUNTER = 0
ADAPTIVE_QUALITY = True
# --- END: Mac M1-M5 Optimizations ---
abs_dir = os.path.dirname(os.path.abspath(__file__))
models_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
)
def pre_check() -> bool:
download_directory_path = abs_dir
conditional_download(
download_directory_path,
[
"https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx"
],
)
return True
def pre_start() -> bool:
# Simplified pre_start, assuming checks happen before calling process functions
model_path = os.path.join(models_dir, "inswapper_128_fp16.onnx")
if not os.path.exists(model_path):
update_status(f"Model not found: {model_path}. Please download it.", NAME)
return False
# Try to get the face swapper to ensure it loads correctly
if get_face_swapper() is None:
# Error message already printed within get_face_swapper
return False
# Add other essential checks if needed, e.g., target/source path validity
return True
def get_face_swapper() -> Any:
global FACE_SWAPPER
with THREAD_LOCK:
if FACE_SWAPPER is None:
model_name = "inswapper_128.onnx"
if "CUDAExecutionProvider" in modules.globals.execution_providers:
model_name = "inswapper_128_fp16.onnx"
model_path = os.path.join(models_dir, model_name)
update_status(f"Loading face swapper model from: {model_path}", NAME)
try:
# Optimized provider configuration for Apple Silicon
providers_config = []
for p in modules.globals.execution_providers:
if p == "CoreMLExecutionProvider" and IS_APPLE_SILICON:
# Enhanced CoreML configuration for M1-M5
providers_config.append((
"CoreMLExecutionProvider",
{
"ModelFormat": "MLProgram",
"MLComputeUnits": "ALL", # Use Neural Engine + GPU + CPU
"SpecializationStrategy": "FastPrediction",
"AllowLowPrecisionAccumulationOnGPU": 1,
"EnableOnSubgraphs": 1,
"RequireStaticShapes": 0,
"MaximumCacheSize": 1024 * 1024 * 512, # 512MB cache
}
))
else:
providers_config.append(p)
FACE_SWAPPER = insightface.model_zoo.get_model(
model_path,
providers=providers_config,
)
update_status("Face swapper model loaded successfully.", NAME)
except Exception as e:
update_status(f"Error loading face swapper model: {e}", NAME)
FACE_SWAPPER = None
return None
return FACE_SWAPPER
def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
face_swapper = get_face_swapper()
if face_swapper is None:
update_status("Face swapper model not loaded or failed to load. Skipping swap.", NAME)
return temp_frame
# Store a copy of the original frame before swapping for opacity blending
original_frame = temp_frame.copy()
# Pre-swap Input Check with optimization
if temp_frame.dtype != np.uint8:
temp_frame = np.clip(temp_frame, 0, 255).astype(np.uint8)
# Apply the face swap with optimized memory handling
try:
# For Apple Silicon, use optimized inference
if IS_APPLE_SILICON:
# Ensure contiguous memory layout for better performance
temp_frame = np.ascontiguousarray(temp_frame)
swapped_frame_raw = face_swapper.get(
temp_frame, target_face, source_face, paste_back=True
)
# --- START: CRITICAL FIX FOR ORT 1.17 ---
# Check the output type and range from the model
if swapped_frame_raw is None:
# print("Warning: face_swapper.get returned None.") # Debug
return original_frame # Return original if swap somehow failed internally
# Ensure the output is a numpy array
if not isinstance(swapped_frame_raw, np.ndarray):
# print(f"Warning: face_swapper.get returned type {type(swapped_frame_raw)}, expected numpy array.") # Debug
return original_frame
# Ensure the output has the correct shape (like the input frame)
if swapped_frame_raw.shape != temp_frame.shape:
# print(f"Warning: Swapped frame shape {swapped_frame_raw.shape} differs from input {temp_frame.shape}.") # Debug
# Attempt resize (might distort if aspect ratio changed, but better than crashing)
try:
swapped_frame_raw = cv2.resize(swapped_frame_raw, (temp_frame.shape[1], temp_frame.shape[0]))
except Exception as resize_e:
# print(f"Error resizing swapped frame: {resize_e}") # Debug
return original_frame
# Explicitly clip values to 0-255 and convert to uint8
# This handles cases where the model might output floats or values outside the valid range
swapped_frame = np.clip(swapped_frame_raw, 0, 255).astype(np.uint8)
# --- END: CRITICAL FIX FOR ORT 1.17 ---
except Exception as e:
print(f"Error during face swap using face_swapper.get: {e}") # More specific error
# import traceback
# traceback.print_exc() # Print full traceback for debugging
return original_frame # Return original if swap fails
# --- Post-swap Processing (Masking, Opacity, etc.) ---
# Now, work with the guaranteed uint8 'swapped_frame'
if getattr(modules.globals, "mouth_mask", False): # Check if mouth_mask is enabled
# Create a mask for the target face
face_mask = create_face_mask(target_face, temp_frame) # Use temp_frame (original shape) for mask creation geometry
# Create the mouth mask using original geometry
mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = (
create_lower_mouth_mask(target_face, temp_frame) # Use temp_frame (original) for cutout
)
# Apply the mouth area only if mouth_cutout exists
if mouth_cutout is not None and mouth_box != (0,0,0,0): # Add check for valid box
# Apply mouth area (from original) onto the 'swapped_frame'
swapped_frame = apply_mouth_area(
swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
)
if getattr(modules.globals, "show_mouth_mask_box", False):
mouth_mask_data = (mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon)
# Draw visualization on the swapped_frame *before* opacity blending
swapped_frame = draw_mouth_mask_visualization(
swapped_frame, target_face, mouth_mask_data
)
# Apply opacity blend between the original frame and the swapped frame
opacity = getattr(modules.globals, "opacity", 1.0)
# Ensure opacity is within valid range [0.0, 1.0]
opacity = max(0.0, min(1.0, opacity))
# Blend the original_frame with the (potentially mouth-masked) swapped_frame
# Ensure both frames are uint8 before blending
final_swapped_frame = cv2.addWeighted(original_frame.astype(np.uint8), 1 - opacity, swapped_frame.astype(np.uint8), opacity, 0)
# Ensure final frame is uint8 after blending (addWeighted should preserve it, but belt-and-suspenders)
final_swapped_frame = final_swapped_frame.astype(np.uint8)
return final_swapped_frame
# --- START: Mac M1-M5 Optimized Face Detection ---
def get_faces_optimized(frame: Frame, use_cache: bool = True) -> Optional[List[Face]]:
"""Optimized face detection for live mode on Apple Silicon"""
global LAST_DETECTION_TIME, FACE_DETECTION_CACHE
if not use_cache or not IS_APPLE_SILICON:
# Standard detection
if modules.globals.many_faces:
return get_many_faces(frame)
else:
face = get_one_face(frame)
return [face] if face else None
# Adaptive detection rate for live mode
current_time = time.time()
time_since_last = current_time - LAST_DETECTION_TIME
# Skip detection if too soon (adaptive frame skipping)
if time_since_last < DETECTION_INTERVAL and FACE_DETECTION_CACHE:
return FACE_DETECTION_CACHE.get('faces')
# Perform detection
LAST_DETECTION_TIME = current_time
if modules.globals.many_faces:
faces = get_many_faces(frame)
else:
face = get_one_face(frame)
faces = [face] if face else None
# Cache results
FACE_DETECTION_CACHE['faces'] = faces
FACE_DETECTION_CACHE['timestamp'] = current_time
return faces
# --- END: Mac M1-M5 Optimized Face Detection ---
# --- START: Helper function for interpolation and sharpening ---
def apply_post_processing(current_frame: Frame, swapped_face_bboxes: List[np.ndarray]) -> Frame:
"""Applies sharpening and interpolation with Apple Silicon optimizations."""
global PREVIOUS_FRAME_RESULT
processed_frame = current_frame.copy()
# 1. Apply Sharpening (if enabled) with optimized kernel for Apple Silicon
sharpness_value = getattr(modules.globals, "sharpness", 0.0)
if sharpness_value > 0.0 and swapped_face_bboxes:
height, width = processed_frame.shape[:2]
for bbox in swapped_face_bboxes:
# Ensure bbox is iterable and has 4 elements
if not hasattr(bbox, '__iter__') or len(bbox) != 4:
# print(f"Warning: Invalid bbox format for sharpening: {bbox}") # Debug
continue
x1, y1, x2, y2 = bbox
# Ensure coordinates are integers and within bounds
try:
x1, y1 = max(0, int(x1)), max(0, int(y1))
x2, y2 = min(width, int(x2)), min(height, int(y2))
except ValueError:
# print(f"Warning: Could not convert bbox coordinates to int: {bbox}") # Debug
continue
if x2 <= x1 or y2 <= y1:
continue
face_region = processed_frame[y1:y2, x1:x2]
if face_region.size == 0: continue
# Apply sharpening with optimized parameters for Apple Silicon
try:
# Use smaller sigma for faster processing on Apple Silicon
sigma = 2 if IS_APPLE_SILICON else 3
blurred = cv2.GaussianBlur(face_region, (0, 0), sigma)
sharpened_region = cv2.addWeighted(
face_region, 1.0 + sharpness_value,
blurred, -sharpness_value,
0
)
sharpened_region = np.clip(sharpened_region, 0, 255).astype(np.uint8)
processed_frame[y1:y2, x1:x2] = sharpened_region
except cv2.error:
pass
# 2. Apply Interpolation (if enabled)
enable_interpolation = getattr(modules.globals, "enable_interpolation", False)
interpolation_weight = getattr(modules.globals, "interpolation_weight", 0.2)
final_frame = processed_frame # Start with the current (potentially sharpened) frame
if enable_interpolation and 0 < interpolation_weight < 1:
if PREVIOUS_FRAME_RESULT is not None and PREVIOUS_FRAME_RESULT.shape == processed_frame.shape and PREVIOUS_FRAME_RESULT.dtype == processed_frame.dtype:
# Perform interpolation
try:
final_frame = cv2.addWeighted(
PREVIOUS_FRAME_RESULT, 1.0 - interpolation_weight,
processed_frame, interpolation_weight,
0
)
# Ensure final frame is uint8
final_frame = np.clip(final_frame, 0, 255).astype(np.uint8)
except cv2.error as interp_e:
# print(f"Warning: OpenCV error during interpolation: {interp_e}") # Debug
final_frame = processed_frame # Use current frame if interpolation fails
PREVIOUS_FRAME_RESULT = None # Reset state if error occurs
# Update the state for the next frame *with the interpolated result*
PREVIOUS_FRAME_RESULT = final_frame.copy()
else:
# If previous frame invalid or doesn't match, use current frame and update state
if PREVIOUS_FRAME_RESULT is not None and PREVIOUS_FRAME_RESULT.shape != processed_frame.shape:
# print("Info: Frame shape changed, resetting interpolation state.") # Debug
pass
PREVIOUS_FRAME_RESULT = processed_frame.copy()
else:
# If interpolation is off or weight is invalid, just use the current frame
# Update state with the current (potentially sharpened) frame
# Reset previous frame state if interpolation was just turned off or weight is invalid
PREVIOUS_FRAME_RESULT = processed_frame.copy()
return final_frame
# --- END: Helper function for interpolation and sharpening ---
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
"""
DEPRECATED / SIMPLER VERSION - Processes a single frame using one source face.
Consider using process_frame_v2 for more complex scenarios.
"""
if getattr(modules.globals, "opacity", 1.0) == 0:
# If opacity is 0, no swap happens, so no post-processing needed.
# Also reset interpolation state if it was active.
global PREVIOUS_FRAME_RESULT
PREVIOUS_FRAME_RESULT = None
return temp_frame
# Color correction removed from here (better applied before swap if needed)
processed_frame = temp_frame # Start with the input frame
swapped_face_bboxes = [] # Keep track of where swaps happened
if modules.globals.many_faces:
many_faces = get_many_faces(processed_frame)
if many_faces:
current_swap_target = processed_frame.copy() # Apply swaps sequentially on a copy
for target_face in many_faces:
current_swap_target = swap_face(source_face, target_face, current_swap_target)
if target_face is not None and hasattr(target_face, "bbox") and target_face.bbox is not None:
swapped_face_bboxes.append(target_face.bbox.astype(int))
processed_frame = current_swap_target # Assign the final result after all swaps
else:
target_face = get_one_face(processed_frame)
if target_face:
processed_frame = swap_face(source_face, target_face, processed_frame)
if target_face is not None and hasattr(target_face, "bbox") and target_face.bbox is not None:
swapped_face_bboxes.append(target_face.bbox.astype(int))
# Apply sharpening and interpolation
final_frame = apply_post_processing(processed_frame, swapped_face_bboxes)
return final_frame
def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
"""Handles complex mapping scenarios (map_faces=True) and live streams."""
if getattr(modules.globals, "opacity", 1.0) == 0:
# If opacity is 0, no swap happens, so no post-processing needed.
# Also reset interpolation state if it was active.
global PREVIOUS_FRAME_RESULT
PREVIOUS_FRAME_RESULT = None
return temp_frame
processed_frame = temp_frame # Start with the input frame
swapped_face_bboxes = [] # Keep track of where swaps happened
# Determine source/target pairs based on mode
source_target_pairs = []
# Ensure maps exist before accessing them
source_target_map = getattr(modules.globals, "source_target_map", None)
simple_map = getattr(modules.globals, "simple_map", None)
# Check if target is a file path (image or video) or live stream
is_file_target = modules.globals.target_path and (is_image(modules.globals.target_path) or is_video(modules.globals.target_path))
if is_file_target:
# Processing specific image or video file with pre-analyzed maps
if source_target_map:
if modules.globals.many_faces:
source_face = default_source_face() # Use default source for all targets
if source_face:
for map_data in source_target_map:
if is_image(modules.globals.target_path):
target_info = map_data.get("target", {})
if target_info: # Check if target info exists
target_face = target_info.get("face")
if target_face:
source_target_pairs.append((source_face, target_face))
elif is_video(modules.globals.target_path):
# Find faces for the current frame_path in video map
target_frames_data = map_data.get("target_faces_in_frame", [])
if target_frames_data: # Check if frame data exists
target_frames = [f for f in target_frames_data if f and f.get("location") == temp_frame_path]
for frame_data in target_frames:
faces_in_frame = frame_data.get("faces", [])
if faces_in_frame: # Check if faces exist
for target_face in faces_in_frame:
source_target_pairs.append((source_face, target_face))
else: # Single face or specific mapping
for map_data in source_target_map:
source_info = map_data.get("source", {})
if not source_info: continue # Skip if no source info
source_face = source_info.get("face")
if not source_face: continue # Skip if no source defined for this map entry
if is_image(modules.globals.target_path):
target_info = map_data.get("target", {})
if target_info:
target_face = target_info.get("face")
if target_face:
source_target_pairs.append((source_face, target_face))
elif is_video(modules.globals.target_path):
target_frames_data = map_data.get("target_faces_in_frame", [])
if target_frames_data:
target_frames = [f for f in target_frames_data if f and f.get("location") == temp_frame_path]
for frame_data in target_frames:
faces_in_frame = frame_data.get("faces", [])
if faces_in_frame:
for target_face in faces_in_frame:
source_target_pairs.append((source_face, target_face))
else:
# Live stream or webcam processing (analyze faces on the fly)
detected_faces = get_many_faces(processed_frame)
if detected_faces:
if modules.globals.many_faces:
source_face = default_source_face() # Use default source for all detected targets
if source_face:
for target_face in detected_faces:
source_target_pairs.append((source_face, target_face))
elif simple_map:
# Use simple_map (source_faces <-> target_embeddings)
source_faces = simple_map.get("source_faces", [])
target_embeddings = simple_map.get("target_embeddings", [])
if source_faces and target_embeddings and len(source_faces) == len(target_embeddings):
# Match detected faces to the closest target embedding
if len(detected_faces) <= len(target_embeddings):
# More targets defined than detected - match each detected face
for detected_face in detected_faces:
if detected_face.normed_embedding is None: continue
closest_idx, _ = find_closest_centroid(target_embeddings, detected_face.normed_embedding)
if 0 <= closest_idx < len(source_faces):
source_target_pairs.append((source_faces[closest_idx], detected_face))
else:
# More faces detected than targets defined - match each target embedding to closest detected face
detected_embeddings = [f.normed_embedding for f in detected_faces if f.normed_embedding is not None]
detected_faces_with_embedding = [f for f in detected_faces if f.normed_embedding is not None]
if not detected_embeddings: return processed_frame # No embeddings to match
for i, target_embedding in enumerate(target_embeddings):
if 0 <= i < len(source_faces): # Ensure source face exists for this embedding
closest_idx, _ = find_closest_centroid(detected_embeddings, target_embedding)
if 0 <= closest_idx < len(detected_faces_with_embedding):
source_target_pairs.append((source_faces[i], detected_faces_with_embedding[closest_idx]))
else: # Fallback: if no map, use default source for the single detected face (if any)
source_face = default_source_face()
target_face = get_one_face(processed_frame, detected_faces) # Use faces already detected
if source_face and target_face:
source_target_pairs.append((source_face, target_face))
# Perform swaps based on the collected pairs
current_swap_target = processed_frame.copy() # Apply swaps sequentially
for source_face, target_face in source_target_pairs:
if source_face and target_face:
current_swap_target = swap_face(source_face, target_face, current_swap_target)
if target_face is not None and hasattr(target_face, "bbox") and target_face.bbox is not None:
swapped_face_bboxes.append(target_face.bbox.astype(int))
processed_frame = current_swap_target # Assign final result
# Apply sharpening and interpolation
final_frame = apply_post_processing(processed_frame, swapped_face_bboxes)
return final_frame
def process_frames(
source_path: str, temp_frame_paths: List[str], progress: Any = None
) -> None:
"""
Processes a list of frame paths (typically for video).
Iterates through frames, applies the appropriate swapping logic based on globals,
and saves the result back to the frame path. Handles multi-threading via caller.
"""
# Determine which processing function to use based on map_faces global setting
use_v2 = getattr(modules.globals, "map_faces", False)
source_face = None # Initialize source_face
# --- Pre-load source face only if needed (Simple Mode: map_faces=False) ---
if not use_v2:
if not source_path or not os.path.exists(source_path):
update_status(f"Error: Source path invalid or not provided for simple mode: {source_path}", NAME)
# Log the error but allow proceeding; subsequent check will stop processing.
else:
try:
source_img = cv2.imread(source_path)
if source_img is None:
# Specific error for file reading failure
update_status(f"Error reading source image file {source_path}. Please check the path and file integrity.", NAME)
else:
source_face = get_one_face(source_img)
if source_face is None:
# Specific message for no face detected after successful read
update_status(f"Warning: Successfully read source image {source_path}, but no face was detected. Swaps will be skipped.", NAME)
except Exception as e:
# Print the specific exception caught
import traceback
print(f"{NAME}: Caught exception during source image processing for {source_path}:")
traceback.print_exc() # Print the full traceback
update_status(f"Error during source image reading or analysis {source_path}: {e}", NAME)
# Log general exception during the process
total_frames = len(temp_frame_paths)
# update_status(f"Processing {total_frames} frames. Use V2 (map_faces): {use_v2}", NAME) # Optional Debug
# --- Stop processing entirely if in Simple Mode and source face is invalid ---
if not use_v2 and source_face is None:
update_status(f"Halting video processing: Invalid or no face detected in source image for simple mode.", NAME)
if progress:
# Ensure the progress bar completes if it was started
remaining_updates = total_frames - progress.n if hasattr(progress, 'n') else total_frames
if remaining_updates > 0:
progress.update(remaining_updates)
return # Exit the function entirely
# --- Process each frame path provided in the list ---
# Note: In the current core.py multi_process_frame, temp_frame_paths will usually contain only ONE path per call.
for i, temp_frame_path in enumerate(temp_frame_paths):
# update_status(f"Processing frame {i+1}/{total_frames}: {os.path.basename(temp_frame_path)}", NAME) # Optional Debug
# Read the target frame
try:
temp_frame = cv2.imread(temp_frame_path)
if temp_frame is None:
print(f"{NAME}: Error: Could not read frame: {temp_frame_path}, skipping.")
if progress: progress.update(1)
continue # Skip this frame if read fails
except Exception as read_e:
print(f"{NAME}: Error reading frame {temp_frame_path}: {read_e}, skipping.")
if progress: progress.update(1)
continue
# Select processing function and execute
result_frame = None
try:
if use_v2:
# V2 uses global maps and needs the frame path for lookup in video mode
# update_status(f"Using process_frame_v2 for: {os.path.basename(temp_frame_path)}", NAME) # Optional Debug
result_frame = process_frame_v2(temp_frame, temp_frame_path)
else:
# Simple mode uses the pre-loaded source_face (already checked for validity above)
# update_status(f"Using process_frame (simple) for: {os.path.basename(temp_frame_path)}", NAME) # Optional Debug
result_frame = process_frame(source_face, temp_frame) # source_face is guaranteed to be valid here
# Check if processing actually returned a frame
if result_frame is None:
print(f"{NAME}: Warning: Processing returned None for frame {temp_frame_path}. Using original.")
result_frame = temp_frame
except Exception as proc_e:
print(f"{NAME}: Error processing frame {temp_frame_path}: {proc_e}")
# import traceback # Optional for detailed debugging
# traceback.print_exc()
result_frame = temp_frame # Use original frame on processing error
# Write the result back to the same frame path
try:
write_success = cv2.imwrite(temp_frame_path, result_frame)
if not write_success:
print(f"{NAME}: Error: Failed to write processed frame to {temp_frame_path}")
except Exception as write_e:
print(f"{NAME}: Error writing frame {temp_frame_path}: {write_e}")
# Update progress bar
if progress:
progress.update(1)
# else: # Basic console progress (optional)
# if (i + 1) % 10 == 0 or (i + 1) == total_frames: # Update every 10 frames or on last frame
# update_status(f"Processed frame {i+1}/{total_frames}", NAME)
def process_image(source_path: str, target_path: str, output_path: str) -> None:
"""Processes a single target image."""
# --- Reset interpolation state for single image processing ---
global PREVIOUS_FRAME_RESULT
PREVIOUS_FRAME_RESULT = None
# ---
use_v2 = getattr(modules.globals, "map_faces", False)
# Read target first
try:
target_frame = cv2.imread(target_path)
if target_frame is None:
update_status(f"Error: Could not read target image: {target_path}", NAME)
return
except Exception as read_e:
update_status(f"Error reading target image {target_path}: {read_e}", NAME)
return
result = None
try:
if use_v2:
if getattr(modules.globals, "many_faces", False):
update_status("Processing image with 'map_faces' and 'many_faces'. Using pre-analysis map.", NAME)
# V2 processes based on global maps, doesn't need source_path here directly
# Assumes maps are pre-populated. Pass target_path for map lookup.
result = process_frame_v2(target_frame, target_path)
else: # Simple mode
try:
source_img = cv2.imread(source_path)
if source_img is None:
update_status(f"Error: Could not read source image: {source_path}", NAME)
return
source_face = get_one_face(source_img)
if not source_face:
update_status(f"Error: No face found in source image: {source_path}", NAME)
return
except Exception as src_e:
update_status(f"Error reading or analyzing source image {source_path}: {src_e}", NAME)
return
result = process_frame(source_face, target_frame)
# Write the result if processing was successful
if result is not None:
write_success = cv2.imwrite(output_path, result)
if write_success:
update_status(f"Output image saved to: {output_path}", NAME)
else:
update_status(f"Error: Failed to write output image to {output_path}", NAME)
else:
# This case might occur if process_frame/v2 returns None unexpectedly
update_status("Image processing failed (result was None).", NAME)
except Exception as proc_e:
update_status(f"Error during image processing: {proc_e}", NAME)
# import traceback
# traceback.print_exc()
def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
"""Sets up and calls the frame processing for video."""
# --- Reset interpolation state before starting video processing ---
global PREVIOUS_FRAME_RESULT
PREVIOUS_FRAME_RESULT = None
# ---
mode_desc = "'map_faces'" if getattr(modules.globals, "map_faces", False) else "'simple'"
if getattr(modules.globals, "map_faces", False) and getattr(modules.globals, "many_faces", False):
mode_desc += " and 'many_faces'. Using pre-analysis map."
update_status(f"Processing video with {mode_desc} mode.", NAME)
# Pass the correct source_path (needed for simple mode in process_frames)
# The core processing logic handles calling the right frame function (process_frames)
modules.processors.frame.core.process_video(
source_path, temp_frame_paths, process_frames # Pass the newly modified process_frames
)
# ==========================
# MASKING FUNCTIONS (Mostly unchanged, added safety checks and minor improvements)
# ==========================
def create_lower_mouth_mask(
face: Face, frame: Frame
) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
mouth_cutout = None
lower_lip_polygon = None # Initialize
mouth_box = (0,0,0,0) # Initialize
# Validate face and landmarks
if face is None or not hasattr(face, 'landmark_2d_106'):
# print("Warning: Invalid face object passed to create_lower_mouth_mask.")
return mask, mouth_cutout, mouth_box, lower_lip_polygon
landmarks = face.landmark_2d_106
# Check landmark validity
if landmarks is None or not isinstance(landmarks, np.ndarray) or landmarks.shape[0] < 106:
# print("Warning: Invalid or insufficient landmarks for mouth mask.")
return mask, mouth_cutout, mouth_box, lower_lip_polygon
try: # Wrap main logic in try-except
# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
lower_lip_order = [65, 66, 62, 70, 69, 18, 19, 20, 21, 22, 23, 24, 0, 8, 7, 6, 5, 4, 3, 2, 65] # 21 points
# Check if all indices are valid for the loaded landmarks (already partially done by < 106 check)
if max(lower_lip_order) >= landmarks.shape[0]:
# print(f"Warning: Landmark index {max(lower_lip_order)} out of bounds for shape {landmarks.shape[0]}.")
return mask, mouth_cutout, mouth_box, lower_lip_polygon
lower_lip_landmarks = landmarks[lower_lip_order].astype(np.float32)
# Filter out potential NaN or Inf values in landmarks
if not np.all(np.isfinite(lower_lip_landmarks)):
# print("Warning: Non-finite values detected in lower lip landmarks.")
return mask, mouth_cutout, mouth_box, lower_lip_polygon
center = np.mean(lower_lip_landmarks, axis=0)
if not np.all(np.isfinite(center)): # Check center calculation
# print("Warning: Could not calculate valid center for mouth mask.")
return mask, mouth_cutout, mouth_box, lower_lip_polygon
mask_down_size = getattr(modules.globals, "mask_down_size", 0.1) # Default 0.1
expansion_factor = 1 + mask_down_size
expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
mask_size = getattr(modules.globals, "mask_size", 1.0) # Default 1.0
toplip_extension = mask_size * 0.5
# Define toplip indices relative to lower_lip_order (safer)
toplip_local_indices = [0, 1, 2, 3, 4, 5, 19] # Indices in lower_lip_order for [65, 66, 62, 70, 69, 18, 2]
for idx in toplip_local_indices:
if idx < len(expanded_landmarks): # Boundary check
direction = expanded_landmarks[idx] - center
norm = np.linalg.norm(direction)
if norm > 1e-6: # Avoid division by zero
direction_normalized = direction / norm
expanded_landmarks[idx] += direction_normalized * toplip_extension
# Define chin indices relative to lower_lip_order
chin_local_indices = [9, 10, 11, 12, 13, 14] # Indices for [22, 23, 24, 0, 8, 7]
chin_extension = 2 * 0.2
for idx in chin_local_indices:
if idx < len(expanded_landmarks): # Boundary check
# Extend vertically based on distance from center y
y_diff = expanded_landmarks[idx][1] - center[1]
expanded_landmarks[idx][1] += y_diff * chin_extension
# Ensure landmarks are finite after adjustments
if not np.all(np.isfinite(expanded_landmarks)):
# print("Warning: Non-finite values detected after expanding landmarks.")
return mask, mouth_cutout, mouth_box, lower_lip_polygon
expanded_landmarks = expanded_landmarks.astype(np.int32)
min_x, min_y = np.min(expanded_landmarks, axis=0)
max_x, max_y = np.max(expanded_landmarks, axis=0)
# Add padding *after* initial min/max calculation
padding_ratio = 0.1 # Percentage padding
padding_x = int((max_x - min_x) * padding_ratio)
padding_y = int((max_y - min_y) * padding_ratio) # Use y-range for y-padding
# Apply padding and clamp to frame boundaries
frame_h, frame_w = frame.shape[:2]
min_x = max(0, min_x - padding_x)
min_y = max(0, min_y - padding_y)
max_x = min(frame_w, max_x + padding_x)
max_y = min(frame_h, max_y + padding_y)
if max_x > min_x and max_y > min_y:
# Create the mask ROI
mask_roi_h = max_y - min_y
mask_roi_w = max_x - min_x
mask_roi = np.zeros((mask_roi_h, mask_roi_w), dtype=np.uint8)
# Shift polygon coordinates relative to the ROI's top-left corner
polygon_relative_to_roi = expanded_landmarks - [min_x, min_y]
# Draw polygon on the ROI mask
cv2.fillPoly(mask_roi, [polygon_relative_to_roi], 255)
# Apply Gaussian blur (ensure kernel size is odd and positive)
blur_k_size = getattr(modules.globals, "mask_blur_kernel", 15) # Default 15
blur_k_size = max(1, blur_k_size // 2 * 2 + 1) # Ensure odd
mask_roi = cv2.GaussianBlur(mask_roi, (blur_k_size, blur_k_size), 0) # Sigma=0 calculates from kernel
# 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 *original* frame
mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
lower_lip_polygon = expanded_landmarks # Return polygon in original frame coords
mouth_box = (min_x, min_y, max_x, max_y) # Return the calculated box
else:
# print("Warning: Invalid mouth mask bounding box after padding/clamping.") # Optional debug
pass
except IndexError as idx_e:
# print(f"Warning: Landmark index out of bounds during mouth mask creation: {idx_e}") # Optional debug
pass
except Exception as e:
print(f"Error in create_lower_mouth_mask: {e}") # Print unexpected errors
# import traceback
# traceback.print_exc()
pass
# Return values, ensuring defaults if errors occurred
return mask, mouth_cutout, mouth_box, lower_lip_polygon
def draw_mouth_mask_visualization(
frame: Frame, face: Face, mouth_mask_data: tuple
) -> Frame:
# Validate inputs
if frame is None or face is None or mouth_mask_data is None or len(mouth_mask_data) != 4:
return frame # Return original frame if inputs are invalid
mask, mouth_cutout, box, lower_lip_polygon = mouth_mask_data
(min_x, min_y, max_x, max_y) = box
# Check if polygon is valid for drawing
if lower_lip_polygon is None or not isinstance(lower_lip_polygon, np.ndarray) or len(lower_lip_polygon) < 3:
return frame # Cannot draw without a valid polygon
vis_frame = frame.copy()
height, width = vis_frame.shape[:2]
# Ensure box coordinates are valid integers within frame bounds
try:
min_x, min_y = max(0, int(min_x)), max(0, int(min_y))
max_x, max_y = min(width, int(max_x)), min(height, int(max_y))
except ValueError:
# print("Warning: Invalid coordinates for mask visualization box.")
return frame
if max_x <= min_x or max_y <= min_y:
return frame # Invalid box
# Draw the lower lip polygon (green outline)
try:
# Ensure polygon points are within frame boundaries before drawing
safe_polygon = lower_lip_polygon.copy()
safe_polygon[:, 0] = np.clip(safe_polygon[:, 0], 0, width - 1)
safe_polygon[:, 1] = np.clip(safe_polygon[:, 1], 0, height - 1)
cv2.polylines(vis_frame, [safe_polygon.astype(np.int32)], isClosed=True, color=(0, 255, 0), thickness=2)
except Exception as e:
print(f"Error drawing polygon for visualization: {e}") # Optional debug
pass
# Optional: Draw bounding box (red rectangle)
# cv2.rectangle(vis_frame, (min_x, min_y), (max_x, max_y), (0, 0, 255), 1)
# Optional: Add labels
label_pos_y = min_y - 10 if min_y > 20 else max_y + 15 # Adjust position based on box location
label_pos_x = min_x
try:
cv2.putText(vis_frame, "Mouth Mask", (label_pos_x, label_pos_y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA)
except Exception as e:
# print(f"Error drawing text for visualization: {e}") # Optional debug
pass
return vis_frame
def apply_mouth_area(
frame: np.ndarray,
mouth_cutout: np.ndarray,
mouth_box: tuple,
face_mask: np.ndarray, # Full face mask (for blending edges)
mouth_polygon: np.ndarray, # Specific polygon for the mouth area itself
) -> np.ndarray:
# Basic validation
if (frame is None or mouth_cutout is None or mouth_box is None or
face_mask is None or mouth_polygon is None):
# print("Warning: Invalid input (None value) to apply_mouth_area") # Optional debug
return frame
if (mouth_cutout.size == 0 or face_mask.size == 0 or len(mouth_polygon) < 3):
# print("Warning: Invalid input (empty array/polygon) to apply_mouth_area") # Optional debug
return frame
try: # Wrap main logic in try-except
min_x, min_y, max_x, max_y = map(int, mouth_box) # Ensure integer coords
box_width = max_x - min_x
box_height = max_y - min_y
# Check box validity
if box_width <= 0 or box_height <= 0:
# print("Warning: Invalid mouth box dimensions in apply_mouth_area.")
return frame
# Define the Region of Interest (ROI) on the target frame (swapped frame)
frame_h, frame_w = frame.shape[:2]
# Clamp coordinates strictly within frame boundaries
min_y, max_y = max(0, min_y), min(frame_h, max_y)
min_x, max_x = max(0, min_x), min(frame_w, max_x)
# Recalculate box dimensions based on clamped coords
box_width = max_x - min_x
box_height = max_y - min_y
if box_width <= 0 or box_height <= 0:
# print("Warning: ROI became invalid after clamping in apply_mouth_area.")
return frame # ROI is invalid
roi = frame[min_y:max_y, min_x:max_x]
# Ensure ROI extraction was successful
if roi.size == 0:
# print("Warning: Extracted ROI is empty in apply_mouth_area.")
return frame
# Resize mouth cutout from original frame to fit the ROI size
resized_mouth_cutout = None
if roi.shape[:2] != mouth_cutout.shape[:2]:
# Check if mouth_cutout has valid dimensions before resizing
if mouth_cutout.shape[0] > 0 and mouth_cutout.shape[1] > 0:
resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height), interpolation=cv2.INTER_LINEAR)
else:
# print("Warning: mouth_cutout has invalid dimensions, cannot resize.")
return frame # Cannot proceed without valid cutout
else:
resized_mouth_cutout = mouth_cutout
# If resize failed or original was invalid
if resized_mouth_cutout is None or resized_mouth_cutout.size == 0:
# print("Warning: Mouth cutout is invalid after resize attempt.")
return frame
# --- Color Correction Step ---
# Apply color transfer from ROI (swapped face region) to the original mouth cutout
# This helps match lighting/color before blending
color_corrected_mouth = resized_mouth_cutout # Default to resized if correction fails
try:
# Ensure both images are 3 channels for color transfer
if len(resized_mouth_cutout.shape) == 3 and resized_mouth_cutout.shape[2] == 3 and \
len(roi.shape) == 3 and roi.shape[2] == 3:
color_corrected_mouth = apply_color_transfer(resized_mouth_cutout, roi)
else:
# print("Warning: Cannot apply color transfer, images not BGR.")
pass
except cv2.error as ct_e: # Handle potential errors in color transfer
# print(f"Warning: Color transfer failed: {ct_e}. Using uncorrected mouth cutout.") # Optional debug
pass
except Exception as ct_gen_e:
# print(f"Warning: Unexpected error during color transfer: {ct_gen_e}")
pass
# --- End Color Correction ---
# --- Mask Creation ---
# Create a mask based *specifically* on the mouth_polygon, relative to the ROI
polygon_mask_roi = np.zeros(roi.shape[:2], dtype=np.uint8)
# Adjust polygon coordinates relative to the ROI's top-left corner
adjusted_polygon = mouth_polygon - [min_x, min_y]
# Draw the filled polygon on the ROI mask
cv2.fillPoly(polygon_mask_roi, [adjusted_polygon.astype(np.int32)], 255)
# Feather the polygon mask (Gaussian blur)
mask_feather_ratio = getattr(modules.globals, "mask_feather_ratio", 12) # Default 12
# Calculate feather amount based on the smaller dimension of the box
feather_base_dim = min(box_width, box_height)
feather_amount = max(1, min(30, feather_base_dim // max(1, mask_feather_ratio))) # Avoid div by zero
# Ensure kernel size is odd and positive
kernel_size = 2 * feather_amount + 1
feathered_polygon_mask = cv2.GaussianBlur(polygon_mask_roi.astype(float), (kernel_size, kernel_size), 0)
# Normalize feathered mask to [0.0, 1.0] range
max_val = feathered_polygon_mask.max()
if max_val > 1e-6: # Avoid division by zero
feathered_polygon_mask = feathered_polygon_mask / max_val
else:
feathered_polygon_mask.fill(0.0) # Mask is all black if max is near zero
# --- End Mask Creation ---
# --- Refined Blending ---
# Get the corresponding ROI from the *full face mask* (already blurred)
# Ensure face_mask is float and normalized [0.0, 1.0]
if face_mask.dtype != np.float64 and face_mask.dtype != np.float32:
face_mask_float = face_mask.astype(float) / 255.0
else: # Assume already float [0,1] if type is float
face_mask_float = face_mask
face_mask_roi = face_mask_float[min_y:max_y, min_x:max_x]
# Combine the feathered mouth polygon mask with the face mask ROI
# Use minimum to ensure we only affect area inside both masks (mouth area within face)
# This helps blend the edges smoothly with the surrounding swapped face region
combined_mask = np.minimum(feathered_polygon_mask, face_mask_roi)
# Expand mask to 3 channels for blending (ensure it matches image channels)
if len(frame.shape) == 3 and frame.shape[2] == 3:
combined_mask_3channel = combined_mask[:, :, np.newaxis]
# Ensure data types are compatible for blending (float or double for mask, uint8 for images)
color_corrected_mouth_uint8 = color_corrected_mouth.astype(np.uint8)
roi_uint8 = roi.astype(np.uint8)
combined_mask_float = combined_mask_3channel.astype(np.float64) # Use float64 for precision in mask
# Blend: (original_mouth * combined_mask) + (swapped_face_roi * (1 - combined_mask))
blended_roi = (color_corrected_mouth_uint8 * combined_mask_float +
roi_uint8 * (1.0 - combined_mask_float))
# Place the blended ROI back into the frame
frame[min_y:max_y, min_x:max_x] = blended_roi.astype(np.uint8)
else:
# print("Warning: Cannot apply mouth mask blending, frame is not 3-channel BGR.")
pass # Don't modify frame if it's not BGR
except Exception as e:
print(f"Error applying mouth area: {e}") # Optional debug
# import traceback
# traceback.print_exc()
pass # Don't crash, just return the frame as is
return frame
def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
"""Creates a feathered mask covering the whole face area based on landmarks."""
mask = np.zeros(frame.shape[:2], dtype=np.uint8) # Start with uint8
# Validate inputs
if face is None or not hasattr(face, 'landmark_2d_106') or frame is None:
# print("Warning: Invalid face or frame for create_face_mask.")
return mask # Return empty mask
landmarks = face.landmark_2d_106
if landmarks is None or not isinstance(landmarks, np.ndarray) or landmarks.shape[0] < 106:
# print("Warning: Invalid or insufficient landmarks for face mask.")
return mask # Return empty mask
try: # Wrap main logic in try-except
# Filter out non-finite landmark values
if not np.all(np.isfinite(landmarks)):
# print("Warning: Non-finite values detected in landmarks for face mask.")
return mask
landmarks_int = landmarks.astype(np.int32)
# Use standard face outline landmarks (0-32)
face_outline_points = landmarks_int[0:33] # Points 0 to 32 cover chin and sides
# Calculate convex hull of these points
# Use try-except as convexHull can fail on degenerate input
try:
hull = cv2.convexHull(full_face_poly.astype(np.float32)) # Use float for accuracy
if hull is None or len(hull) < 3:
# print("Warning: Convex hull calculation failed or returned too few points.")
# Fallback: use bounding box of landmarks? Or just return empty mask?
return mask
# Draw the filled convex hull on the mask
cv2.fillConvexPoly(mask, hull.astype(np.int32), 255)
except Exception as hull_e:
print(f"Error creating convex hull for face mask: {hull_e}")
return mask # Return empty mask on error
# Apply Gaussian blur to feather the mask edges
# Kernel size should be reasonably large, odd, and positive
blur_k_size = getattr(modules.globals, "face_mask_blur", 31) # Default 31
blur_k_size = max(1, blur_k_size // 2 * 2 + 1) # Ensure odd and positive
# Use sigma=0 to let OpenCV calculate from kernel size
# Apply blur to the uint8 mask directly
mask = cv2.GaussianBlur(mask, (blur_k_size, blur_k_size), 0)
# --- Optional: Return float mask for apply_mouth_area ---
# mask = mask.astype(float) / 255.0
# ---
except IndexError:
# print("Warning: Landmark index out of bounds for face mask.") # Optional debug
pass
except Exception as e:
print(f"Error creating face mask: {e}") # Print unexpected errors
# import traceback
# traceback.print_exc()
pass
return mask # Return uint8 mask
def apply_color_transfer(source, target):
"""
Apply color transfer using LAB color space. Handles potential division by zero and ensures output is uint8.
"""
# Input validation
if source is None or target is None or source.size == 0 or target.size == 0:
# print("Warning: Invalid input to apply_color_transfer.")
return source # Return original source if invalid input
# Ensure images are 3-channel BGR uint8
if len(source.shape) != 3 or source.shape[2] != 3 or source.dtype != np.uint8:
# print("Warning: Source image for color transfer is not uint8 BGR.")
# Attempt conversion if possible, otherwise return original
try:
if len(source.shape) == 2: # Grayscale
source = cv2.cvtColor(source, cv2.COLOR_GRAY2BGR)
source = np.clip(source, 0, 255).astype(np.uint8)
if len(source.shape)!= 3 or source.shape[2]!= 3: raise ValueError("Conversion failed")
except Exception:
return source
if len(target.shape) != 3 or target.shape[2] != 3 or target.dtype != np.uint8:
# print("Warning: Target image for color transfer is not uint8 BGR.")
try:
if len(target.shape) == 2: # Grayscale
target = cv2.cvtColor(target, cv2.COLOR_GRAY2BGR)
target = np.clip(target, 0, 255).astype(np.uint8)
if len(target.shape)!= 3 or target.shape[2]!= 3: raise ValueError("Conversion failed")
except Exception:
return source # Return original source if target invalid
result_bgr = source # Default to original source in case of errors
try:
# Convert to float32 [0, 1] range for LAB conversion
source_float = source.astype(np.float32) / 255.0
target_float = target.astype(np.float32) / 255.0
source_lab = cv2.cvtColor(source_float, cv2.COLOR_BGR2LAB)
target_lab = cv2.cvtColor(target_float, cv2.COLOR_BGR2LAB)
# Compute statistics
source_mean, source_std = cv2.meanStdDev(source_lab)
target_mean, target_std = cv2.meanStdDev(target_lab)
# Reshape for broadcasting
source_mean = source_mean.reshape((1, 1, 3))
source_std = source_std.reshape((1, 1, 3))
target_mean = target_mean.reshape((1, 1, 3))
target_std = target_std.reshape((1, 1, 3))
# Avoid division by zero or very small std deviations (add epsilon)
epsilon = 1e-6
source_std = np.maximum(source_std, epsilon)
# target_std = np.maximum(target_std, epsilon) # Target std can be small
# Perform color transfer in LAB space
result_lab = (source_lab - source_mean) * (target_std / source_std) + target_mean
# --- No explicit clipping needed in LAB space typically ---
# Clipping is handled implicitly by the conversion back to BGR and then to uint8
# Convert back to BGR float [0, 1]
result_bgr_float = cv2.cvtColor(result_lab, cv2.COLOR_LAB2BGR)
# Clip final BGR values to [0, 1] range before scaling to [0, 255]
result_bgr_float = np.clip(result_bgr_float, 0.0, 1.0)
# Convert back to uint8 [0, 255]
result_bgr = (result_bgr_float * 255.0).astype("uint8")
except cv2.error as e:
# print(f"OpenCV error during color transfer: {e}. Returning original source.") # Optional debug
return source # Return original source if conversion fails
except Exception as e:
# print(f"Unexpected color transfer error: {e}. Returning original source.") # Optional debug
# import traceback
# traceback.print_exc()
return source
return result_bgr
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