Update metadata.py

better mouth mask showing and tracking the lips part only.

README.md

@@ -1,4 +1,4 @@
-<h1 align="center">Deep-Live-Cam</h1>
+<h1 align="center">Deep-Live-Cam 2.0.2c</h1>
 
 <p align="center">
   Real-time face swap and video deepfake with a single click and only a single image.
@@ -30,7 +30,7 @@ By using this software, you agree to these terms and commit to using it in a man
 
 Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online. We are not responsible for end-user actions.
 
-## Exclusive v2.3c Quick Start - Pre-built (Windows/Mac Silicon)
+## Exclusive v2.4 Quick Start - Pre-built (Windows/Mac Silicon)
 
   <a href="https://deeplivecam.net/index.php/quickstart"> <img src="media/Download.png" width="285" height="77" />
 

modules/core.py

@@ -129,11 +129,22 @@ def suggest_execution_providers() -> List[str]:
 
 
 def suggest_execution_threads() -> int:
+    """Suggest optimal thread count based on hardware and execution provider."""
+    import os
+    
+    # Get CPU count
+    cpu_count = os.cpu_count() or 4
+    
     if 'DmlExecutionProvider' in modules.globals.execution_providers:
         return 1
     if 'ROCMExecutionProvider' in modules.globals.execution_providers:
         return 1
-    return 8
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
+        # For CUDA, use more threads for parallel frame processing
+        return min(cpu_count, 16)
+    
+    # For CPU execution, use most cores but leave some for system
+    return max(4, min(cpu_count - 2, 16))
 
 
 def limit_resources() -> None:
@@ -176,10 +187,16 @@ def update_status(message: str, scope: str = 'DLC.CORE') -> None:
         ui.update_status(message)
 
 def start() -> None:
+    """Start processing with performance monitoring."""
+    import time
+    
+    start_time = time.time()
+    
     for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
         if not frame_processor.pre_start():
             return
     update_status('Processing...')
+    
     # process image to image
     if has_image_extension(modules.globals.target_path):
         if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
@@ -193,26 +210,40 @@ def start() -> None:
             frame_processor.process_image(modules.globals.source_path, modules.globals.output_path, modules.globals.output_path)
             release_resources()
         if is_image(modules.globals.target_path):
-            update_status('Processing to image succeed!')
+            elapsed = time.time() - start_time
+            update_status(f'Processing to image succeed! (Time: {elapsed:.2f}s)')
         else:
             update_status('Processing to image failed!')
         return
+    
     # process image to videos
     if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
         return
 
+    extraction_start = time.time()
     if not modules.globals.map_faces:
         update_status('Creating temp resources...')
         create_temp(modules.globals.target_path)
         update_status('Extracting frames...')
         extract_frames(modules.globals.target_path)
+    extraction_time = time.time() - extraction_start
+    update_status(f'Frame extraction completed in {extraction_time:.2f}s')
 
     temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
+    total_frames = len(temp_frame_paths)
+    update_status(f'Processing {total_frames} frames with {modules.globals.execution_threads} threads...')
+    
+    processing_start = time.time()
     for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
         update_status('Progressing...', frame_processor.NAME)
         frame_processor.process_video(modules.globals.source_path, temp_frame_paths)
         release_resources()
+    processing_time = time.time() - processing_start
+    fps_processing = total_frames / processing_time if processing_time > 0 else 0
+    update_status(f'Frame processing completed in {processing_time:.2f}s ({fps_processing:.2f} fps)')
+    
     # handles fps
+    encoding_start = time.time()
     if modules.globals.keep_fps:
         update_status('Detecting fps...')
         fps = detect_fps(modules.globals.target_path)
@@ -221,6 +252,9 @@ def start() -> None:
     else:
         update_status('Creating video with 30.0 fps...')
         create_video(modules.globals.target_path)
+    encoding_time = time.time() - encoding_start
+    update_status(f'Video encoding completed in {encoding_time:.2f}s')
+    
     # handle audio
     if modules.globals.keep_audio:
         if modules.globals.keep_fps:
@@ -230,10 +264,13 @@ def start() -> None:
         restore_audio(modules.globals.target_path, modules.globals.output_path)
     else:
         move_temp(modules.globals.target_path, modules.globals.output_path)
+    
     # clean and validate
     clean_temp(modules.globals.target_path)
+    
+    total_time = time.time() - start_time
     if is_video(modules.globals.target_path):
-        update_status('Processing to video succeed!')
+        update_status(f'Processing to video succeed! Total time: {total_time:.2f}s')
     else:
         update_status('Processing to video failed!')
 

modules/face_analyser.py

@@ -2,6 +2,7 @@ import os
 import shutil
 from typing import Any
 import insightface
+import threading
 
 import cv2
 import numpy as np
@@ -13,14 +14,22 @@ from modules.utilities import get_temp_directory_path, create_temp, extract_fram
 from pathlib import Path
 
 FACE_ANALYSER = None
+FACE_ANALYSER_LOCK = threading.Lock()
 
 
 def get_face_analyser() -> Any:
+    """Get face analyser with thread-safe initialization."""
     global FACE_ANALYSER
 
     if FACE_ANALYSER is None:
-        FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=modules.globals.execution_providers)
-        FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
+        with FACE_ANALYSER_LOCK:
+            # Double-check after acquiring lock
+            if FACE_ANALYSER is None:
+                FACE_ANALYSER = insightface.app.FaceAnalysis(
+                    name='buffalo_l',
+                    providers=modules.globals.execution_providers
+                )
+                FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
     return FACE_ANALYSER
 
 

modules/globals.py

@@ -27,6 +27,7 @@ keep_audio: bool = True
 keep_frames: bool = False
 many_faces: bool = False         # Process all detected faces with default source
 map_faces: bool = False          # Use source_target_map or simple_map for specific swaps
+poisson_blend: bool = False      # Enable Poisson Blending for smoother face swaps
 color_correction: bool = False   # Enable color correction (implementation specific)
 nsfw_filter: bool = False
 

modules/metadata.py

@@ -1,3 +1,3 @@
 name = 'Deep-Live-Cam'
-version = '2.0c'
-edition = 'GitHub Edition'
+version = '2.0.3c'
+edition = 'GitHub Edition'

modules/processors/frame/core.py

@@ -67,13 +67,29 @@ def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
                  print(f"Warning: Error removing frame processor {frame_processor}: {e}")
 
 def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], progress: Any = None) -> None:
-    with ThreadPoolExecutor(max_workers=modules.globals.execution_threads) as executor:
-        futures = []
-        for path in temp_frame_paths:
-            future = executor.submit(process_frames, source_path, [path], progress)
-            futures.append(future)
-        for future in futures:
-            future.result()
+    """Process frames in parallel with optimized batching and memory management."""
+    max_workers = modules.globals.execution_threads
+    
+    # Determine optimal batch size based on available memory and thread count
+    # Process frames in batches to avoid memory overflow
+    batch_size = max(1, min(32, len(temp_frame_paths) // max(1, max_workers)))
+    
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        # Process in batches to manage memory better
+        for i in range(0, len(temp_frame_paths), batch_size):
+            batch = temp_frame_paths[i:i + batch_size]
+            futures = []
+            
+            for path in batch:
+                future = executor.submit(process_frames, source_path, [path], progress)
+                futures.append(future)
+            
+            # Wait for batch to complete before starting next batch
+            for future in futures:
+                try:
+                    future.result()
+                except Exception as e:
+                    print(f"Error processing frame: {e}")
 
 
 def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:

modules/processors/frame/face_masking.py

@@ -45,6 +45,7 @@ def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
         )  # 5% of face width
 
         # Create a slightly larger convex hull for padding
+        face_outline = landmarks[0:33]
         hull = cv2.convexHull(face_outline)
         hull_padded = []
         for point in hull:
@@ -70,77 +71,30 @@ def create_lower_mouth_mask(
 ) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
     mask = np.zeros(frame.shape[:2], dtype=np.uint8)
     mouth_cutout = None
+    lower_lip_polygon = None
+    mouth_box = (0,0,0,0)
+
     landmarks = face.landmark_2d_106
     if landmarks is not None:
-        #                  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,
-        ]
-        lower_lip_landmarks = landmarks[lower_lip_order].astype(
-            np.float32
-        )  # Use float for precise calculations
+        # Use outer mouth landmarks (52-63) to capture the lips only
+        lower_lip_order = list(range(52, 64))
+        
+        if max(lower_lip_order) >= landmarks.shape[0]:
+            return mask, mouth_cutout, mouth_box, lower_lip_polygon
+
+        lower_lip_landmarks = landmarks[lower_lip_order].astype(np.float32)
 
         # Calculate the center of the landmarks
         center = np.mean(lower_lip_landmarks, axis=0)
 
         # Expand the landmarks outward using the mouth_mask_size
+        # Use a more conservative expansion to avoid affecting face shape
         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
+        # Removed specific top/chin extensions to preserve face shape
 
         # Convert back to integer coordinates
         expanded_landmarks = expanded_landmarks.astype(np.int32)
@@ -165,7 +119,9 @@ def create_lower_mouth_mask(
 
         # 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)
+        # Shift polygon coordinates relative to the ROI's top-left corner
+        polygon_relative_to_roi = expanded_landmarks - [min_x, min_y]
+        cv2.fillPoly(mask_roi, [polygon_relative_to_roi], 255)
 
         # Apply Gaussian blur to soften the mask edges
         mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
@@ -178,8 +134,9 @@ def create_lower_mouth_mask(
 
         # Return the expanded lower lip polygon in original frame coordinates
         lower_lip_polygon = expanded_landmarks
+        mouth_box = (min_x, min_y, max_x, max_y)
 
-    return mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon
+    return mask, mouth_cutout, mouth_box, 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)
@@ -606,4 +563,4 @@ def draw_mask_visualization(
         1,
     )
 
-    return vis_frame 
+    return vis_frame

modules/processors/frame/face_swapper.py

@@ -1,8 +1,9 @@
-from typing import Any, List
+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
@@ -14,9 +15,9 @@ from modules.utilities import (
     is_video,
 )
 from modules.cluster_analysis import find_closest_centroid
-# Removed modules.globals.face_swapper_enabled - assuming controlled elsewhere or implicitly true if used
-# Removed modules.globals.opacity - accessed via getattr
 import os
+from collections import deque
+import time
 
 FACE_SWAPPER = None
 THREAD_LOCK = threading.Lock()
@@ -26,6 +27,16 @@ NAME = "DLC.FACE-SWAPPER"
 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"
@@ -69,55 +80,64 @@ def get_face_swapper() -> Any:
             model_path = os.path.join(models_dir, model_name)
             update_status(f"Loading face swapper model from: {model_path}", NAME)
             try:
-                # Ensure the providers list is correctly passed
-                # Apply CoreML optimization for Mac systems
+                # 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=[
-                        (
-                            (
-                                "CoreMLExecutionProvider",
-                                {
-                                    "ModelFormat": "MLProgram",
-                                    "MLComputeUnits": "CPUAndGPU",
-                                    "SpecializationStrategy": "FastPrediction",
-                                    "AllowLowPrecisionAccumulationOnGPU": 1,
-                                },
-                            )
-                            if p == "CoreMLExecutionProvider"
-                            else p
-                        )
-                        for p in modules.globals.execution_providers
-                    ],
+                    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)
-                # print traceback maybe?
-                # import traceback
-                # traceback.print_exc()
-                FACE_SWAPPER = None # Ensure it remains None on failure
+                FACE_SWAPPER = None
                 return None
     return FACE_SWAPPER
 
 
 def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
+    """Optimized face swapping with better memory management and performance."""
     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 # Return original frame if model failed or not loaded
+        return temp_frame
+
+    # Safety check for faces
+    if source_face is None or target_face is None:
+        return temp_frame
+    if not hasattr(source_face, 'normed_embedding') or source_face.normed_embedding is None:
+        return temp_frame
 
     # Store a copy of the original frame before swapping for opacity blending
     original_frame = temp_frame.copy()
 
-    # --- Pre-swap Input Check (Optional but good practice) ---
+    # Pre-swap Input Check with optimization
     if temp_frame.dtype != np.uint8:
-        # print(f"Warning: Input frame is {temp_frame.dtype}, converting to uint8 before swap.")
         temp_frame = np.clip(temp_frame, 0, 255).astype(np.uint8)
-    # --- End Input Check ---
 
-    # Apply the face swap
+    # Apply the face swap with optimized memory handling
     try:
+        # Ensure contiguous memory layout for better performance on all platforms
+        if not temp_frame.flags['C_CONTIGUOUS']:
+            temp_frame = np.ascontiguousarray(temp_frame)
+        
         swapped_frame_raw = face_swapper.get(
             temp_frame, target_face, source_face, paste_back=True
         )
@@ -174,13 +194,42 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
             )
 
             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
-                )
+                        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
+                        )
+        
+    # --- Poisson Blending ---
+    if getattr(modules.globals, "poisson_blend", False):
+        face_mask = create_face_mask(target_face, temp_frame)
+        if face_mask is not None:
+            # Find bounding box of the mask
+            y_indices, x_indices = np.where(face_mask > 0)
+            if len(x_indices) > 0 and len(y_indices) > 0:
+                x_min, x_max = np.min(x_indices), np.max(x_indices)
+                y_min, y_max = np.min(y_indices), np.max(y_indices)
 
-    # Apply opacity blend between the original frame and the swapped frame
+                # Calculate center
+                center = (int((x_min + x_max) / 2), int((y_min + y_max) / 2))
+
+                # Crop src and mask
+                src_crop = swapped_frame[y_min : y_max + 1, x_min : x_max + 1]
+                mask_crop = face_mask[y_min : y_max + 1, x_min : x_max + 1]
+
+                try:
+                    # Use original_frame as destination to blend the swapped face onto it
+                    swapped_frame = cv2.seamlessClone(
+                        src_crop,
+                        original_frame,
+                        mask_crop,
+                        center,
+                        cv2.NORMAL_CLONE,
+                    )
+                except Exception as e:
+                    print(f"Poisson blending failed: {e}")
+        
+            # 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))
@@ -195,14 +244,50 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
     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."""
+    """Applies sharpening and interpolation with Apple Silicon optimizations."""
     global PREVIOUS_FRAME_RESULT
 
     processed_frame = current_frame.copy()
 
-    # 1. Apply Sharpening (if enabled)
+    # 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]
@@ -225,23 +310,21 @@ def apply_post_processing(current_frame: Frame, swapped_face_bboxes: List[np.nda
                 continue
 
             face_region = processed_frame[y1:y2, x1:x2]
-            if face_region.size == 0: continue # Skip empty regions
+            if face_region.size == 0: continue
 
-            # Apply sharpening using addWeighted for smoother control
-            # Use try-except for GaussianBlur and addWeighted as they can fail on invalid inputs
+            # Apply sharpening with optimized parameters for Apple Silicon
             try:
-                 blurred = cv2.GaussianBlur(face_region, (0, 0), 3) # sigma=3, kernel size auto
-                 sharpened_region = cv2.addWeighted(
+                # 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
-                 )
-                 # Ensure the sharpened region doesn't have invalid values
-                 sharpened_region = np.clip(sharpened_region, 0, 255).astype(np.uint8)
-                 processed_frame[y1:y2, x1:x2] = sharpened_region
-            except cv2.error as sharpen_e:
-                # print(f"Warning: OpenCV error during sharpening: {sharpen_e} for bbox {bbox}") # Debug
-                # Skip sharpening for this region if it fails
+                )
+                sharpened_region = np.clip(sharpened_region, 0, 255).astype(np.uint8)
+                processed_frame[y1:y2, x1:x2] = sharpened_region
+            except cv2.error:
                 pass
 
 
@@ -455,6 +538,7 @@ def process_frames(
 ) -> None:
     """
     Processes a list of frame paths (typically for video).
+    Optimized with better memory management and caching.
     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.
     """
@@ -478,6 +562,8 @@ def process_frames(
                     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)
+                    # Free memory immediately after extracting face
+                    del source_img
             except Exception as e:
                 # Print the specific exception caught
                 import traceback
@@ -505,6 +591,7 @@ def process_frames(
         # update_status(f"Processing frame {i+1}/{total_frames}: {os.path.basename(temp_frame_path)}", NAME) # Optional Debug
 
         # Read the target frame
+        temp_frame = None
         try:
             temp_frame = cv2.imread(temp_frame_path)
             if temp_frame is None:
@@ -539,13 +626,19 @@ def process_frames(
             # traceback.print_exc()
             result_frame = temp_frame # Use original frame on processing error
 
-        # Write the result back to the same frame path
+        # Write the result back to the same frame path with optimized compression
         try:
-            write_success = cv2.imwrite(temp_frame_path, result_frame)
+            # Use PNG compression level 3 (faster) instead of default 9
+            write_success = cv2.imwrite(temp_frame_path, result_frame, [cv2.IMWRITE_PNG_COMPRESSION, 3])
             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}")
+        
+        # Free memory immediately after processing
+        del temp_frame
+        if result_frame is not None:
+            del result_frame
 
         # Update progress bar
         if progress:
@@ -659,8 +752,9 @@ def create_lower_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
+        # Use outer mouth landmarks (52-63) to capture the lips only
+        # This avoids including the chin/jawline, preserving the face shape from the swap
+        lower_lip_order = list(range(52, 64))
 
         # Check if all indices are valid for the loaded landmarks (already partially done by < 106 check)
         if max(lower_lip_order) >= landmarks.shape[0]:
@@ -684,31 +778,6 @@ def create_lower_mouth_mask(
         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.")
@@ -1007,13 +1076,43 @@ def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
         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
+        # Use standard face outline (0-32)
+        face_outline = landmarks_int[0:33]
 
+        # Estimate forehead points to ensure mask covers the whole face (including forehead)
+        # This is critical for Poisson blending to work correctly on the forehead
+        eyebrows = landmarks_int[33:43]
+        if eyebrows.shape[0] > 0:
+            chin = landmarks_int[16]
+            eyebrow_center = np.mean(eyebrows, axis=0)
+            
+            # Vector from chin to eyebrows (upwards)
+            up_vector = eyebrow_center - chin
+            norm = np.linalg.norm(up_vector)
+            if norm > 0:
+                up_vector /= norm
+                
+                # Extend upwards by 1.0 of the chin-to-eyebrow distance (aggressive coverage)
+                # This ensures the mask covers the entire forehead for proper blending
+                forehead_offset = up_vector * (norm * 1.0)
+                
+                # Shift eyebrows up to create forehead points
+                forehead_points = eyebrows + forehead_offset
+                
+                # Expand the top points slightly outwards to cover forehead corners
+                # Calculate the center of the new top points
+                top_center = np.mean(forehead_points, axis=0)
+                
+                # Expand outwards by 20%
+                forehead_points = (forehead_points - top_center) * 1.2 + top_center
+                
+                # Combine outline and forehead points
+                face_outline = np.concatenate((face_outline, forehead_points.astype(np.int32)), axis=0)
 
         # 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
+             hull = cv2.convexHull(face_outline.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?

modules/ui.py

@@ -36,7 +36,7 @@ if platform.system() == "Windows":
 ROOT = None
 POPUP = None
 POPUP_LIVE = None
-ROOT_HEIGHT = 750
+ROOT_HEIGHT = 800
 ROOT_WIDTH = 600
 
 PREVIEW = None
@@ -98,6 +98,7 @@ def save_switch_states():
         "keep_frames": modules.globals.keep_frames,
         "many_faces": modules.globals.many_faces,
         "map_faces": modules.globals.map_faces,
+        "poisson_blend": modules.globals.poisson_blend,
         "color_correction": modules.globals.color_correction,
         "nsfw_filter": modules.globals.nsfw_filter,
         "live_mirror": modules.globals.live_mirror,
@@ -120,6 +121,7 @@ def load_switch_states():
         modules.globals.keep_frames = switch_states.get("keep_frames", False)
         modules.globals.many_faces = switch_states.get("many_faces", False)
         modules.globals.map_faces = switch_states.get("map_faces", False)
+        modules.globals.poisson_blend = switch_states.get("poisson_blend", False)
         modules.globals.color_correction = switch_states.get("color_correction", False)
         modules.globals.nsfw_filter = switch_states.get("nsfw_filter", False)
         modules.globals.live_mirror = switch_states.get("live_mirror", False)
@@ -272,6 +274,19 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
     )
     map_faces_switch.place(relx=0.1, rely=0.65)
 
+    poisson_blend_value = ctk.BooleanVar(value=modules.globals.poisson_blend)
+    poisson_blend_switch = ctk.CTkSwitch(
+        root,
+        text=_("Poisson Blend"),
+        variable=poisson_blend_value,
+        cursor="hand2",
+        command=lambda: (
+            setattr(modules.globals, "poisson_blend", poisson_blend_value.get()),
+            save_switch_states(),
+        ),
+    )
+    poisson_blend_switch.place(relx=0.1, rely=0.7)
+
     show_fps_value = ctk.BooleanVar(value=modules.globals.show_fps)
     show_fps_switch = ctk.CTkSwitch(
         root,
@@ -310,21 +325,21 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
     start_button = ctk.CTkButton(
         root, text=_("Start"), cursor="hand2", command=lambda: analyze_target(start, root)
     )
-    start_button.place(relx=0.15, rely=0.80, relwidth=0.2, relheight=0.05)
+    start_button.place(relx=0.15, rely=0.86, relwidth=0.2, relheight=0.05)
 
     stop_button = ctk.CTkButton(
         root, text=_("Destroy"), cursor="hand2", command=lambda: destroy()
     )
-    stop_button.place(relx=0.4, rely=0.80, relwidth=0.2, relheight=0.05)
+    stop_button.place(relx=0.4, rely=0.86, relwidth=0.2, relheight=0.05)
 
     preview_button = ctk.CTkButton(
         root, text=_("Preview"), cursor="hand2", command=lambda: toggle_preview()
     )
-    preview_button.place(relx=0.65, rely=0.80, relwidth=0.2, relheight=0.05)
+    preview_button.place(relx=0.65, rely=0.86, relwidth=0.2, relheight=0.05)
 
     # --- Camera Selection ---
     camera_label = ctk.CTkLabel(root, text=_("Select Camera:"))
-    camera_label.place(relx=0.1, rely=0.86, relwidth=0.2, relheight=0.05)
+    camera_label.place(relx=0.1, rely=0.92, relwidth=0.2, relheight=0.05)
 
     available_cameras = get_available_cameras()
     camera_indices, camera_names = available_cameras
@@ -343,7 +358,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             root, variable=camera_variable, values=camera_names
         )
 
-    camera_optionmenu.place(relx=0.35, rely=0.86, relwidth=0.25, relheight=0.05)
+    camera_optionmenu.place(relx=0.35, rely=0.92, relwidth=0.25, relheight=0.05)
 
     live_button = ctk.CTkButton(
         root,
@@ -363,7 +378,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
             else "disabled"
         ),
     )
-    live_button.place(relx=0.65, rely=0.86, relwidth=0.2, relheight=0.05)
+    live_button.place(relx=0.65, rely=0.92, relwidth=0.2, relheight=0.05)
     # --- End Camera Selection ---
 
     # 1) Define a DoubleVar for transparency (0 = fully transparent, 1 = fully opaque)
@@ -387,7 +402,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
 
     # 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_label.place(relx=0.15, rely=0.75, relwidth=0.2, relheight=0.05)
 
     transparency_slider = ctk.CTkSlider(
         root,
@@ -403,7 +418,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
         border_width=1,
         corner_radius=3,
     )
-    transparency_slider.place(relx=0.35, rely=0.71, relwidth=0.5, relheight=0.02)
+    transparency_slider.place(relx=0.35, rely=0.77, relwidth=0.5, relheight=0.02)
 
     # 3) Sharpness label & slider
     sharpness_var = ctk.DoubleVar(value=0.0)  # start at 0.0
@@ -412,7 +427,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
         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_label.place(relx=0.15, rely=0.80, relwidth=0.2, relheight=0.05)
 
     sharpness_slider = ctk.CTkSlider(
         root,
@@ -428,17 +443,17 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
         border_width=1,
         corner_radius=3,
     )
-    sharpness_slider.place(relx=0.35, rely=0.76, relwidth=0.5, relheight=0.02)
+    sharpness_slider.place(relx=0.35, rely=0.82, 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)
+    status_label.place(relx=0.1, rely=0.96, relwidth=0.8)
 
     donate_label = ctk.CTkLabel(
         root, text="Deep Live Cam", justify="center", cursor="hand2"
     )
-    donate_label.place(relx=0.1, rely=0.95, relwidth=0.8)
+    donate_label.place(relx=0.1, rely=0.98, relwidth=0.8)
     donate_label.configure(
         text_color=ctk.ThemeManager.theme.get("URL").get("text_color")
     )

modules/utilities.py

@@ -21,13 +21,14 @@ if platform.system().lower() == "darwin":
 
 
 def run_ffmpeg(args: List[str]) -> bool:
+    """Run ffmpeg with hardware acceleration and optimized settings."""
     commands = [
         "ffmpeg",
         "-hide_banner",
-        "-hwaccel",
-        "auto",
-        "-loglevel",
-        modules.globals.log_level,
+        "-hwaccel", "auto",  # Auto-detect hardware acceleration
+        "-hwaccel_output_format", "auto",  # Use hardware format when possible
+        "-threads", str(modules.globals.execution_threads or 0),  # 0 = auto-detect optimal thread count
+        "-loglevel", modules.globals.log_level,
     ]
     commands.extend(args)
     try:
@@ -61,39 +62,131 @@ def detect_fps(target_path: str) -> float:
 
 
 def extract_frames(target_path: str) -> None:
+    """Extract frames with hardware acceleration and optimized settings."""
     temp_directory_path = get_temp_directory_path(target_path)
+    
+    # Use hardware-accelerated decoding and optimized pixel format
     run_ffmpeg(
         [
-            "-i",
-            target_path,
-            "-pix_fmt",
-            "rgb24",
+            "-i", target_path,
+            "-vf", "format=rgb24",  # Use video filter for format conversion (faster)
+            "-vsync", "0",  # Prevent frame duplication
+            "-frame_pts", "1",  # Preserve frame timing
             os.path.join(temp_directory_path, "%04d.png"),
         ]
     )
 
 
 def create_video(target_path: str, fps: float = 30.0) -> None:
+    """Create video with hardware-accelerated encoding and optimized settings."""
     temp_output_path = get_temp_output_path(target_path)
     temp_directory_path = get_temp_directory_path(target_path)
-    run_ffmpeg(
-        [
-            "-r",
-            str(fps),
-            "-i",
-            os.path.join(temp_directory_path, "%04d.png"),
-            "-c:v",
-            modules.globals.video_encoder,
-            "-crf",
-            str(modules.globals.video_quality),
-            "-pix_fmt",
-            "yuv420p",
-            "-vf",
-            "colorspace=bt709:iall=bt601-6-625:fast=1",
+    
+    # Determine optimal encoder based on available hardware
+    encoder = modules.globals.video_encoder
+    encoder_options = []
+    
+    # GPU-accelerated encoding options
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
+        # NVIDIA GPU encoding
+        if encoder == 'libx264':
+            encoder = 'h264_nvenc'
+            encoder_options = [
+                "-preset", "p7",  # Highest quality preset for NVENC
+                "-tune", "hq",  # High quality tuning
+                "-rc", "vbr",  # Variable bitrate
+                "-cq", str(modules.globals.video_quality),  # Quality level
+                "-b:v", "0",  # Let CQ control bitrate
+                "-multipass", "fullres",  # Two-pass encoding for better quality
+            ]
+        elif encoder == 'libx265':
+            encoder = 'hevc_nvenc'
+            encoder_options = [
+                "-preset", "p7",
+                "-tune", "hq",
+                "-rc", "vbr",
+                "-cq", str(modules.globals.video_quality),
+                "-b:v", "0",
+            ]
+    elif 'DmlExecutionProvider' in modules.globals.execution_providers:
+        # AMD/Intel GPU encoding (DirectML on Windows)
+        if encoder == 'libx264':
+            # Try AMD AMF encoder
+            encoder = 'h264_amf'
+            encoder_options = [
+                "-quality", "quality",  # Quality mode
+                "-rc", "vbr_latency",
+                "-qp_i", str(modules.globals.video_quality),
+                "-qp_p", str(modules.globals.video_quality),
+            ]
+        elif encoder == 'libx265':
+            encoder = 'hevc_amf'
+            encoder_options = [
+                "-quality", "quality",
+                "-rc", "vbr_latency",
+                "-qp_i", str(modules.globals.video_quality),
+                "-qp_p", str(modules.globals.video_quality),
+            ]
+    else:
+        # CPU encoding with optimized settings
+        if encoder == 'libx264':
+            encoder_options = [
+                "-preset", "medium",  # Balance speed/quality
+                "-crf", str(modules.globals.video_quality),
+                "-tune", "film",  # Optimize for film content
+            ]
+        elif encoder == 'libx265':
+            encoder_options = [
+                "-preset", "medium",
+                "-crf", str(modules.globals.video_quality),
+                "-x265-params", "log-level=error",
+            ]
+        elif encoder == 'libvpx-vp9':
+            encoder_options = [
+                "-crf", str(modules.globals.video_quality),
+                "-b:v", "0",  # Constant quality mode
+                "-cpu-used", "2",  # Speed vs quality (0-5, lower=slower/better)
+            ]
+    
+    # Build ffmpeg command
+    ffmpeg_args = [
+        "-r", str(fps),
+        "-i", os.path.join(temp_directory_path, "%04d.png"),
+        "-c:v", encoder,
+    ]
+    
+    # Add encoder-specific options
+    ffmpeg_args.extend(encoder_options)
+    
+    # Add common options
+    ffmpeg_args.extend([
+        "-pix_fmt", "yuv420p",
+        "-movflags", "+faststart",  # Enable fast start for web playback
+        "-vf", "colorspace=bt709:iall=bt601-6-625:fast=1",
+        "-y",
+        temp_output_path,
+    ])
+    
+    # Try with hardware encoder first, fallback to software if it fails
+    success = run_ffmpeg(ffmpeg_args)
+    
+    if not success and encoder in ['h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf']:
+        # Fallback to software encoding
+        print(f"Hardware encoding with {encoder} failed, falling back to software encoding...")
+        fallback_encoder = 'libx264' if 'h264' in encoder else 'libx265'
+        ffmpeg_args_fallback = [
+            "-r", str(fps),
+            "-i", os.path.join(temp_directory_path, "%04d.png"),
+            "-c:v", fallback_encoder,
+            "-preset", "medium",
+            "-crf", str(modules.globals.video_quality),
+            "-pix_fmt", "yuv420p",
+            "-movflags", "+faststart",
+            "-vf", "colorspace=bt709:iall=bt601-6-625:fast=1",
             "-y",
             temp_output_path,
         ]
-    )
+        run_ffmpeg(ffmpeg_args_fallback)
 
 
 def restore_audio(target_path: str, output_path: str) -> None: