Update project version in README

- adds poisson blending on the face to make a seamless blending of the face and the swapped image removing the "frame"
- adds the switch on the UI

Advance Merry Christmas everyone!

README.md

@@ -1,4 +1,4 @@
-<h1 align="center">Deep-Live-Cam</h1>
+<h1 align="center">Deep-Live-Cam 2.0.1c</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 v2.3d 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/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'
+version = '2.0.1c'
 edition = 'GitHub Edition'

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"
@@ -69,34 +80,34 @@ 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
 
@@ -105,19 +116,22 @@ 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 # Return original frame if model failed or not loaded
+        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:
+        # 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
         )
@@ -174,13 +188,42 @@ 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
-                )
-
-    # Apply opacity blend between the original frame and the swapped frame
+                        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)
+        
+                        # 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 +238,50 @@ 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 +304,21 @@ 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
 
 

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")
     )