mirror of
https://github.com/hacksider/Deep-Live-Cam.git
synced 2025-12-06 00:20:02 +01:00
optimization with mac
Hoping this would solve the mac issues, if you're a mac user, please report if there is an improvement
This commit is contained in:
Kenneth Estanislao
parent
2411f1e9b1
commit
b3c4ed9250
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@ -1,8 +1,9 @@
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from typing import Any, List
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from typing import Any, List, Optional
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import cv2
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import insightface
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import threading
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import numpy as np
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import platform
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import modules.globals
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import modules.processors.frame.core
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from modules.core import update_status
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@ -14,9 +15,9 @@ from modules.utilities import (
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is_video,
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)
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from modules.cluster_analysis import find_closest_centroid
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# Removed modules.globals.face_swapper_enabled - assuming controlled elsewhere or implicitly true if used
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# Removed modules.globals.opacity - accessed via getattr
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import os
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from collections import deque
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import time
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FACE_SWAPPER = None
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THREAD_LOCK = threading.Lock()
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@ -26,6 +27,16 @@ NAME = "DLC.FACE-SWAPPER"
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PREVIOUS_FRAME_RESULT = None # Stores the final processed frame from the previous step
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# --- END: Added for Interpolation ---
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# --- START: Mac M1-M5 Optimizations ---
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IS_APPLE_SILICON = platform.system() == 'Darwin' and platform.machine() == 'arm64'
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FRAME_CACHE = deque(maxlen=3) # Cache for frame reuse
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FACE_DETECTION_CACHE = {} # Cache face detections
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LAST_DETECTION_TIME = 0
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DETECTION_INTERVAL = 0.033 # ~30 FPS detection rate for live mode
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FRAME_SKIP_COUNTER = 0
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ADAPTIVE_QUALITY = True
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# --- END: Mac M1-M5 Optimizations ---
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abs_dir = os.path.dirname(os.path.abspath(__file__))
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models_dir = os.path.join(
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os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
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@ -69,34 +80,34 @@ def get_face_swapper() -> Any:
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model_path = os.path.join(models_dir, model_name)
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update_status(f"Loading face swapper model from: {model_path}", NAME)
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try:
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# Ensure the providers list is correctly passed
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# Apply CoreML optimization for Mac systems
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FACE_SWAPPER = insightface.model_zoo.get_model(
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model_path,
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providers=[
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(
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(
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# Optimized provider configuration for Apple Silicon
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providers_config = []
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for p in modules.globals.execution_providers:
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if p == "CoreMLExecutionProvider" and IS_APPLE_SILICON:
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# Enhanced CoreML configuration for M1-M5
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providers_config.append((
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"CoreMLExecutionProvider",
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{
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"ModelFormat": "MLProgram",
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"MLComputeUnits": "CPUAndGPU",
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"MLComputeUnits": "ALL", # Use Neural Engine + GPU + CPU
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"SpecializationStrategy": "FastPrediction",
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"AllowLowPrecisionAccumulationOnGPU": 1,
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},
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)
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if p == "CoreMLExecutionProvider"
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else p
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)
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for p in modules.globals.execution_providers
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],
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"EnableOnSubgraphs": 1,
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"RequireStaticShapes": 0,
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"MaximumCacheSize": 1024 * 1024 * 512, # 512MB cache
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}
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))
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else:
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providers_config.append(p)
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FACE_SWAPPER = insightface.model_zoo.get_model(
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model_path,
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providers=providers_config,
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)
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update_status("Face swapper model loaded successfully.", NAME)
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except Exception as e:
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update_status(f"Error loading face swapper model: {e}", NAME)
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# print traceback maybe?
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# import traceback
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# traceback.print_exc()
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FACE_SWAPPER = None # Ensure it remains None on failure
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FACE_SWAPPER = None
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return None
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return FACE_SWAPPER
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@ -105,19 +116,22 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
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face_swapper = get_face_swapper()
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if face_swapper is None:
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update_status("Face swapper model not loaded or failed to load. Skipping swap.", NAME)
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return temp_frame # Return original frame if model failed or not loaded
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return temp_frame
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# Store a copy of the original frame before swapping for opacity blending
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original_frame = temp_frame.copy()
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# --- Pre-swap Input Check (Optional but good practice) ---
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# Pre-swap Input Check with optimization
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if temp_frame.dtype != np.uint8:
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# print(f"Warning: Input frame is {temp_frame.dtype}, converting to uint8 before swap.")
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temp_frame = np.clip(temp_frame, 0, 255).astype(np.uint8)
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# --- End Input Check ---
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# Apply the face swap
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# Apply the face swap with optimized memory handling
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try:
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# For Apple Silicon, use optimized inference
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if IS_APPLE_SILICON:
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# Ensure contiguous memory layout for better performance
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temp_frame = np.ascontiguousarray(temp_frame)
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swapped_frame_raw = face_swapper.get(
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temp_frame, target_face, source_face, paste_back=True
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)
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@ -195,14 +209,50 @@ def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
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return final_swapped_frame
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# --- START: Mac M1-M5 Optimized Face Detection ---
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def get_faces_optimized(frame: Frame, use_cache: bool = True) -> Optional[List[Face]]:
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"""Optimized face detection for live mode on Apple Silicon"""
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global LAST_DETECTION_TIME, FACE_DETECTION_CACHE
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if not use_cache or not IS_APPLE_SILICON:
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# Standard detection
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if modules.globals.many_faces:
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return get_many_faces(frame)
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else:
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face = get_one_face(frame)
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return [face] if face else None
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# Adaptive detection rate for live mode
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current_time = time.time()
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time_since_last = current_time - LAST_DETECTION_TIME
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# Skip detection if too soon (adaptive frame skipping)
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if time_since_last < DETECTION_INTERVAL and FACE_DETECTION_CACHE:
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return FACE_DETECTION_CACHE.get('faces')
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# Perform detection
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LAST_DETECTION_TIME = current_time
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if modules.globals.many_faces:
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faces = get_many_faces(frame)
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else:
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face = get_one_face(frame)
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faces = [face] if face else None
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# Cache results
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FACE_DETECTION_CACHE['faces'] = faces
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FACE_DETECTION_CACHE['timestamp'] = current_time
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return faces
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# --- END: Mac M1-M5 Optimized Face Detection ---
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# --- START: Helper function for interpolation and sharpening ---
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def apply_post_processing(current_frame: Frame, swapped_face_bboxes: List[np.ndarray]) -> Frame:
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"""Applies sharpening and interpolation."""
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"""Applies sharpening and interpolation with Apple Silicon optimizations."""
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global PREVIOUS_FRAME_RESULT
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processed_frame = current_frame.copy()
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# 1. Apply Sharpening (if enabled)
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# 1. Apply Sharpening (if enabled) with optimized kernel for Apple Silicon
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sharpness_value = getattr(modules.globals, "sharpness", 0.0)
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if sharpness_value > 0.0 and swapped_face_bboxes:
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height, width = processed_frame.shape[:2]
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@ -225,23 +275,21 @@ def apply_post_processing(current_frame: Frame, swapped_face_bboxes: List[np.nda
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continue
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face_region = processed_frame[y1:y2, x1:x2]
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if face_region.size == 0: continue # Skip empty regions
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if face_region.size == 0: continue
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# Apply sharpening using addWeighted for smoother control
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# Use try-except for GaussianBlur and addWeighted as they can fail on invalid inputs
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# Apply sharpening with optimized parameters for Apple Silicon
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try:
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blurred = cv2.GaussianBlur(face_region, (0, 0), 3) # sigma=3, kernel size auto
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# Use smaller sigma for faster processing on Apple Silicon
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sigma = 2 if IS_APPLE_SILICON else 3
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blurred = cv2.GaussianBlur(face_region, (0, 0), sigma)
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sharpened_region = cv2.addWeighted(
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face_region, 1.0 + sharpness_value,
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blurred, -sharpness_value,
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0
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)
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# Ensure the sharpened region doesn't have invalid values
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sharpened_region = np.clip(sharpened_region, 0, 255).astype(np.uint8)
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processed_frame[y1:y2, x1:x2] = sharpened_region
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except cv2.error as sharpen_e:
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# print(f"Warning: OpenCV error during sharpening: {sharpen_e} for bbox {bbox}") # Debug
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# Skip sharpening for this region if it fails
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except cv2.error:
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pass
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