mirror of
https://github.com/hacksider/Deep-Live-Cam.git
synced 2026-05-01 21:55:10 +02:00
f65aeae5db
Bundles CoreML graph rewrites, GPU-accelerated pipeline work, Windows CUDA fixes, and Mac/Windows runtime routing into a single drop. CoreML (Apple Silicon): - Decompose Pad(reflect) → Slice+Concat in inswapper_128 so the model runs in one CoreML partition instead of 14 (TEMPORARY: fixed upstream in microsoft/onnxruntime#28073, drop when ORT >= 1.26.0). - Fold Shape/Gather chains to constants in det_10g (21ms → 4ms). - Decompose Split(axis=1) → Slice pairs in GFPGAN (155ms → 89ms). - Route detection model to GPU so the ANE is free for the swap model. - Centralize provider/config selection in create_onnx_session. Pipeline (all platforms): - Parallelize face landmark + recognition post-detection; skip landmark_2d_106 when only face_swapper is active. - Pipeline face detection with swap for ANE overlap. - GPU-accelerated paste_back, MJPEG capture, zero-copy display path. - Standalone pipeline benchmark script. Windows / CUDA: - CUDA graphs + FP16 model + all-GPU pipeline for 1080p 60 FPS. - Auto-detect GPU provider and fix DLL discovery for Windows CUDA execution. Cross-platform: - platform_info helper for Mac/Windows runtime routing. - GFPGAN 30 fps + MSMF camera 60 fps with adaptive pipeline tuning. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
145 lines
5.5 KiB
Python
145 lines
5.5 KiB
Python
import cv2
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import numpy as np
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import sys
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import time
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from typing import Optional, Tuple, Callable
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import platform
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import threading
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# Only import Windows-specific library if on Windows
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if platform.system() == "Windows":
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from pygrabber.dshow_graph import FilterGraph
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class VideoCapturer:
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def __init__(self, device_index: int):
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self.device_index = device_index
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self.frame_callback = None
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self._current_frame = None
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self._frame_ready = threading.Event()
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self.is_running = False
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self.cap = None
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# Actual values reported by the camera after configuration
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self.actual_width: int = 0
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self.actual_height: int = 0
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self.actual_fps: float = 0.0
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# Initialize Windows-specific components if on Windows
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if platform.system() == "Windows":
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self.graph = FilterGraph()
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# Verify device exists
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devices = self.graph.get_input_devices()
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if self.device_index >= len(devices):
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raise ValueError(
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f"Invalid device index {device_index}. Available devices: {len(devices)}"
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)
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def start(self, width: int = 960, height: int = 540, fps: int = 60) -> bool:
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"""Initialize and start video capture"""
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try:
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if platform.system() == "Windows":
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# Windows-specific capture methods.
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# MSMF (Media Foundation) is preferred — DirectShow often
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# caps at 30fps even when the camera supports 60fps.
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capture_methods = [
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(self.device_index, cv2.CAP_MSMF), # Media Foundation first
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(self.device_index, cv2.CAP_DSHOW), # DirectShow fallback
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(self.device_index, cv2.CAP_ANY),
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(0, cv2.CAP_ANY),
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]
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for dev_id, backend in capture_methods:
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try:
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self.cap = cv2.VideoCapture(dev_id, backend)
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if self.cap.isOpened():
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break
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self.cap.release()
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except Exception:
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continue
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else:
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# Unix-like systems (Linux/Mac) capture method
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self.cap = cv2.VideoCapture(self.device_index)
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if not self.cap or not self.cap.isOpened():
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raise RuntimeError("Failed to open camera")
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# Try MJPEG first — avoids USB bandwidth limits with
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# uncompressed YUV at high resolutions. Falls back silently
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# if the camera/backend doesn't support it.
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self.cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
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# Request desired resolution and frame rate
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self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
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self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
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self.cap.set(cv2.CAP_PROP_FPS, fps)
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# Read back resolution (usually reliable)
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self.actual_width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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self.actual_height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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# CAP_PROP_FPS is unreliable on DirectShow — often reports 30
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# even when the camera delivers 60. Measure empirically by
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# timing a burst of frames.
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reported_fps = self.cap.get(cv2.CAP_PROP_FPS)
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self.actual_fps = self._measure_fps(warmup=10, sample=30,
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fallback=reported_fps or fps)
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print(f"[VideoCapturer] {self.actual_width}x{self.actual_height} "
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f"@ {self.actual_fps:.1f}fps (reported={reported_fps:.0f})",
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flush=True)
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self.is_running = True
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return True
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except Exception as e:
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print(f"Failed to start capture: {str(e)}")
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if self.cap:
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self.cap.release()
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return False
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def read(self) -> Tuple[bool, Optional[np.ndarray]]:
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"""Read a frame from the camera"""
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if not self.is_running or self.cap is None:
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return False, None
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ret, frame = self.cap.read()
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if ret:
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self._current_frame = frame
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if self.frame_callback:
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self.frame_callback(frame)
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return True, frame
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return False, None
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def release(self) -> None:
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"""Stop capture and release resources"""
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if self.is_running and self.cap is not None:
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self.cap.release()
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self.is_running = False
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self.cap = None
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def _measure_fps(self, warmup: int = 10, sample: int = 30,
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fallback: float = 30.0) -> float:
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"""Read warmup+sample frames and return measured FPS.
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This is more reliable than CAP_PROP_FPS which often lies on
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DirectShow. Takes ~0.5-1s at startup but gives a ground-truth
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number for adaptive polling/detection intervals.
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"""
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try:
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for _ in range(warmup):
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self.cap.read()
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t0 = time.perf_counter()
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for _ in range(sample):
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ret, _ = self.cap.read()
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if not ret:
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return fallback
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elapsed = time.perf_counter() - t0
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if elapsed <= 0:
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return fallback
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return sample / elapsed
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except Exception:
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return fallback
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def set_frame_callback(self, callback: Callable[[np.ndarray], None]) -> None:
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"""Set callback for frame processing"""
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self.frame_callback = callback
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