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CommunityScripts/plugins/FunscriptHaven/funscript_haven.py
HavenCTO 359ebe7b73
Add FunscriptHaven plugin (#652)
2026-01-05 10:21:10 +02:00

1738 lines
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Python
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"""
Funscript Haven - StashApp Plugin
Generates funscript files from video scenes using optical flow analysis
"""
import gc
import os
import sys
import json
import math
import threading
import concurrent.futures
import random
import subprocess
from multiprocessing import Pool
from typing import Dict, Any, List, Optional, Callable, Tuple
# Hardware acceleration will be tried first, then fallback to software decoding if needed
# We don't set these initially to allow hardware acceleration to be attempted
# ----------------- Setup and Dependencies -----------------
# Use PythonDepManager for dependency management
try:
from PythonDepManager import ensure_import
# Install and ensure all required dependencies with specific versions
ensure_import(
"stashapi:stashapp-tools==0.2.58",
"numpy==1.26.4",
"opencv-python==4.10.0.84",
"decord==0.6.0"
)
# Import the dependencies after ensuring they're available
import stashapi.log as log
from stashapi.stashapp import StashInterface
import numpy as np
import cv2
from decord import VideoReader, cpu
except ImportError as e:
print(f"Failed to import PythonDepManager or required dependencies: {e}")
print("Please ensure PythonDepManager is installed and available.")
sys.exit(1)
except Exception as e:
print(f"Error during dependency management: {e}")
import traceback
print(f"Stack trace: {traceback.format_exc()}")
sys.exit(1)
# Import local config
try:
import funscript_haven_config as config
except ModuleNotFoundError:
log.error("Please provide a funscript_haven_config.py file with the required variables.")
raise Exception("Please provide a funscript_haven_config.py file with the required variables.")
# ----------------- Global Variables -----------------
stash: Optional[StashInterface] = None
progress: float = 0.0
total_tasks: int = 0
completed_tasks: int = 0
# ----------------- Optical Flow Functions -----------------
def max_divergence(flow):
"""
Computes the divergence of the optical flow over the whole image and returns
the pixel (x, y) with the highest absolute divergence along with its value.
"""
div = np.gradient(flow[..., 0], axis=0) + np.gradient(flow[..., 1], axis=1)
y, x = np.unravel_index(np.argmax(np.abs(div)), div.shape)
return x, y, div[y, x]
def radial_motion_weighted(flow, center, is_cut, pov_mode=False, balance_global=True):
"""
Computes signed radial motion: positive for outward motion, negative for inward motion.
Closer pixels have higher weight.
"""
if is_cut:
return 0.0
h, w, _ = flow.shape
y, x = np.indices((h, w))
dx = x - center[0]
dy = y - center[1]
dot = flow[..., 0] * dx + flow[..., 1] * dy
if pov_mode or not balance_global:
return np.mean(dot)
weighted_dot = np.where(x > center[0], dot * (w - x) / w, dot * x / w)
weighted_dot = np.where(y > center[1], weighted_dot * (h - y) / h, weighted_dot * y / h)
return np.mean(weighted_dot)
def precompute_flow_info(p0, p1, params):
"""
Compute optical flow and extract relevant information for funscript generation.
"""
cut_threshold = params.get("cut_threshold", 7)
flow = cv2.calcOpticalFlowFarneback(p0, p1, None, 0.5, 3, 15, 3, 5, 1.2, 0)
if params.get("pov_mode"):
max_val = (p0.shape[1] // 2, p0.shape[0] - 1, 0)
else:
max_val = max_divergence(flow)
pos_center = max_val[0:2]
val_pos = max_val[2]
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
mean_mag = np.mean(mag)
is_cut = mean_mag > cut_threshold
return {
"flow": flow,
"pos_center": pos_center,
"neg_center": pos_center,
"val_pos": val_pos,
"val_neg": val_pos,
"cut": is_cut,
"cut_center": pos_center[0],
"mean_mag": mean_mag
}
def precompute_wrapper(p, params):
return precompute_flow_info(p[0], p[1], params)
def find_intel_arc_render_device() -> Optional[str]:
"""
Find the render device path (/dev/dri/renderD*) for the Intel Arc GPU.
Returns the device path or None if not found.
"""
try:
# Check all render devices
render_devices = []
for item in os.listdir("/dev/dri/"):
if item.startswith("renderD"):
render_devices.append(f"/dev/dri/{item}")
# Try each render device to find the Intel Arc one
for render_dev in sorted(render_devices):
try:
result = subprocess.run(
["vainfo", "--display", "drm", "--device", render_dev],
capture_output=True,
text=True,
timeout=5
)
if result.returncode == 0 and "Intel" in result.stdout:
# Check if it supports AV1 (Arc GPUs support AV1)
if "AV1" in result.stdout or "av1" in result.stdout.lower():
return render_dev
except (FileNotFoundError, subprocess.TimeoutExpired, subprocess.SubprocessError):
continue
# Fallback: if we found Intel but no AV1, still return the first Intel device
for render_dev in sorted(render_devices):
try:
result = subprocess.run(
["vainfo", "--display", "drm", "--device", render_dev],
capture_output=True,
text=True,
timeout=5
)
if result.returncode == 0 and "Intel" in result.stdout:
return render_dev
except (FileNotFoundError, subprocess.TimeoutExpired, subprocess.SubprocessError):
continue
return None
except Exception:
return None
def detect_intel_arc_gpu() -> Tuple[bool, Optional[str], Optional[str]]:
"""
Detect if an Intel Arc GPU is available.
Returns (is_available, device_name_or_error, render_device_path).
"""
render_device: Optional[str] = None
try:
# Method 1: Check /sys/class/drm for Intel graphics devices
drm_path = "/sys/class/drm"
if os.path.exists(drm_path):
for item in os.listdir(drm_path):
if item.startswith("card") and os.path.isdir(os.path.join(drm_path, item)):
device_path = os.path.join(drm_path, item, "device", "vendor")
if os.path.exists(device_path):
with open(device_path, "r") as f:
vendor_id = f.read().strip()
# Intel vendor ID is 0x8086
if vendor_id == "0x8086" or vendor_id == "8086":
# Check device name
uevent_path = os.path.join(drm_path, item, "device", "uevent")
if os.path.exists(uevent_path):
with open(uevent_path, "r") as uf:
uevent_data = uf.read()
# Check for Arc-specific device IDs or names
# Intel Arc GPU device ID ranges:
# - 569x series: Arc A310 (e.g., 0x5690-0x569F)
# - 56Ax series: Arc A380 (e.g., 0x56A0-0x56AF)
# - 56Bx series: Arc A750, A770 (e.g., 0x56B0-0x56BF)
# Format in uevent: PCI_ID=8086:56A5 or PCI_ID=0000:0000:8086:56A5
device_id_line = [line for line in uevent_data.split("\n") if "PCI_ID" in line]
if device_id_line:
device_id = device_id_line[0].split("=")[-1] if "=" in device_id_line[0] else ""
# Extract device ID part (after vendor ID 8086)
# Handle formats like "8086:56A5" or "0000:0000:8086:56A5"
arc_detected = False
if ":" in device_id:
parts = device_id.split(":")
# Find the part after 8086 (vendor ID)
for i, part in enumerate(parts):
if part.upper() == "8086" and i + 1 < len(parts):
device_part = parts[i + 1].upper()
# Check if it's an Arc GPU device ID
if any(arc_id in device_part for arc_id in ["569", "56A", "56B"]):
arc_detected = True
break
# Fallback: check if any Arc ID is in the full device_id string
if not arc_detected:
arc_detected = any(arc_id in device_id.upper() for arc_id in ["569", "56A", "56B"])
if arc_detected:
# Find the corresponding render device
render_device = find_intel_arc_render_device()
return True, f"Intel Arc GPU (device: {device_id})", render_device
# Method 2: Try using lspci if available
try:
result = subprocess.run(
["lspci"],
capture_output=True,
text=True,
timeout=5
)
if result.returncode == 0:
for line in result.stdout.split("\n"):
if "VGA" in line or "Display" in line:
if "Intel" in line and ("Arc" in line or "A" in line.split("Intel")[-1].split()[0] if len(line.split("Intel")) > 1 else False):
# Find the corresponding render device
render_device = find_intel_arc_render_device()
return True, f"Intel Arc GPU detected via lspci: {line.strip()}", render_device
except (FileNotFoundError, subprocess.TimeoutExpired):
pass
# Method 3: Check vaapi devices (try all render devices)
# This is a fallback method that checks VAAPI directly
render_device = find_intel_arc_render_device()
if render_device:
# Verify it supports AV1
try:
result = subprocess.run(
["vainfo", "--display", "drm", "--device", render_device],
capture_output=True,
text=True,
timeout=5
)
if result.returncode == 0 and "Intel" in result.stdout:
# Check if it supports AV1
if "AV1" in result.stdout or "av1" in result.stdout.lower():
return True, "Intel Arc GPU (VAAPI with AV1 support)", render_device
except (FileNotFoundError, subprocess.TimeoutExpired):
pass
return False, "No Intel Arc GPU detected", None
except Exception as e:
return False, f"Error detecting GPU: {e}", None
def enable_intel_arc_hardware_acceleration(render_device: Optional[str] = None) -> None:
"""
Enable Intel Arc GPU hardware acceleration via VAAPI.
This function sets environment variables that force FFmpeg/decord to use the Intel Arc GPU
for hardware-accelerated video decoding. The iHD driver will automatically select the
Intel Arc GPU when LIBVA_DRIVER_NAME is set to "iHD".
Args:
render_device: Path to the render device (e.g., /dev/dri/renderD128).
If None, will try to detect it automatically.
Note: libva typically auto-selects the correct device, but specifying
it explicitly ensures the right GPU is used.
"""
# Remove software-only restrictions
os.environ.pop("DECORD_CPU_ONLY", None)
os.environ.pop("FFMPEG_HWACCEL", None)
os.environ.pop("AVCODEC_FORCE_SOFTWARE", None)
# Enable VAAPI hardware acceleration for Intel Arc
# Setting LIBVA_DRIVER_NAME to "iHD" forces libva to use the Intel HD Graphics driver
# which supports Intel Arc GPUs and AV1 hardware acceleration
os.environ["LIBVA_DRIVER_NAME"] = "iHD"
# Don't set LIBVA_DRIVERS_PATH to allow system to find the driver automatically
os.environ.pop("LIBVA_DRIVERS_PATH", None)
# Enable hardware acceleration in FFmpeg (used by decord)
os.environ["FFMPEG_HWACCEL"] = "vaapi"
# Specify the render device explicitly to ensure we use the Intel Arc GPU
# libva will use this device when initializing the iHD driver
if render_device:
# Some systems respect these environment variables for device selection
os.environ["LIBVA_DRIVER_DEVICE"] = render_device
# Alternative variable name that some tools use
os.environ["VAAPI_DEVICE"] = render_device
else:
# Try to find the device automatically
detected_device = find_intel_arc_render_device()
if detected_device:
os.environ["LIBVA_DRIVER_DEVICE"] = detected_device
os.environ["VAAPI_DEVICE"] = detected_device
# Suppress FFmpeg report messages
os.environ["FFREPORT"] = "file=/dev/null:level=0"
def enable_software_decoding() -> None:
"""
Enable software-only decoding by setting environment variables.
These environment variables are set at the Python process level and will override
any system-wide or user-level settings. They take effect for:
- The current Python process
- All child processes (including FFmpeg subprocesses)
- OpenCV's FFmpeg backend
- Decord's FFmpeg backend
Note: Process-level environment variables (set via os.environ) have the highest
precedence and will override system/user settings.
This function aggressively disables ALL hardware acceleration, including Intel Arc GPU.
"""
# Force software decoding - these override any system/user settings
os.environ["DECORD_CPU_ONLY"] = "1"
os.environ["FFMPEG_HWACCEL"] = "none"
# Aggressively disable VAAPI (Video Acceleration API) completely
# Clear any Intel Arc GPU settings that might have been set
os.environ["LIBVA_DRIVERS_PATH"] = "/dev/null" # Invalid path disables VAAPI
os.environ["LIBVA_DRIVER_NAME"] = "" # Clear driver name (removes iHD setting)
os.environ.pop("LIBVA_DRIVER_DEVICE", None) # Remove device setting
os.environ.pop("VAAPI_DEVICE", None) # Remove alternative device setting
# Force software decoding in libavcodec (FFmpeg's codec library)
os.environ["AVCODEC_FORCE_SOFTWARE"] = "1"
# Suppress FFmpeg logging (warnings about hardware acceleration failures)
os.environ["FFREPORT"] = "file=/dev/null:level=0"
os.environ["FFMPEG_LOGLEVEL"] = "error" # Only show errors, suppress warnings
# Additional FFmpeg options to prevent hardware acceleration attempts
os.environ["FFMPEG_HWACCEL_DEVICE"] = ""
# Explicitly disable all hardware acceleration methods
os.environ["FFMPEG_HWACCEL_OUTPUT_FORMAT"] = ""
def disable_software_decoding() -> None:
"""Disable software-only decoding by removing environment variables."""
os.environ.pop("DECORD_CPU_ONLY", None)
os.environ.pop("FFMPEG_HWACCEL", None)
os.environ.pop("LIBVA_DRIVERS_PATH", None)
os.environ.pop("LIBVA_DRIVER_NAME", None)
os.environ.pop("AVCODEC_FORCE_SOFTWARE", None)
os.environ.pop("FFREPORT", None)
def is_av1_hardware_error(error_msg: str) -> bool:
"""Check if error is related to AV1 hardware acceleration failure."""
error_lower = error_msg.lower()
return (
"av1" in error_lower and
("failed to get pixel format" in error_lower or
"doesn't suppport hardware accelerated" in error_lower or
"hardware accelerated" in error_lower)
)
def probe_video_streams(video_path: str) -> Tuple[bool, Optional[int], Optional[str]]:
"""
Probe video file to find the correct video stream index.
Returns (success, video_stream_index, error_message).
"""
try:
cmd = [
"ffprobe",
"-v", "error",
"-select_streams", "v",
"-show_entries", "stream=index,codec_type",
"-of", "json",
video_path
]
result = subprocess.run(cmd, capture_output=True, text=True, timeout=10)
if result.returncode != 0:
return False, None, f"ffprobe failed: {result.stderr}"
data = json.loads(result.stdout)
streams = data.get("streams", [])
# Find the first video stream
for stream in streams:
if stream.get("codec_type") == "video":
stream_index = stream.get("index")
if stream_index is not None:
return True, int(stream_index), None
return False, None, "No video stream found in file"
except subprocess.TimeoutExpired:
return False, None, "ffprobe timed out"
except json.JSONDecodeError as e:
return False, None, f"Failed to parse ffprobe output: {e}"
except Exception as e:
return False, None, f"Error probing video: {e}"
def validate_video_file(video_path: str) -> Tuple[bool, Optional[str]]:
"""
Validate that a video file can be opened with OpenCV.
Returns (is_valid, error_message).
"""
if not os.path.exists(video_path):
return False, f"Video file does not exist: {video_path}"
if not os.path.isfile(video_path):
return False, f"Path is not a file: {video_path}"
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
cap.release()
return False, f"OpenCV cannot open video file: {video_path}"
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = cap.get(cv2.CAP_PROP_FPS)
cap.release()
if frame_count <= 0:
return False, f"Video file has no frames: {video_path}"
if fps <= 0:
return False, f"Video file has invalid FPS: {video_path}"
return True, None
def fetch_frames_opencv(video_path: str, chunk: List[int], params: Dict[str, Any]) -> List[np.ndarray]:
"""
Fetch frames using OpenCV as fallback when decord fails.
Software decoding is enforced via environment variables set before calling this function.
FFmpeg warnings about AV1 hardware acceleration are suppressed (FFmpeg will fall back to software).
IMPORTANT: FFmpeg has BUILT-IN automatic fallback to software decoding.
Even if environment variables are ignored, FFmpeg will:
1. Try hardware acceleration first (if available)
2. If hardware fails, automatically fall back to software decoding
3. Continue processing successfully with software decoding
The AV1 hardware warnings you see are just warnings - FFmpeg continues with software decoding.
"""
frames_gray = []
target_width = 512 if params.get("vr_mode") else 256
target_height = 512 if params.get("vr_mode") else 256
# Ensure software decoding is enforced (in case it wasn't set properly)
# This is a safety check - environment variables should already be set
enable_software_decoding()
# Suppress FFmpeg stderr output (these AV1 hardware errors are harmless - FFmpeg falls back to software)
import sys
import io
old_stderr = sys.stderr
suppressed_stderr = io.StringIO()
try:
# Temporarily redirect stderr to suppress FFmpeg AV1 hardware warnings
# FFmpeg will try hardware first, fail, then AUTOMATICALLY fall back to software - these are just warnings
sys.stderr = suppressed_stderr
# Try opening with OpenCV - use CAP_FFMPEG backend explicitly and disable hardware acceleration
# OpenCV's VideoCapture may need explicit backend selection to respect our environment variables
cap = cv2.VideoCapture(video_path, cv2.CAP_FFMPEG)
if not cap.isOpened():
# Try without explicit backend
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
# Restore stderr before returning
sys.stderr = old_stderr
suppressed_stderr.close()
return frames_gray
try:
frames_read = 0
frames_failed = 0
# For AV1 videos, seeking can be unreliable. Try sequential reading if seeking fails
# Sort chunk indices to read sequentially when possible
sorted_chunk = sorted(chunk)
current_pos = -1
for frame_idx in sorted_chunk:
# Try seeking first
if current_pos != frame_idx - 1:
# Need to seek
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
current_pos = frame_idx
else:
# Sequential read - more reliable for AV1
current_pos += 1
# Try reading frame - FFmpeg will automatically use software if hardware fails
ret, frame = cap.read()
if not ret or frame is None:
frames_failed += 1
# If seeking failed, try sequential reading from start
if current_pos != frame_idx:
# Reset and read sequentially
cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
for i in range(frame_idx + 1):
ret, frame = cap.read()
if not ret or frame is None:
break
if not ret or frame is None:
continue
else:
# Already sequential, just try one more read
ret, frame = cap.read()
if not ret or frame is None:
continue
frames_read += 1
current_pos = frame_idx
# Resize frame
frame_resized = cv2.resize(frame, (target_width, target_height), interpolation=cv2.INTER_LINEAR)
# Convert to grayscale
if params.get("vr_mode"):
h, w = frame_resized.shape[:2]
gray = cv2.cvtColor(frame_resized[h // 2:, :w // 2], cv2.COLOR_BGR2GRAY)
else:
gray = cv2.cvtColor(frame_resized, cv2.COLOR_BGR2GRAY)
frames_gray.append(gray)
# If we read frames successfully, FFmpeg's automatic fallback worked
# (The AV1 warnings are harmless - FFmpeg fell back to software automatically)
# Log diagnostic info if we failed to read frames
if frames_read == 0 and len(chunk) > 0:
# This would indicate an actual problem, not just warnings
# The AV1 warnings are harmless, but if we can't read frames, there's a real issue
pass # Will return empty list, which is handled by caller
finally:
cap.release()
finally:
# Restore stderr
sys.stderr = old_stderr
# Discard suppressed output (FFmpeg AV1 hardware warnings)
suppressed_stderr.close()
return frames_gray
def fetch_frames(video_path, chunk, params):
"""Fetch and preprocess frames from video."""
frames_gray = []
vr: Optional[VideoReader] = None
target_width = 512 if params.get("vr_mode") else 256
target_height = 512 if params.get("vr_mode") else 256
# Try multiple strategies for VideoReader initialization
initialization_strategies = [
# Strategy 1: With width/height (preferred for performance)
{"width": target_width, "height": target_height, "num_threads": params["threads"]},
# Strategy 2: Without width/height (will resize frames manually)
{"num_threads": params["threads"]},
# Strategy 3: Lower resolution
{"width": target_width // 2, "height": target_height // 2, "num_threads": params["threads"]},
# Strategy 4: Single thread
{"width": target_width, "height": target_height, "num_threads": 1},
# Strategy 5: Minimal parameters
{},
]
batch_frames = None
needs_resize = False
av1_hardware_error_detected = False
# First attempt: Try with current settings (hardware acceleration if enabled)
for strategy in initialization_strategies:
vr = None
try:
vr = VideoReader(video_path, ctx=cpu(0), **strategy)
batch_frames = vr.get_batch(chunk).asnumpy()
# Check if we got frames without the desired size
if batch_frames.size > 0 and "width" not in strategy:
needs_resize = True
# Success - break out of loop, vr will be cleaned up after processing
break
except Exception as e:
error_msg = str(e)
# Check if this is an AV1 hardware acceleration error
if is_av1_hardware_error(error_msg):
av1_hardware_error_detected = True
break # Exit to try software decoding
# Failed with this strategy, try next one
if vr is not None:
vr = None
continue
# If AV1 hardware acceleration failed, retry with software decoding
if batch_frames is None and av1_hardware_error_detected:
enable_software_decoding()
# Retry all strategies with software decoding
for strategy in initialization_strategies:
vr = None
try:
vr = VideoReader(video_path, ctx=cpu(0), **strategy)
batch_frames = vr.get_batch(chunk).asnumpy()
# Check if we got frames without the desired size
if batch_frames.size > 0 and "width" not in strategy:
needs_resize = True
# Success - break out of loop
break
except Exception:
# Failed with this strategy, try next one
if vr is not None:
vr = None
continue
# Clean up VideoReader after getting frames
if vr is not None:
vr = None
gc.collect()
if batch_frames is None or batch_frames.size == 0:
return frames_gray
for f in batch_frames:
# Resize if needed (when VideoReader was initialized without width/height)
if needs_resize:
f = cv2.resize(f, (target_width, target_height), interpolation=cv2.INTER_LINEAR)
if params.get("vr_mode"):
h, w, _ = f.shape
gray = cv2.cvtColor(f[h // 2:, :w // 2], cv2.COLOR_RGB2GRAY)
else:
gray = cv2.cvtColor(f, cv2.COLOR_RGB2GRAY)
frames_gray.append(gray)
return frames_gray
# ----------------- OpenSimplex Noise Generator -----------------
class OpenSimplex:
"""OpenSimplex noise generator for smooth, natural random motion."""
PSIZE = 2048
PMASK = PSIZE - 1
def __init__(self, seed=None):
if seed is None:
seed = random.randint(0, 2**63 - 1)
self._perm = [0] * self.PSIZE
self._grad = [(0.0, 0.0)] * self.PSIZE
grad_base = [
(0.130526192220052, 0.991444861373810),
(0.382683432365090, 0.923879532511287),
(0.608761429008721, 0.793353340291235),
(0.793353340291235, 0.608761429008721),
(0.923879532511287, 0.382683432365090),
(0.991444861373810, 0.130526192220051),
(0.991444861373810, -0.130526192220051),
(0.923879532511287, -0.382683432365090),
(0.793353340291235, -0.608761429008720),
(0.608761429008721, -0.793353340291235),
(0.382683432365090, -0.923879532511287),
(0.130526192220052, -0.991444861373810),
(-0.130526192220052, -0.991444861373810),
(-0.382683432365090, -0.923879532511287),
(-0.608761429008721, -0.793353340291235),
(-0.793353340291235, -0.608761429008721),
(-0.923879532511287, -0.382683432365090),
(-0.991444861373810, -0.130526192220052),
(-0.991444861373810, 0.130526192220051),
(-0.923879532511287, 0.382683432365090),
(-0.793353340291235, 0.608761429008721),
(-0.608761429008721, 0.793353340291235),
(-0.382683432365090, 0.923879532511287),
(-0.130526192220052, 0.991444861373810)
]
n = 0.05481866495625118
self._grad_lookup = [(dx / n, dy / n) for dx, dy in grad_base]
source = list(range(self.PSIZE))
for i in range(self.PSIZE - 1, -1, -1):
seed = (seed * 6364136223846793005 + 1442695040888963407) & 0xFFFFFFFFFFFFFFFF
r = int((seed + 31) % (i + 1))
if r < 0:
r += i + 1
self._perm[i] = source[r]
self._grad[i] = self._grad_lookup[self._perm[i] % len(self._grad_lookup)]
source[r] = source[i]
def calculate_2d(self, x, y):
s = 0.366025403784439 * (x + y)
return self._calculate_2d_impl(x + s, y + s)
def calculate_2d_octaves(self, x, y, octaves=1, persistence=1.0, lacunarity=1.0):
frequency = 1.0
amplitude = 1.0
total_value = 0.0
total_amplitude = 0.0
for _ in range(octaves):
total_value += self.calculate_2d(x * frequency, y * frequency) * amplitude
total_amplitude += amplitude
amplitude *= persistence
frequency *= lacunarity
return total_value / total_amplitude if total_amplitude > 0 else 0
def _calculate_2d_impl(self, xs, ys):
xsb = int(math.floor(xs))
ysb = int(math.floor(ys))
xsi = xs - xsb
ysi = ys - ysb
a = int(xsi + ysi)
ssi = (xsi + ysi) * -0.211324865405187
xi = xsi + ssi
yi = ysi + ssi
value = 0.0
value += self._contribute(xsb, ysb, xi, yi)
value += self._contribute(xsb + 1, ysb + 1, xi - 1 + 2 * 0.211324865405187, yi - 1 + 2 * 0.211324865405187)
if a == 0:
value += self._contribute(xsb + 1, ysb, xi - 1 + 0.211324865405187, yi + 0.211324865405187)
value += self._contribute(xsb, ysb + 1, xi + 0.211324865405187, yi - 1 + 0.211324865405187)
else:
value += self._contribute(xsb + 2, ysb + 1, xi - 2 + 3 * 0.211324865405187, yi - 1 + 3 * 0.211324865405187)
value += self._contribute(xsb + 1, ysb + 2, xi - 1 + 3 * 0.211324865405187, yi - 2 + 3 * 0.211324865405187)
return value
def _contribute(self, xsb, ysb, dx, dy):
attn = 2.0 / 3.0 - dx * dx - dy * dy
if attn <= 0:
return 0
pxm = xsb & self.PMASK
pym = ysb & self.PMASK
grad = self._grad[self._perm[pxm] ^ pym]
extrapolation = grad[0] * dx + grad[1] * dy
attn *= attn
return attn * attn * extrapolation
# ----------------- Multi-Axis Generation -----------------
MULTI_AXIS_CONFIG = {
"surge": {
"name": "L1",
"friendly_name": "Forward/Backward",
"file_suffix": "surge",
"default_value": 50,
"phase_offset": 0.25,
},
"sway": {
"name": "L2",
"friendly_name": "Left/Right",
"file_suffix": "sway",
"default_value": 50,
"phase_offset": 0.5,
},
"twist": {
"name": "R0",
"friendly_name": "Twist",
"file_suffix": "twist",
"default_value": 50,
"phase_offset": 0.0,
},
"roll": {
"name": "R1",
"friendly_name": "Roll",
"file_suffix": "roll",
"default_value": 50,
"phase_offset": 0.33,
},
"pitch": {
"name": "R2",
"friendly_name": "Pitch",
"file_suffix": "pitch",
"default_value": 50,
"phase_offset": 0.66,
},
}
class MultiAxisGenerator:
"""Generates secondary axis funscripts from primary L0 (stroke) data."""
def __init__(self, settings):
self.settings = settings
self.intensity = settings.get("multi_axis_intensity", 0.5)
self.random_speed = settings.get("random_speed", 0.3)
self.smart_limit = settings.get("smart_limit", True)
self.auto_home_delay = settings.get("auto_home_delay", 1.0)
self.auto_home_duration = settings.get("auto_home_duration", 0.5)
self.noise_generators = {
axis_name: OpenSimplex(seed=hash(axis_name) & 0xFFFFFFFF)
for axis_name in MULTI_AXIS_CONFIG.keys()
}
def generate_all_axes(self, l0_actions, fps, log_func=None):
if not l0_actions or len(l0_actions) < 2:
return {}
activity_data = self._analyze_activity(l0_actions)
results = {}
for axis_name, axis_config in MULTI_AXIS_CONFIG.items():
if log_func:
log_func(f"Generating {axis_config['friendly_name']} ({axis_config['name']}) axis...")
axis_actions = self._generate_axis(
axis_name,
axis_config,
l0_actions,
activity_data,
fps
)
axis_actions = self._apply_auto_home(axis_actions, activity_data, axis_config)
results[axis_name] = axis_actions
if log_func:
log_func(f" Generated {len(axis_actions)} actions for {axis_config['file_suffix']}")
return results
def _analyze_activity(self, l0_actions):
velocities = []
activity_levels = []
for i in range(len(l0_actions)):
if i == 0:
velocities.append(0)
else:
dt = (l0_actions[i]["at"] - l0_actions[i-1]["at"]) / 1000.0
if dt > 0:
dp = abs(l0_actions[i]["pos"] - l0_actions[i-1]["pos"])
velocities.append(dp / dt)
else:
velocities.append(0)
max_vel = max(velocities) if velocities else 1
if max_vel > 0:
activity_levels = [min(1.0, v / max_vel) for v in velocities]
else:
activity_levels = [0] * len(velocities)
window_size = min(5, len(activity_levels))
smoothed_activity = []
for i in range(len(activity_levels)):
start = max(0, i - window_size // 2)
end = min(len(activity_levels), i + window_size // 2 + 1)
smoothed_activity.append(sum(activity_levels[start:end]) / (end - start))
idle_periods = []
idle_threshold = 0.1
min_idle_duration_ms = self.auto_home_delay * 1000
idle_start = None
for i, (action, activity) in enumerate(zip(l0_actions, smoothed_activity)):
if activity < idle_threshold:
if idle_start is None:
idle_start = action["at"]
else:
if idle_start is not None:
idle_duration = action["at"] - idle_start
if idle_duration >= min_idle_duration_ms:
idle_periods.append((idle_start, action["at"]))
idle_start = None
if idle_start is not None and l0_actions:
idle_duration = l0_actions[-1]["at"] - idle_start
if idle_duration >= min_idle_duration_ms:
idle_periods.append((idle_start, l0_actions[-1]["at"]))
return {
"velocities": velocities,
"activity_levels": smoothed_activity,
"idle_periods": idle_periods
}
def _generate_axis(self, axis_name, axis_config, l0_actions, activity_data, fps):
noise = self.noise_generators[axis_name]
phase_offset = axis_config["phase_offset"]
default_value = axis_config["default_value"]
actions = []
for i, l0_action in enumerate(l0_actions):
timestamp_ms = l0_action["at"]
time_sec = timestamp_ms / 1000.0
noise_x = time_sec * self.random_speed + phase_offset * 10
noise_y = time_sec * self.random_speed * 0.7 + phase_offset * 5
noise_value = noise.calculate_2d_octaves(
noise_x, noise_y,
octaves=2,
persistence=0.5,
lacunarity=2.0
)
raw_pos = default_value + noise_value * 50 * self.intensity
if self.smart_limit and i < len(activity_data["activity_levels"]):
activity = activity_data["activity_levels"][i]
deviation = raw_pos - default_value
raw_pos = default_value + deviation * activity
pos = int(round(max(0, min(100, raw_pos))))
actions.append({"at": timestamp_ms, "pos": pos})
return actions
def _apply_auto_home(self, actions, activity_data, axis_config):
if not actions or not activity_data["idle_periods"]:
return actions
default_value = axis_config["default_value"]
home_duration_ms = self.auto_home_duration * 1000
result_actions = []
idle_periods = activity_data["idle_periods"]
for action in actions:
timestamp_ms = action["at"]
in_idle = False
for idle_start, idle_end in idle_periods:
if idle_start <= timestamp_ms <= idle_end:
in_idle = True
idle_progress = (timestamp_ms - idle_start) / home_duration_ms
idle_progress = min(1.0, idle_progress)
ease = 1 - (1 - idle_progress) ** 2
current_pos = action["pos"]
homed_pos = int(round(current_pos + (default_value - current_pos) * ease))
result_actions.append({"at": timestamp_ms, "pos": homed_pos})
break
if not in_idle:
result_actions.append(action)
return result_actions
def save_axis_funscript(self, base_path, axis_name, actions, log_func=None):
axis_config = MULTI_AXIS_CONFIG.get(axis_name)
if not axis_config:
return False
output_path = f"{base_path}.{axis_config['file_suffix']}.funscript"
funscript = {
"version": "1.0",
"inverted": False,
"range": 100,
"actions": actions
}
try:
with open(output_path, "w") as f:
json.dump(funscript, f, indent=2)
if log_func:
log_func(f"Multi-axis funscript saved: {output_path}")
return True
except Exception as e:
if log_func:
log_func(f"ERROR: Could not save {output_path}: {e}")
return False
# ----------------- Main Processing Function -----------------
def process_video(video_path: str, params: Dict[str, Any], log_func: Callable,
progress_callback: Optional[Callable] = None,
cancel_flag: Optional[Callable] = None) -> bool:
"""
Process a video file and generate funscript.
Returns True if an error occurred, False otherwise.
"""
error_occurred = False
base, _ = os.path.splitext(video_path)
output_path = base + ".funscript"
if os.path.exists(output_path) and not params.get("overwrite", False):
log_func(f"Skipping: output file exists ({output_path})")
return error_occurred
# Validate video file before attempting to process
is_valid, validation_error = validate_video_file(video_path)
if not is_valid:
log_func(f"ERROR: Video validation failed: {validation_error}")
return True
log_func(f"Processing video: {video_path}")
# Probe video to get stream information
probe_success, video_stream_index, probe_error = probe_video_streams(video_path)
if probe_success and video_stream_index is not None:
log_func(f"Found video stream at index {video_stream_index}")
# Detect Intel Arc GPU and configure hardware acceleration
intel_arc_detected, arc_info, render_device = detect_intel_arc_gpu()
if intel_arc_detected:
log_func(f"Intel Arc GPU detected: {arc_info}")
if render_device:
log_func(f"Using render device: {render_device}")
log_func("Configuring hardware acceleration for Intel Arc AV1 decoding...")
log_func("Note: If hardware acceleration fails, FFmpeg will AUTOMATICALLY fall back to software decoding")
enable_intel_arc_hardware_acceleration(render_device)
else:
log_func(f"No Intel Arc GPU detected ({arc_info}), using software decoding")
enable_software_decoding()
# Try multiple initialization strategies for decord VideoReader
vr: Optional[VideoReader] = None
initialization_strategies = [
# Strategy 1: With width/height (original approach)
{"width": 1024, "height": 1024, "num_threads": params["threads"]},
# Strategy 2: Without width/height (native resolution, resize later)
{"num_threads": params["threads"]},
# Strategy 3: Lower resolution
{"width": 512, "height": 512, "num_threads": params["threads"]},
# Strategy 4: Single thread
{"width": 1024, "height": 1024, "num_threads": 1},
# Strategy 5: No parameters (minimal)
{},
]
last_error: Optional[str] = None
av1_hardware_error_detected = False
# First attempt: Try with detected configuration (Intel Arc if detected, otherwise software)
if intel_arc_detected:
log_func("Attempting to open video with Intel Arc hardware acceleration...")
else:
log_func("Attempting to open video with software decoding...")
for i, strategy in enumerate(initialization_strategies):
try:
log_func(f"Trying VideoReader initialization strategy {i+1}/{len(initialization_strategies)}...")
vr = VideoReader(video_path, ctx=cpu(0), **strategy)
# Test that we can actually read properties
_ = len(vr)
_ = vr.get_avg_fps()
if intel_arc_detected:
log_func(f"Successfully opened video with Intel Arc hardware acceleration using strategy {i+1}")
else:
log_func(f"Successfully opened video with software decoding using strategy {i+1}")
break
except Exception as e:
error_msg = str(e)
last_error = error_msg
# Check if this is an AV1 hardware acceleration error
if is_av1_hardware_error(error_msg):
av1_hardware_error_detected = True
log_func(f"AV1 hardware acceleration error detected, will fallback to software decoding")
break # Exit loop to try software decoding
if "cannot find video stream" in error_msg or "st_nb" in error_msg:
# This is the specific error we're trying to fix, try next strategy
continue
else:
# Other errors, try next strategy
continue
# If AV1 hardware acceleration failed, retry with software decoding
if vr is None and av1_hardware_error_detected:
log_func("Falling back to software decoding due to AV1 hardware acceleration issues...")
enable_software_decoding()
# Retry all strategies with software decoding
for i, strategy in enumerate(initialization_strategies):
try:
log_func(f"Trying VideoReader initialization strategy {i+1}/{len(initialization_strategies)} (software decoding)...")
vr = VideoReader(video_path, ctx=cpu(0), **strategy)
# Test that we can actually read properties
_ = len(vr)
_ = vr.get_avg_fps()
log_func(f"Successfully opened video with software decoding using strategy {i+1}")
break
except Exception as e:
error_msg = str(e)
last_error = error_msg
if "cannot find video stream" in error_msg or "st_nb" in error_msg:
continue
else:
continue
# If decord completely failed, try OpenCV as a fallback
use_opencv_fallback = False
if vr is None:
error_msg = last_error or "Unknown error"
log_func(f"WARNING: Decord failed to open video with all strategies.")
log_func(f"WARNING: Last decord error: {error_msg}")
if probe_success:
log_func(f"WARNING: Video has valid stream at index {video_stream_index}, but decord cannot access it.")
log_func("Attempting to use OpenCV as fallback (slower but more compatible)...")
# CRITICAL: Force software decoding when using OpenCV fallback
# This MUST override any Intel Arc GPU hardware acceleration settings that were enabled earlier
log_func("Disabling all hardware acceleration for OpenCV fallback (including Intel Arc GPU)...")
enable_software_decoding()
# Double-check that hardware acceleration is completely disabled
# Clear any Intel Arc GPU settings that might persist
if os.environ.get("LIBVA_DRIVER_NAME"):
log_func(f"Clearing LIBVA_DRIVER_NAME (was: '{os.environ.get('LIBVA_DRIVER_NAME')}')")
os.environ["LIBVA_DRIVER_NAME"] = ""
if os.environ.get("FFMPEG_HWACCEL") != "none":
log_func(f"Setting FFMPEG_HWACCEL to 'none' (was: '{os.environ.get('FFMPEG_HWACCEL')}')")
os.environ["FFMPEG_HWACCEL"] = "none"
# Remove any device-specific settings
os.environ.pop("LIBVA_DRIVER_DEVICE", None)
os.environ.pop("VAAPI_DEVICE", None)
use_opencv_fallback = True
# Get video properties
if use_opencv_fallback:
# Use OpenCV to get video properties
# Ensure software decoding is enforced (safety check)
enable_software_decoding()
# Suppress FFmpeg stderr warnings during property reading
import sys
import io
old_stderr = sys.stderr
suppressed_stderr = io.StringIO()
try:
sys.stderr = suppressed_stderr
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
log_func(f"ERROR: OpenCV fallback also failed to open video: {video_path}")
return True
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = cap.get(cv2.CAP_PROP_FPS)
cap.release()
finally:
sys.stderr = old_stderr
suppressed_stderr.close()
if total_frames <= 0:
log_func(f"ERROR: Video has no frames: {video_path}")
return True
if fps <= 0:
log_func(f"ERROR: Video has invalid FPS: {video_path}")
return True
log_func(f"Using OpenCV fallback for video reading")
else:
# Use decord video properties
try:
total_frames = len(vr)
fps = vr.get_avg_fps()
if total_frames <= 0:
log_func(f"ERROR: Video has no frames: {video_path}")
return True
if fps <= 0:
log_func(f"ERROR: Video has invalid FPS: {video_path}")
return True
except Exception as e:
log_func(f"ERROR: Unable to read video properties: {e}")
return True
step = max(1, int(math.ceil(fps / 15.0)))
effective_fps = fps / step
indices = list(range(0, total_frames, step))
log_func(f"FPS: {fps:.2f}; downsampled to ~{effective_fps:.2f} fps; {len(indices)} frames selected.")
step = max(1, int(math.ceil(fps / 30.0)))
indices = list(range(0, total_frames, step))
bracket_size = int(params.get("batch_size", 3000))
# Store whether to use OpenCV fallback in params for fetch_frames
params["use_opencv_fallback"] = use_opencv_fallback
final_flow_list = []
next_batch = None
fetch_thread = None
for chunk_start in range(0, len(indices), bracket_size):
if cancel_flag and cancel_flag():
log_func("Processing cancelled by user.")
return error_occurred
chunk = indices[chunk_start:chunk_start + bracket_size]
frame_indices = chunk[:-1]
if len(chunk) < 2:
continue
if fetch_thread:
fetch_thread.join()
if params.get("use_opencv_fallback"):
frames_gray = next_batch if next_batch is not None else fetch_frames_opencv(video_path, chunk, params)
else:
frames_gray = next_batch if next_batch is not None else fetch_frames(video_path, chunk, params)
next_batch = None
else:
if params.get("use_opencv_fallback"):
frames_gray = fetch_frames_opencv(video_path, chunk, params)
else:
frames_gray = fetch_frames(video_path, chunk, params)
if not frames_gray:
log_func(f"ERROR: Unable to fetch frames for chunk {chunk_start} - skipping.")
# If this is a critical chunk and we have no data, we might need to abort
if len(final_flow_list) == 0 and chunk_start == 0:
log_func("ERROR: Failed to fetch initial frames - cannot continue processing")
# Add diagnostic information
if params.get("use_opencv_fallback"):
log_func("DEBUG: Using OpenCV fallback - checking if video can be opened...")
import sys
import io
old_stderr = sys.stderr
suppressed_stderr = io.StringIO()
try:
sys.stderr = suppressed_stderr
test_cap = cv2.VideoCapture(video_path)
if test_cap.isOpened():
test_frame_count = int(test_cap.get(cv2.CAP_PROP_FRAME_COUNT))
test_fps = test_cap.get(cv2.CAP_PROP_FPS)
log_func(f"DEBUG: OpenCV can open video - frame_count={test_frame_count}, fps={test_fps}")
# Try reading a single frame
test_cap.set(cv2.CAP_PROP_POS_FRAMES, chunk[0] if chunk else 0)
ret, test_frame = test_cap.read()
if ret and test_frame is not None:
log_func(f"DEBUG: OpenCV can read frames - frame shape: {test_frame.shape}")
else:
log_func("DEBUG: OpenCV opened video but cannot read frames")
else:
log_func("DEBUG: OpenCV cannot open video file")
test_cap.release()
finally:
sys.stderr = old_stderr
suppressed_stderr.close()
return True
continue
# Ensure we have at least 2 frames to create pairs
if len(frames_gray) < 2:
log_func(f"WARNING: Chunk {chunk_start} has insufficient frames ({len(frames_gray)}) - skipping.")
continue
if chunk_start + bracket_size < len(indices):
next_chunk = indices[chunk_start + bracket_size:chunk_start + 2 * bracket_size]
def fetch_and_store():
global next_batch
if params.get("use_opencv_fallback"):
next_batch = fetch_frames_opencv(video_path, next_chunk, params)
else:
next_batch = fetch_frames(video_path, next_chunk, params)
fetch_thread = threading.Thread(target=fetch_and_store)
fetch_thread.start()
pairs = list(zip(frames_gray[:-1], frames_gray[1:]))
with Pool(processes=params["threads"]) as pool:
precomputed = pool.starmap(precompute_wrapper, [(p, params) for p in pairs])
final_centers = []
for j, info in enumerate(precomputed):
center_list = [info["pos_center"]]
for i in range(1, 7):
if j - i >= 0:
center_list.append(precomputed[j - i]["pos_center"])
if j + i < len(precomputed):
center_list.append(precomputed[j + i]["pos_center"])
center_list = np.array(center_list)
center = np.mean(center_list, axis=0)
final_centers.append(center)
with concurrent.futures.ProcessPoolExecutor(max_workers=params["threads"]) as ex:
dot_futures = []
for j, info in enumerate(precomputed):
dot_futures.append(ex.submit(
radial_motion_weighted,
info["flow"],
final_centers[j],
info["cut"],
params.get("pov_mode", False),
params.get("balance_global", True)
))
dot_vals = [f.result() for f in dot_futures]
for j, dot_val in enumerate(dot_vals):
is_cut = precomputed[j]["cut"]
final_flow_list.append((dot_val, is_cut, frame_indices[j]))
if progress_callback:
prog = min(100, int(100 * (chunk_start + len(chunk)) / len(indices)))
progress_callback(prog)
# Piecewise Integration
if not final_flow_list:
log_func("ERROR: No flow data computed - video processing failed completely")
return True
cum_flow = [0]
time_stamps = [final_flow_list[0][2]]
for i in range(1, len(final_flow_list)):
flow_prev, cut_prev, t_prev = final_flow_list[i - 1]
flow_curr, cut_curr, t_curr = final_flow_list[i]
if cut_curr:
cum_flow.append(0)
else:
mid_flow = (flow_prev + flow_curr) / 2
cum_flow.append(cum_flow[-1] + mid_flow)
time_stamps.append(t_curr)
cum_flow = [(cum_flow[i] + cum_flow[i-1]) / 2 if i > 0 else cum_flow[i] for i in range(len(cum_flow))]
# Detrending & Normalization
detrend_win = int(params["detrend_window"] * effective_fps)
disc_threshold = 1000
detrended_data = np.zeros_like(cum_flow)
weight_sum = np.zeros_like(cum_flow)
disc_indices = np.where(np.abs(np.diff(cum_flow)) > disc_threshold)[0] + 1
segment_boundaries = [0] + list(disc_indices) + [len(cum_flow)]
overlap = detrend_win // 2
for i in range(len(segment_boundaries) - 1):
seg_start = segment_boundaries[i]
seg_end = segment_boundaries[i + 1]
seg_length = seg_end - seg_start
if seg_length < 5:
detrended_data[seg_start:seg_end] = cum_flow[seg_start:seg_end] - np.mean(cum_flow[seg_start:seg_end])
continue
if seg_length <= detrend_win:
segment = cum_flow[seg_start:seg_end]
x = np.arange(len(segment))
trend = np.polyfit(x, segment, 1)
detrended_segment = segment - np.polyval(trend, x)
weights = np.hanning(len(segment))
detrended_data[seg_start:seg_end] += detrended_segment * weights
weight_sum[seg_start:seg_end] += weights
else:
for start in range(seg_start, seg_end - overlap, overlap):
end = min(start + detrend_win, seg_end)
segment = cum_flow[start:end]
x = np.arange(len(segment))
trend = np.polyfit(x, segment, 1)
detrended_segment = segment - np.polyval(trend, x)
weights = np.hanning(len(segment))
detrended_data[start:end] += detrended_segment * weights
weight_sum[start:end] += weights
detrended_data /= np.maximum(weight_sum, 1e-6)
smoothed_data = np.convolve(detrended_data, [1/16, 1/4, 3/8, 1/4, 1/16], mode='same')
norm_win = int(params["norm_window"] * effective_fps)
if norm_win % 2 == 0:
norm_win += 1
half_norm = norm_win // 2
norm_rolling = np.empty_like(smoothed_data)
for i in range(len(smoothed_data)):
start_idx = max(0, i - half_norm)
end_idx = min(len(smoothed_data), i + half_norm + 1)
local_window = smoothed_data[start_idx:end_idx]
local_min = local_window.min()
local_max = local_window.max()
if local_max - local_min == 0:
norm_rolling[i] = 50
else:
norm_rolling[i] = (smoothed_data[i] - local_min) / (local_max - local_min) * 100
# Keyframe Reduction
if params.get("keyframe_reduction", True):
key_indices = [0]
for i in range(1, len(norm_rolling) - 1):
d1 = norm_rolling[i] - norm_rolling[i - 1]
d2 = norm_rolling[i + 1] - norm_rolling[i]
if (d1 < 0) != (d2 < 0):
key_indices.append(i)
key_indices.append(len(norm_rolling) - 1)
else:
key_indices = range(len(norm_rolling))
actions = []
for ki in key_indices:
try:
timestamp_ms = int(((time_stamps[ki]) / fps) * 1000)
pos = int(round(norm_rolling[ki]))
actions.append({"at": timestamp_ms, "pos": 100 - pos})
except Exception as e:
log_func(f"Error computing action at segment index {ki}: {e}")
error_occurred = True
log_func(f"Keyframe reduction: {len(actions)} actions computed.")
funscript = {"version": "1.0", "actions": actions}
try:
with open(output_path, "w") as f:
json.dump(funscript, f, indent=2)
log_func(f"Funscript saved: {output_path}")
except Exception as e:
log_func(f"ERROR: Could not write output: {e}")
error_occurred = True
# Generate multi-axis funscripts if enabled
if params.get("multi_axis", False) and actions:
log_func("Generating multi-axis funscripts...")
multi_gen = MultiAxisGenerator(params)
secondary_axes = multi_gen.generate_all_axes(actions, fps, log_func)
for axis_name, axis_actions in secondary_axes.items():
multi_gen.save_axis_funscript(base, axis_name, axis_actions, log_func)
log_func(f"Multi-axis generation complete: {len(secondary_axes)} additional axes created.")
return error_occurred
# ----------------- StashApp Integration -----------------
def initialize_stash(connection: Dict[str, Any]) -> None:
"""Initialize the StashApp interface."""
global stash
stash = StashInterface(connection)
def get_scenes_with_tag(tag_name: str) -> List[Dict[str, Any]]:
"""Get all scenes that have a specific tag."""
tag = stash.find_tag(tag_name, create=False)
if not tag:
log.warning(f"Tag '{tag_name}' not found")
return []
# Use fragment to limit fields and avoid fingerprint errors
# Only fetch id, files.path, and tags.id/name - avoiding problematic fragments
scenes = stash.find_scenes(
f={"tags": {"value": [tag["id"]], "modifier": "INCLUDES"}},
filter={"per_page": -1},
fragment="id files { path } tags { id name }"
)
return scenes or []
def remove_tag_from_scene(scene_id: str, tag_name: str) -> None:
"""Remove a tag from a scene."""
tag = stash.find_tag(tag_name, create=False)
if not tag:
return
# Use fragment to limit fields and avoid fingerprint errors
scene = stash.find_scene(scene_id, fragment="id tags { id }")
if not scene:
return
current_tags = [t["id"] for t in scene.get("tags", [])]
if tag["id"] in current_tags:
current_tags.remove(tag["id"])
stash.update_scene({"id": scene_id, "tag_ids": current_tags})
def add_tag_to_scene(scene_id: str, tag_name: str) -> None:
"""Add a tag to a scene."""
tag = stash.find_tag(tag_name, create=True)
if not tag:
return
# Use fragment to limit fields and avoid fingerprint errors
scene = stash.find_scene(scene_id, fragment="id tags { id }")
if not scene:
return
current_tags = [t["id"] for t in scene.get("tags", [])]
if tag["id"] not in current_tags:
current_tags.append(tag["id"])
stash.update_scene({"id": scene_id, "tag_ids": current_tags})
def is_vr_scene(scene: Dict[str, Any]) -> bool:
"""Check if a scene is tagged as VR."""
tags = scene.get("tags", [])
vr_tag_names = config.vr_tag_names if hasattr(config, 'vr_tag_names') else ["VR", "Virtual Reality"]
for tag in tags:
if tag.get("name", "").lower() in [t.lower() for t in vr_tag_names]:
return True
return False
def add_scene_marker(scene_id: str, title: str, seconds: float, tag_name: Optional[str] = None) -> None:
"""Add a marker to a scene."""
marker_data = {
"scene_id": scene_id,
"title": title,
"seconds": seconds,
}
if tag_name:
tag = stash.find_tag(tag_name, create=True)
if tag:
marker_data["primary_tag_id"] = tag["id"]
stash.create_scene_marker(marker_data)
def get_scene_file_path(scene: Dict[str, Any]) -> Optional[str]:
"""Get the file path for a scene."""
files = scene.get("files", [])
if files:
return files[0].get("path")
return None
# ----------------- Settings Helper -----------------
def get_plugin_setting(key: str, default: Any = None) -> Any:
"""Get a plugin setting from StashApp, falling back to config file."""
try:
settings = stash.get_configuration().get("plugins", {}).get("funscript_haven", {})
if key in settings and settings[key] is not None:
return settings[key]
except Exception:
pass
# Fall back to config file
return getattr(config, key, default)
def get_trigger_tag() -> str:
"""Get the trigger tag name."""
return get_plugin_setting("trigger_tag", "FunscriptHaven_Process")
def get_complete_tag() -> Optional[str]:
"""Get the completion tag name."""
tag = get_plugin_setting("complete_tag", "FunscriptHaven_Complete")
return tag if tag else None
def get_error_tag() -> str:
"""Get the error tag name."""
return get_plugin_setting("error_tag", "FunscriptHaven_Error")
# ----------------- Task Functions -----------------
def process_tagged_scenes() -> None:
"""Process all scenes tagged with the trigger tag."""
global total_tasks, completed_tasks
trigger_tag = get_trigger_tag()
scenes = get_scenes_with_tag(trigger_tag)
if not scenes:
log.info(f"No scenes found with tag '{trigger_tag}'")
return
total_tasks = len(scenes)
completed_tasks = 0
log.info(f"Found {total_tasks} scenes to process")
log.progress(0.0)
for scene in scenes:
scene_id = scene["id"]
video_path = get_scene_file_path(scene)
if not video_path:
log.error(f"No file path for scene {scene_id}")
completed_tasks += 1
continue
if not os.path.exists(video_path):
log.error(f"Video file not found: {video_path}")
completed_tasks += 1
continue
# Build processing parameters from plugin settings (with config file fallback)
# Convert 0-10 integer settings to their actual decimal values
detrend_window_raw = get_plugin_setting('detrend_window', 2) # Default: 2 (was 1.5)
norm_window_raw = get_plugin_setting('norm_window', 4) # Default: 4 (was 4.0)
multi_axis_intensity_raw = get_plugin_setting('multi_axis_intensity', 5) # Default: 5 (was 0.5, scale 0-10)
random_speed_raw = get_plugin_setting('random_speed', 3) # Default: 3 (was 0.3, scale 0-10)
auto_home_delay_raw = get_plugin_setting('auto_home_delay', 1) # Default: 1 (was 1.0)
auto_home_duration_raw = get_plugin_setting('auto_home_duration', 1) # Default: 1 (was 0.5, rounded)
params = {
"threads": int(get_plugin_setting('threads', os.cpu_count() or 4)),
"detrend_window": float(max(1, min(10, int(detrend_window_raw)))), # Clamp to 1-10 seconds
"norm_window": float(max(1, min(10, int(norm_window_raw)))), # Clamp to 1-10 seconds
"batch_size": int(get_plugin_setting('batch_size', 3000)),
"overwrite": bool(get_plugin_setting('overwrite', False)),
"keyframe_reduction": bool(get_plugin_setting('keyframe_reduction', True)),
"vr_mode": is_vr_scene(scene),
"pov_mode": bool(get_plugin_setting('pov_mode', False)),
"balance_global": bool(get_plugin_setting('balance_global', True)),
"multi_axis": bool(get_plugin_setting('multi_axis', False)),
"multi_axis_intensity": float(max(0, min(10, int(multi_axis_intensity_raw))) / 10.0), # Convert 0-10 to 0.0-1.0
"random_speed": float(max(0, min(10, int(random_speed_raw))) / 10.0), # Convert 0-10 to 0.0-1.0
"auto_home_delay": float(max(0, min(10, int(auto_home_delay_raw)))), # Clamp to 0-10 seconds
"auto_home_duration": float(max(0, min(10, int(auto_home_duration_raw)))), # Clamp to 0-10 seconds
"smart_limit": bool(get_plugin_setting('smart_limit', True)),
}
log.info(f"Processing scene {scene_id}: {video_path}")
def progress_cb(prog: int) -> None:
scene_progress = prog / 100.0
overall_progress = (completed_tasks + scene_progress) / total_tasks
log.progress(overall_progress)
try:
error = process_video(
video_path,
params,
log.info,
progress_callback=progress_cb
)
if error:
log.error(f"Error processing scene {scene_id}")
add_tag_to_scene(scene_id, get_error_tag())
else:
log.info(f"Successfully processed scene {scene_id}")
# Add completion tag if configured
complete_tag = get_complete_tag()
if complete_tag:
add_tag_to_scene(scene_id, complete_tag)
# Add scene marker if configured
if get_plugin_setting('add_marker', True):
add_scene_marker(scene_id, "Funscript Generated", 0, "Funscript")
# Remove trigger tag
remove_tag_from_scene(scene_id, trigger_tag)
except Exception as e:
log.error(f"Exception processing scene {scene_id}: {e}")
add_tag_to_scene(scene_id, get_error_tag())
completed_tasks += 1
log.progress(completed_tasks / total_tasks)
log.info(f"Completed processing {total_tasks} scenes")
log.progress(1.0)
# ----------------- Main Execution -----------------
def main() -> None:
"""Main entry point for the plugin."""
json_input = read_json_input()
output = {}
run(json_input, output)
out = json.dumps(output)
print(out + "\n")
def read_json_input() -> Dict[str, Any]:
"""Read JSON input from stdin."""
json_input = sys.stdin.read()
return json.loads(json_input)
def run(json_input: Dict[str, Any], output: Dict[str, Any]) -> None:
"""Main execution logic."""
plugin_args = None
try:
log.debug(json_input["server_connection"])
os.chdir(json_input["server_connection"]["PluginDir"])
initialize_stash(json_input["server_connection"])
except Exception as e:
log.error(f"Failed to initialize: {e}")
output["output"] = "error"
return
try:
plugin_args = json_input['args'].get("mode")
except (KeyError, TypeError):
pass
if plugin_args == "process_scenes":
process_tagged_scenes()
output["output"] = "ok"
return
# Default action: process tagged scenes
process_tagged_scenes()
output["output"] = "ok"
return
if __name__ == "__main__":
try:
main()
except KeyboardInterrupt:
log.info("Plugin interrupted by user")
except Exception as e:
log.error(f"Plugin failed: {e}")
sys.exit(1)
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