scofano
454eea334f
Replace the bundled GPLv3 license text with AGPLv3 to better reflect network-use distribution terms, and add codescene_analysis to .gitignore so generated analysis artifacts are not committed.chore: switch project license to AGPL and ignore analysis output Replace the bundled GPLv3 license text with AGPLv3 to better reflect network-use distribution terms, and add codescene_analysis to .gitignore so generated analysis artifacts are not committed.
428 lines
14 KiB
Python
428 lines
14 KiB
Python
import os
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import shutil
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import subprocess
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import tempfile
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import math
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from pathlib import Path
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import torch
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import torch.nn.functional as F
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try:
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from comfy.utils import ProgressBar
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except ImportError:
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class ProgressBar:
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def __init__(self, total): self.total = total
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def update(self, value): pass
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# ComfyUI folder_paths
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try:
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import folder_paths
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OUTPUT_DIR = folder_paths.get_output_directory()
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except ImportError:
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OUTPUT_DIR = Path("output")
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FFMPEG = shutil.which("ffmpeg")
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FFPROBE = shutil.which("ffprobe")
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if not FFMPEG or not FFPROBE:
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raise RuntimeError("ffmpeg and ffprobe not found on PATH.")
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# ---------- EASING ------------------------------------------------------------
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def ease_value(t: float, mode: str) -> float:
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key = mode.strip().upper().replace("-", "_")
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if key == "LINEAR":
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return t
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elif key == "EASE_IN":
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return t * t * t
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elif key == "EASE_OUT":
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u = 1.0 - t
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return 1.0 - u * u * u
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elif key == "EASE_IN_OUT":
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return t * t * (3 - 2 * t) # smoothstep
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return t # fallback
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def normalize_mode(mode: str) -> str:
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"""
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Interpret UI mode string as one of our internal shake modes.
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"""
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m = mode.strip().lower()
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if "circle" in m:
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return "CIRCULAR"
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if "random" in m:
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return "RANDOM"
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# Default to circular if unclear
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return "CIRCULAR"
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def get_video_info(video_path):
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"""Get video duration, fps, width, height"""
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cmd = [
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FFPROBE,
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"-v", "quiet",
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"-print_format", "json",
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"-show_format",
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"-show_streams",
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video_path
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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import json
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data = json.loads(result.stdout)
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duration = float(data['format']['duration'])
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# Find video stream
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video_stream = None
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for stream in data['streams']:
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if stream['codec_type'] == 'video':
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video_stream = stream
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break
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if video_stream:
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fps_str = video_stream.get('r_frame_rate', '30/1')
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if '/' in fps_str:
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num, den = fps_str.split('/')
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fps = float(num) / float(den)
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else:
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fps = float(fps_str)
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width = int(video_stream['width'])
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height = int(video_stream['height'])
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else:
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fps = 30.0
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width = 1920
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height = 1080
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return duration, fps, width, height
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def extract_frames(video_path, output_dir, fps):
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"""Extract frames from video"""
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frames_dir = output_dir / "frames"
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frames_dir.mkdir(exist_ok=True)
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cmd = [
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FFMPEG,
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"-i", video_path,
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"-vf", f"fps={fps}",
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str(frames_dir / "frame_%06d.png")
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]
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subprocess.run(cmd, check=True)
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return frames_dir
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def load_frames(frames_dir):
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"""Load frames as torch tensor"""
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from PIL import Image
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import numpy as np
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frame_files = sorted(frames_dir.glob("frame_*.png"))
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if not frame_files:
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raise ValueError("No frames extracted")
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frames = []
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for frame_file in frame_files:
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img = Image.open(frame_file).convert("RGB")
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arr = np.array(img).astype(np.float32) / 255.0
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frames.append(torch.from_numpy(arr))
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if not frames:
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raise ValueError("No frames loaded")
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# Stack to (B, H, W, C)
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images = torch.stack(frames, dim=0)
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return images
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# ---------- NODE --------------------------------------------------------------
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class CameraShakeVideoNode:
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"""
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Camera shake / procedural handheld motion for a video file.
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Takes a video file path and applies shake effects, then outputs a new video
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with the same audio (if present).
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Supports circular or random shake patterns with configurable intensity and easing.
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When loop is enabled, uses circular shake with constant radius for seamless looping.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"video_path": ("STRING", {"default": ""}),
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"mode": (["Circular Shake", "Random Shake"], {"default": "Circular Shake"}),
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"pixels_per_frame": ("FLOAT", {"default": 5.0, "min": 0.0, "step": 0.1}),
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"ease": (["Linear", "Ease_In", "Ease_Out", "Ease_In_Out"], {"default": "Linear"}),
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"loop": ("BOOLEAN", {"default": False}),
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"prefix": ("STRING", {"default": "camera_shake"}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("output_path",)
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FUNCTION = "apply_camera_shake"
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CATEGORY = "Simple Video Effects/Video Processing"
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OUTPUT_NODE = True
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# ---- helpers -------------------------------------------------------------
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def _resize_to(self, frame_hwc: torch.Tensor, size_hw):
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H, W = size_hw
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x = frame_hwc.permute(2, 0, 1).unsqueeze(0) # 1,C,H,W
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x = F.interpolate(x, size=(H, W), mode="bilinear", align_corners=False)
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return x.squeeze(0).permute(1, 2, 0)
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def _aspect_corrected_crop_box(self, W: int, H: int, m_small_int: int):
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"""
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Given an integer scalar margin on the smaller dimension (per-side),
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compute integer crop box (x0,y0,x1,y1) that:
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- preserves aspect (within ±1 px due to rounding),
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- never exceeds bounds.
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"""
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aspect = W / H if H > 0 else 1.0
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# Which side is smaller?
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small = min(W, H)
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if small <= 2:
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return 0, 0, W, H, 0, 0
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# Safe guard: ensure at least 1 px interior
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max_small_margin = max(0, (small // 2) - 1)
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m_small = max(0, min(int(m_small_int), max_small_margin))
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# Fractional level based on smaller half-size
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half_small = small / 2.0
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frac = 0.0 if half_small <= 0 else (m_small / half_small) # 0..~1
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# Proportional margins per axis to keep aspect (before integer correction)
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m_x = int(round(frac * (W / 2.0)))
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m_y = int(round(frac * (H / 2.0)))
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# Compute crop size
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cw = W - 2 * m_x
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ch = H - 2 * m_y
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# Enforce minimum interior
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cw = max(1, cw)
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ch = max(1, ch)
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# ---- Aspect correction (integer) ------------------------------------
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# target: cw / ch == W / H => cw == round(ch * aspect)
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ideal_cw = int(round(ch * aspect))
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ideal_cw = max(1, min(ideal_cw, W - 2)) # keep some border to be safe in edge cases
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if ideal_cw != cw:
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# Recompute m_x from ideal_cw, keep m_y if possible
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cw = ideal_cw
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m_x = (W - cw) // 2
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# ensure symmetric crop; parity fix if needed
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if W - 2 * m_x != cw:
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m_x2 = max(0, min(m_x + 1, (W - 1) // 2))
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if W - 2 * m_x2 == cw:
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m_x = m_x2
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else:
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# else recompute ch from cw to restore symmetry
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ch = max(1, int(round(cw / aspect)))
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m_y = (H - ch) // 2
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# Final clamp to safe range
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m_x = max(0, min(m_x, (W - 1) // 2))
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m_y = max(0, min(m_y, (H - 1) // 2))
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x0, y0 = m_x, m_y
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x1, y1 = W - m_x, H - m_y
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return x0, y0, x1, y1, m_x, m_y
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# ---- main ---------------------------------------------------------------
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def apply_camera_shake(self,
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video_path: str,
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mode: str,
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pixels_per_frame: float,
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ease: str,
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loop: bool,
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prefix: str):
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if not os.path.exists(video_path):
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raise ValueError(f"Video file not found: {video_path}")
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# Get video info
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duration, fps, width, height = get_video_info(video_path)
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# Create output folder
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output_dir = Path(OUTPUT_DIR)
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output_dir.mkdir(parents=True, exist_ok=True)
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counter = 1
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while True:
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filename = f"{prefix}_{counter:03d}.mp4"
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output_path = output_dir / filename
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if not output_path.exists():
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break
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counter += 1
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pbar = ProgressBar(3)
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with tempfile.TemporaryDirectory() as tmpdir:
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tmpdir = Path(tmpdir)
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# Extract frames
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frames_dir = extract_frames(video_path, tmpdir, fps)
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images = load_frames(frames_dir)
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pbar.update(1)
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# Apply camera shake (same logic as image version)
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shaken_images, info = self._apply_shake(images, mode, pixels_per_frame, ease, loop)
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# Save processed frames
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processed_frames_dir = tmpdir / "processed_frames"
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processed_frames_dir.mkdir()
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for i, img_tensor in enumerate(shaken_images):
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# Convert to PIL
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arr = (img_tensor.clamp(0, 1) * 255).byte().numpy()
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from PIL import Image
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img = Image.fromarray(arr)
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img.save(processed_frames_dir / f"frame_{i:06d}.png")
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pbar.update(1)
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# Encode to video with audio
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cmd = [
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FFMPEG,
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"-y",
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"-framerate", str(fps),
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"-i", str(processed_frames_dir / "frame_%06d.png"),
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"-i", video_path, # for audio
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"-c:v", "libx264",
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"-c:a", "copy", # copy audio if exists
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"-pix_fmt", "yuv420p",
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"-map", "0:v:0", # video from frames
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"-map", "1:a?", # audio from original (optional)
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str(output_path)
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]
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subprocess.run(cmd, check=True)
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pbar.update(1)
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return (str(output_path),)
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def _apply_shake(self, images, mode, pixels_per_frame, ease, loop):
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"""Apply camera shake to images (copied from CameraShakeNode)"""
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if images.ndim != 4:
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return (images, "Input is not a batched IMAGE (B,H,W,C).")
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B, H, W, C = images.shape
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if B <= 0:
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return (images, "Empty batch; nothing to do.")
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# Normalize
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shake_mode = normalize_mode(mode)
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if loop:
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shake_mode = "CIRCULAR"
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i_ease = ease
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# Compute scalar radius on the smaller dimension (per-side)
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small = min(W, H)
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max_safe_small_margin = max(0, (small // 2) - 1)
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requested_radius_small_f = max(0.0, float(pixels_per_frame))
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radius_small_f = min(requested_radius_small_f, float(max_safe_small_margin))
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# Convert to integer margin and compute the "safe inner canvas"
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m_small_int = int(round(radius_small_f))
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# This gives us a central crop with margins m_x, m_y
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x0c, y0c, x1c, y1c, m_x, m_y = self._aspect_corrected_crop_box(W, H, m_small_int)
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cw = x1c - x0c
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ch = y1c - y0c
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if cw <= 0 or ch <= 0:
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return (images, "Invalid crop; probably too large shake vs resolution.")
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# Eased progress 0..1 across frames
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ts = [0.0] if B == 1 else [i / (B - 1) for i in range(B)]
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es = [ease_value(t, i_ease) for t in ts]
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out_frames = []
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# How many circles across the sequence for circular shake
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num_cycles = 1.0 if loop else 2.0
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for i in range(B):
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t = ts[i]
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# Envelope (0..1) over time for radius
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radius_factor = 1.0 if loop else (es[i] if B > 1 else 1.0)
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# Per-axis max amplitude for this frame
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ax = m_x * radius_factor
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ay = m_y * radius_factor
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if shake_mode == "CIRCULAR":
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# Smooth sinusoidal circular path (like smooth_camera_shake, no jitter)
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# num_cycles = how many full circles over the whole sequence
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angle = 2.0 * math.pi * num_cycles * t
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dx_f = ax * math.sin(angle)
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dy_f = ay * math.sin(angle + math.pi * 0.5)
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elif shake_mode == "RANDOM":
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# Constant-magnitude random shake.
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# If pixels_per_frame = 1 on the small dimension, this results in
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# a 1-pixel step (left/right/up/down/diagonal) every frame,
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# and it does NOT ramp up over time.
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# Base step size from margins (already derived from requested radius)
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step_x = m_x if m_x > 0 else 0
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step_y = m_y if m_y > 0 else 0
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# Ensure we still get motion if margins are tiny but non-zero
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if step_x == 0 and m_x > 0:
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step_x = 1
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if step_y == 0 and m_y > 0:
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step_y = 1
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# Random direction in {-1, 0, +1} for each axis
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dir_x = int(torch.randint(-1, 2, (1,)).item())
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dir_y = int(torch.randint(-1, 2, (1,)).item())
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# Ignore the easing envelope for magnitude here – keep step size constant
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dx_f = dir_x * step_x
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dy_f = dir_y * step_y
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else:
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# Fallback: no movement
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dx_f = 0.0
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dy_f = 0.0
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# Clamp to safe integer offsets (so crop stays inside original)
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dx = int(round(max(-m_x, min(m_x, dx_f))))
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dy = int(round(max(-m_y, min(m_y, dy_f))))
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# Base central crop is at (m_x, m_y); we offset it by dx, dy
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x0 = m_x + dx
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y0 = m_y + dy
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x1 = x0 + cw
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y1 = y0 + ch
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# Extra safety clamp against rounding drift
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if x0 < 0:
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x0 = 0
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x1 = cw
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if y0 < 0:
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y0 = 0
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y1 = ch
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if x1 > W:
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x1 = W
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x0 = W - cw
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if y1 > H:
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y1 = H
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y0 = H - ch
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cropped = images[i, y0:y1, x0:x1, :]
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resized = self._resize_to(cropped, (H, W))
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out_frames.append(resized)
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out = torch.stack(out_frames, dim=0)
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info_lines = []
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info_lines.append(f"Frames: {B}, Canvas: {W}x{H}")
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info_lines.append(f"Mode: {shake_mode}, Ease: {ease}, Loop: {loop}")
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info_lines.append(f"Requested small-dim radius: {requested_radius_small_f:.2f} px")
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info_lines.append(f"Applied small-dim radius: {radius_small_f:.2f} px (safe limit: {max_safe_small_margin} px)")
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info_lines.append(f"Per-axis margins (max shake): m_x={m_x}, m_y={m_y}")
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info_lines.append("Note: frames are cropped with a safe border then resized back to avoid black edges during shake.")
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info = "\n".join(info_lines)
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return (out, info)
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