scofano
dc9cf8639b
- Moved all script modules into a new `scripts/` folder - Updated import statements in `__init__.py` to reference the new folder - Updated README installation instructions to reflect the new structure for better codebase organization
349 lines
12 KiB
Python
349 lines
12 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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from pathlib import Path
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import torch
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import torch.nn.functional as F
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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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m = mode.strip().lower()
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if m in ("zoom in", "in"):
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return "IN"
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if m in ("zoom out", "out"):
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return "OUT"
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return "IN"
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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 ZoomSequenceVideoNode:
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"""
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Zooms IN or OUT across a video file. The canvas size stays fixed and
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the original aspect ratio is preserved for every frame.
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Takes a video file path and applies zoom effects, then outputs a new video
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with the same audio (if present).
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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": (["Zoom In", "Zoom Out"], {"default": "Zoom In"}),
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"pixels_per_frame": ("FLOAT", {"default": 1.0, "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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"prefix": ("STRING", {"default": "zoom_sequence"}),
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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_zoom_sequence"
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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 zoom 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_zoom_sequence(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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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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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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# Apply zoom sequence (same logic as image version)
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zoomed_images, info = self._apply_zoom(images, mode, pixels_per_frame, ease)
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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(zoomed_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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# 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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return (str(output_path),)
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def _apply_zoom(self, images, mode, pixels_per_frame, ease):
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"""Apply zoom sequence to images (copied from ZoomSequenceNode)"""
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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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i_mode = normalize_mode(mode)
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i_ease = ease
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# Compute max scalar margin 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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# keep as float (allows fractional speed), clamp later when actually cropping
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requested_small_margin_max_f = pixels_per_frame * max(0, B - 1)
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small_margin_max_f = min(requested_small_margin_max_f, float(max_safe_small_margin))
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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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# Per-frame scalar margins (float, on the smaller dimension)
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if i_mode == "IN":
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m_smalls_f = [small_margin_max_f * e for e in es]
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else: # OUT
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m_smalls_f = [small_margin_max_f * (1.0 - e) for e in es]
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# Apply proportional, aspect-corrected crops
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out_frames = []
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clamped = False
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used_mx = []
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used_my = []
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for i in range(B):
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# Round to integer ONLY where slicing happens
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m_small_int = int(round(m_smalls_f[i]))
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# Detect clamping relative to theoretical safe limit
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if m_small_int != max(0, min(m_small_int, (small // 2) - 1)):
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clamped = True
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x0, y0, x1, y1, m_x, m_y = self._aspect_corrected_crop_box(W, H, m_small_int)
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if x1 <= x0 or y1 <= y0:
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out_frames.append(images[i])
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continue
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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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used_mx.append(m_x)
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used_my.append(m_y)
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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}, Mode: {mode}, Ease: {ease}")
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info_lines.append(f"Requested small-dim max margin: {requested_small_margin_max_f:.2f} px")
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info_lines.append(f"Applied small-dim max margin: {small_margin_max_f:.2f} px (safe limit: {max_safe_small_margin} px)")
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info_lines.append("Note: margins are proportional per axis to preserve aspect; integer crop indices are used.")
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if clamped:
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info_lines.append("Warning: requested margins exceeded safe bounds and were clamped.")
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info = "\n".join(info_lines)
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return (out, info)
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