Added all functionality to cut_to_length
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Hannes committed
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*/
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+86
-66
@@ -1,16 +1,66 @@
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#!/usr/bin/env python3
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import subprocess
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import json
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import sys
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import shutil
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from pathlib import Path
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import cv2
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import numpy as np
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from scipy import signal
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def run(cmd):
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return subprocess.run(cmd, capture_output=True, text=True, check=True)
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"""Run a subprocess command and raise on error."""
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subprocess.run(cmd, check=True)
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def get_video_signal(path):
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cap = cv2.VideoCapture(path)
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motion_signal = []
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prev_gray = None
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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# 1. Shrink the frame (makes it faster and removes noise)
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small_frame = cv2.resize(frame, (160, 90))
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gray = cv2.cvtColor(small_frame, cv2.COLOR_BGR2GRAY)
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if prev_gray is not None:
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# 2. Calculate the difference between this frame and the last
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# This detects the "movement" or "cuts"
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diff = cv2.absdiff(gray, prev_gray)
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motion_signal.append(np.sum(diff))
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else:
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motion_signal.append(0)
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prev_gray = gray
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cap.release()
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return np.array(motion_signal)
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def find_offset(video_a, video_b):
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# print("Extracting signals (this may take a moment)...")
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sig_a = get_video_signal(video_a)
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# print("sig_a finished")
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sig_b = get_video_signal(video_b)
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# print("sig_b finished")
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# Normalize signals (subtract mean, divide by std dev) to improve correlation
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sig_a = (sig_a - np.mean(sig_a)) / (np.std(sig_a) + 1e-5)
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sig_b = (sig_b - np.mean(sig_b)) / (np.std(sig_b) + 1e-5)
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# Perform Cross-Correlation
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correlation = signal.correlate(sig_a, sig_b, mode='full')
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lags = signal.correlation_lags(len(sig_a), len(sig_b), mode='full')
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# plt.plot(lags)
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# The peak of the correlation is our offset in frames
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best_lag = lags[np.argmax(correlation)]
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return best_lag
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def probe_video(path):
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"""Get video FPS, duration, codec, and stream start time."""
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import json
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cmd = [
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"ffprobe",
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"-v", "error",
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@@ -19,11 +69,9 @@ def probe_video(path):
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"-show_format",
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path
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]
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data = json.loads(run(cmd).stdout)
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data = json.loads(subprocess.run(cmd, capture_output=True, text=True, check=True).stdout)
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duration = float(data["format"]["duration"])
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for s in data["streams"]:
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if s["codec_type"] == "video":
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num, den = s["r_frame_rate"].split("/")
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@@ -34,95 +82,67 @@ def probe_video(path):
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raise RuntimeError("No video stream found")
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def vaapi_cut(inp, outp, start, end, codec="h264_vaapi"):
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encoder = codec
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cmd = [
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def trim_audio(input_mkv, start_time, end_time, out_audio):
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"""Trim only the first (and only) audio track from input_mkv."""
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run([
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"ffmpeg",
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"-y",
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"-vaapi_device", "/dev/dri/renderD128",
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"-ss", str(start),
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"-to", str(end),
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"-i", inp,
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"-map", "0",
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"-vf", "format=nv12,hwupload",
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"-c:v", encoder,
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"-qp", "20",
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"-c:a", "copy",
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"-c:s", "copy",
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"-c:d", "copy",
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"-c:t", "copy",
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outp
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]
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subprocess.run(cmd, check=True)
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def extract_audio(output_mkv, out_audio):
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# Extract the first (and only) audio track
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run([
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"mkvextract",
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"tracks",
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output_mkv,
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f"1:{out_audio}" # track IDs are usually 0 for video, 1 for audio
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"-i", input_mkv,
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"-ss", str(start_time),
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"-to", str(end_time),
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"-map", "0:a:0", # first audio track
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"-c:a", "copy", # no re-encode
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out_audio
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])
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def mux_audio_into_copy(inp2, audio_file, output_dir):
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def mux_audio_into_output(inp2, audio_file, output_dir):
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"""Mux trimmed audio into inp2 and place final file in output_dir."""
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output_dir = Path(output_dir)
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output_dir.mkdir(exist_ok=True)
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copied_video = output_dir / Path(inp2).name
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shutil.copy2(inp2, copied_video)
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final_output = output_dir / "final_with_audio.mkv"
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final_output = output_dir / f"{Path(inp2).stem}_ger.mkv"
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run([
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"mkvmerge",
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"-o", str(final_output),
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str(copied_video),
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str(inp2),
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"--language", "0:deu", # assign German language
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"--track-name", "0:German (2.0 aac)",
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str(audio_file)
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])
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return final_output
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def main():
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inp = f"/home/honney/projects/mkv/AnoHana The Flower We Saw That Day (2011-2011) [imdbid-tt1913273]/Season 01/AnoHana The Flower We Saw That Day S01E001.mkv"
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inp2 = f"/home/honney/projects/mkv/Anohana - The Flower We Saw That Day - S01 (BD 1080p HEVC Opus) [derpie]/[derpie] Anohana - The Flower We Saw That Day - S01E01 (BD 1080p HEVC Opus) [Dual Audio].mkv"
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outp = "./output.mkv"
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n1 = 24
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for i in range(1, 12):
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# --- Inputs ---
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inp = f"/home/honney/projects/mkv/AnoHana The Flower We Saw That Day (2011-2011) [imdbid-tt1913273]/Season 01/AnoHana The Flower We Saw That Day S01E0{i:02}.mkv"
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inp2 = f"/home/honney/projects/mkv/Anohana - The Flower We Saw That Day/S01/AnoHana The Flower We Saw That Day S01E0{i:02}.mkv"
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n1 = find_offset(inp, inp2) # first frame to keep
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output_dir = "./output_dir"
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# --- Probe video ---
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fps, _, _, vstart = probe_video(inp)
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_, duration, _, _ = probe_video(inp2)
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_, duration2, _, _ = probe_video(inp2)
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# --- Compute timestamps ---
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start = vstart + (n1 / fps)
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end = duration + start
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end = start + duration2
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if end <= start:
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raise ValueError("n2 must be greater than n1")
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print(f"fps: {fps}")
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print(f"cutting from frame {n1} to {end*fps}")
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print(f"time range: {start:.6f}s -> {end:.6f}s")
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print(f"Cutting audio from frame {n1} (timestamp {start:.6f}s) to timestamp {end:.6f}s")
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print("Trying stream copy...")
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vaapi_cut(inp, outp, start, end)
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# --- Trim only audio ---
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audio_file = Path(output_dir) / f"{Path(inp2).stem}_german_audio.aac"
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trim_audio(inp, start, end, audio_file)
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output_dir = "./output_dir"
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audio_file = Path(output_dir) / "german_audio.aac"
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# --- Mux trimmed audio ---
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final_file = mux_audio_into_output(inp2, audio_file, output_dir)
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extract_audio(outp, audio_file)
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final_file = mux_audio_into_copy(inp2, audio_file, output_dir)
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print(f"Final file created at: {final_file}")
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print(f"Final file with trimmed audio created at: {final_file}")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,72 @@
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import cv2
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import numpy as np
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def create_comparison_collage(video_a_path, video_b_path, time_a, offset_b, radius=1, output_name="collage.jpg"):
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caps = {
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"A": cv2.VideoCapture(video_a_path),
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"B": cv2.VideoCapture(video_b_path)
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}
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results = {"A": [], "B": []}
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for label, cap in caps.items():
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if not cap.isOpened():
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print(f"Error: Could not open Video {label}")
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return
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fps = cap.get(cv2.CAP_PROP_FPS)
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# Determine base time for this specific video
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base_time = time_a if label == "A" else (time_a + offset_b)
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center_frame = int(round(base_time * fps))
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# Extract range: from (center - radius) to (center + radius)
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for i in range(-radius, radius + 1):
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target_idx = center_frame + i
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cap.set(cv2.CAP_PROP_POS_FRAMES, target_idx)
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success, frame = cap.read()
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if success:
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# Add overlay text (Timestamp and Frame Index)
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current_time = target_idx / fps
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text = f"Vid {label} | F: {target_idx} | {current_time:.4f}s"
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# Draw black background for text for readability
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cv2.putText(frame, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,0,0), 4, cv2.LINE_AA)
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cv2.putText(frame, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,255,255), 2, cv2.LINE_AA)
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results[label].append(frame)
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else:
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# Placeholder if frame is missing
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blank = np.zeros((720, 1280, 3), dtype=np.uint8)
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cv2.putText(blank, "FRAME MISSING", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
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results[label].append(blank)
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cap.release()
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# Ensure all frames are the same size for the collage (using Video A's dimensions)
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h, w, _ = results["A"][0].shape
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rows = []
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for i in range(len(results["A"])):
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img_a = cv2.resize(results["A"][i], (w, h))
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img_b = cv2.resize(results["B"][i], (w, h))
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# Combine Left (A) and Right (B)
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combined_row = np.hstack((img_a, img_b))
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rows.append(combined_row)
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# Combine all rows Top to Bottom
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final_collage = np.vstack(rows)
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# Save and show
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cv2.imwrite(output_name, final_collage)
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print(f"Collage saved as {output_name}")
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# --- SETTINGS ---
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video_a = "/home/honney/projects/mkv/Anohana - The Flower We Saw That Day - S01 (BD 1080p HEVC Opus) [derpie]/[derpie] Anohana - The Flower We Saw That Day - S01E02 (BD 1080p HEVC Opus) [Dual Audio].mkv"
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video_b = "/home/honney/projects/mkv/AnoHana The Flower We Saw That Day (2011-2011) [imdbid-tt1913273]/Season 01/AnoHana The Flower We Saw That Day S01E002.mkv"
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timestamp_a = 60 * 20#1/(24000/1001) * 250 # Center point for Video A 75
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offset_for_b = 1/(24000/1001) * 24
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frames_around = 10 # 2 before, 2 after (5 frames total per video)
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create_comparison_collage(video_a, video_b, timestamp_a, offset_for_b, frames_around)
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+14
-12
@@ -2,6 +2,7 @@ import cv2
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import numpy as np
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from scipy import signal
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import matplotlib.pyplot as plt
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import sys
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def get_video_signal(path):
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cap = cv2.VideoCapture(path)
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@@ -52,17 +53,18 @@ def find_offset(video_a, video_b):
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return best_lag
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# Usage
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# for i in range(1, 12):
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# # print(f"{i:02}")
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# video_a = f"/home/honney/projects/mkv/AnoHana The Flower We Saw That Day (2011-2011) [imdbid-tt1913273]/Season 01/AnoHana The Flower We Saw That Day S01E0{i:02}.mkv"
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# video_b = f"/home/honney/projects/mkv/Anohana - The Flower We Saw That Day - S01 (BD 1080p HEVC Opus) [derpie]/[derpie] Anohana - The Flower We Saw That Day - S01E{i:02} (BD 1080p HEVC Opus) [Dual Audio].mkv"
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for i in range(1, 12):
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# print(f"{i:02}")
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video_a = f"/home/honney/projects/mkv/AnoHana The Flower We Saw That Day (2011-2011) [imdbid-tt1913273]/Season 01/AnoHana The Flower We Saw That Day S01E0{i:02}.mkv"
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video_b = f"/home/honney/projects/mkv/Anohana - The Flower We Saw That Day/S01/AnoHana The Flower We Saw That Day S01E0{i:02}.mkv"
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offset_frames = find_offset(video_a, video_b)
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print(f"{i}: Video B is offset by {offset_frames} frames relative to Video A.")
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# sys.exit()
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# video_a = f"/home/honney/projects/mkv/output_dir/AnoHana The Flower We Saw That Day S01E001_ger.mkv"
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# video_b = f"/home/honney/projects/mkv/Anohana - The Flower We Saw That Day/S01/AnoHana The Flower We Saw That Day S01E001.mkv"
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# offset_frames = find_offset(video_a, video_b)
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# print(f"{i}: Video B is offset by {offset_frames} frames relative to Video A.")
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# print(f"{i:02}")
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video_a = f"/home/honney/projects/mkv/output.mkv"
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video_b = f"/home/honney/projects/mkv/Anohana - The Flower We Saw That Day - S01 (BD 1080p HEVC Opus) [derpie]/[derpie] Anohana - The Flower We Saw That Day - S01E01 (BD 1080p HEVC Opus) [Dual Audio].mkv"
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offset_frames = find_offset(video_a, video_b)
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print(f"Video B is offset by {offset_frames} frames relative to Video A.")
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# print(f"Video B is offset by {offset_frames} frames relative to Video A.")
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# sys.exit()
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+57
-66
@@ -1,104 +1,95 @@
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import subprocess
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import numpy as np
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import matplotlib.pyplot as plt
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import sys
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from scipy.interpolate import interp1d
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# --- Setup ---
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video_a = "/home/honney/projects/mkv/Saga of Tanya the Evil/Season 1/Saga Of Tanya The Evil - S01E01 - The Devil Of The Rhine.mkv"
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video_b = "/home/honney/projects/mkv/Saga of Tanya the Evil (2017-2017) [imdbid-tt6455986]/Season 01/Saga of Tanya the Evil S01E001.mkv"
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video_a = "/home/honney/projects/mkv/Anohana - The Flower We Saw That Day - S01 (BD 1080p HEVC Opus) [derpie]/[derpie] Anohana - The Flower We Saw That Day - S01E01 (BD 1080p HEVC Opus) [Dual Audio].mkv"
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video_b = "/home/honney/projects/mkv/AnoHana The Flower We Saw That Day (2011-2011) [imdbid-tt1913273]/Season 01/AnoHana The Flower We Saw That Day S01E001.mkv"
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FPS_NATIVE = 24000/1001
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WIDTH = 160
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HEIGHT = int((WIDTH/16)*9)
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FPS = 24000/1001
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MAX_OFFSET_SECONDS = 2 # ±2 Sekunden Suche
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MAX_OFFSET_SECONDS = 1
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# FAKTOR FÜR PRÄZISION:
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# 2 = Doppelte FPS für die Rechnung (Sub-Frame)
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# 10 = Zehnfache Präzision (reicht für Mikrosekunden-Sync)
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UPSAMPLE_FACTOR = 10
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FPS_VIRTUAL = FPS_NATIVE * UPSAMPLE_FACTOR
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def extract_hist_signal(video):
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"""Extrahiert pro Frame einen Histogrammvektor unter Nutzung von VA-API (AMD GPU)."""
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# Wir nutzen vaapi für Hardware-Dekodierung und Scaling
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proc = subprocess.Popen([
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"ffmpeg",
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"-loglevel", "error",
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"-hwaccel", "vaapi", # Nutze VA-API Hardware-Beschleunigung
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"-hwaccel_device", "/dev/dri/renderD128", # Pfad zur RX 7900 XTX
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"-hwaccel_output_format", "vaapi",
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"-i", video,
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"-vf", (
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f"fps={FPS}," # Framerate Anpassung
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f"scale_vaapi=w={WIDTH}:h={HEIGHT}:format=gray8," # GPU-Scaling + Grayscale
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"hwdownload,format=gray8" # Zurück in den RAM schieben
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),
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"-f", "rawvideo",
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"-"
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"ffmpeg", "-loglevel", "error", "-i", video,
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"-vf", f"scale={WIDTH}:{HEIGHT},format=gray",
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"-f", "rawvideo", "-"
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], stdout=subprocess.PIPE)
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frame_size = WIDTH * HEIGHT
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hist_signal = []
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# Der restliche Loop bleibt gleich, da die Histogramm-Berechnung
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# (np.histogram) weiterhin auf der CPU via NumPy erfolgt.
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while True:
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data = proc.stdout.read(frame_size)
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if not data or len(data) < frame_size:
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break
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if not data or len(data) < frame_size: break
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frame = np.frombuffer(data, dtype=np.uint8)
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hist, _ = np.histogram(frame, bins=32, range=(0, 255))
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hist_signal.append(hist / np.sum(hist))
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hist, _ = np.histogram(frame, bins=64, range=(0, 255))
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hist_signal.append(hist / (np.sum(hist) + 1e-6))
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proc.wait()
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return np.array(hist_signal)
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print(f"Extracting signals (Native FPS: {FPS_NATIVE:.4f})...")
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a_raw = extract_hist_signal(video_a)
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b_raw = extract_hist_signal(video_b)
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print("Extracting signal of video A")
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a = extract_hist_signal(video_a)
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# 1. In 1D-Signal umwandeln (Helligkeits-Schnitt)
|
||||
sig_a_raw = np.mean(a_raw, axis=1)
|
||||
sig_b_raw = np.mean(b_raw, axis=1)
|
||||
|
||||
print("Extracting signal of video B")
|
||||
b = extract_hist_signal(video_b)
|
||||
# 2. UPSAMPLING (Die "doppelte FPS" Logik)
|
||||
# Wir erstellen eine glatte Kurve durch die Frames und tasten sie feiner ab
|
||||
def upsample_signal(sig, factor):
|
||||
x = np.arange(len(sig))
|
||||
f = interp1d(x, sig, kind='cubic') # Kubische Interpolation für weiche Übergänge
|
||||
x_new = np.linspace(0, len(sig) - 1, len(sig) * factor)
|
||||
return f(x_new)
|
||||
|
||||
print("Calculating cross correlation")
|
||||
print(f"Upsampling signals by factor {UPSAMPLE_FACTOR}...")
|
||||
sig_a = upsample_signal(sig_a_raw, UPSAMPLE_FACTOR)
|
||||
sig_b = upsample_signal(sig_b_raw, UPSAMPLE_FACTOR)
|
||||
|
||||
max_lag_frames = int(MAX_OFFSET_SECONDS * FPS)
|
||||
n_a, n_bins = a.shape
|
||||
n_b, _ = b.shape
|
||||
# 3. Zentrieren für bessere Korrelation
|
||||
sig_a -= np.mean(sig_a)
|
||||
sig_b -= np.mean(sig_b)
|
||||
|
||||
lags = np.arange(-max_lag_frames, max_lag_frames + 1)
|
||||
corr = []
|
||||
print("Calculating high-precision cross correlation...")
|
||||
corr = np.correlate(sig_a, sig_b, mode='full')
|
||||
lags = np.arange(-len(sig_b) + 1, len(sig_a))
|
||||
|
||||
for lag in lags:
|
||||
if lag < 0:
|
||||
# Offset negativ → Video B vor Video A
|
||||
sub_a = a[:lag] # bis lag Frames von Video A
|
||||
sub_b = b[-lag:] # von -lag bis Ende von Video B
|
||||
else:
|
||||
sub_a = a[lag:] # ab lag bis Ende von Video A
|
||||
sub_b = b[:n_b - lag] # bis n_b - lag von Video B
|
||||
# Suchbereich einschränken (jetzt basierend auf virtueller FPS)
|
||||
max_lag_v = int(MAX_OFFSET_SECONDS * FPS_VIRTUAL)
|
||||
search_range = (lags >= -max_lag_v) & (lags <= max_lag_v)
|
||||
|
||||
# Frames angleichen
|
||||
min_len = min(len(sub_a), len(sub_b))
|
||||
if min_len == 0:
|
||||
corr.append(0)
|
||||
continue
|
||||
sub_a = sub_a[:min_len]
|
||||
sub_b = sub_b[:min_len]
|
||||
filtered_corr = corr[search_range]
|
||||
filtered_lags = lags[search_range]
|
||||
|
||||
corr.append(np.sum(sub_a * sub_b))
|
||||
best_index = np.argmax(filtered_corr)
|
||||
best_lag_v = filtered_lags[best_index]
|
||||
|
||||
corr = np.array(corr)
|
||||
best_index = np.argmax(corr)
|
||||
best_lag = lags[best_index]
|
||||
# Der Offset berechnet sich nun aus dem virtuellen Lag und der virtuellen FPS
|
||||
best_offset_seconds = best_lag_v / FPS_VIRTUAL
|
||||
|
||||
print("Best offset frames:", best_lag)
|
||||
print("Best offset seconds:", best_lag / FPS)
|
||||
|
||||
sys.exit()
|
||||
print("-" * 30)
|
||||
print(f"Best virtual lag: {best_lag_v} units")
|
||||
print(f"Resulting frames: {best_lag_v / UPSAMPLE_FACTOR:.3f}")
|
||||
print(f"Best offset seconds: {best_offset_seconds:.12f}")
|
||||
print("-" * 30)
|
||||
|
||||
# Plot
|
||||
plt.figure(figsize=(10, 5))
|
||||
plt.plot(lags / FPS, corr, marker='o')
|
||||
plt.axvline(best_lag / FPS, color='r', linestyle='--', label=f"Best offset = {best_lag / FPS:.3f}s")
|
||||
plt.figure(figsize=(12, 5))
|
||||
plt.plot(filtered_lags / FPS_VIRTUAL, filtered_corr)
|
||||
plt.axvline(best_offset_seconds, color='r', linestyle='--', label=f"Peak: {best_offset_seconds:.6f}s")
|
||||
plt.title(f"High-Precision Sync (Upsampled x{UPSAMPLE_FACTOR})")
|
||||
plt.xlabel("Offset (seconds)")
|
||||
plt.ylabel("Cross correlation")
|
||||
plt.title("Video cross correlation (Histogram-based)")
|
||||
plt.ylabel("Correlation")
|
||||
plt.legend()
|
||||
plt.grid(True)
|
||||
plt.show()
|
||||
Reference in new issue
Block a user