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• Normalize audio, then reduce the volume in ffmpeg

  27 octobre 2014, par Steve Sheldon

  I have a question relating to ffmpeg. First here is the scenario, I am working on a project where I need to have some audio with a presenter talking and then potentially some background music. I also have the requirement to normalize the audio. I would like to do this without presenting a bunch of options to the user.

  For normalization I use something similar to this post :

  How to normalize audio with ffmpeg.

  In short, I get a volume adjustment which I then apply to ffmpeg like this :

  ffmpeg -i <input /> -af "volume=xxxdB" <output>
  
  </output>

  So far so good. Now let’s consider the backing track, it doesn’t want to be the same volume as the presenters voice, this would be really distracting, so I want to lower that by some percentage. I can also do this with ffmpeg, I could do it like this (example would set volume to 50%) :

  ffmpeg -i <input /> -af "volume=0.5" <output>
  
  </output>

  Using these two commands back to back, I can get the desired result.

  My question has two parts :

  1. Is there a way to do this in one step ?
  2. Is there any benefit to doing it in one step ?

  Thanks for any help !

• moov atom not found (Extracting unique faces from youtube video)

  10 avril 2023, par Tochukwu

  I got the error below

  


  Saved 0 unique faces
[mov,mp4,m4a,3gp,3g2,mj2 @ 0000024f505224c0] moov atom not found


  


  Trying to extract unique faces from a YouTube video with the code below which is designed to download the YouTube video and extract unique faces into a folder named faces. I got an empty video and folder. Please do check the Python code below

  


  import os
import urllib.request
import cv2
import face_recognition
import numpy as np

# Step 1: Download the YouTube video
video_url = "https://www.youtube.com/watch?v=JriaiYZZhbY&t=4s"
urllib.request.urlretrieve(video_url, "video.mp4")

# Step 2: Extract frames from the video
cap = cv2.VideoCapture("video.mp4")
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frames = []
for i in range(frame_count):
    cap.set(cv2.CAP_PROP_POS_FRAMES, i)
    ret, frame = cap.read()
    if ret:
        frames.append(frame)
cap.release()

# Step 3: Detect faces in the frames
detected_faces = []
for i, frame in enumerate(frames):
    face_locations = face_recognition.face_locations(frame)
    for j, location in enumerate(face_locations):
        top, right, bottom, left = location
        face_image = frame[top:bottom, left:right]
        cv2.imwrite(f"detected_{i}_{j}.jpg", face_image)
        detected_faces.append(face_image)

# Step 4: Save the faces as separate images
if not os.path.exists("faces"):
    os.makedirs("faces")
known_faces = []
for i in range(len(detected_faces)):
    face_image = detected_faces[i]
    face_encoding = face_recognition.face_encodings(face_image)[0]
    known_faces.append(face_encoding)
    cv2.imwrite(f"faces/face_{i}.jpg", face_image)
print("Saved", len(known_faces), "unique faces")


  


• Working example on live streaming using FFMPEG+MP4Box+Dash.js

  27 février 2017, par BartMao

  I’m stucked on this issue for a couple of days, couldn’t find a perfect working example.

  Here’s my current method :

  Step 1.

  ffmpeg -re -i sample.mp4 -threads 0 -y \
  
  -map 0:1 -vn -acodec aac -strict -2 -ar 48000 -ac 2 \
  
  -f segment -segment_time 4 -segment_format mpegts Dash\audio_%d.ts \
  
  -map 0:0 -vcodec libx264 -vprofile baseline -preset veryfast \
  
  -s 640x360 -vb 512k -bufsize 1024k -maxrate 512k -level 31 -keyint_min 25 -g 25 \
  
  -sc_threshold 0 -an -bsf h264_mp4toannexb -flags -global_header \
  
  -f segment -segment_time 4 -segment_format mpegts Dash\video_%d.ts

  Step 2.

  mp4box -add Dash\audio_%d.ts Output\audio_%d.ts.mp4
  
  mp4box -add Dash\video_%d.ts Output\video_%d.ts.mp4

  Step 3.

  mp4box -dash-ctx Output\dash-live.txt -dash 4000 \
  
  -rap -ast-offset 12 -no-frags-default -bs-switching no \
  
  -min-buffer 4000 -url-template -time-shift 1800 \
  
  -segment-name live_$RepresentationID$_ -out Output\live \
  
  -dynamic -mpd-refresh 2 -subsegs-per-sidx -1 Output\audio_%d.ts.mp4:id=audio Output\video_%d.ts.mp4:id=video

  I played the live.mpd generated using Dash.js, the player paused a long time before actually playing, and freeze every time getting a new m4s file.

  Are there any wrong on my commands ? Or it would be great if can provide any other good examples.