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• Is it possible to download a song playlist through following description with yt-dlp ?

  26 février 2023, par BhaturaGuy

  Since downloading a thumbnail from youtube music provides really bad quality,
is it possible to download it in following manner :

  


  Download the link as bestvideo+bestaudio formatt and when the video is downloaded then extract frame number 24 then use that as thumbnail and mux it and audio to .mp3 ?

  


  Screenshot of bestvideo :
sc

  


  Screenshot of normally download with yt-dlp :
sc2

  


• How to upload a transcoded file to s3 and create a link to download it

  23 août 2016, par Dotun Longe

  I want to download a video after my module "creates" it by combining a picture and audio file. The output goes to my tmp folder. This works, but I don’t know how to access it.

  My method is to create another Paperclip attachment called "converted" and the module responsible for transcoding should also be responsible for uploading the converted video to a bucket, where I can then access it via @upload.converted.url.

  I have no idea how to go about this, and my eyes hurt from searching. If you have a better way for me to be able to download the transcoded video without this option, I will be open to it.

  Module -> videocreatingproccessor.rb :

   require 'streamio-ffmpeg'
  
   require 'fileutils'
  
  
  
   module VideoCreatingProcessor
  
  
  
   def self.convert_to_video (path_to_audio_file, path_to_image_file)
  
   movie = FFMPEG::Movie.new(path_to_audio_file)
  
   options = {video_codec: "libx264", frame_rate: 60, resolution: "960x720",
  
         x264_vprofile: "high", x264_preset: "slow", pixel_format: "720p",
  
         audio_codec: "libfaac", audio_bitrate: 32, audio_sample_rate: 44100, audio_channels: 2,
  
         threads: 2}
  
    woptions = { watermark: path_to_image_file, resolution: "960x720", watermark_filter: { padding_x: 10, padding_y: 10 } }
  
  
  
   movie.transcode("tmp/output.mp4",woptions ,options )
  
   end

  uploads_controller.rb :

   class UploadsController < ApplicationController
  
   before_action :set_upload, only: [:show, :edit, :update, :destroy]
  
  
  
   def index
  
   @uploads = Upload.all
  
   end
  
  
  
   def paudioaddress
  
   "https:" + @upload.audio.url
  
   end
  
  
  
   def pimageaddress
  
   "https:" + @upload.image.url
  
    end
  
  
  
    def show
  
    require "video_creating_processor"
  
    newvideo =    VideoCreatingProcessor.convert_to_video(paudioaddress,pimageaddress)
  
    end
  
  
  
    ....
  
    end

• Parallelize Youtube video frame download using yt-dlp and cv2

  4 mars 2023, par zulle99

  My task is to download multiple sequences of successive low resolution frames of Youtube videos.

  


  I summarize the main parts of the process :

  


  

  • Each bag of shots have a dimension of half a second (depending on the current fps)

  


  • In order to grab useful frames I've decided to remove the initial and final 10% of each video since it is common to have an intro and outro. Moreover

  


  • I've made an array of pair of initial and final frame to distribute the load on multiple processes using ProcessPoolExecutor(max_workers=multiprocessing.cpu_count())

  


  • In case of failure/exception I completly remove the relative directory

  


  


  The point is that it do not scale up, since while running I noticesd that all CPUs had always a load lower that the 20% more or less. In addition since with these shots I have to run multiple CNNs, to prevent overfitting it is suggested to have a big dataset and not a bounch of shots.

  


  Here it is the code :

  


  import yt_dlp
import os
from tqdm import tqdm
import cv2
import shutil
import time
import random
from concurrent.futures import ProcessPoolExecutor
import multiprocessing
import pandas as pd
import numpy as np
from pathlib import Path
import zipfile


# PARAMETERS
percentage_train_test = 50
percentage_bag_shots = 20
percentage_to_ignore = 10

zip_f_name = f'VideoClassificationDataset_{percentage_train_test}_{percentage_bag_shots}_{percentage_to_ignore}'
dataset_path = Path('/content/VideoClassificationDataset')

# DOWNOAD ZIP FILES
!wget --no-verbose https://github.com/gtoderici/sports-1m-dataset/archive/refs/heads/master.zip

# EXTRACT AND DELETE THEM
!unzip -qq -o '/content/master.zip' 
!rm '/content/master.zip'

DATA = {'train_partition.txt': {},
        'test_partition.txt': {}}

LABELS = []

train_dict = {}
test_dict = {}

path = '/content/sports-1m-dataset-master/original'

for f in os.listdir(path):
  with open(path + '/' + f) as f_txt:
    lines = f_txt.readlines()
    for line in lines:
      splitted_line = line.split(' ')
      label_indices = splitted_line[1].rstrip('\n').split(',') 
      DATA[f][splitted_line[0]] = list(map(int, label_indices))

with open('/content/sports-1m-dataset-master/labels.txt') as f_labels:
  LABELS = f_labels.read().splitlines()


TRAIN = DATA['train_partition.txt']
TEST = DATA['test_partition.txt']
print('Original Train Test length: ', len(TRAIN), len(TEST))

# sample a subset percentage_train_test
TRAIN = dict(random.sample(TRAIN.items(), (len(TRAIN)*percentage_train_test)//100))
TEST = dict(random.sample(TEST.items(), (len(TEST)*percentage_train_test)//100))

print(f'Sampled {percentage_train_test} Percentage  Train Test length: ', len(TRAIN), len(TEST))


if not os.path.exists(dataset_path): os.makedirs(dataset_path)
if not os.path.exists(f'{dataset_path}/train'): os.makedirs(f'{dataset_path}/train')
if not os.path.exists(f'{dataset_path}/test'): os.makedirs(f'{dataset_path}/test')


  


  Function to extract a sequence of continuous frames :

  


  def extract_frames(directory, url, idx_bag, start_frame, end_frame):
  capture = cv2.VideoCapture(url)
  count = start_frame

  capture.set(cv2.CAP_PROP_POS_FRAMES, count)
  os.makedirs(f'{directory}/bag_of_shots{str(idx_bag)}')

  while count < end_frame:

    ret, frame = capture.read()

    if not ret: 
      shutil.rmtree(f'{directory}/bag_of_shots{str(idx_bag)}')
      return False

    filename = f'{directory}/bag_of_shots{str(idx_bag)}/shot{str(count - start_frame)}.png'

    cv2.imwrite(filename, frame)
    count += 1

  capture.release()
  return True


  


  Function to spread the load along multiple processors :

  


  def video_to_frames(video_url, labels_list, directory, dic, percentage_of_bags):
  url_id = video_url.split('=')[1]
  path_until_url_id = f'{dataset_path}/{directory}/{url_id}'
  try:   

    ydl_opts = {
        'ignoreerrors': True,
        'quiet': True,
        'nowarnings': True,
        'simulate': True,
        'ignorenoformatserror': True,
        'verbose':False,
        'cookies': '/content/all_cookies.txt',
        #https://stackoverflow.com/questions/63329412/how-can-i-solve-this-youtube-dl-429
    }
    ydl = yt_dlp.YoutubeDL(ydl_opts)
    info_dict = ydl.extract_info(video_url, download=False)

    if(info_dict is not None and  info_dict['fps'] >= 20):
      # I must have a least 20 frames per seconds since I take half of second bag of shots for every video

      formats = info_dict.get('formats', None)

      # excluding the initial and final 10% of each video to avoid noise
      video_length = info_dict['duration'] * info_dict['fps']

      shots = info_dict['fps'] // 2

      to_ignore = (video_length * percentage_to_ignore) // 100
      new_len = video_length - (to_ignore * 2)
      tot_stored_bags = ((new_len // shots) * percentage_of_bags) // 100   # ((total_possbile_bags // shots) * percentage_of_bags) // 100
      if tot_stored_bags == 0: tot_stored_bags = 1 # minimum 1 bag of shots

      skip_rate_between_bags = (new_len - (tot_stored_bags * shots)) // (tot_stored_bags-1) if tot_stored_bags > 1 else 0

      chunks = [[to_ignore+(bag*(skip_rate_between_bags+shots)), to_ignore+(bag*(skip_rate_between_bags+shots))+shots] for bag in range(tot_stored_bags)]
      # sequence of [[start_frame, end_frame], [start_frame, end_frame], [start_frame, end_frame], ...]


      # ----------- For the moment I download only shots form video that has 144p resolution -----------

      res = {
          '160': '144p',
          '133': '240p',
          '134': '360p',
          '135': '360p',
          '136': '720p'
      }

      format_id = {}
      for f in formats: format_id[f['format_id']] = f
      #for res in resolution_id:
      if list(res.keys())[0] in list(format_id.keys()):
          video = format_id[list(res.keys())[0]]
          url = video.get('url', None)
          if(video.get('url', None) != video.get('manifest_url', None)):

            if not os.path.exists(path_until_url_id): os.makedirs(path_until_url_id)

            with ProcessPoolExecutor(max_workers=multiprocessing.cpu_count()) as executor:
              for idx_bag, f in enumerate(chunks): 
                res = executor.submit(
                  extract_frames, directory = path_until_url_id, url = url, idx_bag = idx_bag, start_frame = f[0], end_frame = f[1])
                
                if res.result() is True: 
                  l = np.zeros(len(LABELS), dtype=int) 
                  for label in labels_list: l[label] = 1
                  l = np.append(l, [shots]) # appending the number of shots taken in the list before adding it on the dictionary

                  dic[f'{directory}/{url_id}/bag_of_shots{str(idx_bag)}'] = l.tolist()


  except Exception as e:
    shutil.rmtree(path_until_url_id)
    pass


  


  Download of TRAIN bag of shots :

  


  start_time = time.time()
pbar = tqdm(enumerate(TRAIN.items()), total = len(TRAIN.items()), leave=False)

for _, (url, labels_list) in pbar: video_to_frames(
  video_url = url, labels_list = labels_list, directory = 'train', dic = train_dict, percentage_of_bags = percentage_bag_shots)

print("--- %s seconds ---" % (time.time() - start_time))


  


  Download of TEST bag of shots :

  


  start_time = time.time()
pbar = tqdm(enumerate(TEST.items()), total = len(TEST.items()), leave=False)

for _, (url, labels_list) in pbar: video_to_frames(
  video_url = url, labels_list = labels_list, directory = 'test', dic = test_dict, percentage_of_bags = percentage_bag_shots)

print("--- %s seconds ---" % (time.time() - start_time))


  


  Save the .csv files

  


  train_df = pd.DataFrame.from_dict(train_dict, orient='index', dtype=int).reset_index(level=0)
train_df = train_df.rename(columns={train_df.columns[-1]: 'shots'})
train_df.to_csv('/content/VideoClassificationDataset/train.csv', index=True)

test_df = pd.DataFrame.from_dict(test_dict, orient='index', dtype=int).reset_index(level=0)
test_df = test_df.rename(columns={test_df.columns[-1]: 'shots'})
test_df.to_csv('/content/VideoClassificationDataset/test.csv', index=True)