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• Could anyone help me understand why moviepy is rendering at 2.5 it/s ?

  23 décembre 2023, par tristan

  I'm writing a program that uses moviepy to make those weird reddit thread videos with mc parkour playing in the background (real original ik), and everything is good except for when im rendering video which seems to consume a ton of memory and moves really... really slow, like 2.5 it/s. could anyone help ? also im a novice programmer that has no bearing on what is conventional or proper, so sorry if my code is very bad.

  


  from moviepy.video.fx.all import resize
from moviepy.video.tools.subtitles import SubtitlesClip
from moviepy.editor import (
    CompositeVideoClip,
    AudioFileClip,
    VideoFileClip,
    ImageClip,
    TextClip
)
import random
import moviepy.config as cfg
import librosa
from imagegenerator import draw_title
from audioeditor import concatenate_audios
import soundfile as sf
import numpy as np

# Constants
VIDEO_FADE_DURATION = 0.4
SPEED_FACTOR = 1.1
TEXT_WIDTH = 600
MINIMUM_FONT_SIZE = 60
FONT_COLOR = "white"
OUTLINE_COLOR = "black"
TITLE_ANIMATION_DURATION = 0.25
ANIMATION_DURATION = 0.2

# Configure imagemagick binary
cfg.change_settings(
    {
        "IMAGEMAGICK_BINARY": "magick/magick.exe"
    }
)

# Ease-out function
def ease_out(t):
    return 1 - (1 - t) ** 2

# Overlap audio files
def overlap_audio_files(audio_path1, audio_path2):
    # Load the first audio file
    audio1, sr1 = librosa.load(audio_path1, sr=None)

    # Load the second audio file
    audio2, sr2 = librosa.load(audio_path2, sr=None)

    # Ensure both audio files have the same sample rate
    if sr1 != sr2:
        raise ValueError("Sample rates of the two audio files must be the same.")

    # Calculate the duration of audio2
    audio2_duration = len(audio2)

    # Tile audio1 to match the duration of audio2
    audio1 = np.tile(audio1, int(np.ceil(audio2_duration / len(audio1))))

    # Trim audio1 to match the duration of audio2
    audio1 = audio1[:audio2_duration]

    # Combine the audio files by superimposing them
    combined_audio = audio1 + audio2

    # Save the combined audio to a new file
    output_path = "temp/ttsclips/combined_audio.wav"
    sf.write(output_path, combined_audio, sr1)

    return output_path

# Generator function for subtitles with centered alignment and outline
def centered_text_generator_white(txt):
    return TextClip(
        txt,
        font=r"fonts/Invisible-ExtraBold.otf",
        fontsize=86,
        color=FONT_COLOR,
        bg_color='transparent',  # Use a transparent background
        align='center',  # Center the text
        size=(1072, 1682),
        method='caption',  # Draw a caption instead of a title
    )

# Generator function for subtitles with centered alignment and blurred outline
def centered_text_generator_black_blurred_outline(txt, blur_factor=3):
    outline_clip = TextClip(
        txt,
        font=r"fonts/Invisible-ExtraBold.otf",
        fontsize=86,
        color=OUTLINE_COLOR,
        bg_color='transparent',  # Use a transparent background
        align='center',  # Center the text
        size=(1080, 1688),
        method='caption',  # Draw a caption instead of a title
    )

    # Blur the black text (outline)
    blurred_outline_clip = outline_clip.fx(resize, 1.0 / blur_factor)
    blurred_outline_clip = blurred_outline_clip.fx(resize, blur_factor)

    return blurred_outline_clip

# Compile video function
def compile_video(title_content, upvotes, comments, tone, subreddit, video_num):
    # Set the dimensions of the video (720x1280 in this case)
    height = 1280

    # Concatenate the audios
    concatenate_audios()

    concatenated_audio_path = r"temp/ttsclips/concatenated_audio.mp3"
    title_audio_path = r"temp/ttsclips/title.mp3"

    title_audio = AudioFileClip(title_audio_path)
    concatenated_audio = AudioFileClip(concatenated_audio_path)

    # Calculate for video duration
    title_duration = title_audio.duration
    duration = concatenated_audio.duration

    # Set background
    background_path = f"saved_videos/newmcparkour.mp4"
    background = VideoFileClip(background_path)
    background_duration = background.duration
    random_start = random.uniform(0, background_duration - duration)
    background = background.subclip(random_start, random_start + duration)

    # Apply fade-out effect to both background clips
    background = background.crossfadeout(VIDEO_FADE_DURATION)

    # Generate the background image with rounded corners
    background_image_path = draw_title(title_content, upvotes, comments, subreddit)

    # Load the background image with rounded corners
    background_image = ImageClip(background_image_path)

    # Set the start of the animated title clip
    animated_background_clip = background_image.set_start(0)

    # Set the initial position of the text at the bottom of the screen
    initial_position = (90, height)

    # Calculate the final position of the text at the center of the screen
    final_position = [90, 630]

    # Animate the title clip to slide up over the course of the animation duration
    animated_background_clip = animated_background_clip.set_position(
        lambda t: (
            initial_position[0],
            initial_position[1]
            - (initial_position[1] - final_position[1])
            * ease_out(t / TITLE_ANIMATION_DURATION),
        )
    )

    # Set the duration of the animated title clip
    animated_background_clip = animated_background_clip.set_duration(
        TITLE_ANIMATION_DURATION
    )

    # Assign start times to title image
    stationary_background_clip = background_image.set_start(TITLE_ANIMATION_DURATION)

    # Assign positions to stationary title image
    stationary_background_clip = stationary_background_clip.set_position(final_position)

    # Assign durations to stationary title image
    stationary_background_clip = stationary_background_clip.set_duration(
        title_duration - TITLE_ANIMATION_DURATION
    )

    #  Select background music
    if tone == "normal":
        music_options = [
            "Anguish",
            "Garden",
            "Limerence",
            "Lost",
            "NoWayOut",
            "Summer",
            "Never",
            "Miss",
            "Touch",
            "Stellar"
        ]
    elif tone == "eerie":
        music_options = [
            "Creepy",
            "Scary",
            "Spooky",
            "Space",
            "Suspense"
        ]
    background_music_choice = random.choice(music_options)
    background_music_path = f"music/eeriemusic/{background_music_choice}.mp3"

    # Create final audio by overlapping background music and concatenated audio
    final_audio = AudioFileClip(
        overlap_audio_files(background_music_path, concatenated_audio_path)
    )

    # Release the concatenated audio
    concatenated_audio.close()

    # Create subtitles clip using the centered_text_generator
    subtitles = SubtitlesClip("temp/ttsclips/content_speechmarks.srt",
                            lambda txt: centered_text_generator_white(txt))
    subtitles_outline = SubtitlesClip("temp/ttsclips/content_speechmarks.srt",
                            lambda txt: centered_text_generator_black_blurred_outline(txt))

    # Overlay subtitles on the blurred background
    final_clip = CompositeVideoClip(
        [background, animated_background_clip, stationary_background_clip, subtitles_outline, subtitles]
    )

    # Set the final video dimensions and export the video
    final_clip = final_clip.set_duration(duration)
    final_clip = final_clip.set_audio(final_audio)

    final_clip.write_videofile(
        f"temp/videos/{video_num}.mp4",
        codec="libx264",
        fps=60,
        bitrate="8000k",
        audio_codec="aac",
        audio_bitrate="192k",
        preset="ultrafast",
        threads=8
    )

    # Release the concatenated audio
    concatenated_audio.close()

    # Release the title audio
    title_audio.close()

    # Release the background video and image
    background.close()
    background_image.close()

    # Release the final audio
    final_audio.close()

    # Release the subtitle clips
    subtitles.close()
    subtitles_outline.close()

    # Release the final video clip
    final_clip.close()


  


  ive tried turning down my settings, like setting it to "ultrafast" and dropping the bitrate, but nothing seems to work. the only thing I can think of now is that there is something Im doing wrong with moviepy.

  


• Segmentation Analytics : How to Leverage It on Your Site

  27 octobre 2023, par Erin — Analytics Tips

  The deeper you go with your customer analytics, the better your insights will be.

  The result ? Your marketing performance soars to new heights.

  Customer segmentation is one of the best ways businesses can align their marketing strategies with an effective output to generate better results. Marketers know that targeting the right people is one of the most important aspects of connecting with and converting web visitors into customers.

  By diving into customer segmentation analytics, you’ll be able to transform your loosely defined and abstract audience into tangible, understandable segments, so you can serve them better.

  In this guide, we’ll break down customer segmentation analytics, the different types, and how you can delve into these analytics on your website to grow your business.

  What is customer segmentation ?

  Before we dive into customer segmentation analytics, let’s take a step back and look at customer segmentation in general. 

  Customer segmentation is the process of dividing your customers up into different groups based on specific characteristics.

  These groups could be based on demographics like age or location or behaviours like recent purchases or website visits. 

  By splitting your audience into different segments, your marketing team will be able to craft highly targeted and relevant marketing campaigns that are more likely to convert.

  Additionally, customer segmentation allows businesses to gain new insights into their audience. For example, by diving deep into different segments, marketers can uncover pain points and desires, leading to increased conversion rates and return on investment.

  But, to grasp the different customer segments, organisations need to know how to collect, digest and interpret the data for usable insights to improve their business. That’s where segmentation analytics comes in.

  What is customer segmentation analytics ?

  Customer segmentation analytics splits customers into different groups within your analytics software to create more detailed customer data and improve targeting.

  

  With customer segmentation, you’re splitting your customers into different groups. With customer segmentation analytics, you’re doing this all within your analytics platform so you can understand them better.

  One example of splitting your customers up is by country. For example, let’s say you have a global customer base. So, you go into your analytics software and find that 90% of your website visitors come from five countries : the UK, the US, Australia, Germany and Japan.

  In this area, you could then create customer segmentation subsets based on these five countries. Moving forward, you could then hop into your analytics tool at any point in time and analyse the segments by country. 

  For example, if you wanted to see how well your recent marketing campaign impacted your Japanese customers, you could look at your Japanese subset within your analytics and dive into the data.

  The primary goal of customer segmentation analytics is to gather actionable data points to give you an in-depth understanding of your customers. By gathering data on your different audience segments, you’ll discover insights on your customers that you can use to optimise your website, marketing campaigns, mobile apps, product offerings and overall customer experience.

  Rather than lumping your entire customer base into a single mass, customer segmentation analytics allows you to meet even more specific and relevant needs and pain points of your customers to serve them better.

  By allowing you to “zoom in” on your audience, segmentation analytics helps you offer more value to your customers, giving you a competitive advantage in the marketplace.

  5 types of segmentation

  There are dozens of different ways to split up your customers into segments. The one you choose depends on your goals and marketing efforts. Each type of segmentation offers a different view of your customers so you can better understand their specific needs to reach them more effectively.

  While you can segment your customers in almost endless ways, five common types the majority fall under are :

  

  Geographic

  Another way to segment is by geography.

  This is important because you could have drastically different interests, pain points and desires based on where you live.

  If you’re running a global e-commerce website that sells a variety of clothing products, geographic segmentation can play a crucial role in optimising your website.

  For instance, you may observe that a significant portion of your website visitors are from countries in the Southern Hemisphere, where it’s currently summer. On the other hand, visitors from the Northern Hemisphere are experiencing winter. Utilising this information, you can tailor your marketing strategy and website accordingly to increase sells.

  Where someone comes from can significantly impact how they will respond to your messaging, brand and offer.

  Geographic segmentation typically includes the following subtypes :

  • Cities (i.e., Austin, Paris, Berlin, etc.)
  • State (i.e., Massachusetts)
  • Country (i.e., Thailand)

  Psychographic

  Another key segmentation type of psychographic. This is where you split your customers into different groups based on their lifestyles.

  Psychographic segmentation is a method of dividing your customers based on their habits, attitudes, values and opinions. You can unlock key emotional elements that impact your customers’ purchasing behaviours through this segmentation type.

  Psychographic segmentation typically includes the following subtypes :

  • Values
  • Habits
  • Opinions

  Behavioural

  While psychographic segmentation looks at your customers’ overall lifestyle and habits, behavioural segmentation aims to dive into the specific individual actions they take daily, especially when interacting with your brand or your website.

  Your customers won’t all interact with your brand the same way. They’ll act differently when interacting with your products and services for several reasons. 

  Behavioural segmentation can help reveal certain use cases, like why customers buy a certain product, how often they buy it, where they buy it and how they use it.

  By unpacking these key details about your audience’s behaviour, you can optimise your campaigns and messaging to get the most out of your marketing efforts to reach new and existing customers.

  Behavioural segmentation typically includes the following subtypes :

  • Interactions
  • Interests
  • Desires

  Technographic

  Another common segmentation type is technographic segmentation. As the name suggests, this technologically driven segment seeks to understand how your customers use technology.

  While this is one of the newest segmentation types marketers use, it’s a powerful method to help you understand the types of tech your customers use, how often they use it and the specific ways they use it.

  Technographic segmentation typically includes the following subtypes :

  • Smartphone type
  • Device type : smartphone, desktop, tablet
  • Apps
  • Video games

  Demographic

  The most common approach to segmentation is to split your customers up by demographics. 

  Demographic segmentation typically includes subtypes like language, job title, age or education.

  This can be helpful for tailoring your content, products, and marketing efforts to specific audience segments. One way to capture this information is by using web analytics tools, where language is often available as a data point.

  However, for accurate insights into other demographic segments like job titles, which may not be available (or accurate) in analytics tools, you may need to implement surveys or add fields to forms on your website to gather this specific information directly from your visitors.

  How to build website segmentation analytics

  With Matomo, you can create a variety of segments to divide your website visitors into different groups. Matomo’s Segments allows you to view segmentation analytics on subsets of your audience, like :

  • The device they used while visiting your site
  • What channel they entered your site from
  • What country they are located
  • Whether or not they visited a key page of your website
  • And more

  While it’s important to collect general data on every visitor you have to your website, a key to website growth is understanding each type of visitor you have.

  For example, here’s a screenshot of how you can segment all of your website’s visitors from New Zealand :

  

  The criteria you use to define these segments are based on the data collected within your web analytics platform.

  Here are some popular ways you can create some common themes on Matomo that can be used to create segments :

  Visit based segments

  Create segments in Matomo based on visitors’ patterns. 

  For example :

  • Do returning visitors show different traits than first-time visitors ?
  • Do people who arrive on your blog experience your website differently than those arriving on a landing page ?

  This information can inform your content strategy, user interface design and marketing efforts.

  Demographic segments

  Create segments in Matomo based on people’s demographics. 

  For example :

  • User’s browser language
  • Location

  This can enable you to tailor your approach to specific demographics, improving the performance of your marketing campaigns.

  Technographic segments

  Create segments in Matomo based on people’s technographics. 

  For example :

  • Web browser being used (i.e., Chrome, Safari, Firefox, etc.)
  • Device type (i.e., smartphone, tablet, desktop)

  This can inform how to optimise your website based on users’ technology preferences, enhancing the effectiveness of your website.

  Interaction based segments

  Create segments in Matomo based on interactions. 

  For example :

  • Events (i.e., when someone clicks a specific URL on your website)
  • Goals (i.e., when someone stays on your site for a certain period)

  Insights from this can empower you to fine-tune your content and user experience for increasing conversion rates.

  

  Campaign-based segments

  Create segments in Matomo based on campaigns. 

  For example :

  • Visitors arriving from specific traffic sources
  • Visitors arriving from specific advertising campaigns

  With these insights, you can assess the performance of your marketing efforts, optimise your ad spend and make data-driven decisions to enhance your campaigns for better results.

  Ecommerce segments

  Create segments in Matomo based on ecommerce. 

  For example :

  • Visitors who purchased vs. those who didn’t
  • Visitors who purchased a specific product

  This allows you to refine your website and marketing strategy for increased conversions and revenue.

  Leverage Matomo for your segmentation analytics

  By now, you can see the power of segmentation analytics and how they can be used to understand your customers and website visitors better. By breaking down your audience into groups, you’ll be able to gain insights into those segments to know how to serve them better with improved messaging and relevant products.

  If you’re ready to begin using segmentation analytics on your website, try Matomo. Start your 21-day free trial now — no credit card required.

  Matomo is an ideal choice for marketers looking for an easy-to-use, out-of-the-box web analytics solution that delivers accurate insights while keeping privacy and compliance at the forefront.

• FFMPEG and libavfilter

  21 mars 2012, par Sergio

  With the currently installed ffmpeg I can't post watermark on converted videos. Can the reason for that be that I have not installed libavfilter library ?

  Currently # ffmpeg -v looks like :

  FFmpeg version SVN-r20374, Copyright (c) 2000-2009 Fabrice Bellard, et al.
  
  built on Oct 26 2009 22:47:01 with gcc 4.1.2 20080704 (Red Hat 4.1.2-46)
  
  configuration: --enable-libmp3lame --enable-libopencore-amrnb --enable-version3 -   enable-libopencore-amrwb --enable-version3 --enable-nonfree --enable-libfaad --enable-gpl - -disable-mmx --enable-shared --enable-libfaac --enable-libvorbis
  
  libavutil     50. 3. 0 / 50. 3. 0
  
  libavcodec    52.37. 1 / 52.37. 1
  
  libavformat   52.39. 2 / 52.39. 2
  
  libavdevice   52. 2. 0 / 52. 2. 0

  As you can see I'm missing libavfilter ? How can I add that library to FFMPEG and successfully add watermark on videos ?

  Thanks for any help.