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Géodiversité
9 septembre 2011, par ,
Mis à jour : Août 2018
Langue : français
Type : Texte
Autres articles (37)
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HTML5 audio and video support
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For older browsers the Flowplayer flash fallback is used.
MediaSPIP allows for media playback on major mobile platforms with the above (...) -
De l’upload à la vidéo finale [version standalone]
31 janvier 2010, parLe chemin d’un document audio ou vidéo dans SPIPMotion est divisé en trois étapes distinctes.
Upload et récupération d’informations de la vidéo source
Dans un premier temps, il est nécessaire de créer un article SPIP et de lui joindre le document vidéo "source".
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Support audio et vidéo HTML5
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Pour les navigateurs plus anciens, le lecteur flash Flowplayer est utilisé.
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Sur d’autres sites (3673)
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[ffmpeg]How to use libfdk_aac to encode pcm to constant bitrate
27 décembre 2024, par qytI have tried both libfdk_aac and aac, but the encoded PCM audio always has a variable bit rate. Why is this happening ? How can I make it encode with a constant bit rate
The code is as follows :


#include 
#include 
#include 
extern "C"
{
#include <libavcodec></libavcodec>avcodec.h>
#include <libavdevice></libavdevice>avdevice.h>
#include <libavfilter></libavfilter>avfilter.h>
#include <libavfilter></libavfilter>buffersrc.h>
#include <libavfilter></libavfilter>buffersink.h>
#include <libavformat></libavformat>avformat.h>
#include <libavutil></libavutil>avutil.h>
#include <libavutil></libavutil>imgutils.h>
#include <libavutil></libavutil>opt.h>
#include <libavutil></libavutil>channel_layout.h> 
#include <libswresample></libswresample>swresample.h>
#include <libswscale></libswscale>swscale.h>
}

int main(int argc, char** argv) {
 AVCodecContext* codec_context = NULL;
 const AVCodec* codec = NULL;
 AVFrame* frame = NULL;
 AVPacket* pkt = NULL;
 FILE* input_file = NULL;
 FILE* output_file = NULL;
 int ret;

 // Open input file
 const char* input_filename = "D:\\audio\\b.pcm";
 const char* output_filename = "D:\\audio\\input.aac";
 input_file = fopen(input_filename, "rb");
 output_file = fopen(output_filename, "wb");
 if (!input_file || !output_file) {
 fprintf(stderr, "Could not open input or output file\n");
 exit(1);
 }

 // Find the AAC encoder
 codec = avcodec_find_encoder_by_name("libfdk_aac");
 if (!codec) {
 fprintf(stderr, "Codec not found\n");
 exit(1);
 }

 codec_context = avcodec_alloc_context3(codec);
 if (!codec_context) {
 fprintf(stderr, "Could not allocate audio codec context\n");
 exit(1);
 }

 // Set codec parameters
 codec_context->sample_fmt = AV_SAMPLE_FMT_S16;
 codec_context->sample_rate = 44100;
 codec_context->bit_rate = 256000;
 codec_context->rc_buffer_size = codec_context->bit_rate; 
 codec_context->rc_min_rate = codec_context->bit_rate; 
 codec_context->rc_max_rate = codec_context->bit_rate; 
 av_channel_layout_default(&codec_context->ch_layout, 2);
 // Open codec
 if (avcodec_open2(codec_context, codec, &opts) < 0) {
 fprintf(stderr, "Could not open codec\n");
 exit(1);
 }

 // Initialize packet
 pkt = av_packet_alloc();
 if (!pkt) {
 fprintf(stderr, "Could not allocate AVPacket\n");
 exit(1);
 }

 // Initialize frame
 frame = av_frame_alloc();
 frame->nb_samples = codec_context->frame_size;
 frame->format = codec_context->sample_fmt;
 frame->ch_layout.nb_channels = 2;

 // Allocate the data buffers
 ret = av_frame_get_buffer(frame, 0);
 if (ret < 0) {
 fprintf(stderr, "Could not allocate audio data buffers\n");
 exit(1);
 }

 // Main loop: read from the input file, encode, write to the output file
 while (fread(frame->data[0], 1, frame->linesize[0], input_file) == frame->linesize[0]) {
 // Send the frame to the encoder
 if (avcodec_send_frame(codec_context, frame) < 0) {
 fprintf(stderr, "Error sending frame to codec\n");
 exit(1);
 }

 // Get the encoded packet
 while (avcodec_receive_packet(codec_context, pkt) == 0) {
 fwrite(pkt->data, 1, pkt->size, output_file);
 av_packet_unref(pkt);
 }
 }

 // Flush the encoder
 avcodec_send_frame(codec_context, NULL);
 while (avcodec_receive_packet(codec_context, pkt) == 0) {
 fwrite(pkt->data, 1, pkt->size, output_file);
 av_packet_unref(pkt);
 }

 // Clean up
 fclose(input_file);
 fclose(output_file);
 av_frame_free(&frame);
 av_packet_free(&pkt);
 avcodec_free_context(&codec_context);

 return 0;
}



I encoded the AAC and then used FFmpeg to write it to an MP4 file : ./ffmpeg -i input.aac -c copy output.mp4, and I checked it using MediaInfo


The PCM file is S16, 2 channels, 44100 Hz


I try to write mp4 directly and check the aac audio through mediainfo which is also a variable bitrate


#include 
#include 
#include 
extern "C"
{
#include <libavcodec></libavcodec>avcodec.h>
#include <libavdevice></libavdevice>avdevice.h>
#include <libavfilter></libavfilter>avfilter.h>
#include <libavfilter></libavfilter>buffersrc.h>
#include <libavfilter></libavfilter>buffersink.h>
#include <libavformat></libavformat>avformat.h>
#include <libavutil></libavutil>avutil.h>
#include <libavutil></libavutil>imgutils.h>
#include <libavutil></libavutil>opt.h>
#include <libavutil></libavutil>channel_layout.h> 
#include <libswresample></libswresample>swresample.h>
#include <libswscale></libswscale>swscale.h>
}

int main(int argc, char** argv) {
 AVCodecContext* codec_context = NULL;
 const AVCodec* codec = NULL;
 AVFormatContext* format_context = NULL;
 AVStream* audio_stream = NULL;
 AVFrame* frame = NULL;
 AVPacket* pkt = NULL;
 FILE* input_file = NULL;
 int ret;
 int64_t next_pts = 0;

 // Open input file
 const char* input_filename = "D:\\audio\\b.pcm";
 const char* output_filename = "D:\\audio\\input.mp4";
 input_file = fopen(input_filename, "rb");
 if (!input_file) {
 fprintf(stderr, "Could not open input file\n");
 exit(1);
 }

 // Find the AAC encoder
 codec = avcodec_find_encoder_by_name("libfdk_aac");
 if (!codec) {
 fprintf(stderr, "Codec not found\n");
 exit(1);
 }

 // Allocate AVFormatContext for MP4 output
 avformat_alloc_output_context2(&format_context, NULL, NULL, output_filename);
 if (!format_context) {
 fprintf(stderr, "Could not allocate output context\n");
 exit(1);
 }

 // Add audio stream to the output file
 audio_stream = avformat_new_stream(format_context, codec);
 if (!audio_stream) {
 fprintf(stderr, "Could not allocate stream\n");
 exit(1);
 }

 codec_context = avcodec_alloc_context3(codec);
 if (!codec_context) {
 fprintf(stderr, "Could not allocate audio codec context\n");
 exit(1);
 }
 audio_stream->codecpar->frame_size = codec_context->frame_size = 1024;

 // Set codec parameters
 codec_context->sample_fmt = AV_SAMPLE_FMT_S16;
 codec_context->sample_rate = 44100;
 codec_context->bit_rate = 256000;
 codec_context->rc_buffer_size = codec_context->bit_rate;
 codec_context->rc_min_rate = codec_context->bit_rate;
 codec_context->rc_max_rate = codec_context->bit_rate;
 av_channel_layout_default(&codec_context->ch_layout, 2);

 // Copy settings to stream
 ret = avcodec_parameters_from_context(audio_stream->codecpar, codec_context);
 if (ret < 0) {
 fprintf(stderr, "Failed to copy codec parameters to stream\n");
 exit(1);
 }

 // Open codec
 if (avcodec_open2(codec_context, codec, NULL) < 0) {
 fprintf(stderr, "Could not open codec\n");
 exit(1);
 }

 // Open output file
 if (!(format_context->oformat->flags & AVFMT_NOFILE)) {
 ret = avio_open(&format_context->pb, output_filename, AVIO_FLAG_WRITE);
 if (ret < 0) {
 fprintf(stderr, "Could not open output file '%s'\n", output_filename);
 exit(1);
 }
 }

 // Write file header
 ret = avformat_write_header(format_context, NULL);
 if (ret < 0) {
 fprintf(stderr, "Error occurred when opening output file\n");
 exit(1);
 }

 // Initialize packet and frame
 pkt = av_packet_alloc();
 if (!pkt) {
 fprintf(stderr, "Could not allocate AVPacket\n");
 exit(1);
 }

 frame = av_frame_alloc();
 frame->nb_samples = codec_context->frame_size;
 frame->format = codec_context->sample_fmt;
 frame->ch_layout = codec_context->ch_layout;

 // Allocate the data buffers
 ret = av_frame_get_buffer(frame, 0);
 if (ret < 0) {
 fprintf(stderr, "Could not allocate audio data buffers\n");
 exit(1);
 }

 // Main loop: read from the input file, encode, write to the output file
 while (fread(frame->data[0], 1, frame->linesize[0], input_file) == frame->linesize[0]) {
 frame->pts = next_pts; // Set PTS for the frame
 next_pts += frame->nb_samples; // Increment the next PTS
 // Send the frame to the encoder
 if (avcodec_send_frame(codec_context, frame) < 0) {
 fprintf(stderr, "Error sending frame to codec\n");
 exit(1);
 }

 // Get the encoded packet
 while (avcodec_receive_packet(codec_context, pkt) == 0) {
 pkt->pts = pkt->dts = frame->pts;
 av_packet_rescale_ts(pkt, codec_context->time_base, audio_stream->time_base);
 pkt->stream_index = audio_stream->index;
 av_interleaved_write_frame(format_context, pkt);
 av_packet_unref(pkt);
 }
 }

 // Flush the encoder
 avcodec_send_frame(codec_context, NULL);
 while (avcodec_receive_packet(codec_context, pkt) == 0) {
 pkt->pts = pkt->dts = next_pts;
 av_packet_rescale_ts(pkt, codec_context->time_base, audio_stream->time_base);
 pkt->stream_index = audio_stream->index;
 av_interleaved_write_frame(format_context, pkt);
 av_packet_unref(pkt);
 next_pts += pkt->duration;
 }

 // Write file trailer
 av_write_trailer(format_context);

 // Clean up
 fclose(input_file);
 av_frame_free(&frame);
 av_packet_free(&pkt);
 avcodec_free_context(&codec_context);
 avio_closep(&format_context->pb);
 avformat_free_context(format_context);

 return 0;
}


-
Six Best Amplitude Alternatives
10 décembre 2024, par Daniel CroughProduct analytics is big business. Gone are the days when we could only guess what customers were doing with our products or services. Now, we can track, visualise, and analyse how they interact with them and, with that, constantly improve and optimise.
The problem is that many product analytics tools are expensive and complicated — especially for smaller businesses. They’re also packed with functionality more attuned to the needs of massive companies.
Amplitude is such a tool. It’s brilliant and it has all the bells and whistles that you’ll probably never need. Fortunately, there are alternatives. In this guide, we’ll explore the best of those alternatives and, along the way, provide the insight you’ll need to select the best analytics tool for your organisation.
Amplitude : a brief overview
To set the stage, it makes sense to understand exactly what Amplitude offers. It’s a real-time data analytics tool for tracking user actions and gaining insight into engagement, retention, and revenue drivers. It helps you analyse that data and find answers to questions about what happened, why it happened, and what to do next.
However, as good as Amplitude is, it has some significant disadvantages. While it does offer data export functionality, that seems deliberately restricted. It allows data exports for specific events, but it’s not possible to export complete data sets to manipulate or format in another tool. Even pulling it into a CSV file has a 10,000-row limit. There is an API, but not many third-party integration options.
Getting data in can also be a problem. Amplitude requires manual tags on events that must be tracked for analysis, which can leave holes in the data if every possible subsequent action isn’t tagged. That’s a time-consuming exercise, and it’s made worse because those tags will have to be updated every time the website or app is updated.
As good as it is, it can also be overwhelming because it’s stacked with features that can create confusion for novice or inexperienced analysts. It’s also expensive. There is a freemium plan that limits functionality and events. Still, when an organisation wants to upgrade for additional functionality or to analyse more events, the step up to the paid plan is massive.
Lastly, Amplitude has made some strides towards being a web analytics option, but it lacks some basic functionality that may frustrate people who are trying to see the full picture from web to app.
Snapshot of Amplitude alternatives
So, in place of Amplitude, what product analytics tools are available that won’t break the bank and still provide the functionality needed to improve your product ? The good news is that there are literally hundreds of alternatives, and we’ve picked out six of the best.
- Matomo – Best privacy-focused web and mobile analytics
- Mixpanel – Best for product analytics
- Google Analytics – Best free option
- Adobe Analytics – Best for predictive analytics
- Umami – Best lightweight tool for product analytics
- Heap – Best for automatic user data capture
A more detailed analysis of the Amplitude alternatives
Now, let’s dive deeper into each of the six Amplitude alternatives. We’ll cover standout features, integrations, pricing, use cases and community critiques. By the end, you’ll know which analytics tool can help optimise website and app performance to grow your business.
1. Matomo – Best privacy-friendly web and app analytics
Privacy is a big concern these days, especially for organisations with a presence in the European Union (EU). Unlike other analytics tools, Matomo ensures you comply with privacy laws and regulations, like the General Data Protection Regulation (GDPR) and California’s Consumer Privacy Act (CCPA).
Matomo helps businesses get the insights they need without compromising user privacy. It’s also one of the few self-hosted tools, ensuring data never has to leave your site.
Matomo is open-source, which is also rare in this class of tools. That means it’s available for anyone to adapt and customise as they wish. Everything you need to build custom APIs is there.
The Locations page in Matomo shows the countries, continents, regions, and cities where website traffic originates. Its most useful capabilities include visitor logs and session recordings to trace the entire customer journey, spot drop-off points, and fine-tune sales funnels. The platform also comes with heatmaps and A/B testing tools. Heatmaps provide a useful visual representation of your data, while A/B testing allows for more informed, data-driven decisions.
Despite its range of features, many reviewers laud Matomo’s user interface for its simplicity and user-friendliness.
Why Matomo : Matomo is an excellent alternative because it fills in the gaps where Amplitude comes up short, like with cookieless tracking. Also, while Amplitude focuses mainly on behavioural analytics, Matomo offers both behavioural and traditional analytics, which allows more profound insight into your data. Furthermore, Matomo fully complies with the strictest privacy regulations worldwide, including GDPR, LGPD, and HIPAA.
Standout features include multi-touch attribution, visits log, content engagement, ecommerce, customer segments, event tracking, goal tracking, custom dimensions, custom reports, automated email reports, tag manager, sessions recordings, roll-up reporting that can pull data from multiple websites or mobile apps, Google Analytics importer, Matomo tag manager, comprehensive visitor tracking, heatmaps, and more.
Integrations with 100+ technologies, including Cloudflare, WordPress, Magento, Google Ads, Drupal, WooCommerce, Vue, SharePoint and Wix.
Pricing is free for Matomo On-Premise and $23 per month for Matomo Cloud, which comes with a 21-day free trial (no credit card required).
Strengths
- Privacy focused
- Cookieless consent banners
- 100% accurate, unsampled data
- Open-source code
- Complete data ownership (no sharing with third parties)
- Self-hosting and cloud-based options
- Built-in GDPR Manager
- Custom alerts, white labelling, dashboards and reports
Community critiques
- Premium features are expensive and proprietary
- Learning curve for non-technical users
2. Mixpanel – Best for product analytics
Mixpanel is a dedicated product analytics tool. It tracks and analyses customer interactions with a product across different platforms and helps optimise digital products to improve the user experience. It works with real-time data and can provide answers from customer and revenue data in seconds.
It also presents data visualisations to show how customers interact with products.
Mixpanel allows you to play around filters and views to reveal and chart some useful customer trends. (Image source)
Why Mixpanel : One of the strengths of this platform is the ability to test hypotheses. Need to test an ambitious idea ? Mixpanel data can do it with real user analytics. That allows you to make data-driven decisions to find the best path forward.
Standout features include automatic funnel segment analysis, behavioural segmentation, cohort segmentation, collaboration support, customisable dashboards, data pipelines, filtered data views, SQL queries, warehouse connectors and a wide range of pre-built integrations.
Integrations available include Appcues, AppsFlyer, AWS, Databox, Figma, Google Cloud, Hotjar, HubSpot, Intercom, Integromat, MailChimp, Microsoft Azure, Segment, Slack, Statsig, VWO, Userpilot, WebEngage, Zapier, ZOH) and dozens of others.
Pricing starts with a freemium plan valid for up to 20 million events per month. The growth plan is affordable at $25 per month and adds features like no-code data transformations and data pipeline add-ons. The enterprise version runs at a monthly cost of $833 and provides the full suite of features and services and premium support.
There’s a caveat. Those prices only allow up to 1,000 Monthly Tracked Users (MTUs), calculated based on the number of visitors that perform a qualifying event each month. Beyond that, MTU plans start at $20,000 per year.
Strengths
- User behaviour and interaction tracking
- Unlimited cohort segmentation capabilities
- Drop-off analysis showing where users get stuck
- A/B testing capabilities
Community critiques
- Expensive enterprise features
- Extensive setup and configuration requirements
3. Google Analytics 4 – Best free web analytics tool
The first thing to know about Google Analytics 4 is that it’s a web analytics tool. In other words, it tracks sessions, not user behaviours in app environments. It can provide details on how people found your website and how they go there, but it doesn’t offer much detail on how people use your product.
There is also an enterprise version, Google Analytics 360, which is not free. We’ve broken down the differences between the two versions elsewhere.
GA4’s audience overview shows visitors, sessions, session lengths, bounce rates, and user engagement data. (Image source)
Why Google Analytics : It’s great for gauging the effectiveness of marketing campaigns, tracking goal completions (purchases, cart additions, etc.) and spotting trends and patterns in user engagement.
Standout features include built-in automation, customisable conversion goals, data drill-down functionality, detailed web acquisition metrics, media spend ROI calculations and out-of-the-box web analytics reporting.
Integrations include all major CRM platforms, CallRail, DoubleClick DCM, Facebook, Hootsuite, Marketo, Shopify, VWO, WordPress, Zapier and Zendesk, among many others.
Pricing is free for the basic version (Google Analytics 4) and scales based on features and data volume. The advanced features (in Google Analytics 360) are pitched at enterprises, and pricing is custom.
Strengths
- Free to start
- Multiple website management
- Traffic source details
- Up-to-date traffic data
Community critiques
- Steep learning curve
- Data sampling
4. Adobe Analytics – Best for predictive analytics
A fully configured Adobe Analytics implementation is the Swiss army knife of analytics tools. It begins with web analytics, adds product analytics, and then wraps it up nicely with predictive analytics.
Unlike all the Amplitude alternatives here, there’s no free version. Adobe Analytics has a complicated pricing matrix with options like website analytics, marketing analytics, attribution, and predictive analytics. It also has a wide range of customisation options that will appeal to large businesses. But for smaller organisations, it may all be a bit too much.
Mixpanel allows you to play around filters and views to reveal and chart some useful customer trends. (Image source)
Adobe Analytics’ cross-channel attribution ties actions from different channels into a single customer journey. (Image source)
Why Adobe Analytics : For current Adobe customers, this is a logical next step. Either way, Adobe Analytics can combine, evaluate, and analyse data from any part of the customer journey. It analyses that data with predictive intelligence to provide insights to enhance customer experiences.
Standout features include AI-powered prediction analysis, attribution analysis, multi-channel data collection, segmentation and detailed customer journey analytics, product analytics and web analytics.
Integrations are available through the Adobe Experience Cloud Exchange. Adobe Analytics also supports data exchange with brands such as BrightEdge, Branch.io, Google Ads, Hootsuite, Invoca, Salesforce and over 200 other integrations.
Pricing starts at $500 monthly, but prospective customers are encouraged to contact the company for a needs-based quotation.
Strengths
- Drag-and-drop interface
- Flexible segmentation
- Easy-to-create conversion funnels
- Threshold-based alerts and notifications
Community critiques
- No free version
- Lack of technical support
- Steep learning curve
5. Umami – Best lightweight tool for web analytics
The second of our open-source analytics solutions is Umami, a favourite in the software development community. Like Matomo, it’s a powerful and privacy-focused alternative that offers complete data control and respects user privacy. It’s also available as a cloud-based freemium plan or as a self-hosted solution.
Umami’s dashboard reveals the busiest times of day and which pages are visited when.(Image source)
Why Umami : Unami has a clear and simple user interface (UI) that lets you measure important metrics such as page visits, referrers, and user agents. It also features event tracking, although some reviewers complain that it’s quite limited.
Standout features can be summed up in five words : privacy, simplicity, lightweight, real-time, and open-source. Unami’s UI is clean, intuitive and modern, and it doesn’t slow down your website.
Integrations include plugins for VuePress, Gatsby, Craft CMS, Docusaurus, WordPress and Publii, and a module for Nuxt. Unami’s API communicates with Javascript, PHP Laravel and Python.
Pricing is free for up to 100k monthly events and three websites, but with limited support and data retention restrictions. The Pro plan costs $20 a month and gives you unlimited websites and team members, a million events (plus $0.00002 for each event over that), five years of data and email support. Their Enterprise plan is priced custom.
Strengths
- Freemium plan
- Open-source
- Lightweight
Community critiques
- Limited support options
- Data retention restrictions
- No funnel functionality
6. Heap – Best for automatic data capture
Product analytics with a twist is a good description of Heap. It features event auto-capture to track user interactions across all touchpoints in the user journey. This lets you fully understand how and why customers engage with your product and website.
Using a single Javascript snippet, Heap automatically collects data on everything users do, including how they got to your website. It also helps identify how different cohorts engage with your product, providing the critical insights teams need to boost conversion rates.
Heap’s journeys feature combines funnel and path analysis. (Image source)
Why Heap : The auto-capture functionality solves a major shortcoming of many product analytics tools — manual tracking. Instead of having to set up manual tags on events, Heap automatically captures all data on user activity from the start.
Standout features include event auto-capture, session replay, heatmaps, segments (or cohorts) and journeys, the last of which combines the functions of funnel and path analysis tools into a single feature.
Integrations include AWS, Google, Microsoft Azure, major CRM platforms, Snowflake and many other data manipulation platforms.
Pricing is quote-based across all payment tiers. There is also a free plan and a 14-day free trial.
Strengths
- Session replay
- Heatmaps
- User segmentation
- Simple setup
- Event auto-capture
Community critiques
- No A/B testing functionality
- No GDPR compliance support
Choosing the best solution for your team
When selecting a tool, it’s crucial to understand how product analytics and web analytics solutions differ.
Product analytics tools track users or accounts and record the features they use, the funnels they move through, and the cohorts they’re part of. Web analytics tools focus more on sessions than users because they’re interested in data that can help improve website usage.
Some tools combine product and web analytics to do both of these jobs.
Area of focus
Product analytics tools track user behaviour within SaaS- or app-based products. They’re helpful for analysing features, user journeys, engagement metrics, product development and iteration.
Web analytics tools analyse web traffic, user demographics, and traffic sources. They’re most often used for marketing and SEO insights.
Level of detail
Product analytics tools provide in-depth tracking and analysis of user interactions, feature usage, and cohort analysis.
Web analytics tools provide broader data on page views, bounce rates, and conversion tracking to analyse overall site performance.
Whatever tools you try, your first step should be to search for reviews online to see what people who’ve used them think about them. There are some great review sites you can try. See what people are saying on Capterra, G2, Gartner Peer Insights, or TrustRadius.
Use Matomo to power your web and app analytics
Web and product analytics is a competitive field, and there are many other tools worth considering. This list is a small cross-section of what’s available.
That said, if you have concerns about privacy and costs, consider choosing Matomo. Start your 21-day free trial today.
Start improving your websites and apps
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ffmpeg piped output producing incorrect metadata frame count
8 décembre 2024, par XorgonThe short version : Using piped output from ffmpeg produces a file with incorrect metadata.


ffmpeg -y -i .\test_mp4.mp4 -f avi -c:v libx264 - > output.avito make an AVI file using the pipe output.

ffprobe -v error -count_frames -show_entries stream=duration,nb_read_frames,r_frame_rate .\output.avi

The output will show that the metadata does not match the actual frames contained in the video.


Details below.



Using Python, I am attempting to use ffmpeg to compress videos and put them in a PowerPoint. This works great, however, the video files themselves have incorrect frame counts which can cause issues when I read from those videos in other code.


Edit for clarification : by "frame count" I mean the metadata frame count. The actual number of frames contained in the video is correct, but querying the metadata gives an incorrect frame count.


Having eliminated the PowerPoint aspect of the code, I've narrowed this down to the following minimal reproducing example of saving an output from an ffmpeg pipe :


from subprocess import Popen, PIPE

video_path = 'test_mp4.mp4'

ffmpeg_pipe = Popen(['ffmpeg',
 '-y', # Overwrite files
 '-i', f'{video_path}', # Input from file
 '-f', 'avi', # Output format
 '-c:v', 'libx264', # Codec
 '-'], # Output to pipe
 stdout=PIPE)

new_path = "piped_video.avi"
vid_file = open(new_path, "wb")
vid_file.write(ffmpeg_pipe.stdout.read())
vid_file.close()


I've tested several different videos. One small example video that I've tested can be found here.


I've tried a few different codecs with
aviformat and triedlibvpxwithwebmformat. For theavioutputs, the frame count usually reads as1073741824(2^30). Weirdly, for thewebmformat, the frame count read as-276701161105643264.

Edit : This issue can also be reproduced with just ffmpeg in command prompt using the following command :

ffmpeg -y -i .\test_mp4.mp4 -f avi -c:v libx264 - > output.avi

This is a snippet I used to read the frame count, but one could also see the error by opening the video details in Windows Explorer and seeing the total time as something like 9942 hours, 3 minutes, and 14 seconds.


import cv2

video_path = 'test_mp4.mp4'
new_path = "piped_video.webm"

cap = cv2.VideoCapture(video_path)
print(f"Original video frame count: = {int(cap.get(cv2.CAP_PROP_FRAME_COUNT)):d}")
cap.release()

cap = cv2.VideoCapture(new_path)
print(f"Piped video frame count: = {int(cap.get(cv2.CAP_PROP_FRAME_COUNT)):d}")
cap.release()


The error can also be observed using
ffprobewith the following command :ffprobe -v error -count_frames -show_entries stream=duration,nb_read_frames,r_frame_rate .\output.avi. Note that the frame rate and number of frames counted by ffprobe do not match with the duration from the metadata.

For completeness, here is the ffmpeg output :


ffmpeg version 2023-06-11-git-09621fd7d9-full_build-www.gyan.dev Copyright (c) 2000-2023 the FFmpeg developers
 built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
 configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint
 libavutil 58. 13.100 / 58. 13.100
 libavcodec 60. 17.100 / 60. 17.100
 libavformat 60. 6.100 / 60. 6.100
 libavdevice 60. 2.100 / 60. 2.100
 libavfilter 9. 8.101 / 9. 8.101
 libswscale 7. 3.100 / 7. 3.100
 libswresample 4. 11.100 / 4. 11.100
 libpostproc 57. 2.100 / 57. 2.100
Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'test_mp4.mp4':
 Metadata:
 major_brand : mp42
 minor_version : 0
 compatible_brands: isommp42
 creation_time : 2022-08-10T12:54:09.000000Z
 Duration: 00:00:06.67, start: 0.000000, bitrate: 567 kb/s
 Stream #0:0[0x1](eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 384x264 [SAR 1:1 DAR 16:11], 563 kb/s, 30 fps, 30 tbr, 30k tbn (default)
 Metadata:
 creation_time : 2022-08-10T12:54:09.000000Z
 handler_name : Mainconcept MP4 Video Media Handler
 vendor_id : [0][0][0][0]
 encoder : AVC Coding
Stream mapping:
 Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 0000018c68c8b9c0] using SAR=1/1
[libx264 @ 0000018c68c8b9c0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0000018c68c8b9c0] profile High, level 2.1, 4:2:0, 8-bit
Output #0, avi, to 'pipe:':
 Metadata:
 major_brand : mp42
 minor_version : 0
 compatible_brands: isommp42
 ISFT : Lavf60.6.100
 Stream #0:0(eng): Video: h264 (H264 / 0x34363248), yuv420p(progressive), 384x264 [SAR 1:1 DAR 16:11], q=2-31, 30 fps, 30 tbn (default)
 Metadata:
 creation_time : 2022-08-10T12:54:09.000000Z
 handler_name : Mainconcept MP4 Video Media Handler
 vendor_id : [0][0][0][0]
 encoder : Lavc60.17.100 libx264
 Side data:
 cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
[out#0/avi @ 0000018c687f47c0] video:82kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 3.631060%
frame= 200 fps=0.0 q=-1.0 Lsize= 85kB time=00:00:06.56 bitrate= 106.5kbits/s speed=76.2x 
[libx264 @ 0000018c68c8b9c0] frame I:1 Avg QP:16.12 size: 3659
[libx264 @ 0000018c68c8b9c0] frame P:80 Avg QP:21.31 size: 647
[libx264 @ 0000018c68c8b9c0] frame B:119 Avg QP:26.74 size: 243
[libx264 @ 0000018c68c8b9c0] consecutive B-frames: 3.0% 53.0% 0.0% 44.0%
[libx264 @ 0000018c68c8b9c0] mb I I16..4: 17.6% 70.6% 11.8%
[libx264 @ 0000018c68c8b9c0] mb P I16..4: 0.8% 1.7% 0.6% P16..4: 17.6% 4.6% 3.3% 0.0% 0.0% skip:71.4%
[libx264 @ 0000018c68c8b9c0] mb B I16..4: 0.1% 0.3% 0.2% B16..8: 11.7% 1.4% 0.4% direct: 0.6% skip:85.4% L0:32.0% L1:59.7% BI: 8.3%
[libx264 @ 0000018c68c8b9c0] 8x8 transform intra:59.6% inter:62.4%
[libx264 @ 0000018c68c8b9c0] coded y,uvDC,uvAC intra: 48.5% 0.0% 0.0% inter: 3.5% 0.0% 0.0%
[libx264 @ 0000018c68c8b9c0] i16 v,h,dc,p: 19% 39% 25% 17%
[libx264 @ 0000018c68c8b9c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 21% 25% 30% 3% 3% 4% 4% 4% 5%
[libx264 @ 0000018c68c8b9c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 22% 20% 16% 6% 8% 8% 8% 5% 6%
[libx264 @ 0000018c68c8b9c0] i8c dc,h,v,p: 100% 0% 0% 0%
[libx264 @ 0000018c68c8b9c0] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0000018c68c8b9c0] ref P L0: 76.2% 7.9% 11.2% 4.7%
[libx264 @ 0000018c68c8b9c0] ref B L0: 85.6% 12.9% 1.5%
[libx264 @ 0000018c68c8b9c0] ref B L1: 97.7% 2.3%
[libx264 @ 0000018c68c8b9c0] kb/s:101.19


So the question is : why does this happen, and how can one avoid it ?


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