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Websites made with MediaSPIP
2 mai 2011, parThis page lists some websites based on MediaSPIP.
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Creating farms of unique websites
13 avril 2011, parMediaSPIP platforms can be installed as a farm, with a single "core" hosted on a dedicated server and used by multiple websites.
This allows (among other things) : implementation costs to be shared between several different projects / individuals rapid deployment of multiple unique sites creation of groups of like-minded sites, making it possible to browse media in a more controlled and selective environment than the major "open" (...) -
Publier sur MédiaSpip
13 juin 2013Puis-je poster des contenus à partir d’une tablette Ipad ?
Oui, si votre Médiaspip installé est à la version 0.2 ou supérieure. Contacter au besoin l’administrateur de votre MédiaSpip pour le savoir
Sur d’autres sites (8289)
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Making Your First-Party Data Work for You and Your Customers
11 mars, par Alex CarmonaAt last count, 162 countries had enacted data privacy policies of one kind or another. These laws or regulations, without exception, intend to eliminate the use of third-party data. That puts marketing under pressure because third-party data has been the foundation of online marketing efforts since the dawn of the Internet.
Marketers need to future-proof their operations by switching to first-party data. This will require considerable adjustment to systems and processes, but the reward will be effective marketing campaigns that satisfy privacy compliance requirements and bring the business closer to its customers.
To do that, you’ll need a coherent first-party data strategy. That’s what this article is all about. We’ll explain the different types of personal data and discuss how to use them in marketing without compromising or breaching data privacy regulations. We’ll also discuss how to build that strategy in your business.
So, let’s dive in.
The different data types
There are four distinct types of personal data used in marketing, each subject to different data privacy regulations.
Before getting into the different types, it’s essential to understand that all four may comprise one or more of the following :
Identifying data Name, email address, phone number, etc. Behavioural data Website activity, app usage, wishlist content, purchase history, etc. Transactional data Orders, payments, subscription details, etc. Account data Communication preferences, product interests, wish lists, etc. Demographic data Age, gender, income level, education, etc. Geographic Data Location-based information, such as zip codes or regional preferences. Psychographic Data Interests, hobbies and lifestyle preferences. First-party data
When businesses communicate directly with customers, any data they exchange is first-party. It doesn’t matter how the interaction occurs : on the telephone, a website, a chat session, or even in person.
Of course, the parties involved aren’t necessarily individuals. They may be companies, but people within those businesses will probably share at least some of the data with colleagues. That’s fine, so long as the data :
- Remains confidential between the original two parties involved, and
- It is handled and stored following applicable data privacy regulations.
The core characteristic of first-party data is that it’s collected directly from customer interactions. This makes it reliable, accurate and inherently compliant with privacy regulations — assuming the collecting party complies with data privacy laws.
A great example of first-party data use is in banking. Data collected from customer interactions is used to provide personalised services, detect fraud, assess credit risk and improve customer retention.
Zero-party data
There’s also a subset of first-party data, sometimes called zero-party data. It’s what users intentionally and proactively share with a business. It can be preferences, intentions, personal information, survey responses, support tickets, etc.
What makes it different is that the collection of this data depends heavily on the user’s trust. Transparency is a critical factor, too ; visitors expect to be informed about how you’ll use their data. Consumers also have the right to withdraw permission to use all or some of their information at any time.
Second-party data
This data is acquired from a separate organisation that collects it firsthand. Second-party data is someone else’s first-party data that’s later shared with or sold to other businesses. The key here is that whoever owns that data must give explicit consent and be informed of who businesses share their data with.
A good example is the cooperation between hotel chains, car rental companies, and airlines. They share joint customers’ flight data, hotel reservations, and car rental bookings, much like travel agents did before the internet undermined that business model.
Third-party data
This type of data is the arch-enemy of lawmakers and regulators trying to protect the personal data of citizens and residents in their country. It’s information collected by entities that have no direct relationship with the individuals whose data it is.
Third-party data is usually gathered, aggregated, and sold by data brokers or companies, often by using third-party cookies on popular websites. It’s an entire business model — these third-party brokers sell data for marketing, analytics, or research purposes.
Most of the time, third-party data subjects are unaware that their data has been gathered and sold. Hence the need for strong data privacy regulations.
Benefits of a first-party data strategy
First-party data is reliable, accurate, and ethically sourced. It’s an essential part of any modern digital marketing strategy.
More personalised experiences
The most important application of first-party data is customising and personalising customers’ interactions based on real behaviours and preferences. Personalised experiences aren’t restricted to websites and can extend to all customer communication.
The result is company communications and marketing messages are far more relevant to customers. It allows businesses to engage more meaningfully with them, building trust and strengthening customer relationships. Inevitably, this also results in stronger customer loyalty and better customer retention.
Greater understanding of customers
Because first-party data is more accurate and reliable, it can be used to derive valuable insights into customer needs and wants. When all the disparate first-party data points are centralised and organised, it’s possible to uncover trends and patterns in customer behaviour that might not be apparent using other data.
This helps businesses predict and respond to customer needs. It also allows marketing teams to be more deliberate when segmenting customers and prospects into like-minded groups. The data can also be used to create more precise personas for future campaigns or reveal how likely a customer would be to purchase in response to a campaign.
Build trust with customers
First-party data is unique to a business and originates from interactions with customers. It’s also data collected with consent and is “owned” by the company — if you can ever own someone else’s data. If treated like the precious resource, it can help businesses build trust with customers.
However, developing that trust requires a transparent, step-by-step approach. This gradually strengthens relationships to the point where customers are more comfortable sharing the information they’re asked for.
However, while building trust is a long and sometimes arduous process, it can be lost in an instant. That’s why first-party data must be protected like the Crown Jewels.
Components of a first-party data strategy
Security is essential to any first-party data strategy, and for good reason. As Gartner puts it, a business must find the optimal balance between business outcomes and data risk mitigation. Once security is baked in, attention can turn to the different aspects of the strategy.
Data collection
There are many ways to collect first-party data ethically, within the law and while complying with data privacy regulations, such as Europe’s General Data Protection Regulation (GDPR). Potential sources include :
Website activity forms and surveys, behavioural tracking, cookies, tracking pixels and chatbots Mobile app interactions in-app analytics, push notifications and in-app forms Email marketing newsletter sign-ups, email engagement tracking, promotions, polls and surveys Events registrations, post-event surveys and virtual event analytics Social media interaction polls and surveys, direct messages and social media analytics Previous transactions purchase history, loyalty programmes and e-receipts Customer service call centre data, live chat, chatbots and feedback forms In-person interactions in-store purchases, customer feedback and Wi-Fi sign-ins Gated content whitepapers, ebooks, podcasts, webinars and video downloads Interactive content quizzes, assessments, calculators and free tools CRM platforms customer profiles and sales data Consent management privacy policies, consent forms, preference setting Consent management
It may be the final item on the list above, but it’s also a key requirement of many data privacy laws and regulations. For example, the GDPR is very clear about consent : “Processing personal data is generally prohibited, unless it is expressly allowed by law, or the data subject has consented to the processing.”
For that reason, your first-party data strategy must incorporate various transparent consent mechanisms, such as cookie banners and opt-in forms. Crucially, you must provide customers with a mechanism to manage their preferences and revoke that consent easily if they wish to.
Data management
Effective first-party data management, mainly its security and storage, is critical. Most data privacy regimes restrict the transfer of personal data to other jurisdictions and even prohibit it in some instances. Many even specify where residents’ data must be stored.
Consider this cautionary tale : The single biggest fine levied for data privacy infringement so far was €1.2 billion. The Irish Data Protection Commission imposed a massive fine on Meta for transferring EU users’ data to the US without adequate data protection mechanisms.
Data security is critical. If first-party data is compromised, it becomes third-party data, and any customer trust developed with the business will evaporate. To add insult to injury, data regulators could come knocking. That’s why the trend is to use encryption and anonymisation techniques alongside standard access controls.
Once security is assured, the focus is on data management. Many businesses use a Customer Data Platform. This software gathers, combines and manages data from many sources to create a complete and central customer profile. Modern CRM systems can also do that job. AI tools could help find patterns and study them. But the most important thing is to keep databases clean and well-organised to make it easier to use and avoid data silos.
Data activation
Once first-party data has been collected and analysed, it needs to be activated, which means a business needs to use it for the intended purpose. This is the implementation phase where a well-constructed first-party strategy pays off.
The activation stage is where businesses use the intelligence they gather to :
- Personalise website and app experiences
- Adapt marketing campaigns
- Improve conversion rates
- Match stated preferences
- Cater to observed behaviours
- Customise recommendations based on purchase history
- Create segmented email campaigns
- Improve retargeting efforts
- Develop more impactful content
Measurement and optimisation
Because first-party data is collected directly from customers or prospects, it’s far more relevant, reliable, and specific. Your analytics and campaign tracking will be more accurate. This gives you direct and actionable insights into your audience’s behaviour, empowering you to optimise your strategies and achieve better results.
The same goes for your collection and activation efforts. An advanced web analytics platform like Matomo lets you identify key user behaviour and optimise your tracking. Heatmaps, marketing attribution tools, user behaviour analytics and custom reports allow you to segment audiences for better traction (and collect even more first-party data).
How to build a first-party data strategy
There are five important and sequential steps to building a first-party data strategy. But this isn’t a one-time process. It must be revisited regularly as operating and regulatory environments change. There are five steps :
- Audit existing data
Chances are that customers already freely provide a lot of first-party data in the normal course of business. The first step is to locate this data, and the easiest way to do that is by mapping the customer journey. This identifies all the touchpoints where first-party data might be found.
- Define objectives
Then, it’s time to step back and figure out the goals of the first-party data strategy. Consider what you’re trying to achieve. For example :
- Reduce churn
- Expand an existing loyalty programme
- Unload excess inventory
- Improve customer experiences
Whatever the objectives are, they should be clear and measurable.
- Implement tools and technology
The first two steps point to data gaps. Now, the focus turns to ethical web analytics with a tool like Matomo.
To further comply with data privacy regulations, it may also be appropriate to implement a Consent Management Platform (CMP) to help manage preferences and consent choices.
- Build trust with transparency
With the tools in place, it’s time to engage customers. To build trust, keep them informed about how their data is used and remind them of their right to withdraw their consent.
Transparency is crucial in such engagement, as outlined in the 7 GDPR principles.
- Continuously improve
Rinse and repeat. The one constant in business and life is change. As things change, they expose weaknesses or flaws in the logic behind systems and processes. That’s why a first-party data strategy needs to be continually reviewed, updated, and revised. It must adapt to changing trends, markets, regulations, etc.
Tools that can help
Looking back at the different types of data, it’s clear that some are harder and more bothersome to get than others. But capturing behaviours and interactions can be easy — especially if you use tools that follow data privacy rules.
But here’s a tip. Google Analytics 4 isn’t compliant by default, especially not with Europe’s GDPR. It may also struggle to comply with some of the newer data privacy regulations planned by different US states and other countries.
Matomo Analytics is compliant with the GDPR and many other data privacy regulations worldwide. Because it’s open source, it can be integrated with any consent manager.
Get started today by trying Matomo for free for 21 days,
no credit card required. -
VLC dead input for RTP stream
27 mars, par CaptainCheeseI'm working on creating an rtp stream that's meant to display live waveform data from Pioneer prolink players. The motivation for sending this video out is to be able to receive it in a flutter frontend. I initially was just sending a base-24 encoding of the raw ARGB packed ints per frame across a Kafka topic to it but processing this data in flutter proved to be untenable and was bogging down the main UI thread. Not sure if this is the most optimal way of going about this but just trying to get anything to work if it means some speedup on the frontend. So the issue the following implementation is experiencing is that when I run
vlc --rtsp-timeout=120000 --network-caching=30000 -vvvv stream_1.sdpwhere

% cat stream_1.sdp
v=0
o=- 0 1 IN IP4 127.0.0.1
s=RTP Stream
c=IN IP4 127.0.0.1
t=0 0
a=tool:libavformat
m=video 5007 RTP/AVP 96
a=rtpmap:96 H264/90000


I see (among other questionable logs) the following :


[0000000144c44d10] live555 demux error: no data received in 10s, aborting
[00000001430ee2f0] main input debug: EOF reached
[0000000144b160c0] main decoder debug: killing decoder fourcc `h264'
[0000000144b160c0] main decoder debug: removing module "videotoolbox"
[0000000144b164a0] main packetizer debug: removing module "h264"
[0000000144c44d10] main demux debug: removing module "live555"
[0000000144c45bb0] main stream debug: removing module "record"
[0000000144a64960] main stream debug: removing module "cache_read"
[0000000144c29c00] main stream debug: removing module "filesystem"
[00000001430ee2f0] main input debug: Program doesn't contain anymore ES
[0000000144806260] main playlist debug: dead input
[0000000144806260] main playlist debug: changing item without a request (current 0/1)
[0000000144806260] main playlist debug: nothing to play
[0000000142e083c0] macosx interface debug: Playback has been ended
[0000000142e083c0] macosx interface debug: Releasing IOKit system sleep blocker (37463)


This is sort of confusing because when I run
ffmpeg -protocol_whitelist file,crypto,data,rtp,udp -i stream_1.sdp -vcodec libx264 -f null -
I see a number logs about

[h264 @ 0x139304080] non-existing PPS 0 referenced
 Last message repeated 1 times
[h264 @ 0x139304080] decode_slice_header error
[h264 @ 0x139304080] no frame!


After which I see the stream is received and I start getting telemetry on it :


Input #0, sdp, from 'stream_1.sdp':
 Metadata:
 title : RTP Stream
 Duration: N/A, start: 0.016667, bitrate: N/A
 Stream #0:0: Video: h264 (Constrained Baseline), yuv420p(progressive), 1200x200, 60 fps, 60 tbr, 90k tbn
Stream mapping:
 Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))
Press [q] to stop, [?] for help
[libx264 @ 0x107f04f40] using cpu capabilities: ARMv8 NEON
[libx264 @ 0x107f04f40] profile High, level 3.1, 4:2:0, 8-bit
Output #0, null, to 'pipe:':
 Metadata:
 title : RTP Stream
 encoder : Lavf61.7.100
 Stream #0:0: Video: h264, yuv420p(tv, progressive), 1200x200, q=2-31, 60 fps, 60 tbn
 Metadata:
 encoder : Lavc61.19.101 libx264
 Side data:
 cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
[out#0/null @ 0x60000069c000] video:144KiB audio:0KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: unknown
frame= 1404 fps= 49 q=-1.0 Lsize=N/A time=00:00:23.88 bitrate=N/A speed=0.834x


Not sure why VLC is turning me down like some kind of Berghain bouncer that lets nobody in the entire night.


I initially tried just converting the ARGB ints to a YUV420p buffer and used this to create the Frame objects but I couldn't for the life of me figure out how to properly initialize it as the attempts I made kept spitting out garbled junk.


Please go easy on me, I've made an unhealthy habit of resolving nearly all of my coding questions by simply lurking the internet for answers but that's not really helping me solve this issue.


Here's the Java I'm working on (the meat of the rtp comms occurs within
updateWaveformForPlayer()) :

package com.bugbytz.prolink;

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.bytedeco.ffmpeg.global.avcodec;
import org.bytedeco.ffmpeg.global.avutil;
import org.bytedeco.javacv.FFmpegFrameGrabber;
import org.bytedeco.javacv.FFmpegFrameRecorder;
import org.bytedeco.javacv.FFmpegLogCallback;
import org.bytedeco.javacv.Frame;
import org.bytedeco.javacv.FrameGrabber;
import org.deepsymmetry.beatlink.CdjStatus;
import org.deepsymmetry.beatlink.DeviceAnnouncement;
import org.deepsymmetry.beatlink.DeviceAnnouncementAdapter;
import org.deepsymmetry.beatlink.DeviceFinder;
import org.deepsymmetry.beatlink.Util;
import org.deepsymmetry.beatlink.VirtualCdj;
import org.deepsymmetry.beatlink.data.BeatGridFinder;
import org.deepsymmetry.beatlink.data.CrateDigger;
import org.deepsymmetry.beatlink.data.MetadataFinder;
import org.deepsymmetry.beatlink.data.TimeFinder;
import org.deepsymmetry.beatlink.data.WaveformDetail;
import org.deepsymmetry.beatlink.data.WaveformDetailComponent;
import org.deepsymmetry.beatlink.data.WaveformFinder;

import java.awt.*;
import java.awt.image.BufferedImage;
import java.io.File;
import java.nio.ByteBuffer;
import java.text.DecimalFormat;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Properties;
import java.util.Set;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.Executors;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.ScheduledFuture;
import java.util.concurrent.TimeUnit;

import static org.bytedeco.ffmpeg.global.avutil.AV_PIX_FMT_RGB24;

public class App {
 public static ArrayList<track> tracks = new ArrayList<>();
 public static boolean dbRead = false;
 public static Properties props = new Properties();
 private static Map recorders = new HashMap<>();
 private static Map frameCount = new HashMap<>();

 private static final ScheduledExecutorService scheduler = Executors.newScheduledThreadPool(1);
 private static final int FPS = 60;
 private static final int FRAME_INTERVAL_MS = 1000 / FPS;

 private static Map schedules = new HashMap<>();

 private static Set<integer> streamingPlayers = new HashSet<>();

 public static String byteArrayToMacString(byte[] macBytes) {
 StringBuilder sb = new StringBuilder();
 for (int i = 0; i < macBytes.length; i++) {
 sb.append(String.format("%02X%s", macBytes[i], (i < macBytes.length - 1) ? ":" : ""));
 }
 return sb.toString();
 }

 private static void updateWaveformForPlayer(int player) throws Exception {
 Integer frame_for_player = frameCount.get(player);
 if (frame_for_player == null) {
 frame_for_player = 0;
 frameCount.putIfAbsent(player, frame_for_player);
 }

 if (!WaveformFinder.getInstance().isRunning()) {
 WaveformFinder.getInstance().start();
 }
 WaveformDetail detail = WaveformFinder.getInstance().getLatestDetailFor(player);

 if (detail != null) {
 WaveformDetailComponent component = (WaveformDetailComponent) detail.createViewComponent(
 MetadataFinder.getInstance().getLatestMetadataFor(player),
 BeatGridFinder.getInstance().getLatestBeatGridFor(player)
 );
 component.setMonitoredPlayer(player);
 component.setPlaybackState(player, TimeFinder.getInstance().getTimeFor(player), true);
 component.setAutoScroll(true);
 int width = 1200;
 int height = 200;
 Dimension dimension = new Dimension(width, height);
 component.setPreferredSize(dimension);
 component.setSize(dimension);
 component.setScale(1);
 component.doLayout();

 // Create a fresh BufferedImage and clear it before rendering
 BufferedImage image = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);
 Graphics2D g = image.createGraphics();
 g.clearRect(0, 0, width, height); // Clear any old content

 // Draw waveform into the BufferedImage
 component.paint(g);
 g.dispose();

 int port = 5004 + player;
 String inputFile = port + "_" + frame_for_player + ".mp4";
 // Initialize the FFmpegFrameRecorder for YUV420P
 FFmpegFrameRecorder recorder_file = new FFmpegFrameRecorder(inputFile, width, height);
 FFmpegLogCallback.set(); // Enable FFmpeg logging for debugging
 recorder_file.setFormat("mp4");
 recorder_file.setVideoCodec(avcodec.AV_CODEC_ID_H264);
 recorder_file.setPixelFormat(avutil.AV_PIX_FMT_YUV420P); // Use YUV420P format directly
 recorder_file.setFrameRate(FPS);

 // Set video options
 recorder_file.setVideoOption("preset", "ultrafast");
 recorder_file.setVideoOption("tune", "zerolatency");
 recorder_file.setVideoOption("x264-params", "repeat-headers=1");
 recorder_file.setGopSize(FPS);
 try {
 recorder_file.start(); // Ensure this is called before recording any frames
 System.out.println("Recorder started successfully for player: " + player);
 } catch (org.bytedeco.javacv.FFmpegFrameRecorder.Exception e) {
 e.printStackTrace();
 }

 // Get all pixels in one call
 int[] pixels = new int[width * height];
 image.getRGB(0, 0, width, height, pixels, 0, width);
 recorder_file.recordImage(width,height,Frame.DEPTH_UBYTE,1,3 * width, AV_PIX_FMT_RGB24, ByteBuffer.wrap(argbToByteArray(pixels, width, height)));
 recorder_file.stop();
 recorder_file.release();
 final FFmpegFrameRecorder recorder = recorders.get(player);
 FFmpegFrameGrabber grabber = new FFmpegFrameGrabber(inputFile);


 try {
 grabber.start();
 } catch (Exception e) {
 e.printStackTrace();
 }
 if (recorder == null) {
 try {
 String outputStream = "rtp://127.0.0.1:" + port;
 FFmpegFrameRecorder initial_recorder = new FFmpegFrameRecorder(outputStream, grabber.getImageWidth(), grabber.getImageHeight());
 initial_recorder.setFormat("rtp");
 initial_recorder.setVideoCodec(avcodec.AV_CODEC_ID_H264);
 initial_recorder.setPixelFormat(avutil.AV_PIX_FMT_YUV420P);
 initial_recorder.setFrameRate(grabber.getFrameRate());
 initial_recorder.setGopSize(FPS);
 initial_recorder.setVideoOption("x264-params", "keyint=60");
 initial_recorder.setVideoOption("rtsp_transport", "tcp");
 initial_recorder.start();
 recorders.putIfAbsent(player, initial_recorder);
 frameCount.putIfAbsent(player, 0);
 putToRTP(player, grabber, initial_recorder);
 }
 catch (Exception e) {
 e.printStackTrace();
 }
 }
 else {
 putToRTP(player, grabber, recorder);
 }
 File file = new File(inputFile);
 if (file.exists() && file.delete()) {
 System.out.println("Successfully deleted file: " + inputFile);
 } else {
 System.out.println("Failed to delete file: " + inputFile);
 }
 }
 }

 public static void putToRTP(int player, FFmpegFrameGrabber grabber, FFmpegFrameRecorder recorder) throws FrameGrabber.Exception {
 final Frame frame = grabber.grabFrame();
 int frameCount_local = frameCount.get(player);
 frame.keyFrame = frameCount_local++ % FPS == 0;
 frameCount.put(player, frameCount_local);
 try {
 recorder.record(frame);
 } catch (FFmpegFrameRecorder.Exception e) {
 throw new RuntimeException(e);
 }
 }
 public static byte[] argbToByteArray(int[] argb, int width, int height) {
 int totalPixels = width * height;
 byte[] byteArray = new byte[totalPixels * 3]; // 4 bytes per pixel (ARGB)

 for (int i = 0; i < totalPixels; i++) {
 int argbPixel = argb[i];

 byteArray[i * 3] = (byte) ((argbPixel >> 16) & 0xFF); // Red
 byteArray[i * 3 + 1] = (byte) ((argbPixel >> 8) & 0xFF); // Green
 byteArray[i * 3 + 2] = (byte) (argbPixel & 0xFF); // Blue
 }

 return byteArray;
 }


 public static void main(String[] args) throws Exception {
 VirtualCdj.getInstance().setDeviceNumber((byte) 4);
 CrateDigger.getInstance().addDatabaseListener(new DBService());
 props.put("bootstrap.servers", "localhost:9092");
 props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
 props.put("value.serializer", "com.bugbytz.prolink.CustomSerializer");
 props.put(ProducerConfig.MAX_REQUEST_SIZE_CONFIG, "20971520");

 VirtualCdj.getInstance().addUpdateListener(update -> {
 if (update instanceof CdjStatus) {
 try (Producer producer = new KafkaProducer<>(props)) {
 DecimalFormat df_obj = new DecimalFormat("#.##");
 DeviceStatus deviceStatus = new DeviceStatus(
 update.getDeviceNumber(),
 ((CdjStatus) update).isPlaying() || !((CdjStatus) update).isPaused(),
 ((CdjStatus) update).getBeatNumber(),
 update.getBeatWithinBar(),
 Double.parseDouble(df_obj.format(update.getEffectiveTempo())),
 Double.parseDouble(df_obj.format(Util.pitchToPercentage(update.getPitch()))),
 update.getAddress().getHostAddress(),
 byteArrayToMacString(DeviceFinder.getInstance().getLatestAnnouncementFrom(update.getDeviceNumber()).getHardwareAddress()),
 ((CdjStatus) update).getRekordboxId(),
 update.getDeviceName()
 );
 ProducerRecord record = new ProducerRecord<>("device-status", "device-" + update.getDeviceNumber(), deviceStatus);
 try {
 producer.send(record).get();
 } catch (InterruptedException ex) {
 throw new RuntimeException(ex);
 } catch (ExecutionException ex) {
 throw new RuntimeException(ex);
 }
 producer.flush();
 if (!WaveformFinder.getInstance().isRunning()) {
 try {
 WaveformFinder.getInstance().start();
 } catch (Exception ex) {
 throw new RuntimeException(ex);
 }
 }
 }
 }
 });
 DeviceFinder.getInstance().addDeviceAnnouncementListener(new DeviceAnnouncementAdapter() {
 @Override
 public void deviceFound(DeviceAnnouncement announcement) {
 if (!streamingPlayers.contains(announcement.getDeviceNumber())) {
 streamingPlayers.add(announcement.getDeviceNumber());
 schedules.putIfAbsent(announcement.getDeviceNumber(), scheduler.scheduleAtFixedRate(() -> {
 try {
 Runnable task = () -> {
 try {
 updateWaveformForPlayer(announcement.getDeviceNumber());
 } catch (InterruptedException e) {
 System.out.println("Thread interrupted");
 } catch (Exception e) {
 throw new RuntimeException(e);
 }
 System.out.println("Lambda thread work completed!");
 };
 task.run();
 } catch (Exception e) {
 e.printStackTrace();
 }
 }, 0, FRAME_INTERVAL_MS, TimeUnit.MILLISECONDS));
 }
 }

 @Override
 public void deviceLost(DeviceAnnouncement announcement) {
 if (streamingPlayers.contains(announcement.getDeviceNumber())) {
 schedules.get(announcement.getDeviceNumber()).cancel(true);
 streamingPlayers.remove(announcement.getDeviceNumber());
 }
 }
 });
 BeatGridFinder.getInstance().start();
 MetadataFinder.getInstance().start();
 VirtualCdj.getInstance().start();
 TimeFinder.getInstance().start();
 DeviceFinder.getInstance().start();
 CrateDigger.getInstance().start();

 try {
 LoadCommandConsumer consumer = new LoadCommandConsumer("localhost:9092", "load-command-group");
 Thread consumerThread = new Thread(consumer::startConsuming);
 consumerThread.start();

 Runtime.getRuntime().addShutdownHook(new Thread(() -> {
 consumer.shutdown();
 try {
 consumerThread.join();
 } catch (InterruptedException e) {
 Thread.currentThread().interrupt();
 }
 }));
 Thread.sleep(60000);
 } catch (InterruptedException e) {
 System.out.println("Interrupted, exiting.");
 }
 }
}
</integer></track>

-
How to stream from camera to browser using ffmpeg [closed]
7 juin, par Gabriel FurstenheimFFMpeg just merged support for WebRTC.


If I understand correctly, this should allow to stream directly from ffmpeg to a website without the use of a server.


How does this work ?


ffmpeg -f dshow -i video=MyCamera ???


I guess something similar to


I'm guessing something like


ffmpeg -f dshow -i video=MyCamera -f webrtc udp://localhost:6666


Also, how would the website look like ?


For simplicity, assume that everything happens in localhost


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