Recherche avancée

Sur d’autres sites (9042)

• How can I develop Image recognition program

  18 juillet 2017, par SeongHyun Lee

  My program will recognize the advertisement between each TV Programs.
  But I don’t know how to recognize the ads.
  I had Sound Recognition in mind but It’s so difficult.
  I’m using FFmpeg Library.
  There is VideoState struct Reference.

  typedef struct VideoState {
  
  SDL_Thread *read_tid;
  
  SDL_Thread *video_tid;
  
  SDL_Thread *refresh_tid;
  
  AVInputFormat *iformat;
  
  int no_background;
  
  int abort_request;
  
  int force_refresh;
  
  int paused;
  
  int last_paused;
  
  int que_attachments_req;
  
  int seek_req;
  
  int seek_flags;
  
  int64_t seek_pos;
  
  int64_t seek_rel;
  
  int read_pause_return;
  
  AVFormatContext *ic;
  
  
  
  int audio_stream;
  
  
  
  int av_sync_type;
  
  double external_clock; /* external clock base */
  
  int64_t external_clock_time;
  
  
  
  double audio_clock;
  
  double audio_diff_cum; /* used for AV difference average computation */
  
  double audio_diff_avg_coef;
  
  double audio_diff_threshold;
  
  int audio_diff_avg_count;
  
  AVStream *audio_st;
  
  PacketQueue audioq;
  
  int audio_hw_buf_size;
  
  DECLARE_ALIGNED(16,uint8_t,audio_buf2)[AVCODEC_MAX_AUDIO_FRAME_SIZE * 4];
  
  uint8_t silence_buf[SDL_AUDIO_BUFFER_SIZE];
  
  uint8_t *audio_buf;
  
  uint8_t *audio_buf1;
  
  unsigned int audio_buf_size; /* in bytes */
  
  int audio_buf_index; /* in bytes */
  
  int audio_write_buf_size;
  
  AVPacket audio_pkt_temp;
  
  AVPacket audio_pkt;
  
  struct AudioParams audio_src;
  
  struct AudioParams audio_tgt;
  
  struct SwrContext *swr_ctx;
  
  double audio_current_pts;
  
  double audio_current_pts_drift;
  
  int frame_drops_early;
  
  int frame_drops_late;
  
  AVFrame *frame;
  
  
  
  enum ShowMode {
  
      SHOW_MODE_NONE = -1, SHOW_MODE_VIDEO = 0, SHOW_MODE_WAVES, 
  
  SHOW_MODE_RDFT, SHOW_MODE_NB
  
  } show_mode;
  
  int16_t sample_array[SAMPLE_ARRAY_SIZE];
  
  int sample_array_index;
  
  int last_i_start;
  
  RDFTContext *rdft;
  
  int rdft_bits;
  
  FFTSample *rdft_data;
  
  int xpos;
  
  
  
  SDL_Thread *subtitle_tid;
  
  int subtitle_stream;
  
  int subtitle_stream_changed;
  
  AVStream *subtitle_st;
  
  PacketQueue subtitleq;
  
  SubPicture subpq[SUBPICTURE_QUEUE_SIZE];
  
  int subpq_size, subpq_rindex, subpq_windex;
  
  SDL_mutex *subpq_mutex;
  
  SDL_cond *subpq_cond;
  
  
  
  double frame_timer;
  
  double frame_last_pts;
  
  double frame_last_duration;
  
  double frame_last_dropped_pts;
  
  double frame_last_returned_time;
  
  double frame_last_filter_delay;
  
  int64_t frame_last_dropped_pos;
  
  double video_clock;                          ///< pts of last decoded frame 
  
  / predicted pts of next decoded frame
  
  int video_stream;
  
  AVStream *video_st;
  
  PacketQueue videoq;
  
  double video_current_pts;                    ///< current displayed pts 
  
  (different from video_clock if frame fifos are used)
  
  double video_current_pts_drift;              ///< video_current_pts - time 
  
  (av_gettime) at which we updated video_current_pts - used to have running 
  
  video pts
  
  int64_t video_current_pos;                   ///< current displayed file pos
  
  VideoPicture pictq[VIDEO_PICTURE_QUEUE_SIZE];
  
  int pictq_size, pictq_rindex, pictq_windex;
  
  SDL_mutex *pictq_mutex;
  
  SDL_cond *pictq_cond;
  
  #if !CONFIG_AVFILTER
  
  struct SwsContext *img_convert_ctx;
  
  #endif
  
  
  
  char filename[1024];
  
  int width, height, xleft, ytop;
  
  int step;
  
  
  
  #if CONFIG_AVFILTER
  
  AVFilterContext *in_video_filter;           ///< the first filter in the 
  
  video chain
  
  AVFilterContext *out_video_filter;          ///< the last filter in the 
  
  video chain
  
  int use_dr1;
  
  FrameBuffer *buffer_pool;
  
  #endif
  
  
  
  int refresh;
  
  int last_video_stream, last_audio_stream, last_subtitle_stream;
  
  
  
  SDL_cond *continue_read_thread;
  
  
  
  enum V_Show_Mode v_show_mode;
  
  } VideoState;

  What can I use for My Program.... I really need your help.. Thank you !!!

• What is a Cohort Report ? A Beginner’s Guide to Cohort Analysis

  3 janvier 2024, par Erin

  Handling your user data as a single mass of numbers is rarely conducive to figuring out meaningful patterns you can use to improve your marketing campaigns.

  A cohort report (or cohort analysis) can help you quickly break down that larger audience into sequential segments and contrast and compare based on various metrics. As such, it is a great tool for unlocking more granular trends and insights — for example, identifying patterns in engagement and conversions based on the date users first interacted with your site.

  In this guide, we explain the basics of the cohort report and the best way to set one up to get the most out of it.

  What is a cohort report ?

  In a cohort report, you divide a data set into groups based on certain criteria — typically a time-based cohort metric like first purchase date — and then analyse the data across those segments, looking for patterns.

  Date-based cohort analysis is the most common approach, often creating cohorts based on the day a user completed a particular action — signed up, purchased something or visited your website. Depending on the metric you choose to measure (like return visits), the cohort report might look something like this :

  

  Note that this is not a universal benchmark or anything of the sort. The above is a theoretical cohort analysis based on app users who downloaded the app, tracking and comparing the retention rates as the days go by. 

  The benchmarks will be drastically different depending on the metric you’re measuring and the basis for your cohorts. For example, if you’re measuring returning visitor rates among first-time visitors to your website, expect single-digit percentages even on the second day.

  Your industry will also greatly affect what you consider positive in a cohort report. For example, if you’re a subscription SaaS, you’d expect high continued usage rates over the first week. If you sell office supplies to companies, much less so.

  What is an example of a cohort ?

  As we just mentioned, a typical cohort analysis separates users or customers by the date they first interacted with your business — in this case, they downloaded your app. Within that larger analysis, the users who downloaded it on May 3 represent a single cohort.

  

  In this case, we’ve chosen behaviour and time — the app download day — to separate the user base into cohorts. That means every specific day denotes a specific cohort within the analysis.

  Diving deeper into an individual cohort may be a good idea for important holidays or promotional events like Black Friday.

  Of course, cohorts don’t have to be based on specific behaviour within certain periods. You can also create cohorts based on other dimensions :

  • Transactional data — revenue per user
  • Churn data — date of churn
  • Behavioural cohort — based on actions taken on your website, app or e-commerce store, like the number of sessions per user or specific product pages visited
  • Acquisition cohort — which channel referred the user or customer

  For more information on different cohort types, read our in-depth guide on cohort analysis.

  How to create a cohort report (and make sense of it)

  Matomo makes it easy to view and analyse different cohorts (without the privacy and legal implications of using Google Analytics).

  Here are a few different ways to set up a cohort report in Matomo, starting with our built-in cohorts report.

  Cohort reports

  With Matomo, cohort reports are automatically compiled based on the first visit date. The default metric is the percentage of returning visitors.

  

  Changing the settings allows you to create multiple variations of cohort analysis reports.

  Break down cohorts by different metrics

  The percentage of returning visits can be valuable if you’re trying to improve early engagement in a SaaS app onboarding process. But it’s far from your only option.

  You can also compare performance by conversion, revenue, bounce rate, actions per visit, average session duration or other metrics.

  

  Change the time and scope of your cohort analysis

  Splitting up cohorts by single days may be useless if you don’t have a high volume of users or visitors. If the average cohort size is only a few users, you won’t be able to identify reliable patterns. 

  Matomo lets you set any time period to create your cohort analysis report. Instead of the most recent days, you can create cohorts by week, month, year or custom date ranges. 

  

  Cohort sizes will depend on your customer base. Make sure each cohort is large enough to encapsulate all the customers in that cohort and not so small that you have insignificant cohorts of only a few customers. Choose a date range that gives you that without scaling it too far so you can’t identify any seasonal trends.

  Cohort analysis can be a great tool if you’ve recently changed your marketing, product offering or onboarding. Set the data range to weekly and look for any impact in conversions and revenue after the changes.

  Using the “compare to” feature, you can also do month-over-month, quarter-over-quarter or any custom date range comparisons. This approach can help you get a rough overview of your campaign’s long-term progress without doing any in-depth analysis.

  You can also use the same approach to compare different holiday seasons against each other.

  If you want to combine time cohorts with segmentation, you can run cohort reports for different subsets of visitors instead of all visitors. This can lead to actionable insights like adjusting weekend or specific seasonal promotions to improve conversion rates.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

  Start for free

  No credit card required

  Easily create custom cohort reports beyond the time dimension

  If you want to split your audience into cohorts by focusing on something other than time, you will need to create a custom report and choose another dimension. In Matomo, you can choose from a wide range of cohort metrics, including referrers, e-commerce signals like viewed product or product category, form submissions and more.

  

  Then, you can create a simple table-based report with all the insights you need by choosing the metrics you want to see. For example, you could choose average visit duration, bounce rate and other usage metrics.

  

  If you want more revenue-focused insights, add metrics like conversions, add-to-cart and other e-commerce events.

  Custom reports make it easy to create cohort reports for almost any dimension. You can use any metric within demographic and behavioural analytics to create a cohort. (You can explore the complete list of our possible segmentation metrics.)

  We cover different types of custom reports (and ideas for specific marketing campaigns) in our guide on custom segmentation.

  Create your first cohort report and gain better insights into your visitors

  Cohort reports can help you identify trends and the impact of short-term marketing efforts like events and promotions.

  With Matomo cohort reports you have the power to create complex custom reports for various cohorts and segments. 

  If you’re looking for a powerful, easy-to-use web analytics solution that gives you 100% accurate data without compromising your users’ privacy, Matomo is a great fit. Get started with a 21-day free trial today. No credit card required. 

  Try Matomo for Free

  21 day free trial. No credit card required.

  
  

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I accept the terms and conditions and data processing agreement (DPA)

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  
  

  Start improving your websites now

  

• My SBC Collection

  31 décembre 2023, par Multimedia Mike — General

  Like many computer nerds in the last decade, I have accumulated more than a few single-board computers, or “SBCs”, which are small computers based around a system-on-a-chip (SoC) that nearly always features an ARM CPU at its core. Surprisingly few of these units are Raspberry Pi units, though that brand has come to exemplify and dominate the product category.

  Also, as is the case for many computer nerds, most of these SBCs lay fallow for years at a time. Equipped with an inexpensive lightbox that I procured in the last year, I decided I could at least create glamour shots of various units and catalog them in a blog post.

  While Raspberry Pi still enjoys the most mindshare far and away, and while I do have a few Raspberry Pi units in my inventory, I have always been a bigger fan of the ODROID brand, which works with convenient importers around the world (in the USA, I can vouch for Ameridroid, to whom I’ve forked over a fair amount of cash for these computing toys).

  As mentioned, Raspberry Pi undisputedly has the most mindshare of all these SBC brands and I often wonder why… and then I immediately remind myself that it has the biggest ecosystem, and has a variety of turnkey projects and applications (such as Pi-hole and PiVPN) that promise a lower barrier to entry — as well as a slightly lower price point — than some of these other options. ODROID had a decent ecosystem for awhile, especially considering the monthly ODROID Magazine, though that ceased publication in July 2020. The Raspberry Pi and its variants were famously difficult to come by due to the global chip shortage from 2021-2023. Meanwhile, I had no trouble procuring these boards during the same timeframe.

  So let’s delve into the collection…
  

  Cubieboard
  The Raspberry Pi came out in 2012 and by 2013 I was somewhat coveting one to hack on. Finally ! An accessible ARM platform to play with. I had heard of the BeagleBoard for years but never tried to get my hands on one. I was thinking about taking the plunge on a new Raspberry Pi, but a colleague told me I should skip that and go with this new hotness called the Cubieboard, based on an Allwinner SoC. The big value-add that this board had vs. a Raspberry Pi was that it had a SATA adapter. Although now that it has been a decade, it only now occurs to me to quander whether it was true SATA or a USB-to-SATA bridge. Looking it up now, I’m led to believe that the SoC supported the functionality natively.

  Anyway, I did get it up and running but never did much with it, thus setting the tone for future SBC endeavors. No photos because I gave it to another tech enthusiast years ago, whose SBC collection dwarfs my own.

  ODROID-XU4
  I can’t recall exactly when or how I first encountered the ODROID brand. I probably read about it on some enthusiast page or another circa 2014 and decided to try one out. I eventually acquired a total of 3 of these ODROID-XU4 units, each with a different case, 1 with a fan and 2 passively-cooled :

  

  Collection of ODROID-XU4 SBCs

  This is based on the Samsung Exynos 5422 SoC, the same series as was used in their Note 3 phone released in 2013. It has been a fun chip to play with. The XU4 was also my first introduction to the eMMC storage solution that is commonly supported on the ODROID SBCs (alongside micro-SD). eMMC offers many benefits over SD in terms of read/write speed as well as well as longevity/write cycles. That’s getting less relevant these days, however, as more and more SBCs are being released with direct NVMe SSD support.

  I had initially wanted to make a retro-gaming device built on this platform (see the handheld section later for more meditations on that). In support of this common hobbyist goal, there is this nifty case XU4 case which apes the aesthetic of the Nintendo N64 :

  

  ODROID-XU4 N64-style case

  It even has a cool programmable LCD screen. Maybe one day I’ll find a use for it.

  For awhile, one of these XU4 units (likely the noisy, fan-cooled one) was contributing results to the FFmpeg FATE system.

  While it features gigabit ethernet and a USB3 port, I once tried to see if I could get 2 Gbps throughput with the unit using a USB3-gigabit dongle. I had curious results in that the total amount of traffic throughput could never exceed 1 Gbps across both interfaces. I.e., if 1 interface was dealing with 1 Gbps and the other interface tried to run at 1 Gbps, they would both only run at 500 Mbps. That remains a mystery to me since I don’t see that limitation with Intel chips.

  Still, the XU4 has been useful for a variety of projects and prototyping over the years.

  ODROID-HC2 NAS
  I find that a lot of my fellow nerds massively overengineer their homelab NAS setups. I’ll explore this in a future post. For my part, people tend to find my homelab NAS solution slightly underengineered. This is the ODROID-HC2 (the “HC” stands for “Home Cloud”) :

  

  ODROID-HC2 NAS

  It has the same guts as the ODROID-XU4 except no video output and the USB3 function is leveraged for a SATA bridge. This allows you to plug a SATA hard drive directly into the unit :

  

  ODROID-HC2 NAS uncovered

  Believe it or not, this has been my home NAS solution for something like 6 or 7 years now– I don’t clearly remember when I purchased it and put it into service.

  But isn’t this sort of irresponsible ? What about a failure of the main drive ? That’s why I have an external drive connected for backing up the most important data via rsync :

  

  ODROID-HC2 NAS backup enclosure

  The power consumption can’t be beat– Profiling for a few weeks of average usage worked out to 4.5 kWh for the ODROID-HC2… per month.

  ODROID-C2
  I was on a kick of ordering more SBCs at one point. This is the ODROID-C2, equipped with a 64-bit Amlogic SoC :

  

  ODROID-C2

  I had this on the FATE farm for awhile, performing 64-bit ARM builds (vs. the XU4’s 32-bit builds). As memory serves, it was unreliable and would occasionally freeze up.

  Here is a view of the eMMC storage through the bottom of the translucent case :

  

  Bottom of ODROID-C2 with view of eMMC storage

  ODROID-N2+
  Out of all my ODROID SBCs, this is the unit that I long to “get back to” the most– the ODROID-N2+ :

  

  ODROID-N2+

  Very capable unit that makes a great little desktop. I have some projects I want to develop using it so that it will force me to have a focused development environment.

  Raspberry Pi
  Eventually, I did break down and get a Raspberry Pi. I had a specific purpose in mind and, much to my surprise, I have stuck to it :

  

  Original Raspberry Pi

  I was using one of the ODROID-XU4 units as a VPN gateway. Eventually, I wanted to convert the XU4 to something else and I decided to run the VPN gateway as an appliance on the simplest device I could. So I procured this complete hand-me-down unit from eBay and went to work. This was also the first time I discovered the DietPi distribution and this box has been in service running Wireguard via PiVPN for many years.

  I also have a Raspberry Pi 3B+ kicking around somewhere. I used it as a Steam Link device for awhile.

  SOPINE + Baseboard
  Also procured when I was on this “let’s buy random SBCs” kick. The Pine64 SOPINE is actually a compute module that comes in the form factor of a memory module.

  

  Pine64 SOPINE Compute Module

  Back to using Allwinner SoCs. In order to make this thing useful, you need to place it in something. It’s possible to get a mini-ITX form factor board that can accommodate 7 of these modules. Before going to that extreme, there is this much simpler baseboard which can also use eMMC for storage.

  

  Baseboard with SOPINE, eMMC, and heat sinks

  I really need to find an appropriate case for this one as it currently performs its duty while sitting on an anti-static bag.

  NanoPi NEO3
  I enjoy running the DietPi distribution on many of these SBCs (as it’s developed not just for Raspberry Pi). I have also found their website to be a useful resource for discovering new SBCs. That’s how I found the NanoPi series and zeroed in on this NEO3 unit, sporting a Rockchip SoC, and photographed here with some American currency in order to illustrate its relative size :

  

  NanoPi NEO3

  I often forget about this computer because it’s off in another room, just quietly performing its assigned duty.

  MangoPi MQ-Pro
  So far, I’ve heard of these fruits prepending the Greek letter pi for naming small computing products :

  • Raspberry – the O.G.
  • Banana – seems to be popular for hobbyist router/switches
  • Orange
  • Atomic
  • Nano
  • Mango

  Okay, so the AtomicPi and NanoPi names don’t really make sense considering the fruit convention.

  Anyway, the newest entry is the MangoPi. These showed up on Ameridroid a few months ago. There are 2 variants : the MQ-Pro and the MQ-Quad. I picked one and rolled with it.

  

  MangoPi MQ-Pro pieces arrive

  When it arrived, I unpacked it, assembled the pieces, downloaded a distro, tossed that on a micro-SD card, connected a monitor and keyboard to it via its USB-C port, got the distro up and running, configured the wireless networking with a static IP address and installed sshd, and it was ready to go as a headless server for an edge application.

  

  MangoPi MQ-Pro components, ready for assembly

  The unit came with no instructions that I can recall. After I got it set up, I remember thinking, “What is wrong with me ? Why is it that I just know how to do all of this without any documentation ?”

  

  MangoPi MQ-Pro in first test

  Only after I got it up and running and poked around a bit did I realize that this SBC doesn’t have an ARM SoC– it’s a RISC-V SoC. It uses the Allwinner D1, so it looks like I came full circle back to Allwinner.

  

  MangoPi MQ-Pro with more US coinage for scale

  So I now have my first piece of RISC-V hobbyist kit, although I learned recently from Kostya that it’s not that great for multimedia.

  Handheld Gaming Units
  The folks at Hardkernel have also produced a series of handheld retro-gaming devices called ODROID-GO. The first one resembled the original Nintendo Game Boy, came as a kit to be assembled, and emulated 5 classic consoles. It also had some hackability to it. Quite a cool little device, and inexpensive too. I have since passed it along to another gaming enthusiast.

  Later came the ODROID-GO Advance, also a kit, but emulating more devices. I was extremely eager to get my hands on this since it could emulate SNES in addition to NES. It also features a headphone jack, unlike the earlier model. True to form, after I received mine, it took me about 13 months before I got around to assembling it. After that, the biggest challenge I had was trying to find an appropriate case for it.

  

  ODROID-GO Advance with case and headphones

  Even though it may try to copy the general aesthetic and form factor of the Game Boy Advance, cases for the GBA don’t fit this correctly.

  Further, Hardkernel have also released the ODROID-GO Super and Ultra models that do more and more. The Advance, Super, and Ultra models have powerful SoCs and feature much more hackability than the first ODROID-GO model.

  I know that the guts of the Advance have been used in other products as well. The same is likely true for the Super and Ultra.

  Ultimately, the ODROID-GO Advance was just another project I assembled and then set aside since I like the idea of playing old games much more than actually doing it. Plus, the fact has finally crystalized in my mind over the past few years that I have never enjoyed handheld gaming and likely will never enjoy handheld gaming, even after I started wearing glasses. Not that I’m averse to old Game Boy / Color / Advance games, but if I’m going to play them, I’d rather emulate them on a large display.

  The Future
  In some of my weaker moments, I consider ordering up certain Banana Pi products (like the Banana Pi BPI-R2) with a case and doing my own router tricks using some open source router/firewall solution. And then I remind myself that my existing prosumer-type home router is doing just fine. But maybe one day…

  The post My SBC Collection first appeared on Breaking Eggs And Making Omelettes.