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Stereo master soundtrack
17 octobre 2011, par
Mis à jour : Octobre 2011
Langue : English
Type : Audio
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#7 Ambience
16 octobre 2011, par
Mis à jour : Juin 2015
Langue : English
Type : Audio
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#6 Teaser Music
16 octobre 2011, par
Mis à jour : Février 2013
Langue : English
Type : Audio
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#5 End Title
16 octobre 2011, par
Mis à jour : Février 2013
Langue : English
Type : Audio
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#3 The Safest Place
16 octobre 2011, par
Mis à jour : Février 2013
Langue : English
Type : Audio
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#4 Emo Creates
15 octobre 2011, par
Mis à jour : Février 2013
Langue : English
Type : Audio
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Sur d’autres sites (6797)
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How to Choose the Optimal Multi-Touch Attribution Model for Your Organisation
13 mars 2023, par Erin — Analytics TipsIf you struggle to connect the dots on your customer journeys, you are researching the correct solution.
Multi-channel attribution models allow you to better understand the users’ paths to conversion and identify key channels and marketing assets that assist them.
That said, each attribution model has inherent limitations, which make the selection process even harder.
This guide explains how to choose the optimal multi-touch attribution model. We cover the pros and cons of popular attribution models, main evaluation criteria and how-to instructions for model implementation.
Pros and Cons of Different Attribution Models
First Interaction
First Interaction attribution model (also known as first touch) assigns full credit to the conversion to the first channel, which brought in a lead. However, it doesn’t report other interactions the visitor had before converting.
Marketers, who are primarily focused on demand generation and user acquisition, find the first touch attribution model useful to evaluate and optimise top-of-the-funnel (ToFU).
Pros
- Reflects the start of the customer journey
- Shows channels that bring in the best-qualified leads
- Helps track brand awareness campaigns
Cons
- Ignores the impact of later interactions at the middle and bottom of the funnel
- Doesn’t provide a full picture of users’ decision-making process
Last Interaction
Last Interaction attribution model (also known as last touch) shifts the entire credit allocation to the last channel before conversion. But it doesn’t account for the contribution of all other channels.
If your focus is conversion optimization, the last-touch model helps you determine which channels, assets or campaigns seal the deal for the prospect.
Pros
- Reports bottom-of-the-funnel events
- Requires minimal data and configurations
- Helps estimate cost-per-lead or cost-per-acquisition
Cons
- No visibility into assisted conversions and prior visitor interactions
- Overemphasise the importance of the last channel (which can often be direct traffic)
Last Non-Direct Interaction
Last Non-Direct attribution excludes direct traffic from the calculation and assigns the full conversion credit to the preceding channel. For example, a paid ad will receive 100% of credit for conversion if a visitor goes directly to your website to buy a product.
Last Non-Direct attribution provides greater clarity into the bottom-of-the-funnel (BoFU). events. Yet, it still under-reports the role other channels played in conversion.
Pros
- Improved channel visibility, compared to Last-Touch
- Avoids over-valuing direct visits
- Reports on lead-generation efforts
Cons
- Doesn’t work for account-based marketing (ABM)
- Devalues the quality over quantity of leads
Linear Model
Linear attribution model assigns equal credit for a conversion to all tracked touchpoints, regardless of their impact on the visitor’s decision to convert.
It helps you understand the full conversion path. But this model doesn’t distinguish between the importance of lead generation activities versus nurturing touches.
Pros
- Focuses on all touch points associated with a conversion
- Reflects more steps in the customer journey
- Helps analyse longer sales cycles
Cons
- Doesn’t accurately reflect the varying roles of each touchpoint
- Can dilute the credit if too many touchpoints are involved
Time Decay Model
Time decay models assumes that the closer a touchpoint is to the conversion, the greater its influence. Pre-conversion touchpoints get the highest credit, while the first ones are ranked lower (5%-5%-10%-15%-25%-30%).
This model better reflects real-life customer journeys. However, it devalues the impact of brand awareness and demand-generation campaigns.
Pros
- Helps track longer sales cycles and reports on each touchpoint involved
- Allows customising the half-life of decay to improve reporting
- Promotes conversion optimization at BoFu stages
Cons
- Can prompt marketers to curtail ToFU spending, which would translate to fewer qualified leads at lower stages
- Doesn’t reflect highly-influential events at earlier stages (e.g., a product demo request or free account registration, which didn’t immediately lead to conversion)
Position-Based Model
Position-Based attribution model (also known as the U-shaped model) allocates the biggest credit to the first and the last interaction (40% each). Then distributes the remaining 20% across other touches.
For many marketers, that’s the preferred multi-touch attribution model as it allows optimising both ToFU and BoFU channels.
Pros
- Helps establish the main channels for lead generation and conversion
- Adds extra layers of visibility, compared to first- and last-touch attribution models
- Promotes budget allocation toward the most strategic touchpoints
Cons
- Diminishes the importance of lead nurturing activities as more credit gets assigned to demand-gen and conversion-generation channels
- Limited flexibility since it always assigns a fixed amount of credit to the first and last touchpoints, and the remaining credit is divided evenly among the other touchpoints
How to Choose the Right Multi-Touch Attribution Model For Your Business
If you’re deciding which attribution model is best for your business, prepare for a heated discussion. Each one has its trade-offs as it emphasises or devalues the role of different channels and marketing activities.
To reach a consensus, the best strategy is to evaluate each model against three criteria : Your marketing objectives, sales cycle length and data availability.
Marketing Objectives
Businesses generate revenue in many ways : Through direct sales, subscriptions, referral fees, licensing agreements, one-off or retainer services. Or any combination of these activities.
In each case, your marketing strategy will look different. For example, SaaS and direct-to-consumer (DTC) eCommerce brands have to maximise both demand generation and conversion rates. In contrast, a B2B cybersecurity consulting firm is more interested in attracting qualified leads (as opposed to any type of traffic) and progressively nurturing them towards a big-ticket purchase.
When selecting a multi-touch attribution model, prioritise your objectives first. Create a simple scoreboard, where your team ranks various channels and campaign types you rely on to close sales.
Alternatively, you can survey your customers to learn how they first heard about your company and what eventually triggered their conversion. Having data from both sides can help you cross-validate your assumptions and eliminate some biases.
Then consider which model would best reflect the role and importance of different channels in your sales cycle. Speaking of which….
Sales Cycle Length
As shoppers, we spend less time deciding on a new toothpaste brand versus contemplating a new IT system purchase. Factors like industry, business model (B2C, DTC, B2B, B2BC), and deal size determine the average cycle length in your industry.
Statistically, low-ticket B2C sales can happen within just several interactions. The average B2B decision-making process can have over 15 steps, spread over several months.
That’s why not all multi-touch attribution models work equally well for each business. Time-decay suits better B2B companies, while B2C usually go for position-based or linear attribution.
Data Availability
Businesses struggle with multi-touch attribution model implementation due to incomplete analytics data.
Our web analytics tool captures more data than Google Analytics. That’s because we rely on a privacy-focused tracking mechanism, which allows you to collect analytics without showing a cookie consent banner in markets outside of Germany and the UK.
Cookie consent banners are mandatory with Google Analytics. Yet, almost 40% of global consumers reject it. This results in gaps in your analytics and subsequent inconsistencies in multi-touch attribution reports. With Matomo, you can compliantly collect more data for accurate reporting.
Some companies also struggle to connect collected insights to individual shoppers. With Matomo, you can cross-attribute users across browning sessions, using our visitors’ tracking feature.
When you already know a user’s identifier (e.g., full name or email address), you can track their on-site behaviours over time to better understand how they interact with your content and complete their purchases. Quick disclaimer, though, visitors’ tracking may not be considered compliant with certain data privacy laws. Please consult with a local authority if you have doubts.
How to Implement Multi-Touch Attribution
Multi-touch attribution modelling implementation is like a “seek and find” game. You have to identify all significant touchpoints in your customers’ journeys. And sometimes also brainstorm new ways to uncover the missing parts. Then figure out the best way to track users’ actions at those stages (aka do conversion and events tracking).
Here’s a step-by-step walkthrough to help you get started.
Select a Multi-Touch Attribution Tool
The global marketing attribution software is worth $3.1 billion. Meaning there are plenty of tools, differing in terms of accuracy, sophistication and price.
To make the right call prioritise five factors :
- Available models : Look for a solution that offers multiple options and allows you to experiment with different modelling techniques or develop custom models.
- Implementation complexity : Some providers offer advanced data modelling tools for creating custom multi-touch attribution models, but offer few out-of-the-box modelling options.
- Accuracy : Check if the shortlisted tool collects the type of data you need. Prioritise providers who are less dependent on third-party cookies and allow you to identify repeat users.
- Your marketing stack : Some marketing attribution tools come with useful add-ons such as tag manager, heatmaps, form analytics, user session recordings and A/B testing tools. This means you can collect more data for multi-channel modelling with them instead of investing in extra software.
- Compliance : Ensure that the selected multi-attribution analytics software wouldn’t put you at risk of GDPR non-compliance when it comes to user privacy and consent to tracking/analysis.
Finally, evaluate the adoption costs. Free multi-channel analytics tools come with data quality and consistency trade-offs. Premium attribution tools may have “hidden” licensing costs and bill you for extra data integrations.
Look for a tool that offers a good price-to-value ratio (i.e., one that offers extra perks for a transparent price).
Set Up Proper Data Collection
Multi-touch attribution requires ample user data. To collect the right type of insights you need to set up :
- Website analytics : Ensure that you have all tracking codes installed (and working correctly !) to capture pageviews, on-site actions, referral sources and other data points around what users do on page.
- Tags : Add tracking parameters to monitor different referral channels (e.g., “facebook”), campaign types (e.g., ”final-sale”), and creative assets (e.g., “banner-1”). Tags help you get a clearer picture of different touchpoints.
- Integrations : To better identify on-site users and track their actions, you can also populate your attribution tool with data from your other tools – CRM system, A/B testing app, etc.
Finally, think about the ideal lookback window — a bounded time frame you’ll use to calculate conversions. For example, Matomo has a default windows of 7, 30 or 90 days. But you can configure a custom period to better reflect your average sales cycle. For instance, if you’re selling makeup, a shorter window could yield better results. But if you’re selling CRM software for the manufacturing industry, consider extending it.
Configure Goals and Events
Goals indicate your main marketing objectives — more traffic, conversions and sales. In web analytics tools, you can measure these by tracking specific user behaviours.
For example : If your goal is lead generation, you can track :
- Newsletter sign ups
- Product demo requests
- Gated content downloads
- Free trial account registration
- Contact form submission
- On-site call bookings
In each case, you can set up a unique tag to monitor these types of requests. Then analyse conversion rates — the percentage of users who have successfully completed the action.
To collect sufficient data for multi-channel attribution modelling, set up Goal Tracking for different types of touchpoints (MoFU & BoFU) and asset types (contact forms, downloadable assets, etc).
Your next task is to figure out how users interact with different on-site assets. That’s when Event Tracking comes in handy.
Event Tracking reports notify you about specific actions users take on your website. With Matomo Event Tracking, you can monitor where people click on your website, on which pages they click newsletter subscription links, or when they try to interact with static content elements (e.g., a non-clickable banner).
Using in-depth user behavioural reports, you can better understand which assets play a key role in the average customer journey. Using this data, you can localise “leaks” in your sales funnel and fix them to increase conversion rates.
Test and Validated the Selected Model
A common challenge of multi-channel attribution modelling is determining the correct correlation and causality between exposure to touchpoints and purchases.
For example, a user who bought a discounted product from a Facebook ad would act differently than someone who purchased a full-priced product via a newsletter link. Their rate of pre- and post-sales exposure will also differ a lot — and your attribution model may not always accurately capture that.
That’s why you have to continuously test and tweak the selected model type. The best approach for that is lift analysis.
Lift analysis means comparing how your key metrics (e.g., revenue or conversion rates) change among users who were exposed to a certain campaign versus a control group.
In the case of multi-touch attribution modelling, you have to monitor how your metrics change after you’ve acted on the model recommendations (e.g., invested more in a well-performing referral channel or tried a new brand awareness Twitter ad). Compare the before and after ROI. If you see a positive dynamic, your model works great.
The downside of this approach is that you have to invest a lot upfront. But if your goal is to create a trustworthy attribution model, the best way to validate is to act on its suggestions and then test them against past results.
Conclusion
A multi-touch attribution model helps you measure the impact of different channels, campaign types, and marketing assets on metrics that matter — conversion rate, sales volumes and ROI.
Using this data, you can invest budgets into the best-performing channels and confidently experiment with new campaign types.
As a Matomo user, you also get to do so without breaching customers’ privacy or compromising on analytics accuracy.
Start using accurate multi-channel attribution in Matomo. Get your free 21-day trial now. No credit card required.
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ffmpeg decoding through C-API leads to artefacts when input resolution is 1200x1600. Am I doing something wrong ?
26 février 2023, par AntonioUsing the C-API and FFmpeg 5.1 I have been able to encode h264 videos with libx264 on Android.
Now I wanted to replay them on Linux inside my C++ application. These videos can be played correctly on a browser, or on other players that I tried like mplayer or ffplay from ffmpeg. Also, I can unroll the frames with
ffmpeg -i recording.mp4 -start_number 0 -qscale:v 5 %06d.jpgand the images look alright.

However in my C++ application every now and then, but in a very repeatable way, I get artifacts (like the bright pixels showing up above the monitor). They do not accumulate, even though they are not related to keyframes. So whatever error is going on, it doesn't seem to have an impact on subsequent frames. I use OpenCV to visualize the output, and I am pretty sure the problem is not the conversion to BGR because the artifact is already there if I simply show the y channel (luminance, grayscale).


These artifacts show up in videos that I have recorded with a 1200x1600 resolution. It is to be noted that 1200 is not divisible by 32 so ffmpeg does add some padding, but I am dealing with it and it's not an issue. Videos recorded at 1920x1440 are replayed with no artifacts. Two sample videos can be found here for download.


Here follows the code I am using, on the bottom you can see a picture of my decoded image with the artifact and the same as unrolled by ffmpeg command line. It should be noted that I am working with a custom built version of ffmpeg, out of conan packages, while the unrolling is done with ffmpeg from command line that comes with Ubuntu.


extern "C" {
#include <libavcodec></libavcodec>avcodec.h>
#include <libavformat></libavformat>avformat.h>
}

#include 
#include 
#include 

#include <iostream>
int main(int argc, char** argv) {

 int ret;

 auto pkt = av_packet_alloc();
 if (!pkt) {
 std::cerr << "Failed av_packet_alloc()" << std::endl;
 exit(1);
 }

 AVFormatContext* av_format = avformat_alloc_context();
 ret = avformat_open_input(&av_format, FILE_NAME, nullptr, nullptr);
 if (ret < 0) {
 std::cerr << "Failed avformat_open_input, Error: " << ret << std::endl;
 ///Error codes https://stackoverflow.com/questions/12780931/ffmpeg-exit-status-1094995529
 exit(1);
 }
 av_dump_format(av_format, 0, FILE_NAME, 0);
 auto video_st_number = av_find_best_stream(av_format, AVMEDIA_TYPE_VIDEO, -1, -1, nullptr, 0);
 if (video_st_number < 0) {
 std::cerr << "av_find_best_stream couldn't find video stream" << std::endl;
 exit(1);
 }
 auto video_st = av_format->streams[video_st_number];
 auto codec_id = video_st->codecpar->codec_id;
 std::cout << "Duration " << video_st->duration << std::endl;
 std::cout << "n_frames " << video_st->nb_frames << std::endl;

 auto frame = av_frame_alloc();
 if (!frame) {
 fprintf(stderr, "Could not allocate video frame\n");
 exit(1);
 }

 auto codec = avcodec_find_decoder(codec_id);
 if (!codec) {
 fprintf(stderr, "Codec not found\n");
 exit(1);
 }

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


 if ((ret = avcodec_parameters_to_context(c, video_st->codecpar))) {
 fprintf(stderr, "Failed avcodec_parameters_to_context\n");
 exit(1);
 }

 c->pix_fmt = AV_PIX_FMT_YUV420P;///Not really necessary
 c->thread_count = 1;///No impact

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

 std::size_t counter = 0;
 std::size_t n_keyframes = 0;

 while (ret >= 0) {
 ret = av_read_frame(av_format, pkt);
 if (pkt->size == 0) {
 std::cout << "Skipping packet of size zero" << std::endl;
 av_packet_unref(pkt);
 continue;
 }
 while (avcodec_send_packet(c, pkt) != 0) {
 if (avcodec_receive_frame(c, frame) != 0) {
 std::cerr << "Error receiving frame" << std::endl;
 exit(1);
 } else {
 n_keyframes += frame->key_frame;
 std::cout << "Decoded " << ++counter << " frames. Frame No. " << frame->pts / pkt->duration << " "
 << frame->decode_error_flags << " " << frame->key_frame << " " << n_keyframes << " "
 << frame->pkt_dts << std::endl;
 }
 display(frame);
 }
 av_packet_unref(pkt);
 }

 avcodec_send_packet(c, nullptr);
 std::cout << "Flushing decoder" << std::endl;

 while (avcodec_receive_frame(c, frame) == 0) {
 n_keyframes += frame->key_frame;
 std::cout << "Decoded " << ++counter << " frames. Frame No. " << frame->pts << " " << frame->decode_error_flags
 << " " << frame->key_frame << " " << n_keyframes << " " << frame->pkt_dts << std::endl;

 display(frame);
 }

 avcodec_free_context(&c);
 avformat_free_context(av_format);
 av_frame_free(&frame);
 av_packet_free(&pkt);

 return 0;
}
</iostream>




For completeness, this is the
displayfunction, using openCV

void display(const AVFrame* frame) {
 static std::vector yuv_buffer;
 yuv_buffer.resize(frame->linesize[0] * 3 / 2 * frame->width);
 cv::Mat mYUV(frame->height * 3 / 2, frame->width, CV_8UC1, yuv_buffer.data(), frame->linesize[0]);
 memcpy(mYUV.ptr(), frame->data[0], frame->linesize[0] * frame->height);
 //cv::imshow("grayscale", mYUV.rowRange(0, frame->height));
 //cv::imshow("u", cv::Mat(frame->height / 2, frame->width / 2, CV_8UC1, frame->data[1], frame->linesize[1]));
 //cv::imshow("v", cv::Mat(frame->height / 2, frame->width / 2, CV_8UC1, frame->data[2], frame->linesize[2]));

 int dest_row = frame->height;
 for (int j = 0; j < frame->height / 2; j++) {
 memcpy(mYUV.ptr(dest_row), frame->data[1] + frame->linesize[1] * j, frame->width);
 j++;
 memcpy(mYUV.ptr(dest_row) + frame->width / 2, frame->data[1] + frame->linesize[1] * j, frame->width);
 dest_row++;
 }
 for (int j = 0; j < frame->height / 2; j++) {
 memcpy(mYUV.ptr(dest_row), frame->data[2] + frame->linesize[2] * j, frame->width);
 j++;
 memcpy(mYUV.ptr(dest_row) + frame->width / 2, frame->data[2] + frame->linesize[2] * j, frame->width);
 dest_row++;
 }
 cv::Mat mRGB(frame->height, frame->width, CV_8UC3);
 cvtColor(mYUV, mRGB, cv::COLOR_YUV2BGR_I420, 3);
 cv::imshow("Video", mRGB);
 cv::waitKey(0);
}



Note : The
AVFrame->cv::Matconverter is now available in corrected version as answer here.

-
Can I Config FFMEG-2.4.3 to use in android windows 64 ?
13 novembre 2014, par Ngo KyI want to build ffmpeg-2.4.3 (lastest c lib) to use it on android for managing video.
my OS is windows 7 Utimate x64
cywin64 in directory : C :/cywin64/ have make installed.
NDK in directory : D :\solfware\Programming\android-ndk-r10ci follow toturial form : http://www.roman10.net/how-to-build-ffmpeg-for-android/comment-page-1/
my project struture like :
i create build_android.sh like roman10 as following code :
# ARMv7+Neon (Cortex-A8)
# Customizing:
# 1. Feel free to change ./configure parameters for more features
# 2. To adapt other ARM variants
# set $CPU and $OPTIMIZE_CFLAGS
# call build_one
######################################################
NDK=D:\solfware\Programming\android-ndk-r10c
PLATFORM=$NDK/platforms/android-8/arch-arm/
PREBUILT=$NDK/prebuilt/windows-x86_64
function build_one
{
./configure --target-os=linux \
--prefix=$PREFIX \
--enable-cross-compile \
--extra-libs="-lgcc" \
--arch=arm \
--cc=$PREBUILT/bin/arm-linux-androideabi-gcc \
--cross-prefix=$PREBUILT/bin/arm-linux-androideabi- \
--nm=$PREBUILT/bin/arm-linux-androideabi-nm \
--sysroot=$PLATFORM \
--extra-cflags=" -O3 -fpic -DANDROID -DHAVE_SYS_UIO_H=1 -Dipv6mr_interface=ipv6mr_ifindex -fasm -Wno-psabi -fno-short-enums -fno-strict-aliasing -finline-limit=300 $OPTIMIZE_CFLAGS " \
--disable-shared \
--enable-static \
--extra-ldflags="-Wl,-rpath-link=$PLATFORM/usr/lib -L$PLATFORM/usr/lib -nostdlib -lc -lm -ldl -llog" \
--disable-everything \
--enable-demuxer=mov \
--enable-demuxer=h264 \
--disable-ffplay \
--enable-protocol=file \
--enable-avformat \
--enable-avcodec \
--enable-decoder=rawvideo \
--enable-decoder=mjpeg \
--enable-decoder=h263 \
--enable-decoder=mpeg4 \
--enable-decoder=h264 \
--enable-parser=h264 \
--disable-network \
--enable-zlib \
--disable-avfilter \
--disable-avdevice \
$ADDITIONAL_CONFIGURE_FLAG
make clean
make -j4 install
$PREBUILT/bin/arm-linux-androideabi-ar d libavcodec/libavcodec.a inverse.o
$PREBUILT/bin/arm-linux-androideabi-ld -rpath-link=$PLATFORM/usr/lib -L$PLATFORM/usr/lib -soname libffmpeg.so -shared -nostdlib -z,noexecstack -Bsymbolic --whole-archive --no-undefined -o $PREFIX/libffmpeg.so libavcodec/libavcodec.a libavformat/libavformat.a libavutil/libavutil.a libswscale/libswscale.a -lc -lm -lz -ldl -llog --warn-once --dynamic-linker=/system/bin/linker $PREBUILT/lib/gcc/arm-linux-androideabi/4.4.3/libgcc.a
}
#arm v6
#CPU=armv6
#OPTIMIZE_CFLAGS="-marm -march=$CPU"
#PREFIX=./android/$CPU
#ADDITIONAL_CONFIGURE_FLAG=
#build_one
#arm v7vfpv3
CPU=armv7-a
OPTIMIZE_CFLAGS="-mfloat-abi=softfp -mfpu=vfpv3-d16 -marm -march=$CPU "
PREFIX=./android/$CPU
ADDITIONAL_CONFIGURE_FLAG=
build_one
#arm v7vfp
#CPU=armv7-a
#OPTIMIZE_CFLAGS="-mfloat-abi=softfp -mfpu=vfp -marm -march=$CPU "
#PREFIX=./android/$CPU-vfp
#ADDITIONAL_CONFIGURE_FLAG=
#build_one
#arm v7n
#CPU=armv7-a
#OPTIMIZE_CFLAGS="-mfloat-abi=softfp -mfpu=neon -marm -march=$CPU -mtune=cortex-a8"
#PREFIX=./android/$CPU
#ADDITIONAL_CONFIGURE_FLAG=--enable-neon
#build_one
#arm v6+vfp
#CPU=armv6
#OPTIMIZE_CFLAGS="-DCMP_HAVE_VFP -mfloat-abi=softfp -mfpu=vfp -marm -march=$CPU"
#PREFIX=./android/${CPU}_vfp
#ADDITIONAL_CONFIGURE_FLAG=
#build_onei have set my path like above
I built it in Eclipse Everything is ok. but when i ran it in Android it crashed Unfortunately.
I found the errors in Logcat monitor :E/AndroidRuntime(1931) : java.lang.UnsatisfiedLinkError : Couldn’t load avcodec-55 from loader dalvik.system.PathClassLoader[DexPathList [1]] : findLibrary returned null
E/AndroidRuntime(1931) : at java.lang.Runtime.loadLibrary(Runtime.java:358)
In my activity i just call add Library from C preference like :
static {
//System.loadLibrary("avutil-52");
System.loadLibrary("avcodec-55");
System.loadLibrary("avformat-55");
System.loadLibrary("swscale-2");
System.loadLibrary("tutorial02");
}i check in DDMS in app-lib there is no existing Library in there.
The question is : Can i can compile the ffmpeg on windows ?
if ok. how can i config build_android.sh to point to Android $PREBUILTPLATFORM=$NDK/platforms/android-8/arch-arm/
PREBUILT=$NDK/prebuilt/windows-x86_64
function build_one
{
./configure --target-os=linux \
--prefix=$PREFIX \
--enable-cross-compile \
--extra-libs="-lgcc" \
--arch=arm \
--cc=$PREBUILT/bin/arm-linux-androideabi-gcc \
--cross-prefix=$PREBUILT/bin/arm-linux-androideabi- \
--nm=$PREBUILT/bin/arm-linux-androideabi-nm \
--sysroot=$PLATFORM \
--extra-cflags=" -O3 -fpic -DANDROID -DHAVE_SYS_UIO_H=1 -Dipv6mr_interface=ipv6mr_ifindex -fasm -Wno-psabi -fno-short-enums -fno-strict-aliasing -finline-limit=300 $OPTIMIZE_CFLAGS " \
--disable-shared \i found that in $NDK/prebuilt/windows-x86_64/bin did not exist arm-linux-androideabi-gcc\
How can i config to be suitable to run on windows ?Thank in advanced
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