Recherche avancée
Autres articles (112)
-
Personnaliser en ajoutant son logo, sa bannière ou son image de fond
5 septembre 2013, parCertains thèmes prennent en compte trois éléments de personnalisation : l’ajout d’un logo ; l’ajout d’une bannière l’ajout d’une image de fond ;
-
Websites made with MediaSPIP
2 mai 2011, parThis page lists some websites based on MediaSPIP.
-
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" (...)
Sur d’autres sites (14984)
-
Unable to split audio using easy_audio_trimmer
27 juillet 2023, par Sana Wasimcan we use the easy_audio_trimmer package to split an audio ? I tried using the ffmpeg but it is conflicting with the above package and not work.


I tried splitting by using these functions and it gave an error at the FlutterFFmpeg() method and i cant find an alternative also the duration(filePath) in the command final durationResult = await flutterSound.duration(filePath) ; shows an error


Future<void> _splitAudio() async {
 setState(() {
 _progressVisibility = true;
 });

 // Get the application documents directory
 final appDocumentsDirectory = await getApplicationDocumentsDirectory();

 // Get the input audio file path
 final inputAudioPath = widget.file.path;

 // Get the output file names for the two parts
 final outputFileName1 = 'split_audio_part1.mp3';
 final outputFileName2 = 'split_audio_part2.mp3';

 // Get the output file paths for the two parts
 final outputPath1 = '${appDocumentsDirectory.path}/$outputFileName1';
 final outputPath2 = '${appDocumentsDirectory.path}/$outputFileName2';

 // Calculate the duration of the original audio
 final originalDuration = await _getAudioDuration(inputAudioPath);

 // Calculate the durations of the two parts
 final part1Duration = _startValue;
 final part2Duration = originalDuration - _endValue;

 // Construct the FFmpeg command to split the audio
 final ffmpeg = FlutterFFmpeg();
 final splitCommand = '-i $inputAudioPath -ss 0 -t $part1Duration -c copy $outputPath1 -ss $_endValue -t $part2Duration -c copy $outputPath2';

 try {
 // Execute the FFmpeg command to split the audio
 final int result = await ffmpeg.execute(splitCommand);

 if (result == 0) {
 setState(() {
 _progressVisibility = false;
 });
 debugPrint('Audio split successfully.');
 } else {
 setState(() {
 _progressVisibility = false;
 });
 debugPrint('Failed to split audio.');
 }
 } catch (error) {
 setState(() {
 _progressVisibility = false;
 });
 debugPrint('Error while splitting audio: $error');
 }
 }

 Future<int> _getAudioDuration(String filePath) async {
 final flutterSound = FlutterSound();
 final durationResult = await flutterSound.duration(filePath);
 return durationResult.inMilliseconds;
 }
</int></void>

Dependencies


path_provider: ^2.0.15
 ffmpeg_kit_flutter: ^5.1.0
 audioplayers: ^4.1.0
 flutter_sound: ^9.2.13


-
FFmpeg : canvas and crop work separately but result in black screen when combined
25 janvier, par didi00I'm working on a video processing pipeline with FFmpeg, where I :


- 

- Create a black canvas using the color filter.
- Crop a region from my video input.
- Overlay the cropped region onto the black canvas.








Both the canvas and the crop display correctly when tested individually. However, when I attempt to combine them (overlay the crop onto the canvas), the result is a black screen.
What Works :


Black Canvas Alone :


ffmpeg -filter_complex "color=c=black:s=1920x1080[out]" -map "[out]" -f nut - | ffplay 
-


This shows a plain black screen, as expected.


Cropped Region Alone :


ffmpeg -f v4l2 -input_format yuyv422 -framerate 60 -video_size 1920x1080 -i /dev/video0 
\ -vf "crop=1024:192:0:0" -f nut - | ffplay -


This shows the cropped region of the video correctly.


When I combine these steps to overlay the crop onto the black canvas, I get a black screen :


ffmpeg -f v4l2 -input_format yuyv422 -framerate 60 -video_size 1920x1080 -i /dev/video0 
\-filter_complex "color=c=black:s=1920x1080,format=yuv420p[background]; \
[0:v]crop=1024:192:0:0,format=yuv420p[region0]; \
[background][region0]overlay=x=0:y=0[out]" \
-map "[out]" -f nut - | ffplay -


Environment :


- 

- OS : Linux (Debian-based)
- FFmpeg Version : [Insert version, e.g., 4.x or 5.x]
- Capture Card Format : yuyv422








Question :


Why does the pipeline result in a black screen when combining the canvas and the crop, even though both work separately ? Is this an issue with pixel format compatibility, or is there something I'm overlooking in the overlay filter setup ?


-
dnn/vf_dnn_detect.c : add tensorflow output parse support
6 mai 2021, par Ting Fudnn/vf_dnn_detect.c : add tensorflow output parse support
Testing model is tensorflow offical model in github repo, please refer
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
to download the detect model as you need.
For example, local testing was carried on with 'ssd_mobilenet_v2_coco_2018_03_29.tar.gz', and
used one image of dog in
https://github.com/tensorflow/models/blob/master/research/object_detection/test_images/image1.jpgTesting command is :
./ffmpeg -i image1.jpg -vf dnn_detect=dnn_backend=tensorflow:input=image_tensor:output=\
"num_detections&detection_scores&detection_classes&detection_boxes":model=ssd_mobilenet_v2_coco.pb,\
showinfo -f null -We will see the result similar as below :
[Parsed_showinfo_1 @ 0x33e65f0] side data - detection bounding boxes :
[Parsed_showinfo_1 @ 0x33e65f0] source : ssd_mobilenet_v2_coco.pb
[Parsed_showinfo_1 @ 0x33e65f0] index : 0, region : (382, 60) -> (1005, 593), label : 18, confidence : 9834/10000.
[Parsed_showinfo_1 @ 0x33e65f0] index : 1, region : (12, 8) -> (328, 549), label : 18, confidence : 8555/10000.
[Parsed_showinfo_1 @ 0x33e65f0] index : 2, region : (293, 7) -> (682, 458), label : 1, confidence : 8033/10000.
[Parsed_showinfo_1 @ 0x33e65f0] index : 3, region : (342, 0) -> (690, 325), label : 1, confidence : 5878/10000.There are two boxes of dog with cores 94.05% & 93.45% and two boxes of person with scores 80.33% & 58.78%.
Signed-off-by : Ting Fu <ting.fu@intel.com>
Signed-off-by : Guo, Yejun <yejun.guo@intel.com>
Syndiquer tout le site
