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Autres articles (40)
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Support de tous types de médias
10 avril 2011Contrairement à beaucoup de logiciels et autres plate-formes modernes de partage de documents, MediaSPIP a l’ambition de gérer un maximum de formats de documents différents qu’ils soient de type : images (png, gif, jpg, bmp et autres...) ; audio (MP3, Ogg, Wav et autres...) ; vidéo (Avi, MP4, Ogv, mpg, mov, wmv et autres...) ; contenu textuel, code ou autres (open office, microsoft office (tableur, présentation), web (html, css), LaTeX, Google Earth) (...)
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HTML5 audio and video support
13 avril 2011, parMediaSPIP uses HTML5 video and audio tags to play multimedia files, taking advantage of the latest W3C innovations supported by modern browsers.
The MediaSPIP player used has been created specifically for MediaSPIP and can be easily adapted to fit in with a specific theme.
For older browsers the Flowplayer flash fallback is used.
MediaSPIP allows for media playback on major mobile platforms with the above (...) -
Support audio et vidéo HTML5
10 avril 2011MediaSPIP utilise les balises HTML5 video et audio pour la lecture de documents multimedia en profitant des dernières innovations du W3C supportées par les navigateurs modernes.
Pour les navigateurs plus anciens, le lecteur flash Flowplayer est utilisé.
Le lecteur HTML5 utilisé a été spécifiquement créé pour MediaSPIP : il est complètement modifiable graphiquement pour correspondre à un thème choisi.
Ces technologies permettent de distribuer vidéo et son à la fois sur des ordinateurs conventionnels (...)
Sur d’autres sites (10502)
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fftools/ffmpeg : open decoders right after we know they are needed
11 avril 2023, par Anton Khirnov -
fftools/ffmpeg : supply hw_device_ctx to filters before initializing them
15 mars 2023, par Anton Khirnovfftools/ffmpeg : supply hw_device_ctx to filters before initializing them
This is more correct, but was not possible before the recently-added
filtergraph parsing API.Also, only pass hw devices to filters that are flagged as capable of
using them.Tested-by : Niklas Haas
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React Native Expo File System : open failed : ENOENT (No such file or directory)
9 février 2023, par coloradayI'm getting this error in a bare React Native project :


Possible Unhandled Promise Rejection (id: 123):
Error: /data/user/0/com.filsufius.VisionishAItest/files/image-new-♥d.jpg: open failed: ENOENT (No such file or directory)


The same code was saving to File System with no problem yesterday, but today as you can see I am getting an ENOENT error, plus I am getting these funny heart shapes ♥d in the path. Any pointers as to what might be causing this, please ? I use npx expo run:android to builld app locally and expo start —dev-client to run on a physical Android device connected through USB.


import { Image, View, Text, StyleSheet } from "react-native";
import * as FileSystem from "expo-file-system";
import RNFFmpeg from "react-native-ffmpeg";
import * as tf from "@tensorflow/tfjs";
import * as cocossd from "@tensorflow-models/coco-ssd";
import { decodeJpeg, bundleResourceIO } from "@tensorflow/tfjs-react-native";

const Record = () => {
 const [frames, setFrames] = useState([]);
 const [currentFrame, setCurrentFrame] = useState(0);
 const [model, setModel] = useState(null);
 const [detections, setDetections] = useState([]);

 useEffect(() => {
 const fileName = "image-new-%03d.jpg";
 const outputPath = FileSystem.documentDirectory + fileName;
 RNFFmpeg.execute(
 "-y -i https://res.cloudinary.com/dannykeane/video/upload/sp_full_hd/q_80:qmax_90,ac_none/v1/dk-memoji-dark.m3u8 -vf fps=25 -f mjpeg " +
 outputPath
 )
 .then((result) => {
 console.log("Extraction succeeded:", result);
 FileSystem.readDirectoryAsync(FileSystem.documentDirectory).then(
 (files) => {
 setFrames(
 files
 .filter((file) => file.endsWith(".jpg"))
 .sort((a, b) => {
 const aNum = parseInt(a.split("-")[2].split(".")[0]);
 const bNum = parseInt(b.split("-")[2].split(".")[0]);
 return aNum - bNum;
 })
 );
 }
 );
 })
 .catch((error) => {
 console.error("Extraction failed:", error);
 });
 }, []);

 useEffect(() => {
 tf.ready().then(() => cocossd.load().then((model) => setModel(model)));
 }, []);
 useEffect(() => {
 if (frames.length && model) {
 const intervalId = setInterval(async () => {
 setCurrentFrame((currentFrame) =>
 currentFrame === frames.length - 1 ? 0 : currentFrame + 1
 );
 const path = FileSystem.documentDirectory + frames[currentFrame];
 const imageAssetPath = await FileSystem.readAsStringAsync(path, {
 encoding: FileSystem.EncodingType.Base64,
 });
 const imgBuffer = tf.util.encodeString(imageAssetPath, "base64").buffer;
 const imageData = new Uint8Array(imgBuffer);
 const imageTensor = decodeJpeg(imageData, 3);
 console.log("after decodeJpeg.");
 const detections = await model.detect(imageTensor);
 console.log(detections);
 setDetections(detections);
 }, 100);
 return () => clearInterval(intervalId);
 }
 }, [frames, model]);

 
 return (
 <view style="{styles.container}">
 
 <view style="{styles.predictions}">
 {detections.map((p, i) => (
 <text key="{i}" style="{styles.text}">
 {p.class}: {(p.score * 100).toFixed(2)}%
 </text>
 ))}
 </view>
 </view>
 );
};

const styles = StyleSheet.create({
 container: {
 flex: 1,
 alignItems: "center",
 justifyContent: "center",
 },
 image: {
 width: 300,
 height: 300,
 resizeMode: "contain",
 },
 predictions: {
 width: 300,
 height: 100,
 marginTop: 20,
 },
 text: {
 fontSize: 14,
 textAlign: "center",
 },
});

export default Record;```


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