How To Convert File URI To File Object In Cordova?

Cordova is an important framework that facilitates developers to build cross-platform or hybrid mobile applications even with a web tech stack. While working with Cordova, it is quite challenging to deal with file handling as the file uploaded will be either in the form of File_URI or file’s Data_Url. In this guide, we will be learning how to convert URI to File Object in Cordova in order to store or save permanently by converting the File_Uri to file Object.

Understanding File URI and File Object

Let’s clarify a few terms before we get into the real coding process.

  • File URI: URI stands for Uniform Resource Identifier. In simple terms, it represents the local location of a file. Working with Cordova, file URIs are usually used for referencing the location of the file being uploaded in the device’s file system, for eg. files obtained from the camera or the file picker.
  • File Object: With reference to Cordova, a File Object is a file as an object that provides essential methods for interacting with the file, such as reading its content or obtaining its metadata.

Steps to Convert File URI to File Object

Let’s now dive into the real purpose of this Blog.

Ensure that you have set up the Cordova Environment with all the necessary steps, then follow the following steps:

  • Install the Cordova File Plugin using the following syntax. Ensure that you are in your Cordova folder. It is a Cordova method for converting URI to file object and one of the best practices for handling URI to file conversion.

Cordova plugin add Cordova-plugin-file

  • Access the File System using the window’s resorceLocalFileSystemURL method in order to obtain the FileEntry object that actually represents a file. 

window.resolveLocalFileSystemURL(fileUri, successCallback, errorCallback);

  • This method takes 3 arguments, namely: FileURI which is the URI of the File, successCallback which runs when the file is successfully resolved, and an errorCallback which handles the errors during the process.  

The Code is as follows:

For Eg: 

window.resolveLocalFileSystemURL(         FileURI,          async fileEntry => {         console.log(“successfully resolved File Uri to File Object.”);          }           error => {            console.log("convert could not take place", error);          }        );
Code language: JavaScript (javascript)
  • Once the File Uri is successfully resolved, we can now access the File Object using the FileEntry.file() method which again takes 2 arguments, success and error callbacks. The code is as follows:
window.resolveLocalFileSystemURL(          FileURI,           async fileEntry => {             await fileEntry.file(               file => {                  console.log(“File Object: ” , file);        // You can write your logic here on how to handle the File Object.               },               error => {                 console.error("Error getting File object:", error);               }             );           },           error => {             console.log("convert could not take place", error);           }         );
Code language: JavaScript (javascript)

Now that we have the File object, we can perform various operations such as reading its content, uploading it, or manipulating its metadata, Like uploading to your DB or displaying the uploaded file.

Important Note: While working with Cordova and any JS-oriented language, it was observed that the File Object of Cordova and Js are different and while wrapping Cordova on your application, Cordova overwrites the default File Object syntax. This might come as an issue when you want to convert the obtained file into a JS File Object. Thus to achieve so, we can first convert the file obtained using the FileEntry method into a Blob and then directly use this Blob object in places where you want to use the JS File Object. The code for the same is as follows:

readFileAsBlob = file => {    return new Promise(resolve => {      const reader = new FileReader();      reader.onloadend = function() {        const blob = new Blob([new Uint8Array(this.result)], {          type: file.type        });        blob.name = file.name;        blob.lastModifiedDate = new Date(file.lastModifiedDate);        resolve(blob);      };      reader.readAsArrayBuffer(file);    });  }; await window.resolveLocalFileSystemURL(  imgData,   fileEntry => {     fileEntry.file(      file => {        const blob =  this.readFileAsBlob(image);        console.log("blob object:", blob);    // we can use this blob object in place of Js File object.      },      error => {        console.error("Error getting File object:", error);      }    );  },  error => {    console.log("convert could not take place", error);  } );
Code language: JavaScript (javascript)

The above code takes the file object obtained from the FileEntry.file() method passes it to readFileAsBlob() method and returns a blob object that can be used in place of the Js File Object. Thus by using this code, we can convert File Uri to Blob

Conclusion

This blog covers all that should be known about URI to Blob conversion and file conversion in Cordova. It is a crucial method that is required if you are dealing with images or camera uploads in your Cordova app. By following the steps provided, we can easily convert the File Uri to an Object. Also, it is important to keep in mind how we can deal with the change of File Object Syntax of Cordova and JS and therefore it is crucial to convert  File to Blob. It is essential and will save a lot of time and related errors. Hope you got a clear understanding of the same. Go ahead and handle File Uploads gracefully.

Recent Post

  • 12 Essential SaaS Metrics to Track Business Growth

    In the dynamic landscape of Software as a Service (SaaS), the ability to leverage data effectively is paramount for long-term success. As SaaS businesses grow, tracking the right SaaS metrics becomes essential for understanding performance, optimizing strategies, and fostering sustainable growth. This comprehensive guide explores 12 essential SaaS metrics that every SaaS business should track […]

  • Bagging vs Boosting: Understanding the Key Differences in Ensemble Learning

    In modern machine learning, achieving accurate predictions is critical for various applications. Two powerful ensemble learning techniques that help enhance model performance are Bagging and Boosting. These methods aim to combine multiple weak learners to build a stronger, more accurate model. However, they differ significantly in their approaches. In this comprehensive guide, we will dive […]

  • What Is Synthetic Data? Benefits, Techniques & Applications in AI & ML

    In today’s data-driven era, information is the cornerstone of technological advancement and business innovation. However, real-world data often presents challenges—such as scarcity, sensitivity, and high costs—especially when it comes to specific or restricted datasets. Synthetic data offers a transformative solution, providing businesses and researchers with a way to generate realistic and usable data without the […]

  • Federated vs Centralized Learning: The Battle for Privacy, Efficiency, and Scalability in AI

    The ever-expanding field of Artificial Intelligence (AI) and Machine Learning (ML) relies heavily on data to train models. Traditionally, this data is centralized, aggregated, and processed in one location. However, with the emergence of privacy concerns, the need for decentralized systems has grown significantly. This is where Federated Learning (FL) steps in as a compelling […]

  • Federated Learning’s Growing Role in Natural Language Processing (NLP)

    Federated learning is gaining traction in one of the most exciting areas: Natural Language Processing (NLP). Predictive text models on your phone and virtual assistants like Google Assistant and Siri constantly learn from how you interact with them. Traditionally, your interactions (i.e., your text messages or voice commands) would need to be sent back to […]

  • What is Knowledge Distillation? Simplifying Complex Models for Faster Inference

    As AI models grow increasingly complex, deploying them in real-time applications becomes challenging due to their computational demands. Knowledge Distillation (KD) offers a solution by transferring knowledge from a large, complex model (the “teacher”) to a smaller, more efficient model (the “student”). This technique allows for significant reductions in model size and computational load without […]

Click to Copy