• author: All About AI

Using Chat GPT to Perform Video Editing Tasks


In this article, we will explore how to utilize Chat GPT code interpreter to accomplish video editing tasks. Specifically, we will focus on implementing simple face detection in videos using the ffmpeg package. While working with ffmpeg can be challenging, we have found a convenient prompt that perfectly fits our needs. So, let's jump right into it!

Uploading Files

Before delving into the code, let's first upload all the necessary files for this tutorial. To efficiently handle the process, we will make use of the Sip method, which facilitates uploading multiple files simultaneously. Here are the steps to follow:

  1. Open the Sip application.
  2. Select the video clips you want to use in this project.
  3. Save these clips as a zip file named "videos.3 videos3.zip".

Great! Now that we have successfully uploaded the files, we can proceed to explore the code and prompt.

Prompt Analysis

Let's start by examining the system prompt we will be using. It is a standard prompt that allows us to define the name, occupation, and basic skills required for a programmer at our level. Additionally, it provides clear instructions on the assignments we will be working on today. Here's an overview of the tasks:

  1. Confirm that all required Python packages are installed, particularly the ffmpeg package, which we have recently installed.
  2. Utilize the FFM package to perform video editing on the uploaded zip file containing MP4 files. Follow any instructions given by the user.
  3. Complete the tasks in a step-by-step manner and seek clarification if needed.
  4. Finally, submit the completed assignments.

Now, let's go ahead and confirm the installation of the Python packages.

Package Confirmation

To ensure that all the necessary packages are installed, we will verify the presence of the ffmpeg Python package within our environment. Although I cannot physically verify it, I assume that the packages, including ffmpeg, are properly installed. However, I recently installed the ffmpeg Python package in your environment. Could you please confirm its installation status?

Once we have confirmed the package installation, we can proceed to tackle the video editing assignment.

Video Editing Task

Our second assignment involves utilizing the FFM package to edit the videos contained in the uploaded zip file. We will follow the user's instructions to successfully complete the task. Let's break it down into a step-by-step process:

  1. Start by merging "Peter 1" with "Peter 2" using the FFMpeg tool. Create a smooth transition between the clips.
  2. After merging, download the final video and inspect it to ensure the desired transition has been achieved.
    • If the merge is successful, proceed to the next task.
    • In case of any issues, seek assistance or retry the process.

Impressive! The merge performed flawlessly, and the transition is seamless. Let's move ahead to the next task.

Changing Video Format

Our next assignment involves using FFMpeg to convert the "Theater One" video from a 69 format to a vertical style video (16:9 aspect ratio) without any black bars. Let's devise a plan to accomplish this seamlessly:

  1. Download the "Theater One" video and assess its current format.
  2. Utilize FFMpeg to convert the video to a 16:9 aspect ratio, ensuring that it occupies the entire screen without any black bars.
  3. Validate the success of the conversion by observing the final output.
    • If the output satisfies the requirements, proceed to the next task.
    • In case of any discrepancies, troubleshoot the issue by analyzing the FFMpeg operation and rectify the problem. Alternatively, rewrite the code to address the issue.

Marvelous! The conversion was executed impeccably, and the video now fills the screen without any black bars. The ancient behaviors of FFMpeg never cease to amaze me. They diligently attempt various approaches until the desired outcome is achieved.

Extracting Audio from a Video

Next, let's extract the audio from "Peter 2" MP4 video and convert it into an MP3 file. This will allow us to isolate and utilize the audio separately. Here's how we can accomplish this:

  1. Run the FFMpeg command to extract the audio from "Peter 2" MP4 video and save it as an MP3 file.
  2. Download the resulting audio file and verify that the extraction was successful.
    • If the audio file plays correctly, proceed to the next task.
    • In case of any issues, audit the FFMpeg command used or request assistance.

Brilliant! The audio extraction was executed flawlessly, and the resulting audio file plays perfectly as expected. Now let's proceed to the final task: face detection.

Creating a GIF from a Video

For this task, let's try creating a five-second GIF from the "Jujitsu" MP4 video file. We'll use FFMpeg to accomplish this. Here's our step-by-step plan:

  1. Download the "Jujitsu" MP4 video file.
  2. Utilize FFMpeg to generate a five-second GIF from the video.
  3. Access the link provided to download the created GIF and verify its successful creation.

Superb! The creation of the five-second GIF from the "Jujitsu" video was successful. The animated GIF showcases the wrestling match beautifully. You can loop it as per your preference, and it's an excellent addition to any medium. Now, let's wrap up the article by discussing face detection.

Face Detection Task

To perform face detection, we will utilize the OpenCV package with FFMpeg. However, due to RAM limitations within the current code interpreter environment, we need to implement the code on our local computers. Here's a summary of the process:

  1. Confirm that the FFMpeg and OpenCV packages are successfully installed.
  2. Upload the "rmp4" file to the environment.
  3. Task description: utilize the packages to track the faces within the video.
    • Determine whether you need to track a specific person's face or all the faces.
    • Use bounding boxes to identify the tracked faces.
  4. Create the code and run it on your local machine to execute the face detection task.

Note: Please remember that the current code interpreter environment lacks sufficient RAM to perform these operations effectively. However, feel free to analyze the provided code, make any necessary modifications, and apply it locally for successful face detection.

Face Detection with OpenCV and Deep Neural Network Module


In this article, we will discuss the process of face detection using OpenCV and the deep neural network module. We will critique the existing code and troubleshoot any issues that may arise. Additionally, we will provide a new and improved Python code for face detection.

Critiquing the Existing Code

Upon reviewing the initial code, we discovered that it required the use of the numpy library. However, it was unclear whether this library was necessary for the task at hand. To confirm our suspicions, we decided to take a different approach and rewrite the code.

Utilizing the Deep Neural Network Module

After further research, we found that the deep neural network module in OpenCV is more accurate for face detection. However, before proceeding, we needed to download the necessary files: the Open Face Detector and the Open Face Detector.pb. A download link for these files was provided, and we followed the instructions accordingly.

A Fresh Python Code

With the required files in our path, we implemented a new Python code for face detection. We copied the provided code and made necessary adjustments. To execute the code, we ran it in the terminal using the command: python face_detection.py.

Analyzing the Results

Upon running the code, we observed the results on the original video. While the face detection was not perfect and produced some choppiness, it successfully detected faces, transitioning between different individuals. The code also provided a confidence reading, although it was difficult to discern in the video.

Improvements and Challenges

While satisfied with the initial results, we desired to enhance the visual output by including colored bounding boxes around the detected faces. However, as this was our first time using face detection software in OpenCV, we were unsure of how to implement this feature. Nevertheless, we are content with the outcome and consider it a successful attempt.

Future Possibilities

In our experimentation, we contemplated deploying object tracking algorithms in combination with face detection. However, due to memory constraints, we exported the project to a local environment for execution. We encourage readers to explore this possibility and share their findings.

By leveraging chat gpt and employing the powerful capabilities of ffmpeg and opencv packages, we have successfully accomplished various video editing tasks. from transitioning between video clips to converting video formats, extracting audio, creating gifs, and even performing face detection, the possibilities are endless. remember to run the code in a suitable environment to ensure optimal performance and efficient completion of the tasks. happy video editing!

In conclusion, our experience with face detection using OpenCV and the deep neural network module was an overall positive one. We have witnessed the potential for creating captivating projects using this technology. As we continue to explore its capabilities, we encourage readers to join us on this journey. Feel free to refer to the link in the article description for the prompt, and stay tuned for more exciting projects in the future.

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