Converting numpy array to video

Explanation of the problem

The problem at hand involves processing a video using OpenCV and saving the processed video. The initial code snippet demonstrates the usage of OpenCV’s VideoCapture and VideoWriter objects to read frames from a video source, apply processing (flipping frames in this case), and save the processed frames as a new video file. However, when attempting to save each frame and provide it as input to the video writer, an error occurs during the output saving process, stating that there is no such file.

import numpy as np
import cv2

cap = cv2.VideoCapture(0)

# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))

while(cap.isOpened()):
    ret, frame = cap.read()
    if ret == True:
        frame = cv2.flip(frame, 0)

        # Write the flipped frame
        out.write(frame)

        cv2.imshow('frame', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    else:
        break

# Release everything if the job is finished
cap.release()
out.release()
cv2.destroyAllWindows()

The subsequent code snippet attempts to solve the problem by using ffmpeg-python library. It showcases a script that saves frames as JPEG files and then uses ffmpeg to create a video file from those frames. However, there is a need to ensure that the saved video file has the same size as the source video.

import ffmpeg

(
    ffmpeg
    .input('/path/to/jpegs/*.jpg', pattern_type='glob', framerate=25)
    .output('movie.mp4')
    .run()
)

Troubleshooting with the Lightrun Developer Observability Platform

Getting a sense of what’s actually happening inside a live application is a frustrating experience, one that relies mostly on querying and observing whatever logs were written during development.
Lightrun is a Developer Observability Platform, allowing developers to add telemetry to live applications in real-time, on-demand, and right from the IDE.

• Instantly add logs to, set metrics in, and take snapshots of live applications
• Insights delivered straight to your IDE or CLI
• Works where you do: dev, QA, staging, CI/CD, and production

Start for free today

Problem solution for Converting numpy array to video

import ffmpeg

input_file = '/path/to/source/video.avi'
output_file = '/path/to/saved/video.mp4'

(
    ffmpeg
    .input(input_file)
    .output(output_file, pix_fmt='yuv420p', vcodec='libx264', vf='scale=1920:1080', video_bitrate='7468k')
    .run()
)

Other popular problems with ffmpeg-python

Problem: Incorrect Input File Format

One of the common issues encountered with ffmpeg-python is when providing an incorrect input file format. When attempting to process a video or perform operations on a file that is not supported by ffmpeg, an error occurs, indicating that the input file is not recognized or readable. This can lead to unexpected behavior or failure in executing the desired operations.

Solution:

To solve this problem, ensure that the input file provided to ffmpeg-python is in a supported format, such as AVI, MP4, MKV, or other commonly used video formats. If the input file is in a different format, consider converting it to a supported format using ffmpeg or a suitable file conversion tool before passing it to ffmpeg-python. Verifying the input file format compatibility with ffmpeg documentation or relevant resources can help ensure smooth execution of the desired operations.

Problem: Incorrect Output File Configuration

Another frequent issue with ffmpeg-python arises when configuring the output file settings incorrectly. This can result in unexpected output file characteristics, such as incorrect resolution, frame rate, or codec selection. In some cases, the output file may not be created at all, or it may be generated with default settings that do not align with the desired requirements.

Solution:

To address this problem, it is crucial to carefully configure the output file parameters according to the desired output characteristics. This includes specifying the desired resolution, frame rate, video codec, audio codec, and any other relevant settings. Consulting the ffmpeg documentation or relevant resources can provide guidance on the available options and their appropriate usage. Verifying the output file configuration ensures that the resulting video meets the desired specifications.

Problem: Lack of Error Handling and Logging

Another challenge when working with ffmpeg-python is the absence of comprehensive error handling and logging mechanisms. In complex video processing pipelines or batch operations, it can be difficult to identify the root cause of errors or failures that occur during the execution. Without proper error handling and logging, troubleshooting becomes cumbersome and time-consuming.

Solution:

To mitigate this problem, implement robust error handling and logging strategies within the ffmpeg-python code. This includes capturing and handling exceptions that may occur during the execution, providing meaningful error messages, and logging relevant information, such as input and output file paths, operation parameters, and error details. Implementing a structured logging framework and incorporating appropriate error handling practices helps in identifying and resolving issues efficiently, improving the overall reliability of the ffmpeg-python workflows.

A brief introduction to ffmpeg-python

Most popular use cases for ffmpeg-python