If you’ve ever wondered how to measure the quality of your videos without spending hours watching them frame by frame, then this article is for you!
To set the stage: what are these mysterious “FFmpeg quality metrics”? Well, theyre a set of tools that allow us to analyze and compare video files based on various factors like resolution, bitrate, and compression artifacts. And the best part? They’re completely free and open source!
So how do we use them in Python? Let me show you an example:
# Import the necessary modules
import subprocess # Module for running external commands
from os import path # Module for manipulating file paths
# Set up input/output paths for your videos
input_path = 'your-video.mp4' # Path to input video file
output_path = 'your-output.txt' # Path to output text file
# Run FFmpeg to analyze the video and output results to a text file
command = f"ffmpeg -i {input_path} -f null 2>&1 | grep 'frame=.*' > {output_path}" # Command to run FFmpeg and output results to text file
subprocess.run(command, shell=True) # Execute the command using the subprocess module
This code uses the `subprocess` module to run FFmpeg in a shell and capture its output (which includes information about frame rates, bitrates, and other metrics). The results are then saved to a text file for later analysis.
But wait! You might be wondering: what kind of insights can we gain from these quality metrics? Well, let me give you an example:
# Import necessary libraries
import pandas as pd # Import pandas library for data analysis
from matplotlib import pyplot as plt # Import pyplot from matplotlib for data visualization
# Load the results into a Pandas DataFrame for easy analysis
df = pd.read_csv('your-output.txt') # Read the output file and store it in a dataframe called 'df'
# Calculate average frame rate over time
frame_rates = df['frame=.*'] \ # Select the 'frame=' column from the dataframe
.str[10:] \ # Remove the first 10 characters from each value in the column
.astype(float) \ # Convert the values to float data type
.groupby(df['time']) \ # Group the values by the 'time' column
.mean() # Calculate the mean of the grouped values and store it in a new dataframe called 'frame_rates'
# Calculate average bitrate over time
bitrates = df['bitrate'] \ # Select the 'bitrate' column from the dataframe
.str[:-3] \ # Remove the last 3 characters from each value in the column
.astype(int) \ # Convert the values to integer data type
.groupby(df['time']) \ # Group the values by the 'time' column
.sum() / (1024 * 1024) # Calculate the sum of the grouped values and divide by 1024*1024 to convert to MB/s. Store it in a new dataframe called 'bitrates'
# Plot the results over time using Matplotlib
plt.figure(figsize=(8, 6)) # Set the figure size to 8x6 inches
plt.subplot(2, 1, 1) # Create a subplot with 2 rows, 1 column, and select the first plot
plt.plot(frame_rates.index, frame_rates.values) # Plot the index values of the 'frame_rates' dataframe on the x-axis and the corresponding values on the y-axis
plt.title('Average Frame Rate') # Set the title of the plot
plt.ylabel('Frames per Second') # Set the label for the y-axis
plt.grid() # Add grid lines to the plot
plt.subplot(2, 1, 2) # Create a subplot with 2 rows, 1 column, and select the second plot
plt.plot(bitrates.index, bitrates.values) # Plot the index values of the 'bitrates' dataframe on the x-axis and the corresponding values on the y-axis
plt.title('Bitrate (MB/s)') # Set the title of the plot
plt.ylabel('Megabytes per Second') # Set the label for the y-axis
plt.grid() # Add grid lines to the plot
plt.show() # Display the plot
This code uses Pandas to load the results into a DataFrame, and then calculates average frame rates and bitrates over time using groupby(). The results are then plotted using Matplotlib for easy visualization.
With just a few lines of Python code, we can analyze our videos in depth and gain valuable insights into their quality metrics. And the best part? It’s all completely free and open source!