How to Show Progress in a Python For Loop with Image Processing

Learn how to count and display the number of images being processed in a for loop using Python for better feedback during image conversion tasks.
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How to Show Progress in a Python For Loop with Image Processing

Processing images in bulk can often lead to a lack of visibility into how much work has been completed and how much remains. If you’re working with Python to convert images, you might find yourself wondering how to output the progress of your operations. This guide addresses a common question: how to count the amount of pictures already converted and display this progress in the console.

The Problem: Visualizing Progress

When performing operations on multiple images in a loop, it's crucial to keep track of how many images have been processed. This is especially useful in scenarios where the process may take several minutes or more. Here’s the original code snippet that needed improvement:

[[See Video to Reveal this Text or Code Snippet]]

In the above code, the only output during the loop is the static message 'converting'.

Desired Output Format

The goal is to modify this output to indicate the current progress in a format that looks like this:

[[See Video to Reveal this Text or Code Snippet]]

Here, xxx represents the total number of images in the directory, providing a clear indication of what percentage of images have been processed at any point in time.

The Solution: Adding a Counter

To implement this functionality, we will modify the code to include a counter that tracks the number of processed images. The complete modified code will look like this:

[[See Video to Reveal this Text or Code Snippet]]

Breakdown of Changes

Counter Initialization:

A counter variable is initialized to 1 before the loop starts. This will count the number of images processed.

Total File Count:

We get the total number of files using len(directory), which stores the value in train_length.

Updating the Print Statement:

Instead of printing a static message, the print statement now displays the current status in a formatted string showing the progress by combining counter and train_length.

Incrementing the Counter:

After processing each image, the counter is incremented by one to reflect the number of images processed.

Alternative Approach: Using enumerate

Although the above method works fine, Python offers a built-in function called enumerate which can simplify the counting and indexing process while ensuring clarity of code:

[[See Video to Reveal this Text or Code Snippet]]

This alternative method starts counting from 1 automatically, eliminating the need to manually initialize and increment a counter variable.

Conclusion

Being able to visualize the progress when processing a large number of images not only improves user experience but also adds a layer of professionalism to your script. With these adjustments to your loop, you can now effectively track how many images have been converted while the operation runs. By incorporating straightforward modifications to your code, you enhance both functionality and clarity, making your image processing tasks much more user-friendly.

Now, go ahead and implement this in your code. You’ll be pleasantly surprised at how much more informative your program can be!