Скачать или смотреть How to Create a Composite Image from Two Photos Using Python and NumPy

Learn how to efficiently create a composite image by merging two photos in Python using NumPy and the Python Imaging Library. This guide helps beginners troubleshoot errors in image processing.
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This video is based on the question https://stackoverflow.com/q/77298872/ asked by the user 'William Hickey' ( https://stackoverflow.com/u/22746402/ ) and on the answer https://stackoverflow.com/a/77298936/ provided by the user 'Sanxofon' ( https://stackoverflow.com/u/4146962/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Creating a Composite Image from Two Photos in Python

Have you ever wondered how to merge two photos into a single composite image using Python? This can be quite handy for creating unique graphics or enhancing images. In this guide, we’ll break down a code snippet that attempts to achieve exactly that, focusing on resolving a common error faced by beginners.

The Problem: Merging Two Images

When newcomers try to work with images in Python, they often face hurdles, such as data type errors. Let’s take a look at the scenario where you are trying to:

Import two images.

Convert those images into NumPy arrays.

Add the arrays together.

Divide the resulting array by 2 to obtain an average.

However, running this code may lead to the following error:

TypeError: Cannot handle this data type: (1, 1, 3), i8

This error typically occurs when attempting to save the final image generated from these operations.

Understanding the Solution

The core of the solution lies in correctly handling the data types of your NumPy arrays. Let's break it down step-by-step:

Step 1: Import Libraries

We start by importing the necessary libraries:

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

Step 2: Load Images

The first step is to open the two images that you want to merge. For this example, we will refer to them as img1 and img2:

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

Step 3: Convert Images to Arrays

Next, we convert the loaded images into NumPy arrays, which allows us to perform mathematical operations on the pixel values:

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

Step 4: Add the Arrays

Now we can add the two arrays together. This step combines the pixel values from both images:

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

Step 5: Divide by 2

To get the average pixel value, we divide the sum by 2:

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

At this point, it's important to recognize a key issue. After division, the data type of output changes to float64, which is not suitable for creating an image.

Step 6: Correcting the Data Type

Here’s where the main fix comes into play. Instead of converting output into int, we need to convert it into np.uint8 (unsigned 8-bit integer), which is the standard data type for image arrays. The corrected line should look like this:

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

Step 7: Save the Final Product

Finally, we can save the composed image as follows:

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

Conclusion

By simply changing the data type conversion step to np.uint8, you can successfully create a composite image from two photos. Data types are crucial in image processing, and understanding them will help you pilot through challenges effectively.

Cheers to exploring the world of Python and image manipulation! If you encounter any more issues, feel free to ask for help. Happy coding!