Скачать или смотреть Solving Image Visualization Issues in Python with np.sqrt and OpenCV

Learn how to correctly handle image types in Python to visualize edge-detection results with OpenCV. This guide addresses common pitfalls, specifically when processing images with `np.sqrt`.
---
This video is based on the question https://stackoverflow.com/q/64033670/ asked by the user 'giorgio segalla' ( https://stackoverflow.com/u/11612610/ ) and on the answer https://stackoverflow.com/a/64034285/ provided by the user 'Mad Physicist' ( https://stackoverflow.com/u/2988730/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Can't visualize image after process it with np.sqrt

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Solving Image Visualization Issues in Python with np.sqrt and OpenCV

When working with image processing in Python, especially using libraries like NumPy and OpenCV, it's not uncommon to run into issues during visualization. One such problem arises from data type incompatibility when processing images, leading to distortions or errors when using functions like cv2.imshow(). In this guide, we’ll explore a specific scenario where users struggle to visualize an edge-detected image after applying the square root transformation using np.sqrt. We’ll break down the problem and its solution step-by-step.

The Problem: Inability to Visualize Processed Images

Imagine you're working on edge detection using the Perwitt kernel and you’ve successfully generated horizontal and vertical gradients of an image. You then attempt to merge these gradients using a formula that involves the square root of their sums. However, upon trying to display the resulting image using OpenCV, you encounter an error indicating that the expected type is incompatible. Your code might resemble the following:

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

Despite processing the image, you face a TypeError. Upon inspection, you realize the problem lies in the type of the array being passed to cv2.imshow().

The Solution: Adjusting Image Data Types

The error you're facing occurs because the image you are trying to display is of type np.float64, while OpenCV expects an image in the np.uint8 format. Although your code normalizes the pixel values correctly by scaling them within the range of 0 to 255, it does not change the data type. This discrepancy causes issues when you attempt to visualize the image.

Step-by-step Fix

Here's how to resolve the visualization issue:

Normalization of Image Edges: Ensure that you're correctly scaling your image data to the range of 0-255.

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

Convert Data Type: The critical step you are missing is converting the array to the np.uint8 type. You can achieve this by either directly converting it during the cv2.imshow() call or after the normalization step.

Option 1: Convert during display

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

Option 2: Convert separately

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

Validate Changes: After making these adjustments, run your code again. You should now be able to visualize your processed image without errors.

Conclusion

Handling data types is crucial when working with image processing in Python. By ensuring that you are passing the correct type to functions like cv2.imshow(), you can avoid runtime errors and visualize your images accurately. If you encounter similar issues in the future, remember to check the data type of your arrays and convert them as needed.

With this corrected approach, you're now well-equipped to handle edge detection and other image processing tasks with confidence. Happy coding!