visual-debugger 0.2.6

A module to help debugging visual workflows

Visual Debugger for Image Processing

The Visual Debugger module provides tools for visually debugging image processing workflows, allowing annotations like points, labels, rectangles, and more on images. It supports a variety of annotation types and includes functionality for merging multiple images into a composite image for comprehensive review.

Features

• Multiple Annotation Types: Support for a variety of annotations such as points, labeled points, rectangles, circles, and orientation vectors based on pitch, yaw, and roll.
• Image Concatenation: Capability to concatenate multiple debugged images into a single composite image, facilitating easier visualization of sequential image processing steps.
• Dynamic Image Handling: Handles a wide range of image inputs including file paths, in-memory image arrays, base64 encoded images, and images from web links, integrating seamlessly with OpenCV.
• Customizable Debugging: Debugging can be turned on or off, and the module supports generating merged debug images for a sequence of operations.
• Orientation Drawing: Special functionality for visualizing orientation in 3D space, projecting pitch, yaw, and roll onto a 2D image plane. Automatic Naming: Automatically handles the naming of output images based on a customizable tag, the sequence number of the operation, and specific conditions of the debugging process. This ensures a clear and organized structure for saved images, making it easy to track and review the progress of image processing.

Installation

To use this module in your projects, install the pypi package by:

pip install visual_debugger

Usage

Here is a basic example of how to use the VisualDebugger class within your image processing workflow:

from visual_debugger import VisualDebugger, Annotation, AnnotationType

# Initialize the debugger

vd = VisualDebugger(tag="Example", debug_folder_path="./visualdebug",  active=True)

# Load your image (this should be replaced with your actual image path or np.array image)
image_path = "path/to/your/image.jpg"

# Create annotations you want to show on the image
annotations = [
    Annotation(type=AnnotationType.RECTANGLE, coordinates=(10, 10, 50, 50)),
    Annotation(type=AnnotationType.POINT_AND_LABEL, coordinates=(100, 100), labels="Center Point")
]

# Process and debug the image.
# This will use "tag" and "process_step" variables to create a file name 
# And save the image into "debug_folder_path" 
vd.visual_debug(image_path, annotations=annotations, process_step="initial_check")

# Generate a merged image of all processed steps
vd.cook_merged_img()

Documentation

For more details on the methods and their parameters, please refer to the inline documentation within the module files.

Contributing

Contributions to enhance the functionality or improve the documentation are welcome. Please follow the standard GitHub pull request process to submit your contributions.

License

Specify your licensing here, for example, MIT, GPL, etc.

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