Vision Stack

Vision Stack is a ros package that makes processing image data intuitive and straight forward. By implementing an array-like data structure to stack processing for feature abstraction and analysis for data extraction, Vision Stack provides all the tools necessary for tackling any vision tasks.

Author: Daniel Parra

Installation

Use the git submodule manager to add vision stack to your repository.

git submodule add https://github.com/uf-mil/vision_stack.git path/to/submodule

Usage

from PIL import Image
import numpy as np
from vision_stack import VisionStack, BinThresholdingLayer, CannyLayer, ColorMagnificationLayer, CustomLayer, GaussianLayer, GrayscaleLayer, HoughTransformLayer, MinMaxNormalizationLayer, ZScoreNormalizationLayer, RobustScalingLayer, ObjectDetectionLayer, ResizeLayer, RGBMagnificationLayer, SobelLayer, UnderWaterImageEnhancementLayer

# Initialize vision stack data structure
def myCustomImageProcessing(img, *args):
     ...
     return (processed_img, extracted_info) # If you prefer just to do preprocessing of an image, return (processed_img, None) instead

vs = VisionStack(
            layers=[
                BinThresholdingLayer(150,250), # Converts image to grayscale if image is not grayscale and extracts pixels with values between 150 and 250.
                CannyLayer(50,100), # Simplified canny filter that uses cv2.Canny to pass a canny filter over an image with the low value (50) threshold for soft edge detection and the high value (100) for strong edges detection.
                ColorMagnification((23,156,234)), # Highlights objects with this color (23,156,234) in an image.
                CustomLayer('your_layer_name', myCustomImageProcessing, arg1, arg2, ...), # Add your own defined function to the vision stack process and pass as many necessary arguments.
                GaussianLayer((11,11), 50), # Pass a gaussian filter of kernal size (11,11) (kernal size must be of odd numbered dimensions) with a sigma value of 50.
                GrayscaleLayer(), # Convert image to grayscale
                HoughTransformLayer(threshold=100, min_line_length=20, max_line_gap=10, pass_post_processing_img=True), # Pass a Hough Transform filter over an image to extract lines. Setting pass_post_processing_img will push the image with hough transform lines to the next layer.
                # Different types of normalization layers:
                MinMaxNormalizationLayer(),
                ZScoreNormalizationLayer(),
                RobustScalingLayer(),
                ObjectDetectionLayer('path/to/weights.pt|tflite', conf_thres=0.5, iou_thres=0.5, class_names_array=['cls1','cls2','cls3',...], colors_array=[(255,0,0),(0,255,0),(0,0,255),...], pass_post_processing_img = False), # Access YOLO weights and make predictions on the image provided by the previous layer. Setting pass_post_processing_img will push the image with bounding boxes to the next layer.
                ResizeLayer(960, 608), # Resize the image from the previous layer.
                RGBMagnificationLayer('R'|'G'|'B'), # Magnifies the provided channel respectively.
                SobelLayer((5,5)), # Passes a sobel edge detection layer with a kernal size of (5,5) over the image.
                UnderWaterImageEnhancementLayer(), # Uses a generative AI model to improve underwater images (good for murky waters).
                # Include as many layers in any combination as you need
            ],
        )

# Pass image through vision stack
file_path = "path/to/your/image.jpg"

# Open the image file
img = Image.open(file_path)
 
# Convert the image to RGB mode
img = img.convert('RGB')
    
# Convert the image to a NumPy array
img_array = np.array(img)

# Pass img_array through vision_stack
vs.run(in_image = image_array, verbose = True)
# With verbose, if ros is running then topics will be created for each layer to visualize processing.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.