Metadata-Version: 2.1
Name: YOLOLogic
Version: 2.1.3
Summary: An educational module for experimenting with the YOLO logic for multi-instance object detection and for generating region proposals with graph-based algorithms
Home-page: https://engineering.purdue.edu/kak/distYOLO/YOLOLogic-2.1.3.html
Author: Avinash Kak
Author-email: kak@purdue.edu
Maintainer: Avinash Kak
Maintainer-email: kak@purdue.edu
License: Python Software Foundation License
Download-URL: https://engineering.purdue.edu/kak/distYOLO/YOLOLogic-2.1.3.tar.gz
Keywords: object detection,image segmentation,computer vision
Platform: All platforms
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Programming Language :: Python :: 3.10



Consult the module API page at

      https://engineering.purdue.edu/kak/distYOLO/YOLOLogic-2.1.3.html

for all information related to this module, including information related
to the latest changes to the code.  The page at the URL shown above lists
all of the module functionality you can invoke in your own code.

::

        Single-Instance and Multi-Instance Object Detection:
    
            Say you wish to experiment with YOLO-like logic for multi-instance
            object detection, you would need to construct an instance of the
            YOLOLogic class and invoke the methods shown below on
            this instance:
        
            rpg = YOLOLogic(
                              dataroot = "./data/",
                              image_size = [128,128],
                              yolo_interval = 20,
                              path_saved_yolo_model = "./saved_yolo_model",
                              momentum = 0.9,
                              learning_rate = 1e-6,
                              epochs = 40,
                              batch_size = 4,
                              classes = ('Dr_Eval','house','watertower'),
                              use_gpu = True,
                          )
            yolo = YOLOLogic.YoloLikeDetector( rpg = rpg )
            yolo.set_dataloaders(train=True)
            yolo.set_dataloaders(test=True)
            model = yolo.NetForYolo(skip_connections=True, depth=8) 
            model = yolo.run_code_for_training_multi_instance_detection(model, display_images=False)
            yolo.run_code_for_training_multi_instance_detection(model, display_images = True)
            
    
        Graph-Based Algorithms for Region Proposals:
    
            To generate region proposals, you would need to construct an instance
            of the YOLOLogic class and invoke the methods shown below
            on this instance:
        
            rpg = YOLOLogic(
                           ###  The first 6 options affect only the graph-based part of the algo
                           sigma = 1.0,
                           max_iterations = 40,
                           kay = 0.05,
                           image_normalization_required = True,
                           image_size_reduction_factor = 4,
                           min_size_for_graph_based_blobs = 4,
                           ###  The next 4 options affect only the Selective Search part of the algo
                           color_homogeneity_thresh = [20,20,20],
                           gray_var_thresh = 16000,           
                           texture_homogeneity_thresh = 120,
                           max_num_blobs_expected = 8,
                  )
            
            image_name = "images/mondrian.jpg"
            segmented_graph,color_map = rpg.graph_based_segmentation(image_name)
            rpg.visualize_segmentation_in_pseudocolor(segmented_graph[0], color_map, "graph_based" )
            merged_blobs, color_map = rpg.selective_search_for_region_proposals( segmented_graph, image_name )
            rpg.visualize_segmentation_with_mean_gray(merged_blobs, "ss_based_segmentation_in_bw" )

    
          

