YOLOLogic 2.1.4

请参阅模块API页面

https://engineering.purdue.edu/kak/distYOLO/YOLOLogic-2.1.4.html

获取有关此模块的所有信息，包括代码的最新更改信息。上面显示的页面列出了您可以在自己的代码中调用的所有模块功能。

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" )