An educational module for experimenting with single-instance and multi-instance object detection and for generating region proposals with graph-based algorithms
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Single-Instance and Multiple-Instance Detections: Say you wish to experiment with YOLO-like logic for multi-instance object detection, you would need to construct an instance of the RegionProposalGenerator class and invoke the methods shown below on this instance: rpg = RegionProposalGenerator( 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 = RegionProposalGenerator.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 RegionProposalGenerator class and invoke the methods shown below on this instance: rpg = RegionProposalGenerator( ### 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, 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" )
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