def evaluate_proposal_file(dataset, proposal_file, output_dir): """Evaluate box proposal average recall.""" roidb = dataset.get_roidb(gt=True, proposal_file=proposal_file) results = task_evaluation.evaluate_box_proposals(dataset, roidb) task_evaluation.log_box_proposal_results(results) recall_file = os.path.join(output_dir, 'rpn_proposal_recall.pkl') save_object(results, recall_file) return results
def evaluate_proposal_file(dataset, proposal_file, output_dir): """Evaluate box proposal average recall.""" roidb = dataset.get_roidb(gt=True, proposal_file=proposal_file) results = task_evaluation.evaluate_box_proposals(dataset, roidb) task_evaluation.log_box_proposal_results(results) recall_file = os.path.join(output_dir, 'rpn_proposal_recall.pkl') save_object(results, recall_file) all_boxes = np.array([[], smd.prep_proposal_file(proposal_file, output_dir, top_k=100)]) """Plot Precision Recall curve for proposal file""" smd.evaluate_boxes(dataset, all_boxes, output_dir) smd.draw_histos(filepath=output_dir) """Plot recall-proposals curve""" boxes = np.array(all_boxes[1:][0]) scores = boxes[:, :, -1] boxes = boxes[:, :, :-1] smd.plot_rec_prop_curve(boxes, scores, dataset, output_dir, n=40) return results