def inference_pass(query, query_id, image_ix, if_data, gm_method='original'): """ Run the full inference pass using a query and image Attributes: query (obj): the query to execute query_id (int): a query identification number image_ix (int): the image to run against the image if_data (obj): an ImageFetchData object Returns: (object) opengm graphical model (object) DetectionTracker (float) energy value (numpy array) array of best-match indices (numpy array) post-inference marginals (float) total pass time """ import time gm = None tracker = None start = time.time() if_data.configure(image_ix, query) if gm_method == 'original': gm, tracker = ifc.generate_pgm(if_data, verbose=False) elif gm_method == 'uniform' or gm_method == 'empirical': gm, tracker = ifc.generate_pgm_all_objects(if_data, verbose=False, method=gm_method) energy, indices, marginals = ifc.do_inference(gm) duration = time.time() - start print "query {0} on image {1}: {2} sec".format(query_id, image_ix, duration) return gm, tracker, energy, indices, marginals, duration
def ex4(srao_query_string, image_index, gm_method='original', verbose=True): """ querygen """ import image_fetch_core as ifc import image_fetch_utils as ifu; reload(ifu) import image_fetch_plot as ifp; reload(ifp) import image_fetch_querygen as ifq; reload(ifq) vgd, potentials, platt_mod, bin_mod, queries, ifdata = dp.get_all_data() query = ifq.gen_srao(srao_query_string) ifdata.configure(image_index, query) gm = None tracker = None if gm_method == 'original': gm, tracker = ifc.generate_pgm(ifdata, verbose) elif gm_method == 'uniform' or gm_method == 'empirical': gm, tracker = ifc.generate_pgm_all_objects(ifdata, verbose=verbose, method=gm_method) energy, best_match_ix, marginals = ifc.do_inference(gm) #ifp.draw_gm(gm) ifp.draw_best_objects(tracker, best_match_ix, energy, filename = out_path + "mq_i{0}.png".format(image_index)) file_prefix = "mq_i{}_".format(image_index) ifp.draw_all_heatmaps(tracker, ifdata.object_detections, marginals, gm_method, out_path, file_prefix)
def alternate_inference_test(query_ix, image_ix, gm_method='original', verbose=True): import image_fetch_core as ifc import opengm as ogm vgd, potentials, platt_mod, bin_mod, queries, ifdata = dp.get_all_data() query = vgd['vg_data_test'][query_ix].annotations ifdata.configure(image_ix, query) gm = None tracker = None if gm_method == 'original': gm, tracker = ifc.generate_pgm(ifdata, verbose) elif gm_method == 'uniform' or gm_method == 'empirical': gm, tracker = ifc.generate_pgm_all_objects(ifdata, verbose, method=gm_method) #inf_param = ogm.InfParam(steps=120, damping=0., convergenceBound=0.001) #infr = ogm.inference.BeliefPropagation(gm, parameter=inf_param) #infr.infer(infr.verboseVisitor()) return gm, tracker#, infr
def ex1(query_index, image_index, inf_alg='bp', gm_method='original', do_suppl_plots=True, save_gm=False, verbose=True): """ generate plots for a query/image pair """ import image_fetch_core as ifc reload(ifc) import image_fetch_plot as ifp reload(ifp) vgd, potentials, platt_mod, bin_mod, queries, ifdata = dp.get_all_data() query = vgd['vg_data_test'][query_index].annotations ifdata.configure(image_index, query) gm = None tracker = None if gm_method == 'original': gm, tracker = ifc.generate_pgm(ifdata, verbose) elif gm_method == 'uniform' or gm_method == 'empirical': gm, tracker = ifc.generate_pgm_all_objects(ifdata, verbose=verbose, method=gm_method) file_prefix = "q{0}_i{1}_".format(query_index, image_index) energy = None best_match_ix = None marginals = None obj_file = None if inf_alg == 'bp': obj_file = out_path + file_prefix + gm_method + '_bp_objects.png' energy, best_match_ix, marginals = ifc.do_inference(gm) if do_suppl_plots: ifp.draw_all_heatmaps(tracker, ifdata.object_detections, marginals, gm_method, out_path, file_prefix) #ifp.p_compare(tracker, ifdata.object_detections, marginals, out_path+file_prefix+'sctr.png') elif inf_alg == 'astar': obj_file = out_path + file_prefix + 'as_objects.png' energy, best_match_ix = ifc.do_inference_astar(gm) ifp.draw_best_objects(tracker, best_match_ix, energy, filename=obj_file) if save_gm: ifp.draw_gm(gm)
def gen_viz_file(query_ixs, image_ixs, output_path): import image_fetch_core as ifc; reload(ifc) import image_fetch_utils as ifu; reload(ifu) vgd, potentials, platt_mod, bin_mod, queries, ifdata = dp.get_all_data() for query_ix in query_ixs: filename = 'q_' + str(query_ix) + '.csv' f = open(output_path+filename, 'w') query = queries['simple_graphs'][query_ix] query_str = ifu.sg_to_str(query.annotations) tp_simple = ifu.get_partial_scene_matches(vgd['vg_data_test'], queries['simple_graphs']) tp_indices = tp_simple[query_ix] for i in image_ixs: #range(0, len(image_ixs)): ifdata.configure(i, query.annotations) gm, tracker = ifc.generate_pgm(ifdata, verbose=False) energy, best_match_ix, marginals = ifc.do_inference(gm) line = '{:03d}, 0, "{}"\n'.format(i, query_str) f.write(line) line = '{:03d}, 2, {:0.4f}\n'.format(i, energy) f.write(line) if i in tp_indices: line = '{:03d}, 3, "match"\n'.format(i) else: line = '{:03d}, 3, "no match"\n'.format(i) f.write(line) for obj_ix in range(0, len(best_match_ix)): obj_name = tracker.object_names[obj_ix] box_ix = best_match_ix[obj_ix] bc = tracker.box_coords[box_ix] line = '{:03d}, 1, {}, "{}", {}, {}, {}, {}\n'.format(i, obj_ix, obj_name, int(bc[0]), int(bc[1]), int(bc[2]), int(bc[3])) f.write(line) f.close()