import os import os.path as op import sys import numpy as np from numpy import * import json from tqdm import tqdm import copy import argparse parser = argparse.ArgumentParser("Scene layout evaluation") parser.add_argument("-f", "--file", type=str) parser.add_argument("-r", "--room", type=str) parser.add_argument('--dataset_dir', type=str) parser.add_argument("--is_atiss", action='store_true') args = parser.parse_args() dataset_prefix = args.dataset_dir with open(os.path.join(dataset_prefix, "dataset_stats.txt"), "r") as fin: stats = json.load(fin) splits = json.load(open(f"./dataset/3D/{args.room}_splits.json", "r")) def load_room_boxes(prefix, id, stats): data = np.load(op.join(prefix, id, 'boxes.npz')) x_c, y_c = data['floor_plan_centroid'][0], data['floor_plan_centroid'][2] x_offset = min(data['floor_plan_vertices'][:,0]) y_offset = min(data['floor_plan_vertices'][:,2]) room_length = max(data['floor_plan_vertices'][:,0]) - min(data['floor_plan_vertices'][:,0]) room_width = max(data['floor_plan_vertices'][:,2]) - min(data['floor_plan_vertices'][:,2]) vertices = np.stack((data['floor_plan_vertices'][:,0]-x_offset, data['floor_plan_vertices'][:,2]-y_offset), axis=1) vertices = np.asarray([list(nxy) for nxy in set(tuple(xy) for xy in vertices)]) vertices = [f'({v[0]:.2f}, {v[1]:.2f})' for v in vertices] objects = [] for label, size, angle, loc in zip(data['class_labels'], data['sizes'], data['angles'], data['translations']): label_idx = np.where(label)[0][0] if label_idx >= len(stats['object_types']): continue cat = stats['object_types'][label_idx] length, height, width = size orientation = round(angle[0] / 3.1415926 * 180) dx,dz,dy = loc objects.append([cat, length, width, height, dx+x_c-x_offset, dy+y_c-y_offset, dz, angle]) return room_length, room_width, objects def roty(t): c = np.cos(t) s = np.sin(t) return np.array([[c, s], [-s,c]]) def invalid_object_size(output, stats, normalized=False, pixel=False, margin=0.1): invalid_objects = [] counter = 0 invalid_scenes = [] for out in output: invalid_scene = False rl, rw, _ = load_room_boxes(dataset_prefix, out['query_id'], stats) data = np.load(op.join(dataset_prefix, out['query_id'], 'boxes.npz')) x_c, y_c = data['floor_plan_centroid'][0], data['floor_plan_centroid'][2] pred_objects = copy.deepcopy(out['object_list']) norm = 1 if pixel: norm = min(rl, rw) / 256. else: if normalized: norm = min(rl, rw) for _, box in pred_objects: for k,v in box.items(): if k == 'orientation': continue box[k] = v*norm for cat, box in pred_objects: R = roty(box['orientation']/180*pi) box_vertices = np.asarray([[-box['length']/2, -box['width']/2], [box['length']/2, -box['width']/2], [-box['length']/2, box['width']/2], [box['length']/2, box['width']/2]]) box_vertices = box_vertices@R box_vertices += np.asarray([[box['left'], box['top']]]) # if box['left'] + box['length']/2 > rl + 0.1 or box['top'] + box['width']/2 > rw + 0.1 or box['left'] - box['length']/2 < -0.1 or box['top'] - box['width']/2 < -0.1: if max(box_vertices[:, 0]) > rl+margin or min(box_vertices[:, 0]) < -margin or max(box_vertices[:, 1]) > rw+margin or min(box_vertices[:, 1]) < -margin: invalid_objects.append([out['query_id'], cat, max(box['left'] + box['length']/2 - rl, box['top'] + box['width']/2 - rw, (box['left'] - box['length']/2)*-1, (box['top'] - box['width']/2)*-1)]) invalid_scene = True counter += 1 if invalid_scene: invalid_scenes.append(out['query_id']) return len(invalid_objects) / counter, invalid_scenes, invalid_objects def categorical_kl(p, q): return (p * (np.log(p + 1e-6) - np.log(q + 1e-6))).sum() def object_category_KL_divergence(output, gt_data, stats): all_categories = stats['object_types'] gt_label_freq = {c: 0 for c in all_categories} pred_label_freq = {c: 0 for c in all_categories} for d in gt_data.values(): for obj in d[2]: gt_label_freq[obj[0]] += 1 gt_label_freq = np.asarray([gt_label_freq[k]/sum(list(gt_label_freq.values())) for k in sorted(all_categories)]) for out in output: for obj in out['object_list']: if obj[0] not in all_categories: continue pred_label_freq[obj[0]] += 1 pred_label_freq = np.asarray([pred_label_freq[k]/sum(list(pred_label_freq.values())) for k in sorted(all_categories)]) kl_div = categorical_kl(gt_label_freq, pred_label_freq) return kl_div, gt_label_freq, pred_label_freq, sorted(all_categories) def postprocess_atiss(output): id2full = {x.split("_")[-1]:x for x in splits['test']} for o in output: o['query_id'] = id2full[o['id']] regular_output = [] for out in output: if out['query_id'] in splits['rect_test']: data = np.load(os.path.join(dataset_prefix, out['query_id'], 'boxes.npz')) x_c, y_c = data['floor_plan_centroid'][0], data['floor_plan_centroid'][2] x_offset = min(data['floor_plan_vertices'][:,0]) y_offset = min(data['floor_plan_vertices'][:,2]) for _, o in out['object_list']: for k in ['length', 'width', 'height']: o[k] = o[k]*2 o['left'] = o['left'] + x_c - x_offset o['top'] = o['top'] + y_c - y_offset o['orientation'] = round((o['orientation']/np.pi) * 180) regular_output.append(out) return regular_output if __name__ == '__main__': test_ids_regular = splits['rect_test'] test_data_regular = {id:load_room_boxes(dataset_prefix, id, stats) for id in tqdm(test_ids_regular)} output = json.load(open(args.file)) if args.is_atiss: output = postprocess_atiss(output) invalid_objs, invalid_scenes, laa = invalid_object_size(output, stats, normalized=True, pixel=True, margin=0.1) print("Out-of-bound rate: ", len(invalid_scenes)/len(output)) KL_divergence, a, b, cats = object_category_KL_divergence(output, {k:v for k, v in test_data_regular.items() if k in splits['rect_test']}, stats) print("KL Divergence: ", KL_divergence)