def dump_results(end_points, dump_dir, config, inference_switch=False):
''' Dump results.
Args:
end_points: dict
{..., pred_mask}
pred_mask is a binary mask array of size (batch_size, num_proposal) computed by running NMS and empty box removal
Returns:
None
'''
if not os.path.exists(dump_dir):
os.system('mkdir %s'%(dump_dir))
# INPUT
point_clouds = end_points['point_clouds'].cpu().numpy()
batch_size = point_clouds.shape[0]
# NETWORK OUTPUTS
seed_xyz = end_points['seed_xyz'].detach().cpu().numpy() # (B,num_seed,3)
if 'vote_xyz' in end_points:
aggregated_vote_xyz = end_points['aggregated_vote_xyz'].detach().cpu().numpy()
vote_xyz = end_points['vote_xyz'].detach().cpu().numpy() # (B,num_seed,3)
aggregated_vote_xyz = end_points['aggregated_vote_xyz'].detach().cpu().numpy()
objectness_scores = end_points['objectness_scores'].detach().cpu().numpy() # (B,K,2)
pred_center = end_points['center'].detach().cpu().numpy() # (B,K,3)
pred_heading_class = torch.argmax(end_points['heading_scores'], -1) # B,num_proposal
pred_heading_residual = torch.gather(end_points['heading_residuals'], 2, pred_heading_class.unsqueeze(-1)) # B,num_proposal,1
pred_heading_class = pred_heading_class.detach().cpu().numpy() # B,num_proposal
pred_heading_residual = pred_heading_residual.squeeze(2).detach().cpu().numpy() # B,num_proposal
pred_size_class = torch.argmax(end_points['size_scores'], -1) # B,num_proposal
pred_size_residual = torch.gather(end_points['size_residuals'], 2, pred_size_class.unsqueeze(-1).unsqueeze(-1).repeat(1,1,1,3)) # B,num_proposal,1,3
pred_size_residual = pred_size_residual.squeeze(2).detach().cpu().numpy() # B,num_proposal,3
# OTHERS
pred_mask = end_points['pred_mask'] # B,num_proposal
idx_beg = 0
for i in range(batch_size):
pc = point_clouds[i,:,:]
objectness_prob = softmax(objectness_scores[i,:,:])[:,1] # (K,)
# Dump various point clouds
pc_util.write_ply(pc, os.path.join(dump_dir, '%06d_pc.ply'%(idx_beg+i)))
pc_util.write_ply(seed_xyz[i,:,:], os.path.join(dump_dir, '%06d_seed_pc.ply'%(idx_beg+i)))
#pc_util.write_ply_rgb(pc[:,0:3], (pc[:,3:]*256).astype(np.int8),os.path.join(dump_dir, '%06d_rgb_pc.ply'%(idx_beg+i))) #jason color
if 'vote_xyz' in end_points:
pc_util.write_ply(end_points['vote_xyz'][i,:,:], os.path.join(dump_dir, '%06d_vgen_pc.ply'%(idx_beg+i)))
pc_util.write_ply(aggregated_vote_xyz[i,:,:], os.path.join(dump_dir, '%06d_aggregated_vote_pc.ply'%(idx_beg+i)))
pc_util.write_ply(aggregated_vote_xyz[i,:,:], os.path.join(dump_dir, '%06d_aggregated_vote_pc.ply'%(idx_beg+i)))
pc_util.write_ply(pred_center[i,:,0:3], os.path.join(dump_dir, '%06d_proposal_pc.ply'%(idx_beg+i)))
if np.sum(objectness_prob>DUMP_CONF_THRESH)>0:
pc_util.write_ply(pred_center[i,objectness_prob>DUMP_CONF_THRESH,0:3], os.path.join(dump_dir, '%06d_confident_proposal_pc.ply'%(idx_beg+i)))
# Dump predicted bounding boxes
if np.sum(objectness_prob>DUMP_CONF_THRESH)>0:
num_proposal = pred_center.shape[1]
obbs = []
for j in range(num_proposal):
obb = config.param2obb(pred_center[i,j,0:3], pred_heading_class[i,j], pred_heading_residual[i,j],
pred_size_class[i,j], pred_size_residual[i,j])
obbs.append(obb)
if len(obbs)>0:
obbs = np.vstack(tuple(obbs)) # (num_proposal, 7)
pc_util.write_oriented_bbox(obbs[objectness_prob>DUMP_CONF_THRESH,:], os.path.join(dump_dir, '%06d_pred_confident_bbox.ply'%(idx_beg+i)))
pc_util.write_oriented_bbox(obbs[np.logical_and(objectness_prob>DUMP_CONF_THRESH, pred_mask[i,:]==1),:], os.path.join(dump_dir, '%06d_pred_confident_nms_bbox.ply'%(idx_beg+i)))
pc_util.write_oriented_bbox(obbs[pred_mask[i,:]==1,:], os.path.join(dump_dir, '%06d_pred_nms_bbox.ply'%(idx_beg+i)))
pc_util.write_oriented_bbox(obbs, os.path.join(dump_dir, '%06d_pred_bbox.ply'%(idx_beg+i)))
# Return if it is at inference time. No dumping of groundtruths
if inference_switch:
return
# LABELS
gt_center = end_points['center_label'].cpu().numpy() # (B,MAX_NUM_OBJ,3)
gt_mask = end_points['box_label_mask'].cpu().numpy() # B,K2
gt_heading_class = end_points['heading_class_label'].cpu().numpy() # B,K2
gt_heading_residual = end_points['heading_residual_label'].cpu().numpy() # B,K2
gt_size_class = end_points['size_class_label'].cpu().numpy() # B,K2
gt_size_residual = end_points['size_residual_label'].cpu().numpy() # B,K2,3
objectness_label = end_points['objectness_label'].detach().cpu().numpy() # (B,K,)
objectness_mask = end_points['objectness_mask'].detach().cpu().numpy() # (B,K,)
for i in range(batch_size):
if np.sum(objectness_label[i,:])>0:
pc_util.write_ply(pred_center[i,objectness_label[i,:]>0,0:3], os.path.join(dump_dir, '%06d_gt_positive_proposal_pc.ply'%(idx_beg+i)))
if np.sum(objectness_mask[i,:])>0:
pc_util.write_ply(pred_center[i,objectness_mask[i,:]>0,0:3], os.path.join(dump_dir, '%06d_gt_mask_proposal_pc.ply'%(idx_beg+i)))
pc_util.write_ply(gt_center[i,:,0:3], os.path.join(dump_dir, '%06d_gt_centroid_pc.ply'%(idx_beg+i)))
pc_util.write_ply_color(pred_center[i,:,0:3], objectness_label[i,:], os.path.join(dump_dir, '%06d_proposal_pc_objectness_label.obj'%(idx_beg+i)))
# Dump GT bounding boxes
obbs = []
for j in range(gt_center.shape[1]):
if gt_mask[i,j] == 0: continue
obb = config.param2obb(gt_center[i,j,0:3], gt_heading_class[i,j], gt_heading_residual[i,j],
gt_size_class[i,j], gt_size_residual[i,j])
obbs.append(obb)
if len(obbs)>0:
obbs = np.vstack(tuple(obbs)) # (num_gt_objects, 7)
pc_util.write_oriented_bbox(obbs, os.path.join(dump_dir, '%06d_gt_bbox.ply'%(idx_beg+i)))
# OPTIONALL, also dump prediction and gt details
if 'batch_pred_map_cls' in end_points:
for ii in range(batch_size):
fout = open(os.path.join(dump_dir, '%06d_pred_map_cls.txt'%(ii)), 'w')
for t in end_points['batch_pred_map_cls'][ii]:
fout.write(str(t[0])+' ')
fout.write(",".join([str(x) for x in list(t[1].flatten())]))
fout.write(' '+str(t[2]))
fout.write('\n')
fout.close()
if 'batch_gt_map_cls' in end_points:
for ii in range(batch_size):
fout = open(os.path.join(dump_dir, '%06d_gt_map_cls.txt'%(ii)), 'w')
for t in end_points['batch_gt_map_cls'][ii]:
fout.write(str(t[0])+' ')
fout.write(",".join([str(x) for x in list(t[1].flatten())]))
fout.write('\n')
fout.close()
# -*- coding: utf-8 -*-
import os
import sys
import numpy as np
import pc_util
BASE_DIR = os.path.dirname(__file__)
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, "../"))
data = np.load("scannet_scenes/scene0001_01.npy")
scene_points = data[:, 0:3]
colors = data[:, 3:6]
instance_labels = data[:, 6]
semantic_labels = data[:, 7]
output_folder = "demo_output"
if not os.path.exists(output_folder):
os.mkdir(output_folder)
# Write scene as OBJ file for visualization
pc_util.write_ply_rgb(scene_points, colors,
os.path.join(output_folder, "scene.obj"))
pc_util.write_ply_color(scene_points, instance_labels,
os.path.join(output_folder, "scene_instance.obj"))
pc_util.write_ply_color(scene_points, semantic_labels,
os.path.join(output_folder, "scene_semantic.obj"))
def build_website_from_mmdet3d_without_pred(cat, level, annotations,
dataset_dir, output_dir,
part_name_list):
dataset_path = Path(dataset_dir)
item_dir = osp.join(output_dir, 'item')
shutil.rmtree(output_dir, True)
mmcv.mkdir_or_exist(item_dir)
num = len(annotations)
num = 10
table = []
item_file_format = """<a href="./item/{page_name}" title="{page_title}"> {page_title} </a>"""
color_map_item_format = """<h2 style="display:block"> <span style = "color:{color_name}"> {class_name} </span> </h2>"""
bar = ProgressBar()
for i in bar(range(num)):
annotation = annotations[i]
gt_box = annotation['annos']['gt_boxes_upright_depth']
num_gt_box = annotation['annos']['gt_num']
box_class = annotation['annos']['class']
point_file = annotation['pts_path']
instance_file = annotation['pts_instance_mask_path']
sem_file = annotation['pts_semantic_mask_path']
#annotation['class']
#annotation['name']
point_file = str(dataset_path / point_file)
sem_file = str(dataset_path / instance_file)
instance_file = str(dataset_path / instance_file)
point = np.fromfile(point_file, np.float32).reshape(-1, 3)
#print(point.shape, point_file)
#exit(0)
#sem = np.fromfile(sem_file).astype(int)
instance = np.fromfile(instance_file, np.long)
pi_path = osp.join(item_dir, f'point_instance_{i}.ply')
bbox_path = osp.join(item_dir, f'gt_box_{i}.ply')
json_bbox_path = osp.join(item_dir, f'gt_box_{i}.json')
gt_box[:, -3:] *= 1.01
colors = write_bbox_color_json(gt_box, box_class, json_bbox_path)
write_bbox_color(gt_box, box_class, bbox_path)
write_ply_color(point, instance, pi_path)
def rgb_to_hex(rgb):
ans = '#%02x%02x%02x' % (rgb[0], rgb[1], rgb[2])
return ans
rpi_path = osp.relpath(pi_path, item_dir)
rbbox_path = osp.relpath(bbox_path, item_dir)
rjbbox_path = osp.relpath(json_bbox_path, item_dir)
next_page_rel_path = osp.relpath(
osp.join(item_dir, f'{(i + 1) % num}.html'), item_dir)
last_page_rel_path = osp.relpath(
osp.join(item_dir, f'{(i - 1 + num) % num}.html'), item_dir)
table.append([
item_file_format.format(page_name=f'{i}.html',
page_title=f'Instance {i}')
] + [0 for i in range(len(part_name_list))])
color_item = ''
for label in colors:
c = rgb_to_hex(colors[label])
l = part_name_list[label]
color_item = color_item + color_map_item_format.format(
color_name=c, class_name=l)
html_from_template(item_template,
out_file=osp.join(item_dir, f'{i}.html'),
file_path=f"'{rpi_path}', '{rjbbox_path}'",
file_name="'pc_instance', 'gt_box'",
file_type="'pcd', 'json_box'",
color_map=color_item,
last_page=last_page_rel_path,
next_page=next_page_rel_path)
df = pd.DataFrame(table, columns=['Obj'] + part_name_list)
main_table = df.to_html(escape=False,
classes="table sortable is-striped is-hoverable",
border=0,
index=False)
html_from_template(table_template,
out_file=osp.join(output_dir, 'main_page.html'),
table_string=main_table,
type=f'{cat}-{level}')
def build_website_from_mmdet3d(cat,
level,
annotations,
dataset_dir,
output_dir,
pred,
part_name_list,
iou_th=(0.5, )):
ret_dict, (aps, precisions, recalls,
aabb_ious) = evaluate(annotations, pred, part_name_list, iou_th)
#print(ret_dict)
#exit(0)
dataset_path = Path(dataset_dir)
item_dir = osp.join(output_dir, 'item')
shutil.rmtree(output_dir, True)
mmcv.mkdir_or_exist(item_dir)
num = len(annotations)
#num = 10
table = []
data_name = []
overall_string = ""
for j, part_name in enumerate(part_name_list):
data_name.append(f'{part_name}_mIOU')
overall_string += f"<th>{np.mean([aabb_ious[i][j] for i in aabb_ious if j in aabb_ious[i]]):.2f}</th>"
for part_name in part_name_list:
for iou in iou_th:
data_name.append(f'{part_name}_AP @ {iou:.2f}')
res = ret_dict[f'{part_name}_AP_{iou:.2f}']
overall_string += f"<th>{res:.2f}</th>"
for part_name in part_name_list:
for iou in iou_th:
data_name.append(f'{part_name}_AR @ {iou:.2f}')
res = ret_dict[f'{part_name}_rec_{iou:.2f}']
overall_string += f"<th>{res:.2f}</th>"
item_file_format = """<a href="./item/{page_name}" title="{page_title}"> {page_title} </a>"""
color_map_item_format = """<h2 style="display:block"> <span style = "color:{color_name}"> {class_name} </span> </h2>"""
bar = ProgressBar()
for i in bar(range(num)):
#if i != 9:
# continue
annotation = annotations[i]
gt_box = annotation['annos']['gt_boxes_upright_depth']
num_gt_box = annotation['annos']['gt_num']
box_class = annotation['annos']['class']
point_file = annotation['pts_path']
instance_file = annotation['pts_instance_mask_path']
sem_file = annotation['pts_semantic_mask_path']
#annotation['class']
#annotation['name']
point_file = str(dataset_path / point_file)
sem_file = str(dataset_path / instance_file)
instance_file = str(dataset_path / instance_file)
point = np.fromfile(point_file, np.float32).reshape(-1, 3)
#print(point.shape, point_file)
#sem = np.fromfile(sem_file).astype(int)
instance = np.fromfile(instance_file, np.long)
pi_path = osp.join(item_dir, f'point_instance_{i}.ply')
bbox_path = osp.join(item_dir, f'gt_box_{i}.ply')
json_bbox_path = osp.join(item_dir, f'gt_box_{i}.json')
colors = write_bbox_color_json(gt_box, box_class, json_bbox_path)
write_bbox_color(gt_box, box_class, bbox_path)
write_ply_color(point, instance, pi_path)
def rgb_to_hex(rgb):
ans = '#%02x%02x%02x' % (rgb[0], rgb[1], rgb[2])
return ans
rpi_path = osp.relpath(pi_path, item_dir)
rbbox_path = osp.relpath(bbox_path, item_dir)
rjbbox_path = osp.relpath(json_bbox_path, item_dir)
next_page_rel_path = osp.relpath(
osp.join(item_dir, f'{(i + 1) % num}.html'), item_dir)
last_page_rel_path = osp.relpath(
osp.join(item_dir, f'{(i - 1 + num) % num}.html'), item_dir)
table_item_i = [
item_file_format.format(page_name=f'{i}.html',
page_title=f'Instance {i}')
]
#for k in range(len(iou_th)):
for j, part_name in enumerate(part_name_list):
table_item_i.append(f'{aabb_ious[i][j]:.2f}')
for k in range(len(iou_th)):
for j, part_name in enumerate(part_name_list):
if j in aps[k][i]:
table_item_i.append(f'{float(aps[k][i][j]):.2f}')
else:
table_item_i.append('N/A')
for k in range(len(iou_th)):
for j, part_name in enumerate(part_name_list):
if j in recalls[k][i]:
table_item_i.append(f'{float(recalls[k][i][j][-1]):.2f}')
else:
table_item_i.append('N/A')
boxes = pred[i]['boxes_3d'].tensor.numpy()[:, :6]
boxes[:, 2] += boxes[:, 5] * 0.5
#print(boxes, gt_box)
#exit(0)
score = pred[i]['scores_3d'].numpy()
label = pred[i]['labels_3d'].numpy()
flag = score > 0.1
# flag = np.logical_and(score > 0.1, label == 0)
boxes = boxes[flag]
label = label[flag]
#print(boxes.shape, label, score[score > 0.1])
table.append(table_item_i)
#print(boxes.shape, label.shape)
pred_bbox_path = osp.join(item_dir, f'pred_box_{i}.ply')
json_pred_bbox_path = osp.join(item_dir, f'pred_box_{i}.json')
write_bbox_color(boxes, label, pred_bbox_path)
colors_ = write_bbox_color_json(boxes, label, json_pred_bbox_path)
colors.update(colors_)
color_item = ''
for label in colors:
c = rgb_to_hex(colors[label])
l = part_name_list[label]
color_item = color_item + color_map_item_format.format(
color_name=c, class_name=l)
pred_rjbbox_path = osp.relpath(json_pred_bbox_path, item_dir)
html_from_template(
item2_template,
out_file=osp.join(item_dir, f'{i}.html'),
file_path=f"'{rpi_path}', '{rjbbox_path}', '{pred_rjbbox_path}'",
file_name="'pc_instance', 'gt_box', 'pred_box'",
file_type="'pcd', 'json_box', 'json_box'",
color_map=color_item,
last_page=last_page_rel_path,
next_page=next_page_rel_path)
df = pd.DataFrame(table, columns=[
'Object',
] + data_name)
main_table = df.to_html(escape=False,
classes="table sortable is-striped is-hoverable",
border=0,
index=False)
html_from_template(table_template,
out_file=osp.join(output_dir, 'main_page.html'),
table_string=main_table,
overal_info=overall_string,
type=f'{cat}-{level}')
import sys
import os
BASE_DIR = os.path.dirname(__file__)
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, '../'))
import numpy as np
import pc_util
data = np.load('scannet_scenes/scene0001_01.npy')
scene_points = data[:,0:3]
colors = data[:,3:6]
instance_labels = data[:,6]
semantic_labels = data[:,7]
output_folder = 'demo_output'
if not os.path.exists(output_folder):
os.mkdir(output_folder)
# Write scene as OBJ file for visualization
pc_util.write_ply_rgb(scene_points, colors, os.path.join(output_folder, 'scene.obj'))
pc_util.write_ply_color(scene_points, instance_labels, os.path.join(output_folder, 'scene_instance.obj'))
pc_util.write_ply_color(scene_points, semantic_labels, os.path.join(output_folder, 'scene_semantic.obj'))
scene_name = 'scannet_train_detection_data/scene0002_00'
output_folder = 'data_viz_dump'
data = np.load(scene_name + '_vert.npy')
scene_points = data[:, 0:3]
colors = data[:, 3:]
instance_labels = np.load(scene_name + '_ins_label.npy')
semantic_labels = np.load(scene_name + '_sem_label.npy')
instance_bboxes = np.load(scene_name + '_bbox.npy')
print(np.unique(instance_labels))
print(np.unique(semantic_labels))
input()
if not os.path.exists(output_folder):
os.mkdir(output_folder)
# Write scene as OBJ file for visualization
pc_util.write_ply_rgb(scene_points, colors,
os.path.join(output_folder, 'scene.obj'))
pc_util.write_ply_color(scene_points, instance_labels,
os.path.join(output_folder, 'scene_instance.obj'))
pc_util.write_ply_color(scene_points, semantic_labels,
os.path.join(output_folder, 'scene_semantic.obj'))
from model_util_scannet import ScannetDatasetConfig
DC = ScannetDatasetConfig()
print(instance_bboxes.shape)
bbox, bbox_color = DC.param2bbox(instance_bboxes, True)
pc_util.write_ply_color(bbox, bbox_color,
os.path.join(output_folder, 'scene_bbox.obj'))
