eval_cfg=eval_cfg)
train_args = configparser.ConfigParser()
eval_args = configparser.ConfigParser()
train_args.read(train_cfg_file_path)
eval_args.read(eval_cfg_file_path)
dataset_name = eval_args.get('DATA', 'DATASET')
cam_type = eval_args.get('DATA', 'cam_type')
estimate_bbs = eval_args.getboolean('BBOXES', 'ESTIMATE_BBS')
icp = eval_args.getboolean('EVALUATION', 'ICP')
gt_trans = eval_args.getboolean('EVALUATION', 'gt_trans')
evaluation_name = evaluation_name + '_icp' if icp else evaluation_name
evaluation_name = evaluation_name + '_bbest' if estimate_bbs else evaluation_name
evaluation_name = evaluation_name + '_gttrans' if gt_trans else evaluation_name
data = dataset_name + '_' + cam_type if len(cam_type) > 0 else dataset_name
log_dir = get_log_dir(workspace_path, experiment_name, experiment_group)
eval_dir = get_eval_dir(log_dir, evaluation_name, data)
eval_calc_errors.eval_calc_errors(eval_args, eval_dir)
eval_loc.match_and_eval_performance_scores(eval_args, eval_dir)
# report = latex_report.Report(eval_dir,log_dir)
# report.write_configuration(train_cfg_file_path,eval_cfg_file_path)
# report.merge_all_tex_files()
# report.include_all_figures()
# report.save(open_pdf=True)
def main():
'''
lxc:
use_euclidean means the similarity between test embedding and template embedding
are computed using Euclidean Distance
'''
#use_euclidean = False
parser = argparse.ArgumentParser()
parser.add_argument('experiment_name')
parser.add_argument('evaluation_name')
parser.add_argument('--eval_cfg', default='eval.cfg', required=False)
parser.add_argument('--at_step', default=None, required=False)
arguments = parser.parse_args()
full_name = arguments.experiment_name.split('/')
experiment_name = full_name.pop()
experiment_group = full_name.pop() if len(full_name) > 0 else ''
evaluation_name = arguments.evaluation_name
eval_cfg = arguments.eval_cfg
at_step = arguments.at_step
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
train_cfg_file_path = u.get_config_file_path(workspace_path,
experiment_name,
experiment_group)
eval_cfg_file_path = u.get_eval_config_file_path(workspace_path,
eval_cfg=eval_cfg)
train_args = configparser.ConfigParser()
eval_args = configparser.ConfigParser()
train_args.read(train_cfg_file_path)
eval_args.read(eval_cfg_file_path)
#[DATA]
# target data params
dataset_name = eval_args.get('DATA', 'DATASET')
obj_id = eval_args.getint('DATA', 'OBJ_ID')
scenes = eval(eval_args.get(
'DATA', 'SCENES')) if len(eval(eval_args.get(
'DATA',
'SCENES'))) > 0 else eval_utils.get_all_scenes_for_obj(eval_args)
cam_type = eval_args.get('DATA', 'cam_type')
model_type = 'reconst' if dataset_name == 'tless' else '' # model_type set to reconst only for tless.
data_params = dataset_params.get_dataset_params(dataset_name,
model_type=model_type,
train_type='',
test_type=cam_type,
cam_type=cam_type)
target_models_info = inout.load_yaml(
data_params['models_info_path']) # lxc
# source data params, lxc
source_dataset_name = 'toyotalight'
# source_dataset_name = train_args.get('DATA','DATASET') # TODO train args no section DATA
# source_obj_id = train_args.getint('DATA','OBJ_ID') # TODO train args no section DATA
source_obj_id = int(train_cfg_file_path[-6:-4]) # TODO workaround
source_data_params = dataset_params.get_dataset_params(source_dataset_name,
model_type='',
train_type='',
test_type='',
cam_type='')
# for tless temporarily.
# source_data_params = dataset_params.get_dataset_params(source_dataset_name, model_type='', train_type='', test_type='kinect', cam_type='kinect')
source_models_info = inout.load_yaml(
source_data_params['models_info_path'])
print("source_models_info_path:", source_data_params['models_info_path'])
# 'diameter' is not equal to sqrt(x^2+y^2+z^2) for hinterstoisser, rutgers, tless, tejaniDB. etc.
# for toyotalight, 'diameter' == sqrt(...).
target_models_3Dlength = np.linalg.norm([
target_models_info[obj_id][key]
for key in ['size_x', 'size_y', 'size_z']
])
source_models_3Dlength = np.linalg.norm([
source_models_info[source_obj_id][key]
for key in ['size_x', 'size_y', 'size_z']
])
target_source_length_ratio = target_models_3Dlength / source_models_3Dlength
print("target_source_length_ratio:", target_source_length_ratio)
print("source id {:02d}, target id {:02d}".format(source_obj_id, obj_id))
print('basepath: ', data_params['base_path'])
#[BBOXES]
estimate_bbs = eval_args.getboolean('BBOXES', 'ESTIMATE_BBS')
#[METRIC]
top_nn = eval_args.getint('METRIC', 'TOP_N')
#[EVALUATION]
icp = eval_args.getboolean('EVALUATION', 'ICP')
evaluation_name = evaluation_name + '_icp' if icp else evaluation_name
evaluation_name = evaluation_name + '_bbest' if estimate_bbs else evaluation_name
data = dataset_name + '_' + cam_type if len(cam_type) > 0 else dataset_name
log_dir = u.get_log_dir(workspace_path, experiment_name, experiment_group)
ckpt_dir = u.get_checkpoint_dir(log_dir)
eval_dir = u.get_eval_dir(log_dir, evaluation_name, data)
# if eval_args.getboolean('EVALUATION','EVALUATE_ERRORS'):
# eval_loc.match_and_eval_performance_scores(eval_args, eval_dir)
# exit()
if not os.path.exists(eval_dir):
os.makedirs(eval_dir)
shutil.copy2(eval_cfg_file_path, eval_dir)
print "eval_args: ", eval_args
codebook, dataset, decoder = factory.build_codebook_from_name(
experiment_name,
experiment_group,
return_dataset=True,
return_decoder=True)
dataset.renderer
gpu_options = tf.GPUOptions(allow_growth=True,
per_process_gpu_memory_fraction=0.5)
config = tf.ConfigProto(gpu_options=gpu_options)
sess = tf.Session(config=config)
factory.restore_checkpoint(sess,
tf.train.Saver(),
ckpt_dir,
at_step=at_step)
if estimate_bbs:
#Object Detection, seperate from main
# sys.path.append('/net/rmc-lx0050/home_local/sund_ma/src/SSD_Tensorflow')
# from ssd_detector import SSD_detector
# #TODO: set num_classes, network etc.
# ssd = SSD_detector(sess, num_classes=31, net_shape=(300,300))
from rmcssd.bin import detector
ssd = detector.Detector(eval_args.get('BBOXES', 'CKPT'))
t_errors = []
R_errors = []
all_test_visibs = []
test_embeddings = []
for scene_id in scenes:
test_imgs = eval_utils.load_scenes(scene_id, eval_args)
test_imgs_depth = eval_utils.load_scenes(
scene_id, eval_args, depth=True) if icp else None
if estimate_bbs:
print eval_args.get('BBOXES', 'EXTERNAL')
if eval_args.get('BBOXES', 'EXTERNAL') == 'False':
bb_preds = {}
for i, img in enumerate(test_imgs):
print img.shape
bb_preds[i] = ssd.detectSceneBBs(img,
min_score=.2,
nms_threshold=.45)
# inout.save_yaml(os.path.join(scene_res_dir,'bb_preds.yml'), bb_preds)
print bb_preds
else:
bb_preds = inout.load_yaml(
os.path.join(eval_args.get('BBOXES', 'EXTERNAL'),
'{:02d}.yml'.format(scene_id)))
test_img_crops, test_img_depth_crops, bbs, bb_scores, visibilities = eval_utils.generate_scene_crops(
test_imgs, test_imgs_depth, bb_preds, eval_args, train_args)
else:
# test_img_crops: each crop contains some bbox(es) for specified object id.
test_img_crops, test_img_depth_crops, bbs, bb_scores, visibilities = eval_utils.get_gt_scene_crops(
scene_id, eval_args, train_args)
if len(test_img_crops) == 0:
print 'ERROR: object %s not in scene %s' % (obj_id, scene_id)
exit()
info = inout.load_info(
data_params['scene_info_mpath'].format(scene_id))
Ks_test = [np.array(v['cam_K']).reshape(3, 3) for v in info.values()]
######remove
gts = inout.load_gt(data_params['scene_gt_mpath'].format(scene_id))
visib_gts = inout.load_yaml(data_params['scene_gt_stats_mpath'].format(
scene_id, 15))
#######
W_test, H_test = data_params['test_im_size']
icp_renderer = icp_utils.SynRenderer(train_args) if icp else None
noof_scene_views = eval_utils.noof_scene_views(scene_id, eval_args)
test_embeddings.append([])
scene_res_dir = os.path.join(
eval_dir, '{scene_id:02d}'.format(scene_id=scene_id))
if not os.path.exists(scene_res_dir):
os.makedirs(scene_res_dir)
for view in xrange(
noof_scene_views
): # for example, LINEMOD ape noof_scene_views = 1236
try:
# only a specified object id is selected throughout the whole scene views.
test_crops, test_crops_depth, test_bbs, test_scores, test_visibs = eval_utils.select_img_crops(
test_img_crops[view][obj_id],
test_img_depth_crops[view][obj_id] if icp else None,
bbs[view][obj_id], bb_scores[view][obj_id],
visibilities[view][obj_id], eval_args)
except:
print 'no detections'
continue
print view
preds = {}
pred_views = []
all_test_visibs.append(test_visibs[0])
t_errors_crop = []
R_errors_crop = []
for i, (test_crop, test_bb, test_score) in enumerate(
zip(test_crops, test_bbs, test_scores)):
# each test_crop is a ground truth patch
if train_args.getint('Dataset', 'C') == 1:
test_crop = cv2.cvtColor(test_crop,
cv2.COLOR_BGR2GRAY)[:, :, None]
start = time.time()
'''modify here to change the pose estimation algorithm. lxc'''
Rs_est, ts_est = codebook.auto_pose6d(
sess,
test_crop,
test_bb,
Ks_test[view].copy(),
top_nn,
train_args,
target_source_length_ratio=target_source_length_ratio)
ae_time = time.time() - start
run_time = ae_time + bb_preds[view][0][
'det_time'] if estimate_bbs else ae_time
if eval_args.getboolean('PLOT', 'EMBEDDING_PCA'):
test_embeddings[-1].append(
codebook.test_embedding(sess,
test_crop,
normalized=True))
# icp = False if view<350 else True
#TODO:
Rs_est_old, ts_est_old = Rs_est.copy(), ts_est.copy()
for p in xrange(top_nn):
if icp:
start = time.time()
# icp only along tz
R_est_refined, t_est_refined = icp_utils.icp_refinement(
test_crops_depth[i],
icp_renderer,
Rs_est[p],
ts_est[p],
Ks_test[view].copy(), (W_test, H_test),
depth_only=True,
max_mean_dist_factor=5.0)
print ts_est[p]
print t_est_refined
# x,y update,does not change tz:
_, ts_est_refined = codebook.auto_pose6d(
sess,
test_crop,
test_bb,
Ks_test[view].copy(),
top_nn,
train_args,
depth_pred=t_est_refined[2])
# commented by lxc
# _, ts_est_refined, _ = codebook.auto_pose6d(sess, test_crop, test_bb, Ks_test[view].copy(), top_nn, train_args,depth_pred=t_est_refined[2])
t_est_refined = ts_est_refined[p]
# rotation icp, only accepted if below 20 deg change
R_est_refined, _ = icp_utils.icp_refinement(
test_crops_depth[i],
icp_renderer,
R_est_refined,
t_est_refined,
Ks_test[view].copy(), (W_test, H_test),
no_depth=True)
print Rs_est[p]
print R_est_refined
icp_time = time.time() - start
Rs_est[p], ts_est[p] = R_est_refined, t_est_refined
preds.setdefault('ests', []).append({
'score': test_score,
'R': Rs_est[p],
't': ts_est[p]
})
run_time = run_time + icp_time if icp else run_time
min_t_err, min_R_err = eval_plots.print_trans_rot_errors(
gts[view], obj_id, ts_est, ts_est_old, Rs_est, Rs_est_old)
t_errors_crop.append(min_t_err)
R_errors_crop.append(min_R_err)
if eval_args.getboolean('PLOT', 'RECONSTRUCTION'):
eval_plots.plot_reconstruction_test(
sess, codebook._encoder, decoder, test_crop)
# eval_plots.plot_reconstruction_train(sess, decoder, nearest_train_codes[0])
if eval_args.getboolean('PLOT',
'NEAREST_NEIGHBORS') and not icp:
for R_est, t_est in zip(Rs_est, ts_est):
pred_views.append(
dataset.render_rot(R_est, downSample=2))
eval_plots.show_nearest_rotation(pred_views, test_crop,
view)
if eval_args.getboolean('PLOT', 'SCENE_WITH_ESTIMATE'):
eval_plots.plot_scene_with_estimate(
test_imgs[view].copy(),
icp_renderer.renderer if icp else dataset.renderer,
Ks_test[view].copy(), Rs_est_old[0], ts_est_old[0],
Rs_est[0], ts_est[0], test_bb, test_score, obj_id,
gts[view], bb_preds[view] if estimate_bbs else None)
if cv2.waitKey(1) == 32:
cv2.waitKey(0)
t_errors.append(t_errors_crop[np.argmin(
np.linalg.norm(np.array(t_errors_crop), axis=1))])
R_errors.append(R_errors_crop[np.argmin(
np.linalg.norm(np.array(t_errors_crop), axis=1))])
# save predictions in sixd format
res_path = os.path.join(scene_res_dir,
'%04d_%02d.yml' % (view, obj_id))
inout.save_results_sixd17(res_path, preds, run_time=run_time)
if not os.path.exists(os.path.join(eval_dir, 'latex')):
os.makedirs(os.path.join(eval_dir, 'latex'))
if not os.path.exists(os.path.join(eval_dir, 'figures')):
os.makedirs(os.path.join(eval_dir, 'figures'))
'''evaluation code
dataset_renderer renders source object model for evaluation;
If we need target object model for evaluation, go get a new renderer.
'''
if eval_args.getboolean('EVALUATION', 'COMPUTE_ERRORS'):
eval_calc_errors.eval_calc_errors(eval_args,
eval_dir,
dataset_renderer=dataset.renderer)
if eval_args.getboolean('EVALUATION', 'EVALUATE_ERRORS'):
eval_loc.match_and_eval_performance_scores(eval_args, eval_dir)
'''plot code'''
cyclo = train_args.getint('Embedding', 'NUM_CYCLO')
if eval_args.getboolean('PLOT', 'EMBEDDING_PCA'):
embedding = sess.run(codebook.embedding_normalized)
eval_plots.compute_pca_plot_embedding(eval_dir,
embedding[::cyclo],
np.array(test_embeddings[0]),
obj_id=obj_id)
if eval_args.getboolean('PLOT', 'VIEWSPHERE'):
eval_plots.plot_viewsphere_for_embedding(
dataset.viewsphere_for_embedding[::cyclo], eval_dir, obj_id=obj_id)
if eval_args.getboolean('PLOT', 'CUM_T_ERROR_HIST'):
eval_plots.plot_t_err_hist(np.array(t_errors), eval_dir, obj_id=obj_id)
eval_plots.plot_t_err_hist2(np.array(t_errors),
eval_dir,
obj_id=obj_id)
if eval_args.getboolean('PLOT', 'CUM_R_ERROR_HIST'):
eval_plots.plot_R_err_hist(eval_args, eval_dir, scenes)
eval_plots.plot_R_err_hist2(np.array(R_errors),
eval_dir,
obj_id=obj_id)
if eval_args.getboolean('PLOT', 'CUM_VSD_ERROR_HIST'):
eval_plots.plot_vsd_err_hist(eval_args, eval_dir, scenes)
if eval_args.getboolean('PLOT', 'VSD_OCCLUSION'):
eval_plots.plot_vsd_occlusion(eval_args, eval_dir, scenes,
np.array(all_test_visibs))
if eval_args.getboolean('PLOT', 'R_ERROR_OCCLUSION'):
eval_plots.plot_re_rect_occlusion(eval_args, eval_dir, scenes,
np.array(all_test_visibs))
if eval_args.getboolean('PLOT', 'ANIMATE_EMBEDDING_PCA'):
eval_plots.animate_embedding_path(test_embeddings[0])
if eval_args.getboolean('PLOT', 'RECONSTRUCTION_TEST_BATCH'):
eval_plots.plot_reconstruction_test_batch(sess,
codebook,
decoder,
test_img_crops,
noof_scene_views,
obj_id,
eval_dir=eval_dir)
# plt.show()
# calculate 6D pose errors
# print 'exiting ...'
# eval_calc_errors.eval_calc_errors(eval_args, eval_dir)
# calculate 6D pose errors
report = latex_report.Report(eval_dir, log_dir)
report.write_configuration(train_cfg_file_path, eval_cfg_file_path)
report.merge_all_tex_files()
report.include_all_figures()
report.save(open_pdf=False)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('experiment_name')
parser.add_argument('evaluation_name')
parser.add_argument('--eval_cfg', default='eval.cfg', required=False)
parser.add_argument('--at_step', default=None, type=str, required=False)
parser.add_argument('--model_path', default=None, required=True)
arguments = parser.parse_args()
full_name = arguments.experiment_name.split('/')
experiment_name = full_name.pop()
experiment_group = full_name.pop() if len(full_name) > 0 else ''
evaluation_name = arguments.evaluation_name
eval_cfg = arguments.eval_cfg
at_step = arguments.at_step
model_path = arguments.model_path
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
log_dir = u.get_log_dir(workspace_path, experiment_name, experiment_group)
train_cfg_file_path = u.get_train_config_exp_file_path(
log_dir, experiment_name)
eval_cfg_file_path = u.get_eval_config_file_path(workspace_path,
eval_cfg=eval_cfg)
train_args = configparser.ConfigParser(inline_comment_prefixes="#")
eval_args = configparser.ConfigParser(inline_comment_prefixes="#")
train_args.read(train_cfg_file_path)
eval_args.read(eval_cfg_file_path)
#[DATA]
dataset_name = eval_args.get('DATA', 'DATASET')
obj_id = eval_args.getint('DATA', 'OBJ_ID')
scenes = eval(eval_args.get(
'DATA', 'SCENES')) if len(eval(eval_args.get(
'DATA',
'SCENES'))) > 0 else eval_utils.get_all_scenes_for_obj(eval_args)
cam_type = eval_args.get('DATA', 'cam_type')
data_params = dataset_params.get_dataset_params(dataset_name,
model_type='',
train_type='',
test_type=cam_type,
cam_type=cam_type)
#[BBOXES]
estimate_bbs = eval_args.getboolean('BBOXES', 'ESTIMATE_BBS')
gt_masks = eval_args.getboolean('BBOXES', 'gt_masks')
estimate_masks = eval_args.getboolean('BBOXES', 'estimate_masks')
#[METRIC]
top_nn = eval_args.getint('METRIC', 'TOP_N')
#[EVALUATION]
icp = eval_args.getboolean('EVALUATION', 'ICP')
gt_trans = eval_args.getboolean('EVALUATION', 'gt_trans')
iterative_code_refinement = eval_args.getboolean(
'EVALUATION', 'iterative_code_refinement')
H_AE = train_args.getint('Dataset', 'H')
W_AE = train_args.getint('Dataset', 'W')
evaluation_name = evaluation_name + '_icp' if icp else evaluation_name
evaluation_name = evaluation_name + '_bbest' if estimate_bbs else evaluation_name
evaluation_name = evaluation_name + '_maskest' if estimate_masks else evaluation_name
evaluation_name = evaluation_name + '_gttrans' if gt_trans else evaluation_name
evaluation_name = evaluation_name + '_gtmasks' if gt_masks else evaluation_name
evaluation_name = evaluation_name + '_refined' if iterative_code_refinement else evaluation_name
data = dataset_name + '_' + cam_type if len(cam_type) > 0 else dataset_name
if at_step is None:
checkpoint_file = u.get_checkpoint_basefilename(
log_dir,
False,
latest=train_args.getint('Training', 'NUM_ITER'),
joint=True)
else:
checkpoint_file = u.get_checkpoint_basefilename(log_dir,
False,
latest=at_step,
joint=True)
print(checkpoint_file)
eval_dir = u.get_eval_dir(log_dir, evaluation_name, data)
if not os.path.exists(eval_dir):
os.makedirs(eval_dir)
shutil.copy2(eval_cfg_file_path, eval_dir)
codebook, dataset = factory.build_codebook_from_name(experiment_name,
experiment_group,
return_dataset=True,
joint=True)
dataset._kw['model_path'] = [model_path]
dataset._kw['model'] = 'cad' if 'cad' in model_path else 'reconst'
dataset._kw['model'] = 'reconst' if 'reconst' in model_path else 'cad'
gpu_options = tf.GPUOptions(allow_growth=True,
per_process_gpu_memory_fraction=0.5)
config = tf.ConfigProto(gpu_options=gpu_options)
sess = tf.Session(config=config)
saver = tf.train.Saver()
saver.restore(sess, checkpoint_file)
t_errors = []
R_errors = []
all_test_visibs = []
external_path = eval_args.get('BBOXES', 'EXTERNAL')
test_embeddings = []
for scene_id in scenes:
test_imgs = eval_utils.load_scenes(scene_id, eval_args)
test_imgs_depth = eval_utils.load_scenes(
scene_id, eval_args, depth=True) if icp else None
if estimate_bbs:
print(external_path)
if external_path == 'False':
bb_preds = {}
for i, img in enumerate(test_imgs):
print((img.shape))
bb_preds[i] = ssd.detectSceneBBs(img,
min_score=.05,
nms_threshold=.45)
print(bb_preds)
else:
if estimate_masks:
bb_preds = inout.load_yaml(
os.path.join(
external_path,
'{:02d}/mask_rcnn_predict.yml'.format(scene_id)))
print(list(bb_preds[0][0].keys()))
mask_paths = glob.glob(
os.path.join(external_path,
'{:02d}/masks/*.npy'.format(scene_id)))
maskrcnn_scene_masks = [np.load(mp) for mp in mask_paths]
else:
maskrcnn_scene_masks = None
bb_preds = inout.load_yaml(
os.path.join(external_path,
'{:02d}.yml'.format(scene_id)))
test_img_crops, test_img_depth_crops, bbs, bb_scores, visibilities = eval_utils.generate_scene_crops(
test_imgs,
test_imgs_depth,
bb_preds,
eval_args, (H_AE, W_AE),
inst_masks=maskrcnn_scene_masks)
else:
test_img_crops, test_img_depth_crops, bbs, bb_scores, visibilities = eval_utils.get_gt_scene_crops(
scene_id,
eval_args,
train_args,
load_gt_masks=external_path if gt_masks else gt_masks)
if len(test_img_crops) == 0:
print(('ERROR: object %s not in scene %s' % (obj_id, scene_id)))
exit()
info = inout.load_info(
data_params['scene_info_mpath'].format(scene_id))
Ks_test = [
np.array(v['cam_K']).reshape(3, 3) for v in list(info.values())
]
######remove
gts = inout.load_gt(data_params['scene_gt_mpath'].format(scene_id))
visib_gts = inout.load_yaml(data_params['scene_gt_stats_mpath'].format(
scene_id, 15))
#######
W_test, H_test = data_params['test_im_size']
icp_renderer = icp_utils.SynRenderer(
train_args, dataset._kw['model_path'][0]) if icp else None
noof_scene_views = eval_utils.noof_scene_views(scene_id, eval_args)
test_embeddings.append([])
scene_res_dir = os.path.join(
eval_dir, '{scene_id:02d}'.format(scene_id=scene_id))
if not os.path.exists(scene_res_dir):
os.makedirs(scene_res_dir)
for view in range(noof_scene_views):
try:
test_crops, test_crops_depth, test_bbs, test_scores, test_visibs = eval_utils.select_img_crops(
test_img_crops[view][obj_id],
test_img_depth_crops[view][obj_id] if icp else None,
bbs[view][obj_id], bb_scores[view][obj_id],
visibilities[view][obj_id], eval_args)
except:
print('no detections')
continue
print(view)
preds = {}
pred_views = []
all_test_visibs.append(test_visibs[0])
t_errors_crop = []
R_errors_crop = []
for i, (test_crop, test_bb, test_score) in enumerate(
zip(test_crops, test_bbs, test_scores)):
start = time.time()
if train_args.getint('Dataset', 'C') == 1:
test_crop = cv2.cvtColor(test_crop,
cv2.COLOR_BGR2GRAY)[:, :, None]
Rs_est, ts_est, _ = codebook.auto_pose6d(
sess,
test_crop,
test_bb,
Ks_test[view].copy(),
top_nn,
train_args,
codebook._get_codebook_name(model_path),
refine=iterative_code_refinement)
Rs_est_old, ts_est_old = Rs_est.copy(), ts_est.copy()
ae_time = time.time() - start
if eval_args.getboolean('PLOT', 'EMBEDDING_PCA'):
test_embeddings[-1].append(
codebook.test_embedding(sess,
test_crop,
normalized=True))
if eval_args.getboolean('EVALUATION', 'gt_trans'):
ts_est = np.empty((top_nn, 3))
for n in range(top_nn):
smallest_diff = np.inf
for visib_gt, gt in zip(visib_gts[view], gts[view]):
if gt['obj_id'] == obj_id:
diff = np.sum(
np.abs(gt['obj_bb'] -
np.array(visib_gt['bbox_obj'])))
if diff < smallest_diff:
smallest_diff = diff
gt_obj = gt.copy()
print('Im there')
ts_est[n] = np.array(gt_obj['cam_t_m2c']).reshape(-1)
try:
run_time = ae_time + bb_preds[view][0][
'det_time'] if estimate_bbs else ae_time
except:
run_time = ae_time
for p in range(top_nn):
if icp:
# note: In the CVPR paper a different ICP was used
start = time.time()
# depth icp
R_est_refined, t_est_refined = icp_utils.icp_refinement(
test_crops_depth[i],
icp_renderer,
Rs_est[p],
ts_est[p],
Ks_test[view].copy(), (W_test, H_test),
depth_only=True,
max_mean_dist_factor=5.0)
print(t_est_refined)
# x,y update,does not change tz:
_, ts_est_refined, _ = codebook.auto_pose6d(
sess,
test_crop,
test_bb,
Ks_test[view].copy(),
top_nn,
train_args,
codebook._get_codebook_name(model_path),
depth_pred=t_est_refined[2],
refine=iterative_code_refinement)
t_est_refined = ts_est_refined[p]
# rotation icp, only accepted if below 20 deg change
R_est_refined, _ = icp_utils.icp_refinement(
test_crops_depth[i],
icp_renderer,
R_est_refined,
t_est_refined,
Ks_test[view].copy(), (W_test, H_test),
no_depth=True)
print((Rs_est[p]))
print(R_est_refined)
icp_time = time.time() - start
Rs_est[p], ts_est[p] = R_est_refined, t_est_refined
preds.setdefault('ests', []).append({
'score': test_score,
'R': Rs_est[p],
't': ts_est[p]
})
run_time = run_time + icp_time if icp else run_time
min_t_err, min_R_err = eval_plots.print_trans_rot_errors(
gts[view], obj_id, ts_est, ts_est_old, Rs_est, Rs_est_old)
t_errors_crop.append(min_t_err)
R_errors_crop.append(min_R_err)
if eval_args.getboolean('PLOT',
'NEAREST_NEIGHBORS') and not icp:
for R_est, t_est in zip(Rs_est, ts_est):
pred_views.append(
dataset.render_rot(R_est, downSample=2))
eval_plots.show_nearest_rotation(pred_views, test_crop,
view)
if eval_args.getboolean('PLOT', 'SCENE_WITH_ESTIMATE'):
eval_plots.plot_scene_with_estimate(
test_imgs[view].copy(),
icp_renderer.renderer if icp else dataset.renderer,
Ks_test[view].copy(), Rs_est_old[0], ts_est_old[0],
Rs_est[0], ts_est[0], test_bb, test_score, obj_id,
gts[view], bb_preds[view] if estimate_bbs else None)
if cv2.waitKey(1) == 32:
cv2.waitKey(0)
t_errors.append(t_errors_crop[np.argmin(
np.linalg.norm(np.array(t_errors_crop), axis=1))])
R_errors.append(R_errors_crop[np.argmin(
np.linalg.norm(np.array(t_errors_crop), axis=1))])
# save predictions in sixd format
res_path = os.path.join(scene_res_dir,
'%04d_%02d.yml' % (view, obj_id))
inout.save_results_sixd17(res_path, preds, run_time=run_time)
if not os.path.exists(os.path.join(eval_dir, 'latex')):
os.makedirs(os.path.join(eval_dir, 'latex'))
if not os.path.exists(os.path.join(eval_dir, 'figures')):
os.makedirs(os.path.join(eval_dir, 'figures'))
if eval_args.getboolean('EVALUATION', 'COMPUTE_ERRORS'):
eval_calc_errors.eval_calc_errors(eval_args, eval_dir)
if eval_args.getboolean('EVALUATION', 'EVALUATE_ERRORS'):
eval_loc.match_and_eval_performance_scores(eval_args, eval_dir)
cyclo = train_args.getint('Embedding', 'NUM_CYCLO')
if eval_args.getboolean('PLOT', 'EMBEDDING_PCA'):
embedding = sess.run(codebook.embedding_normalized)
eval_plots.compute_pca_plot_embedding(eval_dir, embedding[::cyclo],
np.array(test_embeddings[0]))
if eval_args.getboolean('PLOT', 'VIEWSPHERE'):
eval_plots.plot_viewsphere_for_embedding(
dataset.viewsphere_for_embedding[::cyclo], eval_dir)
if eval_args.getboolean('PLOT', 'CUM_T_ERROR_HIST'):
eval_plots.plot_t_err_hist(np.array(t_errors), eval_dir)
eval_plots.plot_t_err_hist2(np.array(t_errors), eval_dir)
if eval_args.getboolean('PLOT', 'CUM_R_ERROR_HIST'):
eval_plots.plot_R_err_recall(eval_args, eval_dir, scenes)
eval_plots.plot_R_err_hist2(np.array(R_errors), eval_dir)
if eval_args.getboolean('PLOT', 'CUM_VSD_ERROR_HIST'):
try:
eval_plots.plot_vsd_err_hist(eval_args, eval_dir, scenes)
except:
pass
if eval_args.getboolean('PLOT', 'VSD_OCCLUSION'):
try:
eval_plots.plot_vsd_occlusion(eval_args, eval_dir, scenes,
np.array(all_test_visibs))
except:
pass
if eval_args.getboolean('PLOT', 'R_ERROR_OCCLUSION'):
try:
eval_plots.plot_re_rect_occlusion(eval_args, eval_dir, scenes,
np.array(all_test_visibs))
except:
pass
if eval_args.getboolean('PLOT', 'ANIMATE_EMBEDDING_PCA'):
eval_plots.animate_embedding_path(test_embeddings[0])
report = latex_report.Report(eval_dir, log_dir)
report.write_configuration(train_cfg_file_path, eval_cfg_file_path)
report.merge_all_tex_files()
report.include_all_figures()
report.save(open_pdf=True)