def plot_re_rect_occlusion(eval_args, eval_dir, scene_ids, all_test_visibs, bins = 10):
obj_id = eval_args.getint('DATA','obj_id')
top_n_eval = eval_args.getint('EVALUATION','TOP_N_EVAL')
top_n = eval_args.getint('METRIC','TOP_N')
# if top_n_eval < 1:
# return
all_angle_errs = []
for scene_id in scene_ids:
if not os.path.exists(os.path.join(eval_dir,'error=re_ntop=%s' % top_n,'errors_{:02d}.yml'.format(scene_id))):
print 'WARNING: ' + os.path.join(eval_dir,'error=re_ntop=%s' % top_n,'errors_{:02d}.yml'.format(scene_id)) + ' not found'
continue
angle_errs_dict = inout.load_yaml(os.path.join(eval_dir,'error=re_ntop=%s' % top_n,'errors_{:02d}.yml'.format(scene_id)))
all_angle_errs += [angle_e['errors'].values()[0] for angle_e in angle_errs_dict]
if len(all_angle_errs) == 0:
return
all_angle_errs = np.array(all_angle_errs)
# print all_vsd_errs
fig = plt.figure()
plt.grid()
plt.ylabel('rot err [deg]')
plt.xlabel('visibility [percent]')
# plt.axis((-0.1, 1.1, -0.1, 1.1))
# plt.xlim((0.0, 1.0))
# plt.ylim((0.0, 1.0))
total_views = len(all_angle_errs)/top_n
angle_errs_rect = np.empty((total_views,))
for view in xrange(total_views):
top_n_errors = all_angle_errs[view*top_n:(view+1)*top_n]
angle_errs_rect[view] = np.min([top_n_errors[0], 180-top_n_errors[0]])
bounds = np.linspace(0,1,bins+1)
bin_angle_errs = []
bin_count = []
for idx in xrange(bins):
bin_idcs = np.where((all_test_visibs>bounds[idx]) & (all_test_visibs<bounds[idx+1]))
# median_angle_err[idx] = np.median(angle_errs_rect[bin_idcs])
bin_angle_errs.append(angle_errs_rect[bin_idcs])
bin_count.append(len(bin_idcs[0]))
middle_bin_vis = bounds[:-1] + (bounds[1]-bounds[0])/2.
# plt.bar(middle_bin_vis,median_angle_err,0.5/bins)
plt.boxplot(bin_angle_errs, positions = middle_bin_vis, widths=0.5/bins, sym='+')
# count_str = 'bin count ' + bins * '%s '
# count_str = count_str % tuple(bin_count)
plt.title('Visibility vs Median Rectified Rotation Error' + str(bin_count))
plt.savefig(os.path.join(eval_dir,'figures','R_err_occlusion_{:02d}.png'.format(obj_id)), dpi=300)
tikz_save(os.path.join(eval_dir,'latex','R_err_occlusion.tex'), figurewidth ='0.45\\textheight', figureheight='0.45\\textheight', show_info=False)
def get_gt_scene_crops(scene_id, eval_args, train_args):
dataset_name = eval_args.get('DATA','DATASET')
cam_type = eval_args.get('DATA','CAM_TYPE')
icp = eval_args.getboolean('EVALUATION','ICP')
delta = eval_args.get('METRIC', 'VSD_DELTA')
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
dataset_path = u.get_dataset_path(workspace_path)
H = train_args.getint('Dataset','H')
# linemod with 13 objects and linemod with 15 objects may use the same md5 file!
# for example, 7 in 13 is duck, but 7 in 15 is cup!
cfg_string = str([scene_id] + eval_args.items('DATA') + eval_args.items('BBOXES') + [H])
current_config_hash = hashlib.md5(cfg_string).hexdigest()
# current_config_hash = cfg_string
current_file_name = os.path.join(dataset_path, current_config_hash + '.npz')
if os.path.exists(current_file_name):
data = np.load(current_file_name)
test_img_crops = data['test_img_crops'].item()
test_img_depth_crops = data['test_img_depth_crops'].item()
bb_scores = data['bb_scores'].item()
bb_vis = data['visib_gt'].item()
bbs = data['bbs'].item()
if not os.path.exists(current_file_name) or len(test_img_crops) == 0 or len(test_img_depth_crops) == 0:
test_imgs = load_scenes(scene_id, eval_args)
test_imgs_depth = load_scenes(scene_id, eval_args, depth=True) if icp else None
data_params = dataset_params.get_dataset_params(dataset_name, model_type='', train_type='', test_type=cam_type, cam_type=cam_type)
# only available for primesense, sixdtoolkit can generate. calc_gt_stats.py
visib_gt = inout.load_yaml(data_params['scene_gt_stats_mpath'].format(scene_id, delta))
bb_gt = inout.load_gt(data_params['scene_gt_mpath'].format(scene_id))
test_img_crops, test_img_depth_crops, bbs, bb_scores, bb_vis = generate_scene_crops(test_imgs, test_imgs_depth, bb_gt, eval_args,
train_args, visib_gt=visib_gt)
np.savez(current_file_name, test_img_crops=test_img_crops, test_img_depth_crops=test_img_depth_crops, bbs = bbs, bb_scores=bb_scores, visib_gt=bb_vis)
current_cfg_file_name = os.path.join(dataset_path, current_config_hash + '.cfg')
with open(current_cfg_file_name, 'w') as f:
f.write(cfg_string)
print 'created new ground truth crops!'
else:
print 'loaded previously generated ground truth crops!'
print len(test_img_crops), len(test_img_depth_crops)
return (test_img_crops, test_img_depth_crops, bbs, bb_scores, bb_vis)
def plot_R_err_hist_vis(eval_args, eval_dir, scene_ids, bins=20):
top_n_eval = eval_args.getint('EVALUATION','TOP_N_EVAL')
top_n = eval_args.getint('METRIC','TOP_N')
cam_type = eval_args.get('DATA','cam_type')
dataset_name = eval_args.get('DATA','dataset')
obj_id = eval_args.getint('DATA','obj_id')
# if top_n_eval < 1:
# return
data_params = dataset_params.get_dataset_params(dataset_name, model_type='', train_type='', test_type=cam_type, cam_type=cam_type)
angle_errs = []
for scene_id in scene_ids:
error_file_path = os.path.join(eval_dir,'error=re_ntop=%s' % top_n,'errors_{:02d}.yml'.format(scene_id))
if not os.path.exists(error_file_path):
print(('WARNING: ' + error_file_path + ' not found'))
continue
# angle_errs_dict = inout.load_yaml(error_file_path)
# angle_errs += [angle_e['errors'].values()[0] for angle_e in angle_errs_dict]
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))
re_dict = inout.load_yaml(error_file_path)
for view in range(len(gts)):
res = re_dict[view*top_n:(view+1)*top_n]
for gt,visib_gt in zip(gts[view],visib_gts[view]):
if gt['obj_id'] == obj_id:
if visib_gt['visib_fract'] > 0.1:
for re_e in res:
angle_errs += [list(re_e['errors'].values())[0]]
if len(angle_errs) == 0:
return
angle_errs = np.array(angle_errs)
plot_R_err_hist2(angle_errs, eval_dir, bins=bins)
def get_all_scenes_for_obj(eval_args):
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
dataset_path = u.get_dataset_path(workspace_path)
dataset_name = eval_args.get('DATA', 'DATASET')
cam_type = eval_args.get('DATA', 'CAM_TYPE')
try:
obj_id = eval_args.getint('DATA', 'OBJ_ID')
except:
obj_id = eval(eval_args.get('DATA', 'OBJECTS'))[0]
cfg_string = str(dataset_name)
current_config_hash = hashlib.md5(cfg_string).hexdigest()
current_file_name = os.path.join(dataset_path,
current_config_hash + '.npy')
if os.path.exists(current_file_name):
obj_scene_dict = np.load(current_file_name).item()
else:
p = dataset_params.get_dataset_params(dataset_name,
model_type='',
train_type='',
test_type=cam_type,
cam_type=cam_type)
obj_scene_dict = {}
scene_gts = []
for scene_id in range(1, p['scene_count'] + 1):
print(scene_id)
scene_gts.append(
inout.load_yaml(p['scene_gt_mpath'].format(scene_id)))
for obj in range(1, p['obj_count'] + 1):
eval_scenes = set()
for scene_i, scene_gt in enumerate(scene_gts):
for view_gt in scene_gt[0]:
if view_gt['obj_id'] == obj:
eval_scenes.add(scene_i + 1)
obj_scene_dict[obj] = list(eval_scenes)
np.save(current_file_name, obj_scene_dict)
eval_scenes = obj_scene_dict[obj_id]
return eval_scenes
def plot_vsd_occlusion(eval_args, eval_dir, scene_ids, all_test_visibs, bins = 10):
top_n_eval = eval_args.getint('EVALUATION','TOP_N_EVAL')
top_n = eval_args.getint('METRIC','TOP_N')
delta = eval_args.getint('METRIC','VSD_DELTA')
tau = eval_args.getint('METRIC','VSD_TAU')
cost = eval_args.get('METRIC','VSD_COST')
obj_id = eval_args.getint('DATA','obj_id')
# if top_n_eval < 1:
# return
all_vsd_errs = []
for scene_id in scene_ids:
error_file_path = os.path.join(eval_dir,'error=vsd_ntop=%s_delta=%s_tau=%s_cost=%s' % (top_n, delta, tau, cost), 'errors_{:02d}.yml'.format(scene_id))
if not os.path.exists(error_file_path):
print 'WARNING: ' + error_file_path + ' not found'
continue
vsd_dict = inout.load_yaml(error_file_path)
all_vsd_errs += [vsd_e['errors'].values()[0] for vsd_e in vsd_dict]
if len(all_vsd_errs) == 0:
return
all_vsd_errs = np.array(all_vsd_errs)
# print all_vsd_errs
fig = plt.figure()
# ax = plt.axes()
# ax.set_xlim((0.0,1.0))
# ax.set_ylim((0.0,1.0))
ax = plt.gca()
ax.set_xlim((0.0,1.0))
plt.grid()
plt.ylabel('vsd err')
plt.xlabel('visibility [percent]')
# plt.xlim((0.0, 1.0))
# plt.ylim((0.0, 1.0))
total_views = len(all_vsd_errs)/top_n
vsd_errs = np.empty((total_views,))
for view in xrange(total_views):
top_n_errors = all_vsd_errs[view*top_n:(view+1)*top_n]
vsd_errs[view] = top_n_errors[0]
bounds = np.linspace(0,1,bins+1)
bin_vsd_errs = []
bin_count = []
for idx in xrange(bins):
bin_idcs = np.where((all_test_visibs>bounds[idx]) & (all_test_visibs<bounds[idx+1]))
bin_vsd_errs.append(vsd_errs[bin_idcs])
bin_count.append(len(bin_idcs[0]))
middle_bin_vis = bounds[:-1] + (bounds[1]-bounds[0])/2.
# plt.bar(middle_bin_vis,mean_vsd_err,0.5/bins)
plt.boxplot(bin_vsd_errs, positions = middle_bin_vis, widths=0.5/bins, sym='+')
# count_str = 'bin count ' + bins * '%s '
# count_str = count_str % tuple(bin_count)
plt.title('Visibility vs Mean VSD Error' + str(bin_count))
plt.savefig(os.path.join(eval_dir,'figures','vsd_occlusion_{:02d}.png'.format(obj_id)), dpi=300)
tikz_save(os.path.join(eval_dir,'latex','vsd_occlusion.tex'), figurewidth ='0.45\\textheight', figureheight='0.45\\textheight', show_info=False)
def plot_vsd_err_hist(eval_args, eval_dir, scene_ids):
# THIS DOES NOT WORK WELL FOR MULTIPLE INSTANCE
# E.G. TEJANI 04 MILK. lxc
top_n_eval = eval_args.getint('EVALUATION','TOP_N_EVAL')
top_n = eval_args.getint('METRIC','TOP_N')
delta = eval_args.getint('METRIC','VSD_DELTA')
tau = eval_args.getint('METRIC','VSD_TAU')
cost = eval_args.get('METRIC','VSD_COST')
cam_type = eval_args.get('DATA','cam_type')
dataset_name = eval_args.get('DATA','dataset')
obj_id = eval_args.getint('DATA','obj_id')
# if top_n_eval < 1:
# return
data_params = dataset_params.get_dataset_params(dataset_name, model_type='', train_type='', test_type=cam_type, cam_type=cam_type)
vsd_errs = []
for scene_id in scene_ids:
error_file_path = os.path.join(eval_dir,'error=vsd_ntop=%s_delta=%s_tau=%s_cost=%s' % (top_n, delta, tau, cost), 'errors_{:02d}.yml'.format(scene_id))
if not os.path.exists(error_file_path):
print 'WARNING: ' + error_file_path + ' not found'
continue
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))
vsd_dict = inout.load_yaml(error_file_path)
for view,vsd_e in enumerate(vsd_dict):
vsds = vsd_dict[view*top_n:(view+1)*top_n]
for gt,visib_gt in zip(gts[view],visib_gts[view]):
if gt['obj_id'] == obj_id:
if visib_gt['visib_fract'] > 0.1:
for vsd_e in vsds:
vsd_errs += [vsd_e['errors'].values()[0]]
if len(vsd_errs) == 0:
return
vsd_errs = np.array(vsd_errs)
print len(vsd_errs)
fig = plt.figure()
ax = plt.gca()
ax.set_xlim((0.0,1.0))
plt.grid()
plt.xlabel('vsd err')
plt.ylabel('recall')
plt.title('VSD Error vs Recall')
legend=[]
for n in np.unique(np.array([top_n, 1])):
total_views = len(vsd_errs)/top_n
min_vsd_errs = np.empty((total_views,))
for view in xrange(total_views):
top_n_errors = vsd_errs[view*top_n:(view+1)*top_n]
if n == 1:
top_n_errors = top_n_errors[np.newaxis,0]
min_vsd_errs[view] = np.min(top_n_errors)
min_vsd_errs_sorted = np.sort(min_vsd_errs)
recall = np.float32(np.arange(total_views)+1.)/total_views
# fill curve
min_vsd_errs_sorted = np.hstack((min_vsd_errs_sorted, np.array([1.])))
recall = np.hstack((recall,np.array([1.])))
AUC_vsd = np.trapz(recall, min_vsd_errs_sorted)
plt.plot(min_vsd_errs_sorted,recall)
legend += ['top {0} vsd err, AUC = {1:.4f}'.format(n,AUC_vsd)]
plt.legend(legend)
plt.savefig(os.path.join(eval_dir,'figures','vsd_err_hist_{:02d}.png'.format(obj_id)), dpi=300)
tikz_save(os.path.join(eval_dir,'latex','vsd_err_hist.tex'), figurewidth ='0.45\\textheight', figureheight='0.45\\textheight', show_info=False)
def plot_R_err_hist(eval_args, eval_dir, scene_ids):
top_n_eval = eval_args.getint('EVALUATION','TOP_N_EVAL')
top_n = eval_args.getint('METRIC','TOP_N')
cam_type = eval_args.get('DATA','cam_type')
dataset_name = eval_args.get('DATA','dataset')
obj_id = eval_args.getint('DATA','obj_id')
# if top_n_eval < 1: # why?
# return
data_params = dataset_params.get_dataset_params(dataset_name, model_type='', train_type='', test_type=cam_type, cam_type=cam_type)
angle_errs = []
for scene_id in scene_ids:
error_file_path = os.path.join(eval_dir,'error=re_ntop=%s' % top_n,'errors_{:02d}.yml'.format(scene_id))
if not os.path.exists(error_file_path):
print 'WARNING: ' + error_file_path + ' not found'
continue
# angle_errs_dict = inout.load_yaml(error_file_path)
# angle_errs += [angle_e['errors'].values()[0] for angle_e in angle_errs_dict]
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))
re_dict = inout.load_yaml(error_file_path)
for view in xrange(len(gts)):
res = re_dict[view*top_n:(view+1)*top_n]
for gt,visib_gt in zip(gts[view],visib_gts[view]):
if gt['obj_id'] == obj_id:
if visib_gt['visib_fract'] > 0.1:
for re_e in res:
angle_errs += [re_e['errors'].values()[0]]
if len(angle_errs) == 0:
return
angle_errs = np.array(angle_errs)
fig = plt.figure()
plt.grid()
plt.xlabel('angle err [deg]')
plt.ylabel('recall')
plt.title('Angle Error vs Recall')
legend=[]
for n in np.unique(np.array([top_n, 1])):
total_views = len(angle_errs)/top_n
min_angle_errs = np.empty((total_views,))
min_angle_errs_rect = np.empty((total_views,))
for view in xrange(total_views):
top_n_errors = angle_errs[view*top_n:(view+1)*top_n]
if n == 1:
top_n_errors = top_n_errors[np.newaxis,0]
min_angle_errs[view] = np.min(top_n_errors)
min_angle_errs_rect[view] = np.min(np.hstack((top_n_errors, 180-top_n_errors)))
min_angle_errs_sorted = np.sort(min_angle_errs)
min_angle_errs_rect_sorted = np.sort(min_angle_errs_rect)
recall = (np.arange(total_views)+1.)/total_views
# fill curve
min_angle_errs_sorted = np.hstack((min_angle_errs_sorted, np.array([180.])))
min_angle_errs_rect_sorted = np.hstack((min_angle_errs_rect_sorted, np.array([90.])))
recall = np.hstack((recall,np.array([1.])))
AUC_angle = np.trapz(recall,min_angle_errs_sorted/180.)
AUC_angle_rect = np.trapz(recall,min_angle_errs_rect_sorted/90.)
plt.plot(min_angle_errs_sorted,recall)
plt.plot(min_angle_errs_rect_sorted,recall)
legend += ['top {0} angle err, AUC = {1:.4f}'.format(n,AUC_angle), 'top {0} rectified angle err, AUC = {1:.4f}'.format(n,AUC_angle_rect)]
plt.legend(legend)
plt.savefig(os.path.join(eval_dir,'figures','R_err_hist_{:02d}.png'.format(obj_id)), dpi=300)
tikz_save(os.path.join(eval_dir,'latex','R_err_hist.tex'), figurewidth ='0.45\\textheight', figureheight='0.45\\textheight', show_info=False)
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_group')
parser.add_argument('--eval_name', default='*', required=False)
args = parser.parse_args()
experiment_group = args.experiment_group
eval_name = args.eval_name
print eval_name
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
exp_group_path = os.path.join(workspace_path, 'experiments',
experiment_group)
print exp_group_path
error_score_files = glob.glob(
os.path.join(exp_group_path, '*/eval', eval_name, '*/error*/scores*'))
print error_score_files
data_re = []
data_auc_re = []
data_auc_rerect = []
data_te = []
data_vsd = []
data_cou = []
data_add = []
data_adi = []
data_proj = []
data_paper_vsd = {}
data_paper_auc = {}
latex_content = []
for error_score_file in error_score_files:
split_path = error_score_file.split('/')
exp_name = split_path[-6]
eval_name = split_path[-4]
occl = 'occlusion' if 'occlusion' in error_score_file else ''
test_data = split_path[-3]
error_type = split_path[-2].split('_')[0].split('=')[1]
print error_type
topn = split_path[-2].split('=')[2].split('_')[0]
error_thres = split_path[-1].split('=')[1].split('_')[0]
eval_cfg_file_path = os.path.join(workspace_path, 'experiments',
experiment_group, exp_name, 'eval',
eval_name, test_data, '*.cfg')
eval_cfg_file_pathes = glob.glob(eval_cfg_file_path)
if len(eval_cfg_file_pathes) == 0:
continue
else:
eval_cfg_file_path = eval_cfg_file_pathes[0]
eval_args = configparser.ConfigParser()
eval_args.read(eval_cfg_file_path)
print eval_cfg_file_path
estimate_bbs = eval_args.getboolean('BBOXES', 'ESTIMATE_BBS')
try:
obj_id = eval_args.getint('DATA', 'OBJ_ID')
except:
obj_id = eval(eval_args.get('DATA', 'OBJECTS'))[0]
scenes = eval_utils.get_all_scenes_for_obj(eval_args)
data = [item[1] for item in eval_args.items('DATA')]
data[2] = 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)
# print str(data)
error_score_dict = inout.load_yaml(error_score_file)
try:
sixd_recall = error_score_dict['obj_recalls'][obj_id]
except:
continue
if error_type == 're':
data_re.append({
'exp_name': exp_name,
'eval_name': eval_name,
'error_type': error_type,
'thres': error_thres,
'top': topn,
'sixd_recall': sixd_recall,
'EST_BBS': estimate_bbs,
'eval_data': str(data[:2] + [occl]),
'eval_scenes': str(data[2]),
'eval_obj': str(data[3])
})
err_file = os.path.join(
os.path.dirname(os.path.dirname(error_score_file)),
'latex/R_err_hist.tex')
try:
with open(err_file, 'r') as f:
for line in f:
if re.match('(.*)legend entries(.*)', line):
auc_re = float(line.split('=')[2].split('}')[0])
auc_rerect = float(
line.split('=')[3].split('}')[0])
data_auc_re.append({
'exp_name': exp_name,
'eval_name': eval_name,
'error_type': 'auc_re',
'thres': 'None',
'top': topn,
'sixd_recall': auc_re,
'EST_BBS': estimate_bbs,
'eval_data': str(data[:2] + [occl]),
'eval_scenes': str(data[2]),
'eval_obj': str(data[3])
})
data_auc_rerect.append({
'exp_name': exp_name,
'eval_name': eval_name,
'error_type': 'auc_rerect',
'thres': 'None',
'top': topn,
'sixd_recall': auc_rerect,
'EST_BBS': estimate_bbs,
'eval_data': str(data[:2] + [occl]),
'eval_scenes': str(data[2]),
'eval_obj': str(data[3])
})
if not data_paper_auc.has_key(int(data[3])):
data_paper_auc[int(data[3])] = {}
data_paper_auc[int(data[3])]['eval_obj'] = int(data[3])
data_paper_auc[int(
data[3])][eval_name + '_' + 'auc_re' + '_' +
str(data[1])] = float(auc_re) * 100
data_paper_auc[int(
data[3])][eval_name + '_' + 'auc_rerect' + '_' +
str(data[1])] = float(auc_rerect) * 100
except:
print err_file, 'not found'
elif error_type == 'te':
data_te.append({
'exp_name': exp_name,
'eval_name': eval_name,
'error_type': error_type,
'thres': error_thres,
'top': topn,
'sixd_recall': sixd_recall,
'EST_BBS': estimate_bbs,
'eval_data': str(data[:2] + [occl]),
'eval_scenes': str(data[2]),
'eval_obj': str(data[3])
})
elif error_type == 'vsd':
data_vsd.append({
'exp_name':
exp_name,
'eval_name':
eval_name,
'error_type':
error_type,
'thres':
error_thres,
'top':
topn,
'sixd_recall':
sixd_recall,
'EST_BBS':
estimate_bbs,
'eval_data':
str(data[:2] + [occl]),
'eval_scenes':
str(data[2]),
'eval_obj':
int(data[3]) if '[' not in data[3] else eval(data[3])[0]
})
if not data_paper_vsd.has_key(int(data[3])):
data_paper_vsd[int(data[3])] = {}
data_paper_vsd[int(data[3])]['eval_obj'] = int(data[3])
data_paper_vsd[int(
data[3])][eval_name + '_' + error_type + '_' +
str(data[1])] = float(sixd_recall) * 100
elif error_type == 'cou':
data_cou.append({
'exp_name': exp_name,
'eval_name': eval_name,
'error_type': error_type,
'thres': error_thres,
'top': topn,
'sixd_recall': sixd_recall,
'EST_BBS': estimate_bbs,
'eval_data': str(data[:2] + [occl]),
'eval_scenes': str(data[2]),
'eval_obj': str(data[3])
})
elif error_type == 'add':
data_add.append({
'exp_name': exp_name,
'eval_name': eval_name,
'error_type': error_type,
'thres': error_thres,
'top': topn,
'sixd_recall': sixd_recall,
'EST_BBS': estimate_bbs,
'eval_data': str(data[:2] + [occl]),
'eval_scenes': str(data[2]),
'eval_obj': str(data[3])
})
elif error_type == 'proj':
data_proj.append({
'exp_name': exp_name,
'eval_name': eval_name,
'error_type': error_type,
'thres': error_thres,
'top': topn,
'sixd_recall': sixd_recall,
'EST_BBS': estimate_bbs,
'eval_data': str(data[:2] + [occl]),
'eval_scenes': str(data[2]),
'eval_obj': str(data[3])
})
elif error_type == 'adi':
data_adi.append({
'exp_name': exp_name,
'eval_name': eval_name,
'error_type': error_type,
'thres': error_thres,
'top': topn,
'sixd_recall': sixd_recall,
'EST_BBS': estimate_bbs,
'eval_data': str(data[:2] + [occl]),
'eval_scenes': str(data[2]),
'eval_obj': str(data[3])
})
else:
print 'error not known: ', error_type
if len(data_re) > 0:
df_re = pd.DataFrame(data_re).sort_values(
by=['eval_obj', 'eval_name', 'eval_data', 'sixd_recall'])
latex_content.append('\\begin{adjustbox}{max width=\\textwidth}')
latex_content.append(df_re.to_latex(index=False, multirow=True))
latex_content.append('\\end{adjustbox}')
latex_content.append('\n')
latex_content.append('\n')
if len(data_auc_re) > 0:
df_re = pd.DataFrame(data_auc_re).sort_values(
by=['eval_obj', 'eval_name', 'eval_data', 'sixd_recall'])
latex_content.append('\\begin{adjustbox}{max width=\\textwidth}')
latex_content.append(df_re.to_latex(index=False, multirow=True))
latex_content.append('\\end{adjustbox}')
latex_content.append('\n')
latex_content.append('\n')
if len(data_auc_rerect) > 0:
df_re = pd.DataFrame(data_auc_rerect).sort_values(
by=['eval_obj', 'eval_name', 'eval_data', 'sixd_recall'])
latex_content.append('\\begin{adjustbox}{max width=\\textwidth}')
latex_content.append(df_re.to_latex(index=False, multirow=True))
latex_content.append('\\end{adjustbox}')
latex_content.append('\n')
latex_content.append('\n')
if len(data_te) > 0:
df_te = pd.DataFrame(data_te).sort_values(
by=['eval_obj', 'eval_name', 'eval_data', 'sixd_recall'])
latex_content.append('\\begin{adjustbox}{max width=\\textwidth}')
latex_content.append(df_te.to_latex(index=False, multirow=True))
latex_content.append('\\end{adjustbox}')
latex_content.append('\n')
latex_content.append('\n')
if len(data_cou) > 0:
df_cou = pd.DataFrame(data_cou).sort_values(
by=['eval_obj', 'eval_name', 'eval_data', 'sixd_recall'])
latex_content.append('\\begin{adjustbox}{max width=\\textwidth}')
latex_content.append(df_cou.to_latex(index=False, multirow=True))
latex_content.append('\\end{adjustbox}')
latex_content.append('\n')
latex_content.append('\n')
if len(data_add) > 0:
df_add = pd.DataFrame(data_add).sort_values(
by=['eval_obj', 'eval_name', 'eval_data', 'sixd_recall'])
latex_content.append('\\begin{adjustbox}{max width=\\textwidth}')
latex_content.append(df_add.to_latex(index=False, multirow=True))
latex_content.append('\\end{adjustbox}')
latex_content.append('\n')
latex_content.append('\n')
if len(data_proj) > 0:
df_proj = pd.DataFrame(data_proj).sort_values(
by=['eval_obj', 'eval_name', 'eval_data', 'sixd_recall'])
latex_content.append('\\begin{adjustbox}{max width=\\textwidth}')
latex_content.append(df_proj.to_latex(index=False, multirow=True))
latex_content.append('\\end{adjustbox}')
latex_content.append('\n')
latex_content.append('\n')
if len(data_adi) > 0:
df_adi = pd.DataFrame(data_adi).sort_values(
by=['eval_obj', 'eval_name', 'eval_data', 'sixd_recall'])
latex_content.append('\\begin{adjustbox}{max width=\\textwidth}')
latex_content.append(df_adi.to_latex(index=False, multirow=True))
latex_content.append('\\end{adjustbox}')
latex_content.append('\n')
latex_content.append('\n')
if len(data_paper_vsd) > 0:
df_paper = pd.DataFrame.from_dict(data_paper_vsd, orient='index')
cols = ['eval_obj'] + [col for col in df_paper if col != 'eval_obj']
df_paper = df_paper[cols]
df_paper = df_paper.sort_index(axis=1)
df_paper.loc['mean'] = df_paper.mean(axis=0)
# df_paper.loc['mean'][0] = 0
latex_content.append('\\begin{adjustbox}{max width=\\textwidth}')
latex_list = df_paper.to_latex(index=False,
multirow=True,
float_format='%.2f').splitlines()
latex_list.insert(len(latex_list) - 3, '\midrule')
latex_new = '\n'.join(latex_list)
latex_content.append(latex_new)
latex_content.append('\\end{adjustbox}')
latex_content.append('\n')
latex_content.append('\n')
if len(data_paper_auc) > 0:
df_paper = pd.DataFrame.from_dict(data_paper_auc, orient='index')
cols = ['eval_obj'] + [col for col in df_paper if col != 'eval_obj']
df_paper = df_paper[cols]
df_paper = df_paper.sort_index(axis=1)
df_paper.loc['mean'] = df_paper.mean(axis=0)
# df_paper.loc['mean'][0] = 0
latex_content.append('\\begin{adjustbox}{max width=\\textwidth}')
latex_list = df_paper.to_latex(index=False,
multirow=True,
float_format='%.2f').splitlines()
latex_list.insert(len(latex_list) - 3, '\midrule')
latex_new = '\n'.join(latex_list)
latex_content.append(latex_new)
latex_content.append('\\end{adjustbox}')
latex_content.append('\n')
latex_content.append('\n')
if len(data_vsd) > 0:
df_vsd = pd.DataFrame(data_vsd).sort_values(
by=['eval_obj', 'eval_name', 'eval_data', 'sixd_recall'])
latex_content.append('\\begin{adjustbox}{max width=\\textwidth}')
latex_content.append(df_vsd.to_latex(index=False, multirow=True))
latex_content.append('\\end{adjustbox}')
latex_content = ''.join(latex_content)
full_filename = os.path.join(exp_group_path, 'latex', 'report.tex')
if not os.path.exists(os.path.join(exp_group_path, 'latex')):
os.makedirs(os.path.join(exp_group_path, 'latex'))
with open(full_filename, 'w') as f:
f.write(prolog % (time.ctime(), experiment_group.replace('_', '\_')))
f.write(latex_content)
f.write(epilog)
from subprocess import check_output, Popen
check_output(['pdflatex', 'report.tex'],
cwd=os.path.dirname(full_filename))
Popen(['okular', 'report.pdf'], cwd=os.path.dirname(full_filename))
print 'finished'
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)
def get_gt_scene_crops(scene_id, eval_args, train_args, load_gt_masks=False):
dataset_name = eval_args.get('DATA', 'DATASET')
cam_type = eval_args.get('DATA', 'CAM_TYPE')
icp = eval_args.getboolean('EVALUATION', 'ICP')
delta = eval_args.get('METRIC', 'VSD_DELTA')
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
dataset_path = u.get_dataset_path(workspace_path)
H_AE = train_args.getint('Dataset', 'H')
W_AE = train_args.getint('Dataset', 'W')
cfg_string = str([scene_id] + eval_args.items('DATA') +
eval_args.items('BBOXES') + [H_AE])
cfg_string = cfg_string.encode('utf-8')
current_config_hash = hashlib.md5(cfg_string).hexdigest()
current_file_name = os.path.join(dataset_path,
current_config_hash + '.npz')
if os.path.exists(current_file_name):
data = np.load(current_file_name)
test_img_crops = data['test_img_crops'].item()
test_img_depth_crops = data['test_img_depth_crops'].item()
bb_scores = data['bb_scores'].item()
bb_vis = data['visib_gt'].item()
bbs = data['bbs'].item()
if not os.path.exists(current_file_name) or len(
test_img_crops) == 0 or len(test_img_depth_crops) == 0:
test_imgs = load_scenes(scene_id, eval_args)
test_imgs_depth = load_scenes(scene_id, eval_args,
depth=True) if icp else None
data_params = dataset_params.get_dataset_params(dataset_name,
model_type='',
train_type='',
test_type=cam_type,
cam_type=cam_type)
# only available for primesense, sixdtoolkit can generate
visib_gt = inout.load_yaml(data_params['scene_gt_stats_mpath'].format(
scene_id, delta))
gt = inout.load_gt(data_params['scene_gt_mpath'].format(scene_id))
gt_inst_masks = None
if load_gt_masks:
mask_paths = glob.glob(
os.path.join(load_gt_masks,
'{:02d}/masks/*.npy'.format(scene_id)))
gt_inst_masks = [np.load(mp) for mp in mask_paths]
test_img_crops, test_img_depth_crops, bbs, bb_scores, bb_vis = generate_scene_crops(
test_imgs,
test_imgs_depth,
gt,
eval_args, (H_AE, W_AE),
visib_gt=visib_gt,
inst_masks=gt_inst_masks)
np.savez(current_file_name,
test_img_crops=test_img_crops,
test_img_depth_crops=test_img_depth_crops,
bbs=bbs,
bb_scores=bb_scores,
visib_gt=bb_vis)
current_cfg_file_name = os.path.join(dataset_path,
current_config_hash + '.cfg')
with open(current_cfg_file_name, 'w') as f:
f.write(cfg_string)
print('created new ground truth crops!')
else:
print('loaded previously generated ground truth crops!')
print((len(test_img_crops), len(test_img_depth_crops)))
return (test_img_crops, test_img_depth_crops, bbs, bb_scores, bb_vis)
def match_and_eval_performance_scores(eval_args, eval_dir):
# Paths to pose errors (calculated using eval_calc_errors.py)
#---------------------------------------------------------------------------
# error_bpath = '/path/to/eval/'
# error_paths = [
# pjoin(error_bpath, 'hodan-iros15_hinterstoisser'),
# # pjoin(error_bpath, 'hodan-iros15_tless_primesense'),
# ]
#[METHOD]
method = eval_args.get('METHOD', 'METHOD')
#[DATA]
dataset = eval_args.get('DATA', 'DATASET')
test_type = eval_args.get('DATA', 'CAM_TYPE')
#[METRIC]
# Top N pose estimates (with the highest score) to be evaluated for each
# object in each image
n_top = eval_args.getint(
'EVALUATION', 'TOP_N_EVAL'
) # 0 = all estimates, -1 = given by the number of GT poses
n_top_str = eval_args.getint('METRIC', 'TOP_N')
# Pose error function
error_types = eval(eval_args.get(
'METRIC', 'ERROR_TYPE')) # 'vsd', 'adi', 'add', 'cou', 're', 'te'
error_thresh = eval(eval_args.get('METRIC', 'ERROR_THRES'))
error_thresh_fact = eval(eval_args.get('METRIC', 'ERROR_THRESH_FACT'))
# VSD parameters
vsd_delta = eval_args.getint('METRIC', 'VSD_DELTA')
vsd_tau = eval_args.getint('METRIC', 'VSD_TAU')
vsd_cost = eval_args.get('METRIC', 'VSD_COST') # 'step', 'tlinear'
idx_th = 0
idx_thf = 0
for error_type in error_types:
# Error signature
error_sign = 'error=' + error_type + '_ntop=' + str(n_top_str)
if error_type == 'vsd':
error_sign += '_delta={}_tau={}_cost={}'.format(
vsd_delta, vsd_tau, vsd_cost)
error_path = os.path.join(eval_dir, error_sign)
# error_dir = 'error=vsd_ntop=1_delta=15_tau=20_cost=step'
# Other paths
#---------------------------------------------------------------------------
# Mask of path to the input file with calculated errors
errors_mpath = pjoin('{error_path}', 'errors_{scene_id:02d}.yml')
# Mask of path to the output file with established matches and calculated scores
matches_mpath = pjoin('{error_path}', 'matches_{eval_sign}.yml')
scores_mpath = pjoin('{error_path}', 'scores_{eval_sign}.yml')
# Parameters
#---------------------------------------------------------------------------
require_all_errors = False # Whether to break if some errors are missing
visib_gt_min = 0.1 # Minimum visible surface fraction of valid GT pose
visib_delta = 15 # [mm]
# # Threshold of correctness
# error_thresh = {
# 'vsd': 0.3,
# 'cou': 0.5,
# 'te': 5.0, # [cm]
# 're': 5.0 # [deg]
# }
# # Factor k; threshold of correctness = k * d, where d is the object diameter
# error_thresh_fact = {
# 'add': 0.1,
# 'adi': 0.1
# }
# Evaluation
#---------------------------------------------------------------------------
# Evaluation signature
if error_type in ['add', 'adi']:
if type(error_thresh_fact[error_type]) is list:
cur_thres_f = error_thresh_fact[error_type][idx_thf]
idx_thf += 1
else:
cur_thres_f = error_thresh_fact[error_type]
eval_sign = 'thf=' + str(cur_thres_f)
else:
if type(error_thresh[error_type]) is list:
cur_thres = error_thresh[error_type][idx_th]
idx_th += 1
else:
cur_thres = error_thresh[error_type]
eval_sign = 'th=' + str(cur_thres)
eval_sign += '_min-visib=' + str(visib_gt_min)
print('--- Processing: {}, {}, {}'.format(method, dataset, error_type))
# Load dataset parameters
dp = get_dataset_params(dataset, test_type=test_type)
obj_ids = range(1, dp['obj_count'] + 1)
scene_ids = range(1, dp['scene_count'] + 1)
# Set threshold of correctness (might be different for each object)
error_threshs = {}
if error_type in ['add', 'adi']:
# Relative to object diameter
models_info = inout.load_yaml(dp['models_info_path'])
for obj_id in obj_ids:
obj_diameter = models_info[obj_id]['diameter']
error_threshs[obj_id] = cur_thres_f * obj_diameter
else:
# The same threshold for all objects
for obj_id in obj_ids:
error_threshs[obj_id] = cur_thres
# Go through the test scenes and match estimated poses to GT poses
#-----------------------------------------------------------------------
matches = [] # Stores info about the matching estimate for each GT
for scene_id in scene_ids:
# Load GT poses
gts = inout.load_gt(dp['scene_gt_mpath'].format(scene_id))
# Load statistics (e.g. visibility fraction) of the GT poses
gt_stats_path = dp['scene_gt_stats_mpath'].format(
scene_id, visib_delta)
gt_stats = inout.load_yaml(gt_stats_path)
# Load pre-calculated errors of the pose estimates
scene_errs_path = errors_mpath.format(error_path=error_path,
scene_id=scene_id)
if os.path.isfile(scene_errs_path):
errs = inout.load_errors(scene_errs_path)
matches += match_poses(gts, gt_stats, errs, scene_id,
visib_gt_min, error_threshs, n_top)
elif require_all_errors:
raise IOError(
'{} is missing, but errors for all scenes are required'
' (require_all_results = True).'.format(scene_errs_path))
# Calculate the performance scores
#-----------------------------------------------------------------------
# Split the dataset of Hinterstoisser to the original LINEMOD dataset
# and the Occlusion dataset by TUD (i.e. the extended GT for scene #2)
if dataset == 'hinterstoisser':
print('-- LINEMOD dataset')
eval_sign_lm = 'linemod_' + eval_sign
matches_lm = [m for m in matches if m['scene_id'] == m['obj_id']]
scores_lm = calc_scores(scene_ids, obj_ids, matches_lm, n_top)
# Save scores
scores_lm_path = scores_mpath.format(error_path=error_path,
eval_sign=eval_sign_lm)
inout.save_yaml(scores_lm_path, scores_lm)
# Save matches
matches_path = matches_mpath.format(error_path=error_path,
eval_sign=eval_sign_lm)
inout.save_yaml(matches_path, matches_lm)
print('-- Occlusion dataset')
eval_sign_occ = 'occlusion_' + eval_sign
matches_occ = [m for m in matches if m['scene_id'] == 2]
scene_ids_occ = [2]
obj_ids_occ = [1, 2, 5, 6, 8, 9, 10, 11, 12]
scores_occ = calc_scores(scene_ids_occ, obj_ids_occ, matches_occ,
n_top)
# Save scores
scores_occ_path = scores_mpath.format(error_path=error_path,
eval_sign=eval_sign_occ)
inout.save_yaml(scores_occ_path, scores_occ)
# Save matches
matches_path = matches_mpath.format(error_path=error_path,
eval_sign=eval_sign_occ)
inout.save_yaml(matches_path, matches_occ)
else:
scores = calc_scores(scene_ids, obj_ids, matches, n_top)
# Save scores
scores_path = scores_mpath.format(error_path=error_path,
eval_sign=eval_sign)
inout.save_yaml(scores_path, scores)
# Save matches
matches_path = matches_mpath.format(error_path=error_path,
eval_sign=eval_sign)
inout.save_yaml(matches_path, matches)
print('Done.')
