def main():
args, cfg = parse_args()
checkpoint_path = utils.find_checkpoint(cfg.INTERACTIVE_MODELS_PATH, args.checkpoint)
model = utils.load_is_model(checkpoint_path, args.device, num_max_clicks=args.n_clicks)
eval_exp_name = get_eval_exp_name(args)
eval_exp_path = args.logs_path / eval_exp_name
eval_exp_path.mkdir(parents=True, exist_ok=True)
print_header = True
for dataset_name in args.datasets.split(','):
dataset = utils.get_dataset(dataset_name, cfg)
zoom_in_target_size = 600 if dataset_name == 'DAVIS' else 400
predictor = get_predictor(model, args.mode, args.device,
prob_thresh=args.thresh,
predictor_params={'num_max_points': args.n_clicks},
zoom_in_params={'target_size': zoom_in_target_size})
dataset_results = evaluate_dataset(dataset, predictor, pred_thr=args.thresh,
max_iou_thr=args.target_iou,
max_clicks=args.n_clicks)
save_results(args, dataset_name, eval_exp_path, dataset_results,
print_header=print_header)
print_header = False
def main():
args, cfg = parse_args()
checkpoints_list, logs_path, logs_prefix = get_checkpoints_list_and_logs_path(
args, cfg)
logs_path.mkdir(parents=True, exist_ok=True)
single_model_eval = len(checkpoints_list) == 1
assert not args.iou_analysis if not single_model_eval else True, \
"Can't perform IoU analysis for multiple checkpoints"
print_header = single_model_eval
for dataset_name in args.datasets.split(','):
dataset = utils.get_dataset(dataset_name, cfg)
for checkpoint_path in checkpoints_list:
model = utils.load_is_model(checkpoint_path, args.device)
predictor_params, zoomin_params = get_predictor_and_zoomin_params(
args, dataset_name)
predictor = get_predictor(model,
args.mode,
args.device,
prob_thresh=args.thresh,
predictor_params=predictor_params,
zoom_in_params=zoomin_params)
vis_callback = get_prediction_vis_callback(
logs_path, dataset_name,
args.thresh) if args.vis_preds else None
dataset_results = evaluate_dataset(dataset,
predictor,
pred_thr=args.thresh,
max_iou_thr=args.target_iou,
min_clicks=args.min_n_clicks,
max_clicks=args.n_clicks,
callback=vis_callback)
row_name = args.mode if single_model_eval else checkpoint_path.stem
if args.iou_analysis:
save_iou_analysis_data(args,
dataset_name,
logs_path,
logs_prefix,
dataset_results,
model_name=args.model_name)
save_results(args,
row_name,
dataset_name,
logs_path,
logs_prefix,
dataset_results,
save_ious=single_model_eval and args.save_ious,
single_model_eval=single_model_eval,
print_header=print_header)
print_header = False
def main():
args, cfg = parse_args()
# get model
torch.backends.cudnn.deterministic = True
checkpoint_path = utils.find_checkpoint(cfg.INTERACTIVE_MODELS_PATH,
args.checkpoint)
model = utils.load_is_model(checkpoint_path,
args.device,
cpu_dist_maps=True,
norm_radius=args.norm_radius)
predictor = get_predictor(model.to(args.device),
device=args.device,
brs_mode=args.mode)
clicker = clicker_.Clicker()
input_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([.485, .456, .406], [.229, .224, .225])
])
# get image
image = cv2.cvtColor(cv2.imread(args.imgpath), cv2.COLOR_BGR2RGB)
image_nd = input_transform(image).to(args.device)
if image_nd is not None:
predictor.set_input_image(image_nd)
probs_history = []
clicks = json.load(open('clicks.json', 'r'))['clicks']
for c in clicks:
click = clicker_.Click(is_positive=c['is_positive'],
coords=(c['y'], c['x']))
clicker.add_click(click)
pred = predictor.get_prediction(clicker)
torch.cuda.empty_cache()
if probs_history:
probs_history.append((probs_history[-1][0], pred))
else:
probs_history.append((np.zeros_like(pred), pred))
if probs_history:
current_prob_total, current_prob_additive = probs_history[-1]
final_pred = np.maximum(current_prob_total, current_prob_additive)
final_mask = (final_pred > 0.5).astype(np.uint8)
else:
final_mask = np.ones_like(pred).astype(np.uint8)
if args.sis:
final_mask = structural_integrity_strategy(final_mask, clicker)
cv2.imwrite(
args.outpath,
cv2.cvtColor(image, cv2.COLOR_RGB2BGR) *
np.expand_dims(final_mask, axis=2))
def main():
args, cfg = parse_args()
torch.backends.cudnn.deterministic = True
checkpoint_path = utils.find_checkpoint(cfg.INTERACTIVE_MODELS_PATH, args.checkpoint)
model = utils.load_is_model(checkpoint_path, args.device, cpu_dist_maps=True, norm_radius=args.norm_radius)
root = tk.Tk()
root.minsize(960, 480)
app = InteractiveDemoApp(root, args, model)
root.deiconify()
app.mainloop()
def __init__(self):
torch.backends.cudnn.deterministic = True
base_dir = os.path.abspath(
os.environ.get("MODEL_PATH", "/opt/nuclio/hrnet"))
model_path = os.path.join(base_dir)
self.net = None
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
checkpoint_path = utils.find_checkpoint(model_path,
"coco_lvis_h18_itermask.pth")
self.net = utils.load_is_model(checkpoint_path, self.device)
def main():
args, cfg = parse_args()
torch.backends.cudnn.deterministic = True
checkpoint_path = utils.find_checkpoint(cfg.INTERACTIVE_MODELS_PATH, args.checkpoint)
model = utils.load_is_model(checkpoint_path, args.device, cpu_dist_maps=True, norm_radius=args.norm_radius)
x_coords = (300, 300, 150)
y_coords = (300, 400, 300)
is_pos = (1, 1, 0)
img_pth = '/Users/jason/Desktop/img_1205.jpg'
image = cv2.cvtColor(cv2.imread(img_pth), cv2.COLOR_BGR2RGB)
print(type(image))
print(sys.getsizeof(image))
var1 = BetterThanApp.AppReplacement(image, args, model, x_coords, y_coords, is_pos)
cfg = exp.load_config_file('./config.yml', return_edict=True)
## init dateset
# Possible choices: 'GrabCut', 'Berkeley', 'DAVIS', 'COCO_MVal', 'SBD'
DATASET = 'Berkeley'
dataset = utils.get_dataset(DATASET, cfg)
## init model
from isegm.inference.predictors import get_predictor
EVAL_MAX_CLICKS = 20
MODEL_THRESH = 0.49
checkpoint_path = utils.find_checkpoint(cfg.INTERACTIVE_MODELS_PATH,
'coco_lvis_h18_itermask')
model = utils.load_is_model(checkpoint_path, device)
# Possible choices: 'NoBRS', 'f-BRS-A', 'f-BRS-B', 'f-BRS-C', 'RGB-BRS', 'DistMap-BRS'
brs_mode = 'f-BRS-B'
predictor = get_predictor(model, brs_mode, device, prob_thresh=MODEL_THRESH)
## single sample test
sample_id = random.sample(range(len(dataset)), 1)[0]
TARGET_IOU = 0.95
sample = dataset.get_sample(sample_id)
gt_mask = sample.gt_mask
clicks_list, ious_arr, pred = evaluate_sample(sample.image,
gt_mask,
predictor,
