def main():
fn_list = open("/data/unagi0/dataset/SUNCG-Seg/data_goodlist_v2.txt").readlines()
fn_list = [fn.strip() for fn in fn_list]
cls_hist = np.zeros(256)
counter = Counter()
for fn in tqdm(fn_list):
gt_fn = os.path.join("/data/unagi0/dataset/SUNCG-Seg/category_v2/", fn + "_category40.png")
gt_im = np.array(Image.open(gt_fn))
print (counter)
mul = lambda x, y: x * y
n_pixel_per_img = reduce(mul, gt_im.size)
correct_n_pixel = n_pixel_per_img * len(fn_list)
got_n_pixel = sum(counter.values())
assert got_n_pixel == correct_n_pixel
print (got_n_pixel)
save_dic_to_json(counter, "suncg_gt_distribution.json")
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='')
parser.add_argument("--gt_dir", type=str, default=None,
help="gt dir")
parser.add_argument("--way", type=str, default="legend", help="legend or colorize",
choices=['legend', 'colorize'])
parser.add_argument("--ext", type=str, default="pdf")
parser.add_argument("--dataset", type=str, default="suncg")
parser.add_argument("--title_names", type=str, default=None, nargs='*')
args = parser.parse_args()
dataset_dic = {
"suncg": {
"json_fn": "./dataset/nyu_info.json",
"raw_rgb_dir": "/data/unagi0/dataset/SUNCG-Seg/mlt_v2",
"raw_optional_img_dir": "/data/unagi0/dataset/SUNCG-Seg/hha_v2",
"gt_dir": "/data/unagi0/dataset/SUNCG-Seg/category_v2",
# "gt_dir": "/data/unagi0/watanabe/DomainAdaptation/Segmentation/VisDA2017/test_output/suncg-train_rgbhhab_only_3ch---suncg-train_rgbhha/normal-drn_d_38-20.tar/label",
# "gt_dir": "/data/unagi0/watanabe/DomainAdaptation/Segmentation/VisDA2017/test_output/suncg-train_rgbhha_only_6ch---suncg-train_rgbhha/b16-drn_d_38-10.tar/label",
}
}
raw_rgb_dir = dataset_dic[args.dataset]["raw_rgb_dir"]
raw_optional_img_dir = dataset_dic[args.dataset]["raw_optional_img_dir"]
gt_dir = dataset_dic[args.dataset]["gt_dir"]
with open(dataset_dic[args.dataset]["json_fn"], 'r') as f:
info = json.load(f)
label_list = np.array(info['label'] + ["background"], dtype=np.str)
if args.way == "legend":
vis_with_legend(indir_list=[], outdir=args.outdir, label_list=label_list, raw_rgb_dir=raw_rgb_dir,
raw_optional_img_dir=raw_optional_img_dir, gt_dir=gt_dir, ext=args.ext,
title_names=args.title_names)
elif args.way == "colorize": # TODO
NotImplementedError()
list(model_g2.parameters()) +
list(model_f1.parameters()),
opt=args.opt,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
model_g1.load_state_dict(checkpoint['g1_state_dict'])
model_g2.load_state_dict(checkpoint['g2_state_dict'])
model_f1.load_state_dict(checkpoint['f1_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}'".format(args.resume))
json_fn = os.path.join(args.outdir, "param_%s_resume.json" % args.savename)
check_if_done(json_fn)
args.machine = os.uname()[1]
save_dic_to_json(args.__dict__, json_fn)
start_epoch = checkpoint['epoch']
else:
[model_g1, model_g2], model_f1 = get_multichannel_model(
net_name=args.net,
input_ch_list=args.inch_list,
n_class=args.n_class,
method=detailed_method,
res=args.res,
is_data_parallel=args.is_data_parallel)
optimizer = get_optimizer(list(model_g1.parameters()) +
list(model_g2.parameters()) +
list(model_f1.parameters()),
from util import save_dic_to_json
fn_list = open(
"/data/unagi0/dataset/SUNCG-Seg/data_goodlist_v2.txt").readlines()
fn_list = [fn.strip() for fn in fn_list]
cls_hist = np.zeros(256)
counter = Counter()
for fn in tqdm(fn_list):
gt_fn = os.path.join("/data/unagi0/dataset/SUNCG-Seg/category_v2/",
fn + "_category40.png")
gt_im = Image.open(gt_fn)
counter += Counter(np.array(gt_im).flatten().astype(long))
print(counter)
mul = lambda x, y: x * y
n_pixel_per_img = reduce(mul, gt_im.size)
correct_n_pixel = n_pixel_per_img * len(fn_list)
got_n_pixel = sum(counter.values())
assert got_n_pixel == correct_n_pixel
print(got_n_pixel)
save_dic_to_json(counter, "suncg_gt_distribution.json")
