global is_draw, output_txt
args = GetArgs()
input_root = args.input_root
output_root = args.output_root
is_draw = args.is_draw
pool = Pool(int(cpu_count() * 7 / 8))
for input_dir in os.listdir(input_root):
input_txt_list = []
input_jpg_list = []
output_jpg_list = []
input_txt_dir = os.path.join(input_root, input_dir, 'txt')
input_jpg_dir = os.path.join(input_root, input_dir, 'jpg')
input_txt_list += GetFileList(input_txt_dir, ['.txt'])
temp_list = GetFileList(input_jpg_dir, ['.jpg'])
input_jpg_list += temp_list
output_dir = os.path.join(output_root, input_dir)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if is_draw:
output_jpg_dir = os.path.join(output_dir, 'jpg')
if not os.path.exists(output_jpg_dir):
os.mkdir(output_jpg_dir)
for pth in temp_list:
complete_path = os.path.join(output_jpg_dir,
pth.split('/')[-1])
output_jpg_list.append(complete_path)
results = pool.map(
get_box_range,
IoU = args.IoU
input_txt_root = args.input_root
output_txt_root = args.output_root
box_range_df = pd.read_csv(args.box_range)
box_range_dict = {box_range_df['picture'][i]:[box_range_df['top_left'][i],\
box_range_df['top_right'][i],\
box_range_df['bottom_left'][i],\
box_range_df['bottom_right'][i]]\
for i in range(len(box_range_df))}
input_txt_list = []
output_txt_list = []
box_range_list = []
for txt_dir in os.listdir(input_txt_root):
input_txt_dir = os.path.join(input_txt_root, txt_dir)
temp_list = GetFileList(input_txt_dir, ['.txt'])
input_txt_list += temp_list
output_txt_dir = os.path.join(output_txt_root, txt_dir)
if not os.path.exists(output_txt_dir):
os.mkdir(output_txt_dir)
for pth in temp_list:
complete_path = os.path.join(output_txt_dir, pth.split('/')[-1])
output_txt_list.append(complete_path)
jpg_name = pth.split('/')[-1].split('.')[0] + '.jpg'
box_range_list.append(box_range_dict[jpg_name])
pool = Pool(int(cpu_count()*7/8))
pool.map(filter_with_box_range, zip(input_txt_list, output_txt_list, box_range_list))
toc = time.clock()
print('running time:{:.3f} seconds'.format(toc-tic))
args = parser.parse_args()
return args
if __name__=='__main__':
tic = time.clock()
global categories, threshold, vec_num
args = get_args()
input_root = args.input_root
output_dir = args.output_dir
input_txt_list = []
for input_dir in os.listdir(input_root):
input_txt_dir = os.path.join(input_root, input_dir, 'txt')
input_txt_list += GetFileList(input_txt_dir, ['.txt'])
num = len(input_txt_list)
pool = Pool(int(cpu_count()*7/8))
labels = pool.map(get_labels, input_txt_list)
categories = list(set(list(itertools.chain.from_iterable(labels))))
optimal_infs = {ele:5*[0] for ele in categories}
for threshold in np.arange(0.1, 1.0, 0.1):
results = pool.map(get_label_dict, zip(input_txt_list, [categories]*num, [threshold]*num))
label_infs = {}
for ele in categories:
label_infs[ele] = sum([label_dict[ele] for label_dict in results])
entropy = get_entropy(label_infs[ele][1:-1])
label_infs[ele] = [threshold] + list(label_infs[ele][1:-1]) + [entropy]
if optimal_infs[ele][-1] < entropy:
default=None,
type=str)
parser.add_argument('-out',
dest='cluster_jpg_root',
help='output pictures for each cluster',
default=None,
type=str)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
if __name__ == '__main__':
tic = time.clock()
global cluster_jpg_root, cat_jpg_dir
args = get_args()
cluster_txt_dir = args.cluster_txt_dir
cat_jpg_dir = args.cat_jpg_dir
cluster_jpg_root = args.cluster_jpg_root
cluster_txt_list = GetFileList(cluster_txt_dir, ['.rois.txt'])
pool = Pool(int(cpu_count() * 7 / 8))
pool.map(get_pic_of_cluster, cluster_txt_list)
toc = time.clock()
print('running time:{:.3f}'.format(toc - tic))
with open(args.cfg_Visualize, 'r') as f:
for line in f:
infos = line.strip().split('=')
conf[infos[0]] = infos[1]
categories = conf['cls_names'].split(',')
real_txt_list = []
real_jpg_list = []
output_jpg_list = []
real_root = conf['real_root']
output_jpg_root = conf['output_root']
for data_dir in conf['real_dir'].split(','):
txt_pth = os.path.join(real_root, data_dir, 'txt')
jpg_pth = os.path.join(real_root, data_dir, 'jpg')
real_txt_list += GetFileList(txt_pth, ['.txt'])
temp_jpg_list = GetFileList(jpg_pth, ['.jpg'])
real_jpg_list += temp_jpg_list
output_jpg_dir = output_jpg_root
for idir in data_dir.split('/'):
output_jpg_dir = os.path.join(output_jpg_dir, idir)
if not os.path.exists(output_jpg_dir):
os.mkdir(output_jpg_dir)
for pth in temp_jpg_list:
complete_path = os.path.join(output_jpg_dir,
'merged_' + pth.split('/')[-1])
output_jpg_list.append(complete_path)
predicted_txt_list = []
predicted_root = conf['predicted_root']
for data_dir in conf['predicted_dir'].split(','):
abs_distance = args.abs_distance
unIoU = 1 - args.IoU
conf = {}
with open(args.cfg_GetOptimalThreshold, 'r') as f:
for line in f:
infos = line.strip().split('=')
conf[infos[0]] = infos[1]
categories = conf['cls_names'].split(',')
real_txt_list = []
real_root = conf['real_root']
for data_dir in conf['real_dir'].split(','):
complete_pth = os.path.join(real_root, data_dir)
real_txt_list += GetFileList(complete_pth, ['.txt'])
predicted_txt_list = []
predicted_root = conf['predicted_root']
for data_dir in conf['predicted_dir'].split(','):
complete_pth = os.path.join(predicted_root, data_dir)
predicted_txt_list += GetFileList(complete_pth, ['.txt'])
else:
if len(sys.argv) == 1:
args = GetArgs()
categories = ['apple','beer','broccoli','chineseCabbage','cucumber','egg',\
'eggplant','grape','ham','milk','onion','orange','papaya','pear',\
'potato','radish','strawberry','tomato','watermelon','whiteradish']
abs_distance = np.inf
unIoU = 0.5
if __name__ == '__main__':
tic = time.clock()
global handle_categories1, handle_categories2, threshold
handle_categories1 = ['cucumber', 'eggplant', 'ham']
handle_categories2 = ['Hcucumber','Scucumber','Xcucumber',\
'Heggplant','Seggplant','Xeggplant',\
'Hham','Sham','Xham']
args = GetArgs()
input_txt_root, output_txt_root = args.input, args.output
input_txt_list, output_txt_list = [], []
pool = Pool(int(cpu_count() * 7 / 8))
if args.mode == 'to_hsx':
threshold = args.threshold
for input_txt_dir in GetFileList(input_txt_root, []):
temp_list = GetFileList(input_txt_dir + '/txt', ['.txt'])
input_txt_list += temp_list
output_txt_dir = output_txt_root
for idir in [input_txt_dir.split('/')[-1], 'txt']:
output_txt_dir = os.path.join(output_txt_dir, idir)
if not os.path.exists(output_txt_dir):
os.mkdir(output_txt_dir)
for pth in temp_list:
complete_path = os.path.join(output_txt_dir,
pth.split('/')[-1])
output_txt_list.append(complete_path)
pool.map(modify_labels_to_hsx, zip(input_txt_list, output_txt_list))
elif args.mode == 'to_origin':
for input_txt_dir in GetFileList(input_txt_root, []):
conf = {}
with open(args.cfg_FilterWithThreshold, 'r') as f:
for line in f:
infos = line.strip().split('=')
conf[infos[0]] = infos[1]
categories = conf['cls_names'].split(',')
input_txt_list = []
output_txt_list = []
input_txt_root = conf['input_txt_root']
output_txt_root = conf['output_txt_root']
for txt_dir in conf['input_txt_dir'].split(','):
input_txt_dir = os.path.join(input_txt_root, txt_dir)
temp_list = GetFileList(input_txt_dir, ['.txt'])
input_txt_list += temp_list
output_txt_dir = output_txt_root
for idir in txt_dir.split('/'):
output_txt_dir = os.path.join(output_txt_dir, idir)
if not os.path.exists(output_txt_dir):
os.mkdir(output_txt_dir)
for pth in temp_list:
complete_path = os.path.join(output_txt_dir, pth.split('/')[-1])
output_txt_list.append(complete_path)
thre_df = pd.read_csv(conf['optimal_threshold_csv_path'])
thre_dict = {categories[i]:thre_df['optimal_threshold'][i] for i in range(len(categories))}
else:
if len(sys.argv) == 1:
args = GetArgs()