DL-Art-School/codes/data/image_label_parser.py

60 lines
2.5 KiB
Python

import os
import orjson as json
# Given a JSON file produced by the VS.net image labeler utility, produces a dict where the keys are image file names
# and the values are a list of object with the following properties:
# [patch_top, patch_left, patch_height, patch_width, label]
import torch
class VsNetImageLabeler:
def __init__(self, label_file):
with open(label_file, "r") as read_file:
# Format of JSON file:
# "<nonsense>" {
# "label": "<label>"
# "keyBinding": "<nonsense>"
# "labeledImages": [
# { "path", "label", "patch_top", "patch_left", "patch_height", "patch_width" }
# ]
# }
categories = json.loads(read_file.read())
labeled_images = {}
available_labels = []
for cat in categories.values():
for lbli in cat['labeledImages']:
pth = lbli['path']
if pth not in labeled_images.keys():
labeled_images[pth] = []
labeled_images[pth].append(lbli)
if lbli['label'] not in available_labels:
available_labels.append(lbli['label'])
# Build the label values, from [1,inf]
label_value_dict = {}
for i, l in enumerate(available_labels):
label_value_dict[l] = i
# Insert "labelValue" for each entry.
for v in labeled_images.values():
for l in v:
l['labelValue'] = label_value_dict[l['label']]
self.labeled_images = labeled_images
self.str_labels = available_labels
def get_labeled_paths(self, base_path):
return [os.path.join(base_path, pth) for pth in self.labeled_images]
def get_labels_as_tensor(self, hq, img_key, resize_factor):
_, h, w = hq.shape
labels = torch.zeros((1,h,w), dtype=torch.long)
mask = torch.zeros((1,h,w), dtype=torch.float)
lbl_list = self.labeled_images[img_key]
for patch_lbl in lbl_list:
t, l, h, w = patch_lbl['patch_top'] // resize_factor, patch_lbl['patch_left'] // resize_factor, \
patch_lbl['patch_height'] // resize_factor, patch_lbl['patch_width'] // resize_factor
val = patch_lbl['labelValue']
labels[:,t:t+h,l:l+w] = val
mask[:,t:t+h,l:l+w] = 1.0
return labels, mask, self.str_labels