DL-Art-School/codes/data/image_label_parser.py
2020-12-18 08:53:18 -07:00

130 lines
5.6 KiB
Python

import os
from collections import OrderedDict
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):
if not isinstance(label_file, list):
label_file = [label_file]
self.labeled_images = {}
for lfil in label_file:
with open(lfil, "r") as read_file:
self.label_file = label_file
# Format of JSON file:
# "key_binding" {
# "label": "<label>"
# "index": <num>
# "keyBinding": "key_binding"
# "labeledImages": [
# { "path", "label", "patch_top", "patch_left", "patch_height", "patch_width" }
# ]
# }
categories = json.loads(read_file.read())
available_labels = {}
label_value_dict = {}
for cat in categories.values():
available_labels[cat['index']] = cat['label']
label_value_dict[cat['label']] = cat['index']
for lbli in cat['labeledImages']:
pth = lbli['path']
if pth not in self.labeled_images.keys():
self.labeled_images[pth] = []
self.labeled_images[pth].append(lbli)
# Insert "labelValue" for each entry.
for v in self.labeled_images.values():
for l in v:
l['labelValue'] = label_value_dict[l['label']]
self.categories = categories
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
def add_label(self, binding, img_name, top, left, dim):
lbl = {"path": img_name, "label": self.categories[binding]['label'], "patch_top": top, "patch_left": left,
"patch_height": dim, "patch_width": dim}
self.categories[binding]['labeledImages'].append(lbl)
def save(self):
with open(self.label_file[-1], "wb") as file:
file.write(json.dumps(self.categories))
# A more compact format that is simpler to parse and understand.
class CompactJsonLabeler:
def __init__(self, lbl_files):
if not isinstance(lbl_files, list):
lbl_files = [lbl_files]
self.label_files = lbl_files
self.config, self.labels, self.label_map, self.images = None, None, None, None
for lfil in lbl_files:
with open(lfil, "r") as read_file:
# Format:
# {
# 'config': { 'dim' }
# 'labels': [{ 'label', 'key'}] <- ordered by label index.
# 'images': {'file': [{ 'lid', 'top', 'left' }}
# 'labelMap' {<mapping of string labels to ids>}
# }
parsed = json.loads(read_file.read())
if self.config is None:
self.config = parsed['config']
self.labels = parsed['labels']
self.images = parsed['images']
self.label_map = parsed['label_map']
self.binding_map = {}
for i, lbl in enumerate(self.labels):
self.binding_map[lbl['key']] = i
else:
assert self.config == parsed['config']
assert self.labels == parsed['labels']
assert self.label_map == parsed['label_map']
self.images.update(parsed['images']) # This will overwrite existing images, which is acceptable.
def get_labeled_paths(self, base_path):
return [os.path.join(base_path, pth) for pth in self.images.keys()]
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.images[img_key]
for patch_lbl in lbl_list:
t, l, h, w = patch_lbl['top'] // resize_factor, patch_lbl['left'] // resize_factor, \
self.config['dim'] // resize_factor, self.config['dim'] // 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
def add_label(self, binding, img_name, top, left, dim):
lbl = {'lid': self.binding_map[binding], 'top': top, 'left': left}
if img_name not in self.images.keys():
self.images[img_name] = []
self.images[img_name].append(lbl)
def save(self):
with open(self.label_file[-1], "wb") as file:
file.write(json.dumps(self.categories))