130 lines
5.6 KiB
Python
130 lines
5.6 KiB
Python
import os
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from collections import OrderedDict
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import orjson as json
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# Given a JSON file produced by the VS.net image labeler utility, produces a dict where the keys are image file names
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# and the values are a list of object with the following properties:
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# [patch_top, patch_left, patch_height, patch_width, label]
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import torch
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class VsNetImageLabeler:
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def __init__(self, label_file):
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if not isinstance(label_file, list):
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label_file = [label_file]
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self.labeled_images = {}
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for lfil in label_file:
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with open(lfil, "r") as read_file:
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self.label_file = label_file
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# Format of JSON file:
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# "key_binding" {
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# "label": "<label>"
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# "index": <num>
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# "keyBinding": "key_binding"
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# "labeledImages": [
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# { "path", "label", "patch_top", "patch_left", "patch_height", "patch_width" }
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# ]
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# }
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categories = json.loads(read_file.read())
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available_labels = {}
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label_value_dict = {}
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for cat in categories.values():
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available_labels[cat['index']] = cat['label']
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label_value_dict[cat['label']] = cat['index']
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for lbli in cat['labeledImages']:
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pth = lbli['path']
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if pth not in self.labeled_images.keys():
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self.labeled_images[pth] = []
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self.labeled_images[pth].append(lbli)
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# Insert "labelValue" for each entry.
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for v in self.labeled_images.values():
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for l in v:
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l['labelValue'] = label_value_dict[l['label']]
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self.categories = categories
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self.str_labels = available_labels
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def get_labeled_paths(self, base_path):
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return [os.path.join(base_path, pth) for pth in self.labeled_images]
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def get_labels_as_tensor(self, hq, img_key, resize_factor):
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_, h, w = hq.shape
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labels = torch.zeros((1,h,w), dtype=torch.long)
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mask = torch.zeros((1,h,w), dtype=torch.float)
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lbl_list = self.labeled_images[img_key]
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for patch_lbl in lbl_list:
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t, l, h, w = patch_lbl['patch_top'] // resize_factor, patch_lbl['patch_left'] // resize_factor, \
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patch_lbl['patch_height'] // resize_factor, patch_lbl['patch_width'] // resize_factor
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val = patch_lbl['labelValue']
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labels[:,t:t+h,l:l+w] = val
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mask[:,t:t+h,l:l+w] = 1.0
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return labels, mask, self.str_labels
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def add_label(self, binding, img_name, top, left, dim):
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lbl = {"path": img_name, "label": self.categories[binding]['label'], "patch_top": top, "patch_left": left,
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"patch_height": dim, "patch_width": dim}
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self.categories[binding]['labeledImages'].append(lbl)
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def save(self):
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with open(self.label_file[-1], "wb") as file:
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file.write(json.dumps(self.categories))
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# A more compact format that is simpler to parse and understand.
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class CompactJsonLabeler:
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def __init__(self, lbl_files):
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if not isinstance(lbl_files, list):
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lbl_files = [lbl_files]
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self.label_files = lbl_files
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self.config, self.labels, self.label_map, self.images = None, None, None, None
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for lfil in lbl_files:
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with open(lfil, "r") as read_file:
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# Format:
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# {
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# 'config': { 'dim' }
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# 'labels': [{ 'label', 'key'}] <- ordered by label index.
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# 'images': {'file': [{ 'lid', 'top', 'left' }}
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# 'labelMap' {<mapping of string labels to ids>}
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# }
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parsed = json.loads(read_file.read())
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if self.config is None:
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self.config = parsed['config']
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self.labels = parsed['labels']
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self.images = parsed['images']
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self.label_map = parsed['label_map']
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self.binding_map = {}
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for i, lbl in enumerate(self.labels):
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self.binding_map[lbl['key']] = i
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else:
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assert self.config == parsed['config']
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assert self.labels == parsed['labels']
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assert self.label_map == parsed['label_map']
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self.images.update(parsed['images']) # This will overwrite existing images, which is acceptable.
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def get_labeled_paths(self, base_path):
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return [os.path.join(base_path, pth) for pth in self.images.keys()]
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def get_labels_as_tensor(self, hq, img_key, resize_factor):
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_, h, w = hq.shape
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labels = torch.zeros((1,h,w), dtype=torch.long)
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mask = torch.zeros((1,h,w), dtype=torch.float)
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lbl_list = self.images[img_key]
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for patch_lbl in lbl_list:
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t, l, h, w = patch_lbl['top'] // resize_factor, patch_lbl['left'] // resize_factor, \
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self.config['dim'] // resize_factor, self.config['dim'] // resize_factor
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val = patch_lbl['labelValue']
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labels[:,t:t+h,l:l+w] = val
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mask[:,t:t+h,l:l+w] = 1.0
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return labels, mask, self.str_labels
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def add_label(self, binding, img_name, top, left, dim):
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lbl = {'lid': self.binding_map[binding], 'top': top, 'left': left}
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if img_name not in self.images.keys():
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self.images[img_name] = []
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self.images[img_name].append(lbl)
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def save(self):
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with open(self.label_file[-1], "wb") as file:
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file.write(json.dumps(self.categories))
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