58 lines
2.4 KiB
Python
58 lines
2.4 KiB
Python
import os
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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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with open(label_file, "r") as read_file:
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# Format of JSON file:
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# "<nonsense>" {
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# "label": "<label>"
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# "keyBinding": "<nonsense>"
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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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labeled_images = {}
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available_labels = []
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for cat in categories.values():
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for lbli in cat['labeledImages']:
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pth = lbli['path']
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if pth not in labeled_images.keys():
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labeled_images[pth] = []
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labeled_images[pth].append(lbli)
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if lbli['label'] not in available_labels:
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available_labels.append(lbli['label'])
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# Build the label values, from [1,inf]
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label_value_dict = {}
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for i, l in enumerate(available_labels):
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label_value_dict[l] = i+1
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# Insert "labelValue" for each entry.
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for v in 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.labeled_images = labeled_images
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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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labels = torch.zeros(hq.shape, dtype=torch.long)
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mask = torch.zeros_like(hq)
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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 |