Add labeling feature to image_folder_dataset
This commit is contained in:
James Betker
parent
ec0ee25f4b
commit
087e9280ed
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@ -10,6 +10,7 @@ import os
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from data import util
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# Builds a dataset created from a simple folder containing a list of training/test/validation images.
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from data.image_corruptor import ImageCorruptor
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from data.image_label_parser import VsNetImageLabeler
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class ImageFolderDataset:
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@ -28,21 +29,29 @@ class ImageFolderDataset:
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else:
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self.weights = opt['weights']
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# Just scan the given directory for images of standard types.
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supported_types = ['jpg', 'jpeg', 'png', 'gif']
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self.image_paths = []
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for path, weight in zip(self.paths, self.weights):
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cache_path = os.path.join(path, 'cache.pth')
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if os.path.exists(cache_path):
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imgs = torch.load(cache_path)
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else:
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print("Building image folder cache, this can take some time for large datasets..")
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imgs = []
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for ext in supported_types:
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imgs.extend(glob.glob(os.path.join(path, "*." + ext)))
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torch.save(imgs, cache_path)
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for w in range(weight):
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self.image_paths.extend(imgs)
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if 'labeler' in opt.keys():
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if opt['labeler']['type'] == 'patch_labels':
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self.labeler = VsNetImageLabeler(opt['labeler']['label_file'])
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assert len(self.paths) == 1 # Only a single base-path is supported for labeled images.
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self.image_paths = self.labeler.get_labeled_paths(self.paths[0])
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else:
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self.labeler = None
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# Just scan the given directory for images of standard types.
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supported_types = ['jpg', 'jpeg', 'png', 'gif']
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self.image_paths = []
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for path, weight in zip(self.paths, self.weights):
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cache_path = os.path.join(path, 'cache.pth')
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if os.path.exists(cache_path):
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imgs = torch.load(cache_path)
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else:
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print("Building image folder cache, this can take some time for large datasets..")
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imgs = []
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for ext in supported_types:
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imgs.extend(glob.glob(os.path.join(path, "*." + ext)))
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torch.save(imgs, cache_path)
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for w in range(weight):
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self.image_paths.extend(imgs)
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self.len = len(self.image_paths)
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def get_paths(self):
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@ -74,6 +83,7 @@ class ImageFolderDataset:
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h, w, _ = hs[0].shape
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ls = []
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if self.corrupt_before_downsize:
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hs = [h.copy() for h in hs]
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hs = self.corruptor.corrupt_images(hs)
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for hq in hs:
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ls.append(cv2.resize(hq, (h // self.scale, w // self.scale), interpolation=cv2.INTER_AREA))
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@ -87,6 +97,9 @@ class ImageFolderDataset:
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def __getitem__(self, item):
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hq = util.read_img(None, self.image_paths[item], rgb=True)
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if self.labeler:
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assert hq.shape[0] == hq.shape[1] # This just has not been accomodated yet.
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dim = hq.shape[0]
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hs = self.resize_hq([hq])
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ls = self.synthesize_lq(hs)
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@ -95,13 +108,19 @@ class ImageFolderDataset:
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hq = torch.from_numpy(np.ascontiguousarray(np.transpose(hs[0], (2, 0, 1)))).float()
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lq = torch.from_numpy(np.ascontiguousarray(np.transpose(ls[0], (2, 0, 1)))).float()
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return {'lq': lq, 'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item]}
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out_dict = {'lq': lq, 'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item]}
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if self.labeler:
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base_file = self.image_paths[item].replace(self.paths[0], "")
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assert dim % hq.shape[1] == 0
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lbls, lbl_masks = self.labeler.get_labels_as_tensor(hq, base_file, dim // hq.shape[1])
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out_dict['labels'] = lbls
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out_dict['labels_mask'] = lbl_masks
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return out_dict
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if __name__ == '__main__':
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opt = {
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'name': 'amalgam',
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'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\random_100_1024px'],
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'paths': ['F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\'],
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'weights': [1],
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'target_size': 128,
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'force_multiple': 32,
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@ -110,20 +129,19 @@ if __name__ == '__main__':
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'random_corruptions': ['noise-5', 'none'],
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'num_corrupts_per_image': 1,
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'corrupt_before_downsize': True,
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'labeler': {
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'type': 'patch_labels',
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'label_file': 'F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\categories.json'
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}
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}
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ds = ImageFolderDataset(opt)
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import os
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os.makedirs("debug", exist_ok=True)
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for i in range(0, len(ds)):
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o = ds[random.randint(0, len(ds))]
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#for k, v in o.items():
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k = 'lq'
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v = o[k]
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#if 'LQ' in k and 'path' not in k and 'center' not in k:
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#if 'full' in k:
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#masked = v[:3, :, :] * v[3]
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#torchvision.utils.save_image(masked.unsqueeze(0), "debug/%i_%s_masked.png" % (i, k))
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#v = v[:3, :, :]
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o = ds[random.randint(0, len(ds)-1)]
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hq = o['hq']
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masked = (o['labels_mask'] * .5 + .5) * hq
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import torchvision
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torchvision.utils.save_image(v.unsqueeze(0), "debug/%i_%s.png" % (i, k))
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torchvision.utils.save_image(hq.unsqueeze(0), "debug/%i_hq.png" % (i,))
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torchvision.utils.save_image(masked.unsqueeze(0), "debug/%i_masked.png" % (i,))
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58
codes/data/image_label_parser.py
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58
codes/data/image_label_parser.py
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@ -0,0 +1,58 @@
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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
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