dataannotation

DataAnnotation/labelme/examples/instance_segmentation/README.md

+# Instance Segmentation Example
+
+## Annotation
+
+```bash
+labelme data_annotated --labels labels.txt --nodata
+labelme data_annotated --labels labels.txt --nodata --labelflags '{.*: [occluded, truncated], person-\d+: [male]}'
+```
+
+![](.readme/annotation.jpg)
+
+## Convert to VOC-format Dataset
+
+```bash
+# It generates:
+#   - data_dataset_voc/JPEGImages
+#   - data_dataset_voc/SegmentationClass
+#   - data_dataset_voc/SegmentationClassVisualization
+#   - data_dataset_voc/SegmentationObject
+#   - data_dataset_voc/SegmentationObjectVisualization
+./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt
+```
+
+<img src="data_dataset_voc/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationClassVisualization/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationObjectVisualization/2011_000003.jpg" width="33%" />  
+Fig 1. JPEG image (left), JPEG class label visualization (center), JPEG instance label visualization (right)
+
+
+Note that the label file contains only very low label values (ex. `0, 4, 14`), and
+`255` indicates the `__ignore__` label value (`-1` in the npy file).  
+You can see the label PNG file by following.
+
+```bash
+labelme_draw_label_png data_dataset_voc/SegmentationClassPNG/2011_000003.png   # left
+labelme_draw_label_png data_dataset_voc/SegmentationObjectPNG/2011_000003.png  # right
+```
+
+<img src=".readme/draw_label_png_class.jpg" width="33%" /> <img src=".readme/draw_label_png_object.jpg" width="33%" />
+
+
+## Convert to COCO-format Dataset
+
+```bash
+# It generates:
+#   - data_dataset_coco/JPEGImages
+#   - data_dataset_coco/annotations.json
+./labelme2coco.py data_annotated data_dataset_coco --labels labels.txt
+```

DataAnnotation/labelme/examples/instance_segmentation/labelme2coco.py

+#!/usr/bin/env python
+
+import argparse
+import collections
+import datetime
+import glob
+import json
+import os
+import os.path as osp
+import sys
+
+import numpy as np
+import PIL.Image
+
+import labelme
+
+try:
+    import pycocotools.mask
+except ImportError:
+    print('Please install pycocotools:\n\n    pip install pycocotools\n')
+    sys.exit(1)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument('input_dir', help='input annotated directory')
+    parser.add_argument('output_dir', help='output dataset directory')
+    parser.add_argument('--labels', help='labels file', required=True)
+    args = parser.parse_args()
+
+    if osp.exists(args.output_dir):
+        print('Output directory already exists:', args.output_dir)
+        sys.exit(1)
+    os.makedirs(args.output_dir)
+    os.makedirs(osp.join(args.output_dir, 'JPEGImages'))
+    print('Creating dataset:', args.output_dir)
+
+    now = datetime.datetime.now()
+
+    data = dict(
+        info=dict(
+            description=None,
+            url=None,
+            version=None,
+            year=now.year,
+            contributor=None,
+            date_created=now.strftime('%Y-%m-%d %H:%M:%S.%f'),
+        ),
+        licenses=[dict(
+            url=None,
+            id=0,
+            name=None,
+        )],
+        images=[
+            # license, url, file_name, height, width, date_captured, id
+        ],
+        type='instances',
+        annotations=[
+            # segmentation, area, iscrowd, image_id, bbox, category_id, id
+        ],
+        categories=[
+            # supercategory, id, name
+        ],
+    )
+
+    class_name_to_id = {}
+    for i, line in enumerate(open(args.labels).readlines()):
+        class_id = i - 1  # starts with -1
+        class_name = line.strip()
+        if class_id == -1:
+            assert class_name == '__ignore__'
+            continue
+        class_name_to_id[class_name] = class_id
+        data['categories'].append(dict(
+            supercategory=None,
+            id=class_id,
+            name=class_name,
+        ))
+
+    out_ann_file = osp.join(args.output_dir, 'annotations.json')
+    label_files = glob.glob(osp.join(args.input_dir, '*.json'))
+    for image_id, label_file in enumerate(label_files):
+        print('Generating dataset from:', label_file)
+        with open(label_file) as f:
+            label_data = json.load(f)
+
+        base = osp.splitext(osp.basename(label_file))[0]
+        out_img_file = osp.join(
+            args.output_dir, 'JPEGImages', base + '.jpg'
+        )
+
+        img_file = osp.join(
+            osp.dirname(label_file), label_data['imagePath']
+        )
+        img = np.asarray(PIL.Image.open(img_file))
+        PIL.Image.fromarray(img).save(out_img_file)
+        data['images'].append(dict(
+            license=0,
+            url=None,
+            file_name=osp.relpath(out_img_file, osp.dirname(out_ann_file)),
+            height=img.shape[0],
+            width=img.shape[1],
+            date_captured=None,
+            id=image_id,
+        ))
+
+        masks = {}                                     # for area
+        segmentations = collections.defaultdict(list)  # for segmentation
+        for shape in label_data['shapes']:
+            points = shape['points']
+            label = shape['label']
+            shape_type = shape.get('shape_type', None)
+            mask = labelme.utils.shape_to_mask(
+                img.shape[:2], points, shape_type
+            )
+
+            if label in masks:
+                masks[label] = masks[label] | mask
+            else:
+                masks[label] = mask
+
+            points = np.asarray(points).flatten().tolist()
+            segmentations[label].append(points)
+
+        for label, mask in masks.items():
+            cls_name = label.split('-')[0]
+            if cls_name not in class_name_to_id:
+                continue
+            cls_id = class_name_to_id[cls_name]
+
+            mask = np.asfortranarray(mask.astype(np.uint8))
+            mask = pycocotools.mask.encode(mask)
+            area = float(pycocotools.mask.area(mask))
+            bbox = pycocotools.mask.toBbox(mask).flatten().tolist()
+
+            data['annotations'].append(dict(
+                id=len(data['annotations']),
+                image_id=image_id,
+                category_id=cls_id,
+                segmentation=segmentations[label],
+                area=area,
+                bbox=bbox,
+                iscrowd=0,
+            ))
+
+    with open(out_ann_file, 'w') as f:
+        json.dump(data, f)
+
+
+if __name__ == '__main__':
+    main()

DataAnnotation/labelme/examples/instance_segmentation/labelme2voc.py

+#!/usr/bin/env python
+
+from __future__ import print_function
+
+import argparse
+import glob
+import json
+import os
+import os.path as osp
+import sys
+
+import numpy as np
+import PIL.Image
+
+import labelme
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument('input_dir', help='input annotated directory')
+    parser.add_argument('output_dir', help='output dataset directory')
+    parser.add_argument('--labels', help='labels file', required=True)
+    args = parser.parse_args()
+
+    if osp.exists(args.output_dir):
+        print('Output directory already exists:', args.output_dir)
+        sys.exit(1)
+    os.makedirs(args.output_dir)
+    os.makedirs(osp.join(args.output_dir, 'JPEGImages'))
+    os.makedirs(osp.join(args.output_dir, 'SegmentationClass'))
+    os.makedirs(osp.join(args.output_dir, 'SegmentationClassPNG'))
+    os.makedirs(osp.join(args.output_dir, 'SegmentationClassVisualization'))
+    os.makedirs(osp.join(args.output_dir, 'SegmentationObject'))
+    os.makedirs(osp.join(args.output_dir, 'SegmentationObjectPNG'))
+    os.makedirs(osp.join(args.output_dir, 'SegmentationObjectVisualization'))
+    print('Creating dataset:', args.output_dir)
+
+    class_names = []
+    class_name_to_id = {}
+    for i, line in enumerate(open(args.labels).readlines()):
+        class_id = i - 1  # starts with -1
+        class_name = line.strip()
+        class_name_to_id[class_name] = class_id
+        if class_id == -1:
+            assert class_name == '__ignore__'
+            continue
+        elif class_id == 0:
+            assert class_name == '_background_'
+        class_names.append(class_name)
+    class_names = tuple(class_names)
+    print('class_names:', class_names)
+    out_class_names_file = osp.join(args.output_dir, 'class_names.txt')
+    with open(out_class_names_file, 'w') as f:
+        f.writelines('\n'.join(class_names))
+    print('Saved class_names:', out_class_names_file)
+
+    colormap = labelme.utils.label_colormap(255)
+
+    for label_file in glob.glob(osp.join(args.input_dir, '*.json')):
+        print('Generating dataset from:', label_file)
+        with open(label_file) as f:
+            base = osp.splitext(osp.basename(label_file))[0]
+            out_img_file = osp.join(
+                args.output_dir, 'JPEGImages', base + '.jpg')
+            out_cls_file = osp.join(
+                args.output_dir, 'SegmentationClass', base + '.npy')
+            out_clsp_file = osp.join(
+                args.output_dir, 'SegmentationClassPNG', base + '.png')
+            out_clsv_file = osp.join(
+                args.output_dir,
+                'SegmentationClassVisualization',
+                base + '.jpg',
+            )
+            out_ins_file = osp.join(
+                args.output_dir, 'SegmentationObject', base + '.npy')
+            out_insp_file = osp.join(
+                args.output_dir, 'SegmentationObjectPNG', base + '.png')
+            out_insv_file = osp.join(
+                args.output_dir,
+                'SegmentationObjectVisualization',
+                base + '.jpg',
+            )
+
+            data = json.load(f)
+
+            img_file = osp.join(osp.dirname(label_file), data['imagePath'])
+            img = np.asarray(PIL.Image.open(img_file))
+            PIL.Image.fromarray(img).save(out_img_file)
+
+            cls, ins = labelme.utils.shapes_to_label(
+                img_shape=img.shape,
+                shapes=data['shapes'],
+                label_name_to_value=class_name_to_id,
+                type='instance',
+            )
+            ins[cls == -1] = 0  # ignore it.
+
+            # class label
+            labelme.utils.lblsave(out_clsp_file, cls)
+            np.save(out_cls_file, cls)
+            clsv = labelme.utils.draw_label(
+                cls, img, class_names, colormap=colormap)
+            PIL.Image.fromarray(clsv).save(out_clsv_file)
+
+            # instance label
+            labelme.utils.lblsave(out_insp_file, ins)
+            np.save(out_ins_file, ins)
+            instance_ids = np.unique(ins)
+            instance_names = [str(i) for i in range(max(instance_ids) + 1)]
+            insv = labelme.utils.draw_label(ins, img, instance_names)
+            PIL.Image.fromarray(insv).save(out_insv_file)
+
+
+if __name__ == '__main__':
+    main()

DataAnnotation/labelme/examples/instance_segmentation/labels.txt

+__ignore__
+_background_
+aeroplane
+bicycle
+bird
+boat
+bottle
+bus
+car
+cat
+chair
+cow
+diningtable
+dog
+horse
+motorbike
+person
+potted plant
+sheep
+sofa
+train
+tv/monitor
\ No newline at end of file

DataAnnotation/labelme/examples/primitives/primitives.json

+{
+  "version": "3.5.0",
+  "flags": {},
+  "shapes": [
+    {
+      "label": "rectangle",
+      "line_color": null,
+      "fill_color": null,
+      "points": [
+        [
+          32,
+          35
+        ],
+        [
+          132,
+          135
+        ]
+      ],
+      "shape_type": "rectangle"
+    },
+    {
+      "label": "circle",
+      "line_color": null,
+      "fill_color": null,
+      "points": [
+        [
+          195,
+          84
+        ],
+        [
+          225,
+          125
+        ]
+      ],
+      "shape_type": "circle"
+    },
+    {
+      "label": "rectangle",
+      "line_color": null,
+      "fill_color": null,
+      "points": [
+        [
+          391,
+          33
+        ],
+        [
+          542,
+          135
+        ]
+      ],
+      "shape_type": "rectangle"
+    },
+    {
+      "label": "polygon",
+      "line_color": null,
+      "fill_color": null,
+      "points": [
+        [
+          69,
+          318
+        ],
+        [
+          45,
+          403
+        ],
+        [
+          173,
+          406
+        ],
+        [
+          198,
+          321
+        ]
+      ],
+      "shape_type": "polygon"
+    },
+    {
+      "label": "line",
+      "line_color": null,
+      "fill_color": null,
+      "points": [
+        [
+          188,
+          178
+        ],
+        [
+          160,
+          224
+        ]
+      ],
+      "shape_type": "line"
+    },
+    {
+      "label": "point",
+      "line_color": null,
+      "fill_color": null,
+      "points": [
+        [
+          345,
+          174
+        ]
+      ],
+      "shape_type": "point"
+    },
+    {
+      "label": "line_strip",
+      "line_color": null,
+      "fill_color": null,
+      "points": [
+        [
+          441,
+          181
+        ],
+        [
+          403,
+          274
+        ],
+        [
+          545,
+          275
+        ]
+      ],
+      "shape_type": "linestrip"
+    }
+  ],
+  "lineColor": [
+    0,
+    255,
+    0,
+    128
+  ],
+  "fillColor": [
+    255,
+    0,
+    0,
+    128
+  ],
+  "imagePath": "primitives.jpg",
+  "imageData": null
+}
\ No newline at end of file

DataAnnotation/labelme/examples/semantic_segmentation/README.md

+# Semantic Segmentation Example
+
+## Annotation
+
+```bash
+labelme data_annotated --labels labels.txt --nodata
+```
+
+![](.readme/annotation.jpg)
+
+
+## Convert to VOC-format Dataset
+
+```bash
+# It generates:
+#   - data_dataset_voc/JPEGImages
+#   - data_dataset_voc/SegmentationClass
+#   - data_dataset_voc/SegmentationClassVisualization
+./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt
+```
+
+<img src="data_dataset_voc/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationClassPNG/2011_000003.png" width="33%" /> <img src="data_dataset_voc/SegmentationClassVisualization/2011_000003.jpg" width="33%" />
+
+Fig 1. JPEG image (left), PNG label (center), JPEG label visualization (right)  
+
+
+Note that the label file contains only very low label values (ex. `0, 4, 14`), and
+`255` indicates the `__ignore__` label value (`-1` in the npy file).  
+You can see the label PNG file by following.
+
+```bash
+labelme_draw_label_png data_dataset_voc/SegmentationClassPNG/2011_000003.png
+```
+
+<img src=".readme/draw_label_png.jpg" width="33%" />

DataAnnotation/labelme/examples/semantic_segmentation/data_annotated/2011_000003.json

+{
+  "shapes": [
+    {
+      "label": "person",
+      "line_color": null,
+      "fill_color": null,
+      "points": [
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+        ],
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+      ]
+    },
+    {
+      "label": "person",
+      "line_color": null,
+      "fill_color": null,
+      "points": [
+        [
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DataAnnotation/labelme/examples/semantic_segmentation/data_annotated/2011_000006.json

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DataAnnotation/labelme/examples/semantic_segmentation/data_annotated/2011_000025.json

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DataAnnotation/labelme/examples/semantic_segmentation/data_dataset_voc/class_names.txt

+_background_
+aeroplane
+bicycle
+bird
+boat
+bottle
+bus
+car
+cat
+chair
+cow
+diningtable
+dog
+horse
+motorbike
+person
+potted plant
+sheep
+sofa
+train
+tv/monitor
\ No newline at end of file

DataAnnotation/labelme/examples/semantic_segmentation/labelme2voc.py

+#!/usr/bin/env python
+
+from __future__ import print_function
+
+import argparse
+import glob
+import json
+import os
+import os.path as osp
+import sys
+
+import numpy as np
+import PIL.Image
+
+import labelme
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument('input_dir', help='input annotated directory')
+    parser.add_argument('output_dir', help='output dataset directory')
+    parser.add_argument('--labels', help='labels file', required=True)
+    args = parser.parse_args()
+
+    if osp.exists(args.output_dir):
+        print('Output directory already exists:', args.output_dir)
+        sys.exit(1)
+    os.makedirs(args.output_dir)
+    os.makedirs(osp.join(args.output_dir, 'JPEGImages'))
+    os.makedirs(osp.join(args.output_dir, 'SegmentationClass'))
+    os.makedirs(osp.join(args.output_dir, 'SegmentationClassPNG'))
+    os.makedirs(osp.join(args.output_dir, 'SegmentationClassVisualization'))
+    print('Creating dataset:', args.output_dir)
+
+    class_names = []
+    class_name_to_id = {}
+    for i, line in enumerate(open(args.labels).readlines()):
+        class_id = i - 1  # starts with -1
+        class_name = line.strip()
+        class_name_to_id[class_name] = class_id
+        if class_id == -1:
+            assert class_name == '__ignore__'
+            continue
+        elif class_id == 0:
+            assert class_name == '_background_'
+        class_names.append(class_name)
+    class_names = tuple(class_names)
+    print('class_names:', class_names)
+    out_class_names_file = osp.join(args.output_dir, 'class_names.txt')
+    with open(out_class_names_file, 'w') as f:
+        f.writelines('\n'.join(class_names))
+    print('Saved class_names:', out_class_names_file)
+
+    colormap = labelme.utils.label_colormap(255)
+
+    for label_file in glob.glob(osp.join(args.input_dir, '*.json')):
+        print('Generating dataset from:', label_file)
+        with open(label_file) as f:
+            base = osp.splitext(osp.basename(label_file))[0]
+            out_img_file = osp.join(
+                args.output_dir, 'JPEGImages', base + '.jpg')
+            out_lbl_file = osp.join(
+                args.output_dir, 'SegmentationClass', base + '.npy')
+            out_png_file = osp.join(
+                args.output_dir, 'SegmentationClassPNG', base + '.png')
+            out_viz_file = osp.join(
+                args.output_dir,
+                'SegmentationClassVisualization',
+                base + '.jpg',
+            )
+
+            data = json.load(f)
+
+            img_file = osp.join(osp.dirname(label_file), data['imagePath'])
+            img = np.asarray(PIL.Image.open(img_file))
+            PIL.Image.fromarray(img).save(out_img_file)
+
+            lbl = labelme.utils.shapes_to_label(
+                img_shape=img.shape,
+                shapes=data['shapes'],
+                label_name_to_value=class_name_to_id,
+            )
+            labelme.utils.lblsave(out_png_file, lbl)
+
+            np.save(out_lbl_file, lbl)
+
+            viz = labelme.utils.draw_label(
+                lbl, img, class_names, colormap=colormap)
+            PIL.Image.fromarray(viz).save(out_viz_file)
+
+
+if __name__ == '__main__':
+    main()

DataAnnotation/labelme/examples/semantic_segmentation/labels.txt

+__ignore__
+_background_
+aeroplane
+bicycle
+bird
+boat
+bottle
+bus
+car
+cat
+chair
+cow
+diningtable
+dog
+horse
+motorbike
+person
+potted plant
+sheep
+sofa
+train
+tv/monitor
\ No newline at end of file

DataAnnotation/labelme/examples/tutorial/README.md

+# Tutorial (Single Image Example)
+
+## Annotation
+
+```bash
+labelme apc2016_obj3.jpg -O apc2016_obj3.json
+```
+
+![](.readme/annotation.jpg)
+
+
+## Visualization
+
+To view the json file quickly, you can use utility script:
+
+```bash
+labelme_draw_json apc2016_obj3.json
+```
+
+<img src=".readme/draw_json.jpg" width="70%" />
+
+
+## Convert to Dataset
+
+To convert the json to set of image and label, you can run following:
+
+
+```bash
+labelme_json_to_dataset apc2016_obj3.json -o apc2016_obj3_json
+```
+
+It generates standard files from the JSON file.
+
+- [img.png](apc2016_obj3_json/img.png): Image file.
+- [label.png](apc2016_obj3_json/label.png): uint8 label file.
+- [label_viz.png](apc2016_obj3_json/label_viz.png): Visualization of `label.png`.
+- [label_names.txt](apc2016_obj3_json/label_names.txt): Label names for values in `label.png`.
+
+## How to load label PNG file?
+
+Note that loading `label.png` is a bit difficult
+(`scipy.misc.imread`, `skimage.io.imread` may not work correctly),
+and please use `PIL.Image.open` to avoid unexpected behavior:
+
+```python
+# see load_label_png.py also.
+>>> import numpy as np
+>>> import PIL.Image
+
+>>> label_png = 'apc2016_obj3_json/label.png'
+>>> lbl = np.asarray(PIL.Image.open(label_png))
+>>> print(lbl.dtype)
+dtype('uint8')
+>>> np.unique(lbl)
+array([0, 1, 2, 3], dtype=uint8)
+>>> lbl.shape
+(907, 1210)
+```
+
+Also, you can see the label PNG file by:
+
+```python
+labelme_draw_label_png apc2016_obj3_json/label.png
+```
+
+<img src=".readme/draw_label_png.jpg" width="35%" />

DataAnnotation/labelme/examples/tutorial/apc2016_obj3_json/info.yaml

+label_names:
+- _background_
+- shelf
+- highland_6539_self_stick_notes
+- mead_index_cards
+- kong_air_dog_squeakair_tennis_ball

DataAnnotation/labelme/examples/tutorial/apc2016_obj3_json/label_names.txt

+_background_
+shelf
+highland_6539_self_stick_notes
+mead_index_cards
+kong_air_dog_squeakair_tennis_ball

DataAnnotation/labelme/examples/tutorial/load_label_png.py

+#!/usr/bin/env python
+
+from __future__ import print_function
+
+import os.path as osp
+
+import numpy as np
+import PIL.Image
+
+
+here = osp.dirname(osp.abspath(__file__))
+
+
+def main():
+    label_png = osp.join(here, 'apc2016_obj3_json/label.png')
+    print('Loading:', label_png)
+    print()
+
+    lbl = np.asarray(PIL.Image.open(label_png))
+    labels = np.unique(lbl)
+
+    label_names_txt = osp.join(here, 'apc2016_obj3_json/label_names.txt')
+    label_names = [name.strip() for name in open(label_names_txt)]
+    print('# of labels:', len(labels))
+    print('# of label_names:', len(label_names))
+    if len(labels) != len(label_names):
+        print('Number of unique labels and label_names must be same.')
+        quit(1)
+    print()
+
+    print('label: label_name')
+    for label, label_name in zip(labels, label_names):
+        print('%d: %s' % (label, label_name))
+
+
+if __name__ == '__main__':
+    main()

DataAnnotation/labelme/examples/video_annotation/README.md

+# Video Annotation Example
+
+
+## Annotation
+
+```bash
+labelme data_annotated --labels labels.txt --nodata --keep-prev
+```
+
+<img src=".readme/00000100.jpg" width="49%" /> <img src=".readme/00000101.jpg" width="49%" />
+
+*Fig 1. Video annotation example. A frame (left), The next frame (right).*
+
+
+<img src=".readme/data_annotated.gif" width="98%" />
+
+*Fig 2. Visualization of video semantic segmentation.*
+
+
+## How to Convert a Video File to Images for Annotation?
+
+```bash
+# Download and install software for converting a video file (MP4) to images
+wget https://raw.githubusercontent.com/wkentaro/dotfiles/f3c5ad1f47834818d4f123c36ed59a5943709518/local/bin/video_to_images
+pip install imageio imageio-ffmpeg tqdm
+
+python video_to_images your_video.mp4  # this creates your_video/ directory
+ls your_video/
+
+labelme your_video/
+```

DataAnnotation/labelme/examples/video_annotation/data_annotated/00000100.json

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+  "imagePath": "00000100.jpg",
+  "imageData": null
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DataAnnotation/labelme/examples/video_annotation/data_annotated/00000101.json

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DataAnnotation/labelme/examples/video_annotation/data_annotated/00000102.json

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DataAnnotation/labelme/examples/video_annotation/data_annotated/00000103.json

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DataAnnotation/labelme/examples/video_annotation/data_annotated/00000104.json

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+    0,
+    128
+  ],
+  "fillColor": [
+    255,
+    0,
+    0,
+    128
+  ],
+  "imagePath": "00000104.jpg",
+  "imageData": null
+}
\ No newline at end of file

DataAnnotation/labelme/examples/video_annotation/data_dataset_voc/class_names.txt

+_background_
+car
+track
\ No newline at end of file

DataAnnotation/labelme/examples/video_annotation/labelme2voc.py

+../semantic_segmentation/labelme2voc.py
\ No newline at end of file

DataAnnotation/labelme/examples/video_annotation/labels.txt

+__ignore__
+_background_
+car
+track

DataAnnotation/labelme/github2pypi/.travis.yml

+sudo: false
+
+cache:
+  - pip
+
+dist: trusty
+
+language: python
+
+python:
+  - '3.6'
+  - '2.7'
+
+branches:
+  only:
+    - master
+
+notifications:
+  email: false
+
+install:
+  - true  # drop pip install -r requirements.txt
+
+script:
+  - pip install flake8
+  - flake8 .

DataAnnotation/labelme/github2pypi/README.md

+<h1 align="center">
+  github2pypi
+</h1>
+
+<h4 align="center">
+  Utils to release Python repository on GitHub to PyPi
+</h4>
+
+<div align="center">
+  <a href="https://travis-ci.com/wkentaro/github2pypi"><img src="https://travis-ci.com/wkentaro/github2pypi.svg?branch=master"></a>
+</div>
+
+
+## Usage
+
+
+### 1. Add `github2pypi` as submodule.
+
+See [imgviz](https://github.com/wkentaro/imgviz) as an example.
+
+```bash
+git clone https://github.com/wkentaro/imgviz
+cd imgviz
+
+git submodule add https://github.com/wkentaro/github2pypi.git
+```
+
+
+### 2. Edit `setup.py`.
+
+```python
+import github2pypi
+
+...
+with open('README.md') as f:
+    # e.g., ![](examples/image.jpg) ->
+    #       ![](https://github.com/wkentaro/imgviz/blob/master/examples/image.jpg)
+    long_description = github2pypi.replace_url(
+        slug='wkentaro/imgviz', content=f.read()
+    )
+
+setup(
+    ...
+    long_description=long_description,
+    long_description_content_type='text/markdown',
+)
+```

DataAnnotation/labelme/github2pypi/__init__.py

+# flake8: noqa
+
+from .replace_url import replace_url

DataAnnotation/labelme/github2pypi/replace_url.py

+import re
+
+
+def replace_url(slug, content, branch='master'):
+
+    def repl(match):
+        if not match:
+            return
+
+        url = match.group(1)
+        if url.startswith('http'):
+            return match.group(0)
+
+        url_new = (
+            'https://github.com/{slug}/blob/{branch}/{url}'
+            .format(slug=slug, branch=branch, url=url)
+        )
+        if re.match(r'.*[\.jpg|\.png]$', url_new):
+            url_new += '?raw=true'
+
+        start0, end0 = match.regs[0]
+        start, end = match.regs[1]
+        start -= start0
+        end -= start0
+
+        res = match.group(0)
+        res = res[:start] + url_new + res[end:]
+        return res
+
+    lines = []
+    for line in content.splitlines():
+        patterns = [
+            r'!\[.*?\]\((.*?)\)',
+            r'<img.*?src="(.*?)".*?>',
+            r'\[.*?\]\((.*?)\)',
+            r'<a.*?href="(.*?)".*?>',
+        ]
+        for pattern in patterns:
+            line = re.sub(pattern, repl, line)
+        lines.append(line)
+    return '\n'.join(lines)

DataAnnotation/labelme/labelme.spec

+# -*- mode: python -*-
+# vim: ft=python
+
+
+block_cipher = None
+
+
+a = Analysis(
+    ['labelme/main.py'],
+    pathex=['labelme'],
+    binaries=[],
+    datas=[
+        ('labelme/config/default_config.yaml', 'labelme/config'),
+        ('labelme/icons/*', 'labelme/icons'),
+    ],
+    hiddenimports=[],
+    hookspath=[],
+    runtime_hooks=[],
+    excludes=['matplotlib'],
+    win_no_prefer_redirects=False,
+    win_private_assemblies=False,
+    cipher=block_cipher,
+)
+pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.zipfiles,
+    a.datas,
+    name='labelme',
+    debug=False,
+    strip=False,
+    upx=True,
+    runtime_tmpdir=None,
+    console=False,
+    icon='labelme/icons/icon.ico',
+)
+app = BUNDLE(
+    exe,
+    name='labelme.app',
+    icon='labelme/icons/icon.icns',
+    bundle_identifier=None,
+    info_plist={'NSHighResolutionCapable': 'True'},
+)

DataAnnotation/labelme/labelme/__init__.py

+# flake8: noqa
+
+import logging
+import sys
+
+from qtpy import QT_VERSION
+
+
+__appname__ = 'labelme'
+
+QT4 = QT_VERSION[0] == '4'
+QT5 = QT_VERSION[0] == '5'
+del QT_VERSION
+
+PY2 = sys.version[0] == '2'
+PY3 = sys.version[0] == '3'
+del sys
+
+
+from labelme._version import __version__
+
+from labelme import testing
+from labelme import utils

DataAnnotation/labelme/labelme/_version.py

+# Semantic Versioning 2.0.0: https://semver.org/
+# 1. MAJOR version when you make incompatible API changes;
+# 2. MINOR version when you add functionality in a backwards-compatible manner;
+# 3. PATCH version when you make backwards-compatible bug fixes.
+__version__ = '3.16.3'

DataAnnotation/labelme/labelme/cli/__init__.py

+# flake8: noqa
+
+from . import draw_json
+from . import draw_label_png
+from . import json_to_dataset
+from . import on_docker

DataAnnotation/labelme/labelme/cli/draw_json.py

+#!/usr/bin/env python
+
+import argparse
+import base64
+import json
+import os
+import sys
+
+import matplotlib.pyplot as plt
+
+from labelme import utils
+
+
+PY2 = sys.version_info[0] == 2
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('json_file')
+    args = parser.parse_args()
+
+    json_file = args.json_file
+
+    data = json.load(open(json_file))
+
+    if data['imageData']:
+        imageData = data['imageData']
+    else:
+        imagePath = os.path.join(os.path.dirname(json_file), data['imagePath'])
+        with open(imagePath, 'rb') as f:
+            imageData = f.read()
+            imageData = base64.b64encode(imageData).decode('utf-8')
+    img = utils.img_b64_to_arr(imageData)
+
+    label_name_to_value = {'_background_': 0}
+    for shape in sorted(data['shapes'], key=lambda x: x['label']):
+        label_name = shape['label']
+        if label_name in label_name_to_value:
+            label_value = label_name_to_value[label_name]
+        else:
+            label_value = len(label_name_to_value)
+            label_name_to_value[label_name] = label_value
+    lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
+
+    label_names = [None] * (max(label_name_to_value.values()) + 1)
+    for name, value in label_name_to_value.items():
+        label_names[value] = name
+    lbl_viz = utils.draw_label(lbl, img, label_names)
+
+    plt.subplot(121)
+    plt.imshow(img)
+    plt.subplot(122)
+    plt.imshow(lbl_viz)
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()