Add examples/instance_segmentation

examples/instance_segmentation/README.md

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+# Instance Segmentation Example
+
+## Annotation
+
+```bash
+labelme data_annotated --labels labels.txt --nodata
+```

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+        [
+          353.936170212766,
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+      ],
+      "fill_color": null,
+      "label": "sofa"
+    },
+    {
+      "line_color": null,
+      "points": [
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+        [
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+      "fill_color": null,
+      "label": "sofa"
+    }
+  ],
+  "imageData": null,
+  "lineColor": [
+    0,
+    255,
+    0,
+    128
+  ],
+  "fillColor": [
+    255,
+    0,
+    0,
+    128
+  ]
+}

examples/instance_segmentation/data_annotated/2011_000025.json

@@ -0,0 +1,215 @@
+{
+  "imageData": null,
+  "shapes": [
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+      "fill_color": null,
+      "line_color": null,
+      "label": "bus-1",
+      "points": [
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+  ],
+  "fillColor": [
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+  "lineColor": [
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+    0,
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+  "imagePath": "2011_000025.jpg"
+}

examples/instance_segmentation/data_dataset_voc/class_names.txt

@@ -0,0 +1,21 @@
+_background_
+aeroplane
+bicycle
+bird
+boat
+bottle
+bus
+car
+cat
+chair
+cow
+diningtable
+dog
+horse
+motorbike
+person
+potted plant
+sheep
+sofa
+train
+tv/monitor

examples/instance_segmentation/labelme2voc.py

@@ -0,0 +1,109 @@
+#!/usr/bin/env python
+
+from __future__ import print_function
+
+import argparse
+import glob
+import io
+import json
+import os
+import os.path as osp
+
+import matplotlib
+matplotlib.use('Agg')
+
+import matplotlib.pyplot as plt
+import numpy as np
+import PIL.Image
+import PIL.ImagePalette
+
+import labelme
+from labelme.utils import label2rgb
+from labelme.utils import label_colormap
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument('labels_file')
+    parser.add_argument('in_dir')
+    parser.add_argument('out_dir')
+    args = parser.parse_args()
+
+    if osp.exists(args.out_dir):
+        print('Output directory already exists:', args.out_dir)
+        quit(1)
+    os.makedirs(args.out_dir)
+    os.makedirs(osp.join(args.out_dir, 'JPEGImages'))
+    os.makedirs(osp.join(args.out_dir, 'SegmentationClass'))
+    os.makedirs(osp.join(args.out_dir, 'SegmentationClassVisualization'))
+    os.makedirs(osp.join(args.out_dir, 'SegmentationObject'))
+    os.makedirs(osp.join(args.out_dir, 'SegmentationObjectVisualization'))
+    print('Creating dataset:', args.out_dir)
+
+    class_names = []
+    class_name_to_id = {}
+    for i, line in enumerate(open(args.labels_file).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.out_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.in_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.out_dir, 'JPEGImages', base + '.jpg')
+            out_cls_file = osp.join(
+                args.out_dir, 'SegmentationClass', base + '.png')
+            out_clsv_file = osp.join(
+                args.out_dir, 'SegmentationClassVisualization', base + '.jpg')
+            out_ins_file = osp.join(
+                args.out_dir, 'SegmentationObject', base + '.png')
+            out_insv_file = osp.join(
+                args.out_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',
+            )
+
+            PIL.Image.fromarray(cls).save(out_cls_file)
+            label_names = ['%d: %s' % (cls_id, cls_name)
+                           for cls_id, cls_name in enumerate(class_names)]
+            clsv = labelme.utils.draw_label(
+                cls, img, label_names, colormap=colormap)
+            PIL.Image.fromarray(clsv).save(out_clsv_file)
+
+            PIL.Image.fromarray(ins).save(out_ins_file)
+            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()

examples/instance_segmentation/labels.txt

@@ -0,0 +1,22 @@
+__ignore__
+_background_
+aeroplane
+bicycle
+bird
+boat
+bottle
+bus
+car
+cat
+chair
+cow
+diningtable
+dog
+horse
+motorbike
+person
+potted plant
+sheep
+sofa
+train
+tv/monitor