Add example of semantic_segmentation

examples/semantic_segmentation/README.md

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+# Semantic Segmentation Example
+
+## Annotation
+
+```bash
+labelme data_annotated --labels="$(tr '\n' , < labels.txt)" --nodata
+```
+
+![](.readme/annotation.jpg)
+
+
+## Convert to VOC-like Dataset
+
+```bash
+# It generates:
+#   - data_dataset_voc/JPEGImages
+#   - data_dataset_voc/SegmentationClass
+#   - data_dataset_voc/SegmentationClassVisualization
+./labelme2voc.py labels.txt data_annotated data_dataset_voc
+```
+
+<img src="data_dataset/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset/SegmentationClass/2011_000003.png" width="33%" /> <img src="data_dataset/SegmentationClassVisualization/2011_000003.jpg" width="33%" />
+
+Fig 1. JPEG image (left), PNG label (center), JPEG label visualization (right)  
+*Note that the reason why the label file is mostly black is it contains only very low label values (ex. `-1, 0, 4, 14`).*

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+      "line_color": null,
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+  "imagePath": "2011_000006.jpg",
+  "imageData": null
+}

examples/semantic_segmentation/data_annotated/2011_000025.json

@@ -0,0 +1,215 @@
+{
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+      "line_color": null,
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+  "imageData": null
+}

examples/semantic_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/semantic_segmentation/labelme2voc.py

@@ -0,0 +1,162 @@
+#!/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 skimage.color
+import skimage.io
+
+import labelme
+from labelme.utils import label2rgb
+from labelme.utils import label_colormap
+
+
+# TODO(wkentaro): Move to labelme/utils.py
+# contrib
+# -----------------------------------------------------------------------------
+
+
+def labelme_shapes_to_label(img_shape, shapes, label_name_to_value):
+    lbl = np.zeros(img_shape[:2], dtype=np.int32)
+    for shape in shapes:
+        polygons = shape['points']
+        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
+        mask = labelme.utils.polygons_to_mask(img_shape[:2], polygons)
+        lbl[mask] = label_value
+
+    return lbl
+
+
+def draw_label(label, img, label_names, colormap=None):
+    plt.subplots_adjust(left=0, right=1, top=1, bottom=0,
+                        wspace=0, hspace=0)
+    plt.margins(0, 0)
+    plt.gca().xaxis.set_major_locator(plt.NullLocator())
+    plt.gca().yaxis.set_major_locator(plt.NullLocator())
+
+    if colormap is None:
+        colormap = label_colormap(len(label_names))
+
+    label_viz = label2rgb(
+        label, img, n_labels=len(label_names), alpha=.5)
+    plt.imshow(label_viz)
+    plt.axis('off')
+
+    plt_handlers = []
+    plt_titles = []
+    for label_value, label_name in enumerate(label_names):
+        if label_value not in label:
+            continue
+        if label_name.startswith('_'):
+            continue
+        fc = colormap[label_value]
+        p = plt.Rectangle((0, 0), 1, 1, fc=fc)
+        plt_handlers.append(p)
+        plt_titles.append(label_name)
+    plt.legend(plt_handlers, plt_titles, loc='lower right', framealpha=.5)
+
+    f = io.BytesIO()
+    plt.savefig(f, bbox_inches='tight', pad_inches=0)
+    plt.cla()
+    plt.close()
+
+    out_size = (img.shape[1], img.shape[0])
+    out = PIL.Image.open(f).resize(out_size, PIL.Image.BILINEAR).convert('RGB')
+    out = np.asarray(out)
+    return out
+
+
+# -----------------------------------------------------------------------------
+
+
+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'))
+    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_lbl_file = osp.join(
+                args.out_dir, 'SegmentationClass', base + '.png')
+            out_viz_file = osp.join(
+                args.out_dir, 'SegmentationClassVisualization', base + '.jpg')
+
+            data = json.load(f)
+
+            img_file = osp.join(osp.dirname(label_file), data['imagePath'])
+            img = skimage.io.imread(img_file)
+            skimage.io.imsave(out_img_file, img)
+
+            lbl = labelme_shapes_to_label(
+                img_shape=img.shape,
+                shapes=data['shapes'],
+                label_name_to_value=class_name_to_id,
+            )
+            lbl_pil = PIL.Image.fromarray(lbl)
+            # Only works with uint8 label
+            # lbl_pil = PIL.Image.fromarray(lbl, mode='P')
+            # lbl_pil.putpalette((colormap * 255).flatten())
+            lbl_pil.save(out_lbl_file)
+
+            viz = draw_label(
+                lbl, img, class_names, colormap=colormap)
+            skimage.io.imsave(out_viz_file, viz)
+
+
+if __name__ == '__main__':
+    main()

examples/semantic_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