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Add example of semantic_segmentation

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examples/semantic_segmentation/README.md 0 → 100644
浏览文件 @ 435fbef6
# 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`).*
examples/semantic_segmentation/data_annotated/2011_000003.json 0 → 100644
浏览文件 @ 435fbef6
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63.93617021276599,
281.56382978723406
],
[
89.93617021276599,
252.56382978723406
],
[
100.93617021276599,
183.56382978723406
],
[
108.93617021276599,
145.56382978723406
],
[
81.93617021276599,
151.56382978723406
]
]
},
{
"label": "car",
"line_color": null,
"fill_color": null,
"points": [
[
413.936170212766,
168.56382978723406
],
[
497.936170212766,
168.56382978723406
],
[
497.936170212766,
256.56382978723406
],
[
431.936170212766,
258.56382978723406
],
[
430.936170212766,
236.56382978723406
],
[
408.936170212766,
218.56382978723406
]
]
}
],
"lineColor": [
0,
255,
0,
128
],
"fillColor": [
255,
0,
0,
128
],
"imagePath": "2011_000025.jpg",
"imageData": null
}
\ No newline at end of file
examples/semantic_segmentation/data_dataset_voc/class_names.txt 0 → 100644
浏览文件 @ 435fbef6
_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
examples/semantic_segmentation/labelme2voc.py 0 → 100755
浏览文件 @ 435fbef6
#!/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 → 100644
浏览文件 @ 435fbef6
__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
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