single_image -> tutorial

README.md

@@ -58,7 +58,7 @@ wget https://raw.githubusercontent.com/wkentaro/labelme/master/scripts/labelme_o
 chmod u+x labelme_on_docker
 
 # Maybe you need http://sourabhbajaj.com/blog/2017/02/07/gui-applications-docker-mac/ on macOS
-labelme_on_docker examples/single_image/apc2016_obj3.jpg -O examples/single_image/apc2016_obj3.json
+labelme_on_docker examples/tutorial/apc2016_obj3.jpg -O examples/tutorial/apc2016_obj3.json
 labelme_on_docker examples/semantic_segmentation/data_annotated
 ```
 
@@ -92,8 +92,8 @@ The annotations are saved as a [JSON](http://www.json.org/) file.
 ```bash
 labelme  # just open gui
 
-# single image example
-cd examples/single_image
+# tutorial (single image example)
+cd examples/tutorial
 labelme apc2016_obj3.jpg  # specify image file
 labelme apc2016_obj3.jpg -O apc2016_obj3.json  # close window after the save
 labelme apc2016_obj3.jpg --nodata  # not include image data but relative image path in JSON file
@@ -108,7 +108,7 @@ labelme data_annotated/ --labels labels.txt  # specify label list with a file
 
 For more advanced usage, please refer to the examples:
 
-* [Single Image Example](examples/single_image)
+* [Tutorial (Single Image Example)](examples/tutorial)
 * [Semantic Segmentation Example](examples/semantic_segmentation)
 * [Instance Segmentation Example](examples/instance_segmentation)
 

examples/single_image/README.md → examples/tutorial/README.md

@@ -1,4 +1,4 @@
-# Single Image Example
+# Tutorial (Single Image Example)
 
 ## Annotation
 
@@ -14,7 +14,7 @@ labelme apc2016_obj3.jpg -O apc2016_obj3.json
 To view the json file quickly, you can use utility script:
 
 ```bash
-labelme_draw_json examples/single_image/apc2016_obj3.json
+labelme_draw_json apc2016_obj3.json
 ```
 
 <img src=".readme/draw_json.jpg" width="70%" />
@@ -26,26 +26,26 @@ To convert the json to set of image and label, you can run following:
 
 
 ```bash
-labelme_json_to_dataset examples/single_image/apc2016_obj3.json -o examples/single_image/apc2016_obj3_json
+labelme_json_to_dataset apc2016_obj3.json -o apc2016_obj3_json
 ```
 
 It generates standard files from the JSON file.
 
-- [img.png](examples/single_image/apc2016_obj3_json/img.png): Image file.
-- [label.png](examples/single_image/apc2016_obj3_json/label.png): Int32 label file.
-- [label_viz.png](examples/single_image/apc2016_obj3_json/label_viz.png): Visualization of `label.png`.
-- [label_names.txt](examples/single_image/apc2016_obj3_json/label_names.txt): Label names for values in `label.png`.
+- [img.png](apc2016_obj3_json/img.png): Image file.
+- [label.png](apc2016_obj3_json/label.png): Int32 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`.
 
 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 examples/single_image/load_label_png.py also.
+# see load_label_png.py also.
 >>> import numpy as np
 >>> import PIL.Image
 
->>> label_png = 'examples/single_image/apc2016_obj3_json/label.png'
+>>> label_png = 'apc2016_obj3_json/label.png'
 >>> lbl = np.asarray(PIL.Image.open(label_png))
 >>> print(lbl.dtype)
 dtype('int32')
@@ -54,4 +54,3 @@ array([0, 1, 2, 3], dtype=int32)
 >>> lbl.shape
 (907, 1210)
 ```
-

tests/data/apc2016_obj3.jpg

@@ -1 +1 @@
-../../examples/single_image/apc2016_obj3.jpg
+../../examples/tutorial/apc2016_obj3.jpg

tests/data/apc2016_obj3.json

@@ -1 +1 @@
-../../examples/single_image/apc2016_obj3.json
+../../examples/tutorial/apc2016_obj3.json