Merge pull request #13 from GreatV/markdown_lint

markdown lint

Author: GreatV

README.md

@@ -1,45 +1,50 @@
-**Forked from [rooneysh/Labelme2YOLO](https://github.com/rooneysh/Labelme2YOLO)**
-
 # Labelme2YOLO
 
+**Forked from [rooneysh/Labelme2YOLO](https://github.com/rooneysh/Labelme2YOLO)**
+
 [![PyPI - Version](https://img.shields.io/pypi/v/labelme2yolo.svg)](https://pypi.org/project/labelme2yolo)
 ![PyPI - Downloads](https://img.shields.io/pypi/dm/labelme2yolo?style=flat)
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/labelme2yolo.svg)](https://pypi.org/project/labelme2yolo)
-[![Codacy Badge](https://app.codacy.com/project/badge/Grade/12122fe86f8643c4aa5667c20d528f61)](https://www.codacy.com/gh/GreatV/labelme2yolo/dashboard?utm_source=github.com&utm_medium=referral&utm_content=GreatV/labelme2yolo&utm_campaign=Badge_Grade)
+[![Codacy Badge](https://app.codacy.com/project/badge/Grade/12122fe86f8643c4aa5667c20d528f61)](https://www.codacy.com/gh/GreatV/labelme2yolo/dashboard?utm_source=github.com\&utm_medium=referral\&utm_content=GreatV/labelme2yolo\&utm_campaign=Badge_Grade)
 
-Help converting LabelMe Annotation Tool JSON format to YOLO text file format. 
+Help converting LabelMe Annotation Tool JSON format to YOLO text file format.
 If you've already marked your segmentation dataset by LabelMe, it's easy to use this tool to help converting to YOLO format dataset.
 
----------
-
 ## New
-- export data as yolo polygon annotation (for YOLOv5 v7.0 segmentation)
-- Now you can choose the output format of the label text. The available options are `plygon` and `bbox`.
+
+* export data as yolo polygon annotation (for YOLOv5 v7.0 segmentation)
+* Now you can choose the output format of the label text. The available options are `plygon` and `bbox`.
+
 ## Installation
 
 ```console
 pip install labelme2yolo
 ```
 
 ## Parameters Explain
-**--json_dir** LabelMe JSON files folder path.
 
-**--val_size (Optional)** Validation dataset size, for example 0.2 means 20% for validation.
+**--json\_dir** LabelMe JSON files folder path.
+
+**--val\_size (Optional)** Validation dataset size, for example 0.2 means 20% for validation.
 
-**--test_size (Optional)** Test dataset size, for example 0.2 means 20% for Test.
+**--test\_size (Optional)** Test dataset size, for example 0.2 means 20% for Test.
 
-**--json_name (Optional)** Convert single LabelMe JSON file.
+**--json\_name (Optional)** Convert single LabelMe JSON file.
 
-**--output_format (Optional)** The output format of label.
+**--output\_format (Optional)** The output format of label.
 
 ## How to Use
 
-### 1. Convert JSON files, split training, validation and test dataset by --val_size and --test_size
-Put all LabelMe JSON files under **labelme_json_dir**, and run this python command.
+### 1. Convert JSON files, split training, validation and test dataset by --val\_size and --test\_size
+
+Put all LabelMe JSON files under **labelme\_json\_dir**, and run this python command.
+
 ```bash
 labelme2yolo --json_dir /path/to/labelme_json_dir/ --val_size 0.15 --test_size 0.15
 ```
+
 Script would generate YOLO format dataset labels and images under different folders, for example,
+
 ```bash
 /path/to/labelme_json_dir/YOLODataset/labels/train/
 /path/to/labelme_json_dir/YOLODataset/labels/test/
@@ -52,18 +57,24 @@ Script would generate YOLO format dataset labels and images under different fold
 ```
 
 ### 2. Convert JSON files, split training and validation dataset by folder
-If you already split train dataset and validation dataset for LabelMe by yourself, please put these folder under labelme_json_dir, for example,
+
+If you already split train dataset and validation dataset for LabelMe by yourself, please put these folder under labelme\_json\_dir, for example,
+
 ```bash
 /path/to/labelme_json_dir/train/
 /path/to/labelme_json_dir/val/
 ```
-Put all LabelMe JSON files under **labelme_json_dir**. 
+
+Put all LabelMe JSON files under **labelme\_json\_dir**.
 Script would read train and validation dataset by folder.
 Run this python command.
+
 ```bash
 labelme2yolo --json_dir /path/to/labelme_json_dir/
 ```
+
 Script would generate YOLO format dataset labels and images under different folders, for example,
+
 ```bash
 /path/to/labelme_json_dir/YOLODataset/labels/train/
 /path/to/labelme_json_dir/YOLODataset/labels/val/
@@ -74,11 +85,15 @@ Script would generate YOLO format dataset labels and images under different fold
 ```
 
 ### 3. Convert single JSON file
-Put LabelMe JSON file under **labelme_json_dir**. , and run this python command.
+
+Put LabelMe JSON file under **labelme\_json\_dir**. , and run this python command.
+
 ```bash
 labelme2yolo --json_dir /path/to/labelme_json_dir/ --json_name 2.json
 ```
-Script would generate YOLO format text label and image under **labelme_json_dir**, for example,
+
+Script would generate YOLO format text label and image under **labelme\_json\_dir**, for example,
+
 ```bash
 /path/to/labelme_json_dir/2.text
 /path/to/labelme_json_dir/2.png

src/labelme2yolo/l2y.py

@@ -93,15 +93,15 @@ def get_label_id_map(json_dir):
     return OrderedDict([(label, label_id) for label_id, label in enumerate(label_set)])
 
 
-def extend_point_list(point_list, format="polygon"):
+def extend_point_list(point_list, out_format="polygon"):
     xmin = min([float(point) for point in point_list[::2]])
     xmax = max([float(point) for point in point_list[::2]])
     ymin = min([float(point) for point in point_list[1::2]])
     ymax = max([float(point) for point in point_list[1::2]])
 
-    if (format == "polygon"):
+    if (out_format == "polygon"):
         return np.array([xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax])
-    if (format == "bbox"):
+    if (out_format == "bbox"):
         return np.array([xmin, ymin, xmax - xmin, ymax - ymin])