Added voc2coco for rotated bounding boxes

README.md

@@ -5,8 +5,9 @@
 Then you can run the `voc2coco.py` script to generate a COCO data formatted JSON file for you.
 ```
 python voc2coco.py ./data/VOC/Annotations ./data/coco/output.json
+python voc2coco_rotate.py ./data/VOC/Annotations ./data/coco/output.json
 ```
 Then you can run the following Jupyter notebook to visualize the coco annotations. `COCO_Image_Viewer.ipynb`
 
 
-Further instruction on how to create your own datasets, read the [tutorial](https://www.dlology.com/blog/how-to-create-custom-coco-data-set-for-object-detection/).
+Further instruction on how to create your own datasets, read the [tutorial](https://www.dlology.com/blog/how-to-create-custom-coco-data-set-for-object-detection/).

voc2coco_rotate.py

@@ -0,0 +1,152 @@
+#!/usr/bin/python
+
+# pip install lxml
+
+import sys
+import os
+import json
+import xml.etree.ElementTree as ET
+import glob
+
+START_BOUNDING_BOX_ID = 1
+PRE_DEFINE_CATEGORIES = None
+# If necessary, pre-define category and its id
+#  PRE_DEFINE_CATEGORIES = {"aeroplane": 1, "bicycle": 2, "bird": 3, "boat": 4,
+#  "bottle":5, "bus": 6, "car": 7, "cat": 8, "chair": 9,
+#  "cow": 10, "diningtable": 11, "dog": 12, "horse": 13,
+#  "motorbike": 14, "person": 15, "pottedplant": 16,
+#  "sheep": 17, "sofa": 18, "train": 19, "tvmonitor": 20}
+
+
+def get(root, name):
+    vars = root.findall(name)
+    return vars
+
+
+def get_and_check(root, name, length):
+    vars = root.findall(name)
+    if len(vars) == 0:
+        raise ValueError("Can not find %s in %s." % (name, root.tag))
+    if length > 0 and len(vars) != length:
+        raise ValueError(
+            "The size of %s is supposed to be %d, but is %d."
+            % (name, length, len(vars))
+        )
+    if length == 1:
+        vars = vars[0]
+    return vars
+
+
+def get_filename_as_int(filename):
+    try:
+        filename = filename.replace("\\", "/")
+        filename = os.path.splitext(os.path.basename(filename))[0]
+        return int(filename)
+    except:
+        raise ValueError("Filename %s is supposed to be an integer." % (filename))
+
+
+def get_categories(xml_files):
+    """Generate category name to id mapping from a list of xml files.
+
+    Arguments:
+        xml_files {list} -- A list of xml file paths.
+
+    Returns:
+        dict -- category name to id mapping.
+    """
+    classes_names = []
+    for xml_file in xml_files:
+        tree = ET.parse(xml_file)
+        root = tree.getroot()
+        for member in root.findall("object"):
+            classes_names.append(member[0].text)
+    classes_names = list(set(classes_names))
+    classes_names.sort()
+    return {name: i for i, name in enumerate(classes_names)}
+
+
+def convert(xml_files, json_file):
+    json_dict = {"images": [], "type": "instances", "annotations": [], "categories": []}
+    if PRE_DEFINE_CATEGORIES is not None:
+        categories = PRE_DEFINE_CATEGORIES
+    else:
+        categories = get_categories(xml_files)
+    bnd_id = START_BOUNDING_BOX_ID
+    for xml_file in xml_files:
+        tree = ET.parse(xml_file)
+        root = tree.getroot()
+        path = get(root, "path")
+        if len(path) == 1:
+            filename = os.path.basename(path[0].text)
+        elif len(path) == 0:
+            filename = get_and_check(root, "filename", 1).text
+        else:
+            raise ValueError("%d paths found in %s" % (len(path), xml_file))
+        ## The filename must be a number
+        image_id = get_filename_as_int(filename)
+        size = get_and_check(root, "size", 1)
+        width = int(get_and_check(size, "width", 1).text)
+        height = int(get_and_check(size, "height", 1).text)
+        image = {
+            "file_name": filename,
+            "height": height,
+            "width": width,
+            "id": image_id,
+        }
+        json_dict["images"].append(image)
+        ## Currently we do not support segmentation.
+        #  segmented = get_and_check(root, 'segmented', 1).text
+        #  assert segmented == '0'
+        for obj in get(root, "object"):
+            category = get_and_check(obj, "name", 1).text
+            if category not in categories:
+                new_id = len(categories)
+                categories[category] = new_id
+            category_id = categories[category]
+            bndbox = get_and_check(obj, "robndbox", 1)
+            xmin = int(float(get_and_check(bndbox, "cx", 1).text)) - 1
+            ymin = int(float(get_and_check(bndbox, "cy", 1).text)) - 1
+            width = int(float(get_and_check(bndbox, "w", 1).text))
+            height = int(float(get_and_check(bndbox, "h", 1).text))
+            theta = round(float(get_and_check(bndbox, "angle", 1).text),2)
+
+            ann = {
+                "area": width * height,
+                "iscrowd": 0,
+                "image_id": image_id,
+                "bbox": [xmin, ymin, width, height, theta],
+                "category_id": category_id,
+                "id": bnd_id,
+                "ignore": 0,
+                "segmentation": [],
+            }
+            json_dict["annotations"].append(ann)
+            bnd_id = bnd_id + 1
+
+    for cate, cid in categories.items():
+        cat = {"supercategory": "none", "id": cid, "name": cate}
+        json_dict["categories"].append(cat)
+
+    os.makedirs(os.path.dirname(json_file), exist_ok=True)
+    json_fp = open(json_file, "w")
+    json_str = json.dumps(json_dict)
+    json_fp.write(json_str)
+    json_fp.close()
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="Convert Pascal VOC annotation to COCO format."
+    )
+    parser.add_argument("xml_dir", help="Directory path to xml files.", type=str)
+    parser.add_argument("json_file", help="Output COCO format json file.", type=str)
+    args = parser.parse_args()
+    xml_files = glob.glob(os.path.join(args.xml_dir, "*.xml"))
+
+    # If you want to do train/test split, you can pass a subset of xml files to convert function.
+    print("Number of xml files: {}".format(len(xml_files)))
+    convert(xml_files, args.json_file)
+    print("Success: {}".format(args.json_file))