kerascv

kerascv/extra_reading.txt

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+KerasCV List of Models
+https://keras.io/api/keras_cv/models/
+
+Fast R-CNN (Ross Girshick)
+https://arxiv.org/pdf/1504.08083.pdf
+
+Focal Loss for Dense Object Detection (Lin et al.)
+https://arxiv.org/abs/1708.02002

kerascv/makelist.py

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+'''
+Use this script to generate a list of all XML files in a folder.
+'''
+
+from glob import glob
+
+files = glob('*.xml')
+with open('xml_list.txt', 'w') as f:
+  for fn in files:
+    f.write("%s\n" % fn)

kerascv/pascal2coco.py

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+# adapted from https://blog.roboflow.com/how-to-convert-annotations-from-voc-xml-to-coco-json/
+
+import os
+import argparse
+import json
+import xml.etree.ElementTree as ET
+from typing import Dict, List
+from tqdm import tqdm
+import re
+
+
+def get_label2id(labels_path: str) -> Dict[str, int]:
+    """id is 1 start"""
+    with open(labels_path, 'r') as f:
+        labels_str = f.read().split()
+    labels_ids = list(range(0, len(labels_str)))
+    return dict(zip(labels_str, labels_ids))
+
+
+def get_annpaths(ann_dir_path: str = None,
+                 ann_ids_path: str = None,
+                 ext: str = '',
+                 annpaths_list_path: str = None) -> List[str]:
+    # If use annotation paths list
+    if annpaths_list_path is not None:
+        with open(annpaths_list_path, 'r') as f:
+            ann_paths = f.read().split()
+        return ann_paths
+
+    # If use annotaion ids list
+    ext_with_dot = '.' + ext if ext != '' else ''
+    with open(ann_ids_path, 'r') as f:
+        ann_ids = f.read().split()
+    ann_paths = [os.path.join(ann_dir_path, aid+ext_with_dot) for aid in ann_ids]
+    return ann_paths
+
+
+def get_image_info(annotation_root, extract_num_from_imgid=True):
+    path = annotation_root.findtext('path')
+    if path is None:
+        filename = annotation_root.findtext('filename')
+    else:
+        filename = os.path.basename(path)
+    img_name = os.path.basename(filename)
+    img_id = os.path.splitext(img_name)[0]
+    if extract_num_from_imgid and isinstance(img_id, str):
+        img_id = int(re.findall(r'\d+', img_id)[0])
+
+    size = annotation_root.find('size')
+    width = int(size.findtext('width'))
+    height = int(size.findtext('height'))
+
+    image_info = {
+        'file_name': filename,
+        'height': height,
+        'width': width,
+        'id': img_id
+    }
+    return image_info
+
+
+def get_coco_annotation_from_obj(obj, label2id):
+    label = obj.findtext('name')
+    assert label in label2id, f"Error: {label} is not in label2id !"
+    category_id = label2id[label]
+    bndbox = obj.find('bndbox')
+    xmin = int(bndbox.findtext('xmin')) - 1
+    ymin = int(bndbox.findtext('ymin')) - 1
+    xmax = int(bndbox.findtext('xmax'))
+    ymax = int(bndbox.findtext('ymax'))
+    assert xmax > xmin and ymax > ymin, f"Box size error !: (xmin, ymin, xmax, ymax): {xmin, ymin, xmax, ymax}"
+    o_width = xmax - xmin
+    o_height = ymax - ymin
+    ann = {
+        'area': o_width * o_height,
+        'iscrowd': 0,
+        'bbox': [xmin, ymin, o_width, o_height],
+        'category_id': category_id,
+        'ignore': 0,
+        'segmentation': []  # This script is not for segmentation
+    }
+    return ann
+
+
+def convert_xmls_to_cocojson(annotation_paths: List[str],
+                             label2id: Dict[str, int],
+                             output_jsonpath: str,
+                             extract_num_from_imgid: bool = True):
+    output_json_dict = {
+        "images": [],
+        "type": "instances",
+        "annotations": [],
+        "categories": []
+    }
+    bnd_id = 1  # START_BOUNDING_BOX_ID, TODO input as args ?
+    print('Start converting !')
+    for a_path in tqdm(annotation_paths):
+        # Read annotation xml
+        ann_tree = ET.parse(a_path)
+        ann_root = ann_tree.getroot()
+
+        img_info = get_image_info(annotation_root=ann_root,
+                                  extract_num_from_imgid=extract_num_from_imgid)
+        img_id = img_info['id']
+        output_json_dict['images'].append(img_info)
+
+        for obj in ann_root.findall('object'):
+            ann = get_coco_annotation_from_obj(obj=obj, label2id=label2id)
+            ann.update({'image_id': img_id, 'id': bnd_id})
+            output_json_dict['annotations'].append(ann)
+            bnd_id = bnd_id + 1
+
+    for label, label_id in label2id.items():
+        category_info = {'supercategory': 'none', 'id': label_id, 'name': label}
+        output_json_dict['categories'].append(category_info)
+
+    with open(output_jsonpath, 'w') as f:
+        output_json = json.dumps(output_json_dict)
+        f.write(output_json)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description='This script support converting voc format xmls to coco format json')
+    parser.add_argument('--ann_dir', type=str, default=None,
+                        help='path to annotation files directory. It is not need when use --ann_paths_list')
+    parser.add_argument('--ann_ids', type=str, default=None,
+                        help='path to annotation files ids list. It is not need when use --ann_paths_list')
+    parser.add_argument('--ann_paths_list', type=str, default=None,
+                        help='path of annotation paths list. It is not need when use --ann_dir and --ann_ids')
+    parser.add_argument('--labels', type=str, default=None,
+                        help='path to label list.')
+    parser.add_argument('--output', type=str, default='output.json', help='path to output json file')
+    parser.add_argument('--ext', type=str, default='', help='additional extension of annotation file')
+    args = parser.parse_args()
+    label2id = get_label2id(labels_path=args.labels)
+    ann_paths = get_annpaths(
+        ann_dir_path=args.ann_dir,
+        ann_ids_path=args.ann_ids,
+        ext=args.ext,
+        annpaths_list_path=args.ann_paths_list
+    )
+    convert_xmls_to_cocojson(
+        annotation_paths=ann_paths,
+        label2id=label2id,
+        output_jsonpath=args.output,
+        extract_num_from_imgid=True
+    )
+
+
+if __name__ == '__main__':
+    main()