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231 lines
9.0 KiB
231 lines
9.0 KiB
import json
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from collections import defaultdict
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from pathlib import Path
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import cv2
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import numpy as np
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from tqdm import tqdm
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from ultralytics.utils.checks import check_requirements
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from ultralytics.utils.files import make_dirs
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def coco91_to_coco80_class():
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"""Converts 91-index COCO class IDs to 80-index COCO class IDs.
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Returns:
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(list): A list of 91 class IDs where the index represents the 80-index class ID and the value is the
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corresponding 91-index class ID.
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"""
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return [
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, None, 24, 25, None,
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None, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, None, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
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51, 52, 53, 54, 55, 56, 57, 58, 59, None, 60, None, None, 61, None, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
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None, 73, 74, 75, 76, 77, 78, 79, None]
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def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keypoints=False, cls91to80=True):
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"""Converts COCO dataset annotations to a format suitable for training YOLOv5 models.
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Args:
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labels_dir (str, optional): Path to directory containing COCO dataset annotation files.
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use_segments (bool, optional): Whether to include segmentation masks in the output.
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use_keypoints (bool, optional): Whether to include keypoint annotations in the output.
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cls91to80 (bool, optional): Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs.
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Raises:
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FileNotFoundError: If the labels_dir path does not exist.
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Example Usage:
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convert_coco(labels_dir='../coco/annotations/', use_segments=True, use_keypoints=True, cls91to80=True)
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Output:
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Generates output files in the specified output directory.
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"""
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save_dir = make_dirs('yolo_labels') # output directory
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coco80 = coco91_to_coco80_class()
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# Import json
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for json_file in sorted(Path(labels_dir).resolve().glob('*.json')):
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fn = Path(save_dir) / 'labels' / json_file.stem.replace('instances_', '') # folder name
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fn.mkdir(parents=True, exist_ok=True)
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with open(json_file) as f:
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data = json.load(f)
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# Create image dict
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images = {f'{x["id"]:d}': x for x in data['images']}
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# Create image-annotations dict
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imgToAnns = defaultdict(list)
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for ann in data['annotations']:
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imgToAnns[ann['image_id']].append(ann)
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# Write labels file
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for img_id, anns in tqdm(imgToAnns.items(), desc=f'Annotations {json_file}'):
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img = images[f'{img_id:d}']
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h, w, f = img['height'], img['width'], img['file_name']
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bboxes = []
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segments = []
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keypoints = []
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for ann in anns:
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if ann['iscrowd']:
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continue
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# The COCO box format is [top left x, top left y, width, height]
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box = np.array(ann['bbox'], dtype=np.float64)
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box[:2] += box[2:] / 2 # xy top-left corner to center
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box[[0, 2]] /= w # normalize x
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box[[1, 3]] /= h # normalize y
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if box[2] <= 0 or box[3] <= 0: # if w <= 0 and h <= 0
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continue
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cls = coco80[ann['category_id'] - 1] if cls91to80 else ann['category_id'] - 1 # class
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box = [cls] + box.tolist()
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if box not in bboxes:
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bboxes.append(box)
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if use_segments and ann.get('segmentation') is not None:
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if len(ann['segmentation']) == 0:
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segments.append([])
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continue
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if isinstance(ann['segmentation'], dict):
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ann['segmentation'] = rle2polygon(ann['segmentation'])
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if len(ann['segmentation']) > 1:
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s = merge_multi_segment(ann['segmentation'])
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s = (np.concatenate(s, axis=0) / np.array([w, h])).reshape(-1).tolist()
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else:
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s = [j for i in ann['segmentation'] for j in i] # all segments concatenated
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s = (np.array(s).reshape(-1, 2) / np.array([w, h])).reshape(-1).tolist()
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s = [cls] + s
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if s not in segments:
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segments.append(s)
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if use_keypoints and ann.get('keypoints') is not None:
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k = (np.array(ann['keypoints']).reshape(-1, 3) / np.array([w, h, 1])).reshape(-1).tolist()
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k = box + k
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keypoints.append(k)
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# Write
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with open((fn / f).with_suffix('.txt'), 'a') as file:
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for i in range(len(bboxes)):
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if use_keypoints:
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line = *(keypoints[i]), # cls, box, keypoints
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else:
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line = *(segments[i]
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if use_segments and len(segments[i]) > 0 else bboxes[i]), # cls, box or segments
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file.write(('%g ' * len(line)).rstrip() % line + '\n')
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def rle2polygon(segmentation):
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"""
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Convert Run-Length Encoding (RLE) mask to polygon coordinates.
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Args:
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segmentation (dict, list): RLE mask representation of the object segmentation.
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Returns:
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(list): A list of lists representing the polygon coordinates for each contour.
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Note:
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Requires the 'pycocotools' package to be installed.
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"""
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check_requirements('pycocotools')
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from pycocotools import mask
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m = mask.decode(segmentation)
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m[m > 0] = 255
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contours, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)
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polygons = []
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for contour in contours:
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epsilon = 0.001 * cv2.arcLength(contour, True)
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contour_approx = cv2.approxPolyDP(contour, epsilon, True)
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polygon = contour_approx.flatten().tolist()
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polygons.append(polygon)
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return polygons
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def min_index(arr1, arr2):
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"""
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Find a pair of indexes with the shortest distance between two arrays of 2D points.
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Args:
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arr1 (np.array): A NumPy array of shape (N, 2) representing N 2D points.
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arr2 (np.array): A NumPy array of shape (M, 2) representing M 2D points.
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Returns:
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(tuple): A tuple containing the indexes of the points with the shortest distance in arr1 and arr2 respectively.
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"""
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dis = ((arr1[:, None, :] - arr2[None, :, :]) ** 2).sum(-1)
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return np.unravel_index(np.argmin(dis, axis=None), dis.shape)
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def merge_multi_segment(segments):
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"""
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Merge multiple segments into one list by connecting the coordinates with the minimum distance between each segment.
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This function connects these coordinates with a thin line to merge all segments into one.
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Args:
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segments (List[List]): Original segmentations in COCO's JSON file.
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Each element is a list of coordinates, like [segmentation1, segmentation2,...].
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Returns:
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s (List[np.ndarray]): A list of connected segments represented as NumPy arrays.
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"""
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s = []
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segments = [np.array(i).reshape(-1, 2) for i in segments]
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idx_list = [[] for _ in range(len(segments))]
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# record the indexes with min distance between each segment
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for i in range(1, len(segments)):
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idx1, idx2 = min_index(segments[i - 1], segments[i])
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idx_list[i - 1].append(idx1)
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idx_list[i].append(idx2)
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# use two round to connect all the segments
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for k in range(2):
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# forward connection
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if k == 0:
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for i, idx in enumerate(idx_list):
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# middle segments have two indexes
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# reverse the index of middle segments
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if len(idx) == 2 and idx[0] > idx[1]:
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idx = idx[::-1]
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segments[i] = segments[i][::-1, :]
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segments[i] = np.roll(segments[i], -idx[0], axis=0)
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segments[i] = np.concatenate([segments[i], segments[i][:1]])
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# deal with the first segment and the last one
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if i in [0, len(idx_list) - 1]:
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s.append(segments[i])
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else:
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idx = [0, idx[1] - idx[0]]
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s.append(segments[i][idx[0]:idx[1] + 1])
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else:
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for i in range(len(idx_list) - 1, -1, -1):
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if i not in [0, len(idx_list) - 1]:
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idx = idx_list[i]
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nidx = abs(idx[1] - idx[0])
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s.append(segments[i][nidx:])
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return s
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def delete_dsstore(path='../datasets'):
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"""Delete Apple .DS_Store files in the specified directory and its subdirectories."""
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from pathlib import Path
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files = list(Path(path).rglob('.DS_store'))
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print(files)
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for f in files:
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f.unlink()
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if __name__ == '__main__':
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source = 'COCO'
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if source == 'COCO':
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convert_coco(
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'../datasets/coco/annotations', # directory with *.json
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use_segments=False,
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use_keypoints=True,
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cls91to80=False)
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