# Riferimento per `ultralytics/data/split_dota.py`

Nota

Questo file è disponibile su https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/data/split_dota .py. Se riscontri un problema, contribuisci a risolverlo inviando una Pull Request 🛠️. Grazie 🙏!

## `ultralytics.data.split_dota.bbox_iof(polygon1, bbox2, eps=1e-06)`

Calcola gli iof tra bbox1 e bbox2.

Parametri:

Nome Tipo Descrizione Predefinito
`polygon1` `ndarray`

Coordinate del poligono, (n, 8).

richiesto
`bbox2` `ndarray`

Caselle di delimitazione, (n ,4).

richiesto
Codice sorgente in `ultralytics/data/split_dota.py`
 ```20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53``` ``````def bbox_iof(polygon1, bbox2, eps=1e-6): """ Calculate iofs between bbox1 and bbox2. Args: polygon1 (np.ndarray): Polygon coordinates, (n, 8). bbox2 (np.ndarray): Bounding boxes, (n ,4). """ polygon1 = polygon1.reshape(-1, 4, 2) lt_point = np.min(polygon1, axis=-2) # left-top rb_point = np.max(polygon1, axis=-2) # right-bottom bbox1 = np.concatenate([lt_point, rb_point], axis=-1) lt = np.maximum(bbox1[:, None, :2], bbox2[..., :2]) rb = np.minimum(bbox1[:, None, 2:], bbox2[..., 2:]) wh = np.clip(rb - lt, 0, np.inf) h_overlaps = wh[..., 0] * wh[..., 1] left, top, right, bottom = (bbox2[..., i] for i in range(4)) polygon2 = np.stack([left, top, right, top, right, bottom, left, bottom], axis=-1).reshape(-1, 4, 2) sg_polys1 = [Polygon(p) for p in polygon1] sg_polys2 = [Polygon(p) for p in polygon2] overlaps = np.zeros(h_overlaps.shape) for p in zip(*np.nonzero(h_overlaps)): overlaps[p] = sg_polys1[p[0]].intersection(sg_polys2[p[-1]]).area unions = np.array([p.area for p in sg_polys1], dtype=np.float32) unions = unions[..., None] unions = np.clip(unions, eps, np.inf) outputs = overlaps / unions if outputs.ndim == 1: outputs = outputs[..., None] return outputs ``````

## `ultralytics.data.split_dota.load_yolo_dota(data_root, split='train')`

Carica il set di dati DOTA.

Parametri:

Nome Tipo Descrizione Predefinito
`data_root` `str`

Radice dei dati.

richiesto
`split` `str`

Il set di dati diviso può essere train o val.

`'train'`
Note

La struttura di directory assunta per il set di dati DOTA: - data_root - immagini - addestramento - val - etichette - addestramento - val

Codice sorgente in `ultralytics/data/split_dota.py`
 ```56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86``` ``````def load_yolo_dota(data_root, split="train"): """ Load DOTA dataset. Args: data_root (str): Data root. split (str): The split data set, could be train or val. Notes: The directory structure assumed for the DOTA dataset: - data_root - images - train - val - labels - train - val """ assert split in {"train", "val"}, f"Split must be 'train' or 'val', not {split}." im_dir = Path(data_root) / "images" / split assert im_dir.exists(), f"Can't find {im_dir}, please check your data root." im_files = glob(str(Path(data_root) / "images" / split / "*")) lb_files = img2label_paths(im_files) annos = [] for im_file, lb_file in zip(im_files, lb_files): w, h = exif_size(Image.open(im_file)) with open(lb_file) as f: lb = [x.split() for x in f.read().strip().splitlines() if len(x)] lb = np.array(lb, dtype=np.float32) annos.append(dict(ori_size=(h, w), label=lb, filepath=im_file)) return annos ``````

## `ultralytics.data.split_dota.get_windows(im_size, crop_sizes=(1024), gaps=(200), im_rate_thr=0.6, eps=0.01)`

Ottieni le coordinate delle finestre.

Parametri:

Nome Tipo Descrizione Predefinito
`im_size` `tuple`

Dimensioni dell'immagine originale (h, w).

richiesto
`crop_sizes` `List(int`

Ritaglia le dimensioni delle finestre.

`(1024)`
`gaps` `List(int`

Spazio tra le colture.

`(200)`
`im_rate_thr` `float`

Soglia delle aree delle finestre divisa per le aree dell'immagine.

`0.6`
`eps` `float`

Valore Epsilon per le operazioni matematiche.

`0.01`
Codice sorgente in `ultralytics/data/split_dota.py`
 ``` 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130``` ``````def get_windows(im_size, crop_sizes=(1024,), gaps=(200,), im_rate_thr=0.6, eps=0.01): """ Get the coordinates of windows. Args: im_size (tuple): Original image size, (h, w). crop_sizes (List(int)): Crop size of windows. gaps (List(int)): Gap between crops. im_rate_thr (float): Threshold of windows areas divided by image ares. eps (float): Epsilon value for math operations. """ h, w = im_size windows = [] for crop_size, gap in zip(crop_sizes, gaps): assert crop_size > gap, f"invalid crop_size gap pair [{crop_size} {gap}]" step = crop_size - gap xn = 1 if w <= crop_size else ceil((w - crop_size) / step + 1) xs = [step * i for i in range(xn)] if len(xs) > 1 and xs[-1] + crop_size > w: xs[-1] = w - crop_size yn = 1 if h <= crop_size else ceil((h - crop_size) / step + 1) ys = [step * i for i in range(yn)] if len(ys) > 1 and ys[-1] + crop_size > h: ys[-1] = h - crop_size start = np.array(list(itertools.product(xs, ys)), dtype=np.int64) stop = start + crop_size windows.append(np.concatenate([start, stop], axis=1)) windows = np.concatenate(windows, axis=0) im_in_wins = windows.copy() im_in_wins[:, 0::2] = np.clip(im_in_wins[:, 0::2], 0, w) im_in_wins[:, 1::2] = np.clip(im_in_wins[:, 1::2], 0, h) im_areas = (im_in_wins[:, 2] - im_in_wins[:, 0]) * (im_in_wins[:, 3] - im_in_wins[:, 1]) win_areas = (windows[:, 2] - windows[:, 0]) * (windows[:, 3] - windows[:, 1]) im_rates = im_areas / win_areas if not (im_rates > im_rate_thr).any(): max_rate = im_rates.max() im_rates[abs(im_rates - max_rate) < eps] = 1 return windows[im_rates > im_rate_thr] ``````

## `ultralytics.data.split_dota.get_window_obj(anno, windows, iof_thr=0.7)`

Ottiene gli oggetti per ogni finestra.

Codice sorgente in `ultralytics/data/split_dota.py`
 ```133 134 135 136 137 138 139 140 141 142 143 144``` ``````def get_window_obj(anno, windows, iof_thr=0.7): """Get objects for each window.""" h, w = anno["ori_size"] label = anno["label"] if len(label): label[:, 1::2] *= w label[:, 2::2] *= h iofs = bbox_iof(label[:, 1:], windows) # Unnormalized and misaligned coordinates return [(label[iofs[:, i] >= iof_thr]) for i in range(len(windows))] # window_anns else: return [np.zeros((0, 9), dtype=np.float32) for _ in range(len(windows))] # window_anns ``````

## `ultralytics.data.split_dota.crop_and_save(anno, windows, window_objs, im_dir, lb_dir)`

Ritaglia le immagini e salva le nuove etichette.

Parametri:

Nome Tipo Descrizione Predefinito
`anno` `dict`

Dettatura delle annotazioni, tra cui `filepath`, `label`, `ori_size` come chiavi.

richiesto
`windows` `list`

Un elenco di coordinate delle finestre.

richiesto
`window_objs` `list`

Un elenco di etichette all'interno di ogni finestra.

richiesto
`im_dir` `str`

Il percorso della directory di output delle immagini.

richiesto
`lb_dir` `str`

Il percorso della directory di output delle etichette.

richiesto
Note

La struttura di directory assunta per il set di dati DOTA: - data_root - immagini - addestramento - val - etichette - addestramento - val

Codice sorgente in `ultralytics/data/split_dota.py`
 ```147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188``` ``````def crop_and_save(anno, windows, window_objs, im_dir, lb_dir): """ Crop images and save new labels. Args: anno (dict): Annotation dict, including `filepath`, `label`, `ori_size` as its keys. windows (list): A list of windows coordinates. window_objs (list): A list of labels inside each window. im_dir (str): The output directory path of images. lb_dir (str): The output directory path of labels. Notes: The directory structure assumed for the DOTA dataset: - data_root - images - train - val - labels - train - val """ im = cv2.imread(anno["filepath"]) name = Path(anno["filepath"]).stem for i, window in enumerate(windows): x_start, y_start, x_stop, y_stop = window.tolist() new_name = f"{name}__{x_stop - x_start}__{x_start}___{y_start}" patch_im = im[y_start:y_stop, x_start:x_stop] ph, pw = patch_im.shape[:2] cv2.imwrite(str(Path(im_dir) / f"{new_name}.jpg"), patch_im) label = window_objs[i] if len(label) == 0: continue label[:, 1::2] -= x_start label[:, 2::2] -= y_start label[:, 1::2] /= pw label[:, 2::2] /= ph with open(Path(lb_dir) / f"{new_name}.txt", "w") as f: for lb in label: formatted_coords = ["{:.6g}".format(coord) for coord in lb[1:]] f.write(f"{int(lb[0])} {' '.join(formatted_coords)}\n") ``````

## `ultralytics.data.split_dota.split_images_and_labels(data_root, save_dir, split='train', crop_sizes=(1024), gaps=(200))`

Dividi sia le immagini che le etichette.

Note

La struttura di directory assunta per il set di dati DOTA: - data_root - immagini - split - etichette - split e la struttura della directory di output è - save_dir - immagini - split - etichette - split

Codice sorgente in `ultralytics/data/split_dota.py`
 ```191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218``` ``````def split_images_and_labels(data_root, save_dir, split="train", crop_sizes=(1024,), gaps=(200,)): """ Split both images and labels. Notes: The directory structure assumed for the DOTA dataset: - data_root - images - split - labels - split and the output directory structure is: - save_dir - images - split - labels - split """ im_dir = Path(save_dir) / "images" / split im_dir.mkdir(parents=True, exist_ok=True) lb_dir = Path(save_dir) / "labels" / split lb_dir.mkdir(parents=True, exist_ok=True) annos = load_yolo_dota(data_root, split=split) for anno in tqdm(annos, total=len(annos), desc=split): windows = get_windows(anno["ori_size"], crop_sizes, gaps) window_objs = get_window_obj(anno, windows) crop_and_save(anno, windows, window_objs, str(im_dir), str(lb_dir)) ``````

## `ultralytics.data.split_dota.split_trainval(data_root, save_dir, crop_size=1024, gap=200, rates=(1.0))`

Treno diviso e set di val di DOTA.

Note

La struttura di directory assunta per il set di dati DOTA: - data_root - immagini - addestramento - val - etichette - addestramento - val e la struttura della directory di output è - save_dir - immagini - treno - val - etichette - treno - val

Codice sorgente in `ultralytics/data/split_dota.py`
 ```221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248``` ``````def split_trainval(data_root, save_dir, crop_size=1024, gap=200, rates=(1.0,)): """ Split train and val set of DOTA. Notes: The directory structure assumed for the DOTA dataset: - data_root - images - train - val - labels - train - val and the output directory structure is: - save_dir - images - train - val - labels - train - val """ crop_sizes, gaps = [], [] for r in rates: crop_sizes.append(int(crop_size / r)) gaps.append(int(gap / r)) for split in ["train", "val"]: split_images_and_labels(data_root, save_dir, split, crop_sizes, gaps) ``````

## `ultralytics.data.split_dota.split_test(data_root, save_dir, crop_size=1024, gap=200, rates=(1.0))`

Set di test diviso di DOTA, le etichette non sono incluse in questo set.

Note

La struttura di directory assunta per il set di dati DOTA: - data_root - immagini - test e la struttura della directory di output è: - save_dir - immagini - test

Codice sorgente in `ultralytics/data/split_dota.py`
 ```251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284``` ``````def split_test(data_root, save_dir, crop_size=1024, gap=200, rates=(1.0,)): """ Split test set of DOTA, labels are not included within this set. Notes: The directory structure assumed for the DOTA dataset: - data_root - images - test and the output directory structure is: - save_dir - images - test """ crop_sizes, gaps = [], [] for r in rates: crop_sizes.append(int(crop_size / r)) gaps.append(int(gap / r)) save_dir = Path(save_dir) / "images" / "test" save_dir.mkdir(parents=True, exist_ok=True) im_dir = Path(data_root) / "images" / "test" assert im_dir.exists(), f"Can't find {im_dir}, please check your data root." im_files = glob(str(im_dir / "*")) for im_file in tqdm(im_files, total=len(im_files), desc="test"): w, h = exif_size(Image.open(im_file)) windows = get_windows((h, w), crop_sizes=crop_sizes, gaps=gaps) im = cv2.imread(im_file) name = Path(im_file).stem for window in windows: x_start, y_start, x_stop, y_stop = window.tolist() new_name = f"{name}__{x_stop - x_start}__{x_start}___{y_start}" patch_im = im[y_start:y_stop, x_start:x_stop] cv2.imwrite(str(save_dir / f"{new_name}.jpg"), patch_im) ``````

Creato 2024-01-05, Aggiornato 2024-06-02
Autori: glenn-jocher (4), Burhan-Q (1)