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image_to_pixle_params_yoloSAM/ultralytics-main/ultralytics/data/converter.py

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2025-07-14 17:36:53 +08:00
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import json
import random
import shutil
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
from typing import List, Optional, Union
import cv2
import numpy as np
from PIL import Image
from ultralytics.utils import DATASETS_DIR, LOGGER, NUM_THREADS, TQDM
from ultralytics.utils.downloads import download, zip_directory
from ultralytics.utils.files import increment_path
def coco91_to_coco80_class() -> List[int]:
"""
Convert 91-index COCO class IDs to 80-index COCO class IDs.
Returns:
(List[int]): A list of 91 class IDs where the index represents the 80-index class ID and the value
is the corresponding 91-index class ID.
"""
return [
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,
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,
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,
None,
73,
74,
75,
76,
77,
78,
79,
None,
]
def coco80_to_coco91_class() -> List[int]:
r"""
Convert 80-index (val2014) to 91-index (paper).
Returns:
(List[int]): A list of 80 class IDs where each value is the corresponding 91-index class ID.
References:
https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
Examples:
>>> import numpy as np
>>> a = np.loadtxt("data/coco.names", dtype="str", delimiter="\n")
>>> b = np.loadtxt("data/coco_paper.names", dtype="str", delimiter="\n")
Convert the darknet to COCO format
>>> x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]
Convert the COCO to darknet format
>>> x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]
"""
return [
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
27,
28,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
60,
61,
62,
63,
64,
65,
67,
70,
72,
73,
74,
75,
76,
77,
78,
79,
80,
81,
82,
84,
85,
86,
87,
88,
89,
90,
]
def convert_coco(
labels_dir: str = "../coco/annotations/",
save_dir: str = "coco_converted/",
use_segments: bool = False,
use_keypoints: bool = False,
cls91to80: bool = True,
lvis: bool = False,
):
"""
Convert COCO dataset annotations to a YOLO annotation format suitable for training YOLO models.
Args:
labels_dir (str, optional): Path to directory containing COCO dataset annotation files.
save_dir (str, optional): Path to directory to save results to.
use_segments (bool, optional): Whether to include segmentation masks in the output.
use_keypoints (bool, optional): Whether to include keypoint annotations in the output.
cls91to80 (bool, optional): Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs.
lvis (bool, optional): Whether to convert data in lvis dataset way.
Examples:
>>> from ultralytics.data.converter import convert_coco
Convert COCO annotations to YOLO format
>>> convert_coco("coco/annotations/", use_segments=True, use_keypoints=False, cls91to80=False)
Convert LVIS annotations to YOLO format
>>> convert_coco("lvis/annotations/", use_segments=True, use_keypoints=False, cls91to80=False, lvis=True)
"""
# Create dataset directory
save_dir = increment_path(save_dir) # increment if save directory already exists
for p in save_dir / "labels", save_dir / "images":
p.mkdir(parents=True, exist_ok=True) # make dir
# Convert classes
coco80 = coco91_to_coco80_class()
# Import json
for json_file in sorted(Path(labels_dir).resolve().glob("*.json")):
lname = "" if lvis else json_file.stem.replace("instances_", "")
fn = Path(save_dir) / "labels" / lname # folder name
fn.mkdir(parents=True, exist_ok=True)
if lvis:
# NOTE: create folders for both train and val in advance,
# since LVIS val set contains images from COCO 2017 train in addition to the COCO 2017 val split.
(fn / "train2017").mkdir(parents=True, exist_ok=True)
(fn / "val2017").mkdir(parents=True, exist_ok=True)
with open(json_file, encoding="utf-8") as f:
data = json.load(f)
# Create image dict
images = {f"{x['id']:d}": x for x in data["images"]}
# Create image-annotations dict
annotations = defaultdict(list)
for ann in data["annotations"]:
annotations[ann["image_id"]].append(ann)
image_txt = []
# Write labels file
for img_id, anns in TQDM(annotations.items(), desc=f"Annotations {json_file}"):
img = images[f"{img_id:d}"]
h, w = img["height"], img["width"]
f = str(Path(img["coco_url"]).relative_to("http://images.cocodataset.org")) if lvis else img["file_name"]
if lvis:
image_txt.append(str(Path("./images") / f))
bboxes = []
segments = []
keypoints = []
for ann in anns:
if ann.get("iscrowd", False):
continue
# The COCO box format is [top left x, top left y, width, height]
box = np.array(ann["bbox"], dtype=np.float64)
box[:2] += box[2:] / 2 # xy top-left corner to center
box[[0, 2]] /= w # normalize x
box[[1, 3]] /= h # normalize y
if box[2] <= 0 or box[3] <= 0: # if w <= 0 and h <= 0
continue
cls = coco80[ann["category_id"] - 1] if cls91to80 else ann["category_id"] - 1 # class
box = [cls] + box.tolist()
if box not in bboxes:
bboxes.append(box)
if use_segments and ann.get("segmentation") is not None:
if len(ann["segmentation"]) == 0:
segments.append([])
continue
elif len(ann["segmentation"]) > 1:
s = merge_multi_segment(ann["segmentation"])
s = (np.concatenate(s, axis=0) / np.array([w, h])).reshape(-1).tolist()
else:
s = [j for i in ann["segmentation"] for j in i] # all segments concatenated
s = (np.array(s).reshape(-1, 2) / np.array([w, h])).reshape(-1).tolist()
s = [cls] + s
segments.append(s)
if use_keypoints and ann.get("keypoints") is not None:
keypoints.append(
box + (np.array(ann["keypoints"]).reshape(-1, 3) / np.array([w, h, 1])).reshape(-1).tolist()
)
# Write
with open((fn / f).with_suffix(".txt"), "a", encoding="utf-8") as file:
for i in range(len(bboxes)):
if use_keypoints:
line = (*(keypoints[i]),) # cls, box, keypoints
else:
line = (
*(segments[i] if use_segments and len(segments[i]) > 0 else bboxes[i]),
) # cls, box or segments
file.write(("%g " * len(line)).rstrip() % line + "\n")
if lvis:
filename = Path(save_dir) / json_file.name.replace("lvis_v1_", "").replace(".json", ".txt")
with open(filename, "a", encoding="utf-8") as f:
f.writelines(f"{line}\n" for line in image_txt)
LOGGER.info(f"{'LVIS' if lvis else 'COCO'} data converted successfully.\nResults saved to {save_dir.resolve()}")
def convert_segment_masks_to_yolo_seg(masks_dir: str, output_dir: str, classes: int):
"""
Convert a dataset of segmentation mask images to the YOLO segmentation format.
This function takes the directory containing the binary format mask images and converts them into YOLO segmentation
format. The converted masks are saved in the specified output directory.
Args:
masks_dir (str): The path to the directory where all mask images (png, jpg) are stored.
output_dir (str): The path to the directory where the converted YOLO segmentation masks will be stored.
classes (int): Total classes in the dataset i.e. for COCO classes=80
Examples:
>>> from ultralytics.data.converter import convert_segment_masks_to_yolo_seg
The classes here is the total classes in the dataset, for COCO dataset we have 80 classes
>>> convert_segment_masks_to_yolo_seg("path/to/masks_directory", "path/to/output/directory", classes=80)
Notes:
The expected directory structure for the masks is:
- masks
├─ mask_image_01.png or mask_image_01.jpg
├─ mask_image_02.png or mask_image_02.jpg
├─ mask_image_03.png or mask_image_03.jpg
└─ mask_image_04.png or mask_image_04.jpg
After execution, the labels will be organized in the following structure:
- output_dir
├─ mask_yolo_01.txt
├─ mask_yolo_02.txt
├─ mask_yolo_03.txt
└─ mask_yolo_04.txt
"""
pixel_to_class_mapping = {i + 1: i for i in range(classes)}
for mask_path in Path(masks_dir).iterdir():
if mask_path.suffix in {".png", ".jpg"}:
mask = cv2.imread(str(mask_path), cv2.IMREAD_GRAYSCALE) # Read the mask image in grayscale
img_height, img_width = mask.shape # Get image dimensions
LOGGER.info(f"Processing {mask_path} imgsz = {img_height} x {img_width}")
unique_values = np.unique(mask) # Get unique pixel values representing different classes
yolo_format_data = []
for value in unique_values:
if value == 0:
continue # Skip background
class_index = pixel_to_class_mapping.get(value, -1)
if class_index == -1:
LOGGER.warning(f"Unknown class for pixel value {value} in file {mask_path}, skipping.")
continue
# Create a binary mask for the current class and find contours
contours, _ = cv2.findContours(
(mask == value).astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
) # Find contours
for contour in contours:
if len(contour) >= 3: # YOLO requires at least 3 points for a valid segmentation
contour = contour.squeeze() # Remove single-dimensional entries
yolo_format = [class_index]
for point in contour:
# Normalize the coordinates
yolo_format.append(round(point[0] / img_width, 6)) # Rounding to 6 decimal places
yolo_format.append(round(point[1] / img_height, 6))
yolo_format_data.append(yolo_format)
# Save Ultralytics YOLO format data to file
output_path = Path(output_dir) / f"{mask_path.stem}.txt"
with open(output_path, "w", encoding="utf-8") as file:
for item in yolo_format_data:
line = " ".join(map(str, item))
file.write(line + "\n")
LOGGER.info(f"Processed and stored at {output_path} imgsz = {img_height} x {img_width}")
def convert_dota_to_yolo_obb(dota_root_path: str):
"""
Convert DOTA dataset annotations to YOLO OBB (Oriented Bounding Box) format.
The function processes images in the 'train' and 'val' folders of the DOTA dataset. For each image, it reads the
associated label from the original labels directory and writes new labels in YOLO OBB format to a new directory.
Args:
dota_root_path (str): The root directory path of the DOTA dataset.
Examples:
>>> from ultralytics.data.converter import convert_dota_to_yolo_obb
>>> convert_dota_to_yolo_obb("path/to/DOTA")
Notes:
The directory structure assumed for the DOTA dataset:
- DOTA
├─ images
│ ├─ train
│ └─ val
└─ labels
├─ train_original
└─ val_original
After execution, the function will organize the labels into:
- DOTA
└─ labels
├─ train
└─ val
"""
dota_root_path = Path(dota_root_path)
# Class names to indices mapping
class_mapping = {
"plane": 0,
"ship": 1,
"storage-tank": 2,
"baseball-diamond": 3,
"tennis-court": 4,
"basketball-court": 5,
"ground-track-field": 6,
"harbor": 7,
"bridge": 8,
"large-vehicle": 9,
"small-vehicle": 10,
"helicopter": 11,
"roundabout": 12,
"soccer-ball-field": 13,
"swimming-pool": 14,
"container-crane": 15,
"airport": 16,
"helipad": 17,
}
def convert_label(image_name: str, image_width: int, image_height: int, orig_label_dir: Path, save_dir: Path):
"""Convert a single image's DOTA annotation to YOLO OBB format and save it to a specified directory."""
orig_label_path = orig_label_dir / f"{image_name}.txt"
save_path = save_dir / f"{image_name}.txt"
with orig_label_path.open("r") as f, save_path.open("w") as g:
lines = f.readlines()
for line in lines:
parts = line.strip().split()
if len(parts) < 9:
continue
class_name = parts[8]
class_idx = class_mapping[class_name]
coords = [float(p) for p in parts[:8]]
normalized_coords = [
coords[i] / image_width if i % 2 == 0 else coords[i] / image_height for i in range(8)
]
formatted_coords = [f"{coord:.6g}" for coord in normalized_coords]
g.write(f"{class_idx} {' '.join(formatted_coords)}\n")
for phase in {"train", "val"}:
image_dir = dota_root_path / "images" / phase
orig_label_dir = dota_root_path / "labels" / f"{phase}_original"
save_dir = dota_root_path / "labels" / phase
save_dir.mkdir(parents=True, exist_ok=True)
image_paths = list(image_dir.iterdir())
for image_path in TQDM(image_paths, desc=f"Processing {phase} images"):
if image_path.suffix != ".png":
continue
image_name_without_ext = image_path.stem
img = cv2.imread(str(image_path))
h, w = img.shape[:2]
convert_label(image_name_without_ext, w, h, orig_label_dir, save_dir)
def min_index(arr1: np.ndarray, arr2: np.ndarray):
"""
Find a pair of indexes with the shortest distance between two arrays of 2D points.
Args:
arr1 (np.ndarray): A NumPy array of shape (N, 2) representing N 2D points.
arr2 (np.ndarray): A NumPy array of shape (M, 2) representing M 2D points.
Returns:
idx1 (int): Index of the point in arr1 with the shortest distance.
idx2 (int): Index of the point in arr2 with the shortest distance.
"""
dis = ((arr1[:, None, :] - arr2[None, :, :]) ** 2).sum(-1)
return np.unravel_index(np.argmin(dis, axis=None), dis.shape)
def merge_multi_segment(segments: List[List]):
"""
Merge multiple segments into one list by connecting the coordinates with the minimum distance between each segment.
This function connects these coordinates with a thin line to merge all segments into one.
Args:
segments (List[List]): Original segmentations in COCO's JSON file.
Each element is a list of coordinates, like [segmentation1, segmentation2,...].
Returns:
s (List[np.ndarray]): A list of connected segments represented as NumPy arrays.
"""
s = []
segments = [np.array(i).reshape(-1, 2) for i in segments]
idx_list = [[] for _ in range(len(segments))]
# Record the indexes with min distance between each segment
for i in range(1, len(segments)):
idx1, idx2 = min_index(segments[i - 1], segments[i])
idx_list[i - 1].append(idx1)
idx_list[i].append(idx2)
# Use two round to connect all the segments
for k in range(2):
# Forward connection
if k == 0:
for i, idx in enumerate(idx_list):
# Middle segments have two indexes, reverse the index of middle segments
if len(idx) == 2 and idx[0] > idx[1]:
idx = idx[::-1]
segments[i] = segments[i][::-1, :]
segments[i] = np.roll(segments[i], -idx[0], axis=0)
segments[i] = np.concatenate([segments[i], segments[i][:1]])
# Deal with the first segment and the last one
if i in {0, len(idx_list) - 1}:
s.append(segments[i])
else:
idx = [0, idx[1] - idx[0]]
s.append(segments[i][idx[0] : idx[1] + 1])
else:
for i in range(len(idx_list) - 1, -1, -1):
if i not in {0, len(idx_list) - 1}:
idx = idx_list[i]
nidx = abs(idx[1] - idx[0])
s.append(segments[i][nidx:])
return s
def yolo_bbox2segment(
im_dir: Union[str, Path], save_dir: Optional[Union[str, Path]] = None, sam_model: str = "sam_b.pt", device=None
):
"""
Convert existing object detection dataset (bounding boxes) to segmentation dataset or oriented bounding box (OBB) in
YOLO format. Generate segmentation data using SAM auto-annotator as needed.
Args:
im_dir (str | Path): Path to image directory to convert.
save_dir (str | Path, optional): Path to save the generated labels, labels will be saved
into `labels-segment` in the same directory level of `im_dir` if save_dir is None.
sam_model (str): Segmentation model to use for intermediate segmentation data.
device (int | str, optional): The specific device to run SAM models.
Notes:
The input directory structure assumed for dataset:
- im_dir
├─ 001.jpg
├─ ...
└─ NNN.jpg
- labels
├─ 001.txt
├─ ...
└─ NNN.txt
"""
from ultralytics import SAM
from ultralytics.data import YOLODataset
from ultralytics.utils.ops import xywh2xyxy
# NOTE: add placeholder to pass class index check
dataset = YOLODataset(im_dir, data=dict(names=list(range(1000))))
if len(dataset.labels[0]["segments"]) > 0: # if it's segment data
LOGGER.info("Segmentation labels detected, no need to generate new ones!")
return
LOGGER.info("Detection labels detected, generating segment labels by SAM model!")
sam_model = SAM(sam_model)
for label in TQDM(dataset.labels, total=len(dataset.labels), desc="Generating segment labels"):
h, w = label["shape"]
boxes = label["bboxes"]
if len(boxes) == 0: # skip empty labels
continue
boxes[:, [0, 2]] *= w
boxes[:, [1, 3]] *= h
im = cv2.imread(label["im_file"])
sam_results = sam_model(im, bboxes=xywh2xyxy(boxes), verbose=False, save=False, device=device)
label["segments"] = sam_results[0].masks.xyn
save_dir = Path(save_dir) if save_dir else Path(im_dir).parent / "labels-segment"
save_dir.mkdir(parents=True, exist_ok=True)
for label in dataset.labels:
texts = []
lb_name = Path(label["im_file"]).with_suffix(".txt").name
txt_file = save_dir / lb_name
cls = label["cls"]
for i, s in enumerate(label["segments"]):
if len(s) == 0:
continue
line = (int(cls[i]), *s.reshape(-1))
texts.append(("%g " * len(line)).rstrip() % line)
with open(txt_file, "a", encoding="utf-8") as f:
f.writelines(text + "\n" for text in texts)
LOGGER.info(f"Generated segment labels saved in {save_dir}")
def create_synthetic_coco_dataset():
"""
Create a synthetic COCO dataset with random images based on filenames from label lists.
This function downloads COCO labels, reads image filenames from label list files,
creates synthetic images for train2017 and val2017 subsets, and organizes
them in the COCO dataset structure. It uses multithreading to generate images efficiently.
Examples:
>>> from ultralytics.data.converter import create_synthetic_coco_dataset
>>> create_synthetic_coco_dataset()
Notes:
- Requires internet connection to download label files.
- Generates random RGB images of varying sizes (480x480 to 640x640 pixels).
- Existing test2017 directory is removed as it's not needed.
- Reads image filenames from train2017.txt and val2017.txt files.
"""
def create_synthetic_image(image_file: Path):
"""Generate synthetic images with random sizes and colors for dataset augmentation or testing purposes."""
if not image_file.exists():
size = (random.randint(480, 640), random.randint(480, 640))
Image.new(
"RGB",
size=size,
color=(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)),
).save(image_file)
# Download labels
dir = DATASETS_DIR / "coco"
url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/"
label_zip = "coco2017labels-segments.zip"
download([url + label_zip], dir=dir.parent)
# Create synthetic images
shutil.rmtree(dir / "labels" / "test2017", ignore_errors=True) # Remove test2017 directory as not needed
with ThreadPoolExecutor(max_workers=NUM_THREADS) as executor:
for subset in {"train2017", "val2017"}:
subset_dir = dir / "images" / subset
subset_dir.mkdir(parents=True, exist_ok=True)
# Read image filenames from label list file
label_list_file = dir / f"{subset}.txt"
if label_list_file.exists():
with open(label_list_file, encoding="utf-8") as f:
image_files = [dir / line.strip() for line in f]
# Submit all tasks
futures = [executor.submit(create_synthetic_image, image_file) for image_file in image_files]
for _ in TQDM(as_completed(futures), total=len(futures), desc=f"Generating images for {subset}"):
pass # The actual work is done in the background
else:
LOGGER.warning(f"Labels file {label_list_file} does not exist. Skipping image creation for {subset}.")
LOGGER.info("Synthetic COCO dataset created successfully.")
def convert_to_multispectral(path: Union[str, Path], n_channels: int = 10, replace: bool = False, zip: bool = False):
"""
Convert RGB images to multispectral images by interpolating across wavelength bands.
This function takes RGB images and interpolates them to create multispectral images with a specified number
of channels. It can process either a single image or a directory of images.
Args:
path (str | Path): Path to an image file or directory containing images to convert.
n_channels (int): Number of spectral channels to generate in the output image.
replace (bool): Whether to replace the original image file with the converted one.
zip (bool): Whether to zip the converted images into a zip file.
Examples:
Convert a single image
>>> convert_to_multispectral("path/to/image.jpg", n_channels=10)
Convert a dataset
>>> convert_to_multispectral("coco8", n_channels=10)
"""
from scipy.interpolate import interp1d
from ultralytics.data.utils import IMG_FORMATS
path = Path(path)
if path.is_dir():
# Process directory
im_files = sum([list(path.rglob(f"*.{ext}")) for ext in (IMG_FORMATS - {"tif", "tiff"})], [])
for im_path in im_files:
try:
convert_to_multispectral(im_path, n_channels)
if replace:
im_path.unlink()
except Exception as e:
LOGGER.info(f"Error converting {im_path}: {e}")
if zip:
zip_directory(path)
else:
# Process a single image
output_path = path.with_suffix(".tiff")
img = cv2.cvtColor(cv2.imread(str(path)), cv2.COLOR_BGR2RGB)
# Interpolate all pixels at once
rgb_wavelengths = np.array([650, 510, 475]) # R, G, B wavelengths (nm)
target_wavelengths = np.linspace(450, 700, n_channels)
f = interp1d(rgb_wavelengths.T, img, kind="linear", bounds_error=False, fill_value="extrapolate")
multispectral = f(target_wavelengths)
cv2.imwritemulti(str(output_path), np.clip(multispectral, 0, 255).astype(np.uint8).transpose(2, 0, 1))
LOGGER.info(f"Converted {output_path}")