数据转化
import json
from pathlib import Path
from tqdm import tqdm
r"""
coco_anno_dict is like {
"images": list of image_info's
"annotations": list of annotation_info's
"categories": list of categorie_info's
}
where img_info is like: {
"id": ..., # 0-indexed
"width": ...,
"height": ...,
"file_name": ...,
}, annotation_info is like: {
"id": ..., # 0-indexed
"image_id": ...,
"category_id": ...,
"segmentation": ...,
"iscrowd": ...,
"area": ...,
"bbox": ..., # (x, y, w, h)
}, and category_info is like: {
"id": ..., # 1-indexed
"name": ...,
}
"""
def sim10k_to_coco(
src_path: str = "VOC2012/Annotations",
des_path: str = "annotations/sim10k_caronly.json",
categories: tuple = ("car",)
) -> None:
r""" Convert Sim10k (in VOC format) into COCO format.
Args:
src_path: path of the directory containing VOC-format annotations
des_path: destination of the converted COCO-fomat annotation
categories: only category ``car`` is considered by default
"""
from xml.etree import ElementTree
src_path = Path(src_path)
des_path = Path(des_path)
assert src_path.exists(), "Annotation directory does not exist"
if des_path.exists():
print(f"{des_path} exists. Override? (y/n)", end=" ")
if input() != "y":
print("Abort")
return
else:
des_path.parent.mkdir(parents=True, exist_ok=True)
coco_anno_dict = {
"images": [],
"categories": [],
"annotations": [],
}
num_images = 0
num_categories = 0
num_annotations = 0
category_to_id = {}
for category in categories:
coco_anno_dict["categories"].append({
"id": num_categories + 1,
"name": category
})
category_to_id[category] = num_categories + 1
num_categories += 1
for anno_file in tqdm(list(src_path.glob("*.xml"))):
et_root = ElementTree.parse(anno_file).getroot()
img_info = {
"id": num_images,
"file_name": anno_file.stem + ".jpg",
}
num_images += 1
size = et_root.find("size")
img_info["width"] = int(size.find("width").text)
img_info["height"] = int(size.find("height").text)
coco_anno_dict["images"].append(img_info)
for anno_object in et_root.findall("object"):
category = anno_object.find("name").text
if category not in categories:
continue
anno_info = {
"id": num_annotations,
"image_id": img_info["id"],
"category_id": category_to_id[category],
"segmentation": [],
"iscrowd": 0
}
num_annotations += 1
bndbox = anno_object.find("bndbox")
xmin = float(bndbox.find("xmin").text)
ymin = float(bndbox.find("ymin").text)
xmax = float(bndbox.find("xmax").text)
ymax = float(bndbox.find("ymax").text)
anno_info["bbox"] = [xmin, ymin, round(xmax - xmin, 2), round(ymax - ymin, 2)]
anno_info["area"] = round(anno_info["bbox"][2] * anno_info["bbox"][3], 2)
coco_anno_dict["annotations"].append(anno_info)
print("# of images:", num_images)
print("# of categories:", num_categories)
print("# of annotations:", num_annotations)
with open(des_path, 'w') as f:
f.write(json.dumps(coco_anno_dict, indent=4))
print(f"Convert successfully to {des_path}")
def bdd100k_daytime_to_coco(
src_path: str = "labels/bdd100k_labels_images_train.json",
des_path: str = "annotations/bdd_daytime_train.json",
categories: tuple = (
"pedestrian", "car", "bicycle", "rider", "truck", "bus", "motorcycle", "train")
) -> None:
r""" Extract ``daytime`` subset from BDD100k dataset and convert into COCO format.
Args:
src_path: source of the annotation json file
des_path: destination of the converted COCO-fomat annotation
categories: categories used
"""
src_path = Path(src_path)
des_path = Path(des_path)
assert src_path.exists(), "Source annotation file does not exist"
if des_path.exists():
print(f"{des_path} exists. Override? (y/n)", end=" ")
if input() != "y":
print("Abort")
return
else:
des_path.parent.mkdir(parents=True, exist_ok=True)
coco_anno_dict = {
"images": [],
"categories": [],
"annotations": [],
}
num_images = 0
num_categories = 0
num_annotations = 0
category_to_id = {}
for category in categories:
coco_anno_dict["categories"].append({
"id": num_categories + 1,
"name": category
})
category_to_id[category] = num_categories + 1
num_categories += 1
with open(src_path, 'r') as f:
raw_img_annos = json.load(f)
for raw_img_anno in tqdm(raw_img_annos):
if raw_img_anno["attributes"]["timeofday"] != "daytime":
continue
img_info = {
"id": num_images,
"file_name": raw_img_anno["name"],
"height": 720,
"width": 1280
}
coco_anno_dict["images"].append(img_info)
num_images += 1
for label in raw_img_anno["labels"]:
if label["category"] not in category_to_id or "box2d" not in label:
continue
anno_info = {
"id": num_annotations,
"image_id": img_info["id"],
"category_id": category_to_id[label["category"]],
"segmentation": [],
"iscrowd": 0,
}
num_annotations += 1
x1 = label["box2d"]["x1"]
y1 = label["box2d"]["y1"]
x2 = label["box2d"]["x2"]
y2 = label["box2d"]["y2"]
anno_info["bbox"] = [x1, y1, x2 - x1, y2 - y1]
anno_info["area"] = float((x2 - x1) * (y2 - y1))
coco_anno_dict["annotations"].append(anno_info)
print("# of images:", num_images)
print("# of categories:", num_categories)
print("# of annotations:", num_annotations)
with open(des_path, 'w') as f:
f.write(json.dumps(coco_anno_dict, indent=2))
print(f"Convert successfully to {des_path}")
def cityscapes_to_coco(
src_path: str = "gtFine/train",
des_path: str = "annotations/cityscapes_train.json",
car_only: bool = False,
foggy: bool = False,
categories: tuple = (
"person", "car", "bicycle", "rider", "truck", "bus", "motorcycle", "train")
) -> None:
r"""Convert Cityscapes into COCO format.
Ref: https://github.com/facebookresearch/Detectron/blob/7aa91aa/tools/convert_cityscapes_to_coco.py
Args:
src_path: path of the directory containing Cityscapes annotations
des_path: destination of the converted COCO-fomat annotation
car_only: whether extract category ``car`` only. used in Syn-to-real adaptation
foggy: whether extract from foggy cityscapes. used in weather adaptation
categories: categories used
"""
def get_instances_with_polygons(imageFileName):
r""" Ref: https://github.com/facebookresearch/Detectron/issues/111#issuecomment-363425465"""
import os
import sys
import cv2
import numpy as np
from PIL import Image
from cityscapesscripts.evaluation.instance import Instance
from cityscapesscripts.helpers.csHelpers import labels, id2label
img = Image.open(imageFileName)
imgNp = np.array(img)
instances = {}
for label in labels:
instances[label.name] = []
for instanceId in np.unique(imgNp):
if instanceId < 1000:
continue
instanceObj = Instance(imgNp, instanceId)
instanceObj_dict = instanceObj.toDict()
if id2label[instanceObj.labelID].hasInstances:
mask = (imgNp == instanceId).astype(np.uint8)
contour, hier = cv2.findContours(
mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
polygons = [c.reshape(-1).tolist() for c in contour]
instanceObj_dict["contours"] = polygons
instances[id2label[instanceObj.labelID].name].append(
instanceObj_dict)
return instances
def polygon_to_bbox(polygon: list) -> list:
"""Convert polygon into COCO-format bounding box."""
TO_REMOVE = 1
x0 = min(min(p[::2]) for p in polygon)
x1 = max(max(p[::2]) for p in polygon)
y0 = min(min(p[1::2]) for p in polygon)
y1 = max(max(p[1::2]) for p in polygon)
bbox = [x0, y0, x1 -x0 + TO_REMOVE, y1 - y0 + TO_REMOVE]
return bbox
src_path = Path(src_path)
des_path = Path(des_path)
assert src_path.exists(), "Source annotation file does not exist"
if des_path.exists():
print(f"{des_path} exists. Override? (y/n)", end=" ")
if input() != "y":
print("Abort")
return
else:
des_path.parent.mkdir(parents=True, exist_ok=True)
coco_anno_dict = {
"images": [],
"categories": [],
"annotations": [],
}
num_images = 0
num_categories = 0
num_annotations = 0
if car_only:
categories = ("car",)
category_to_id = {}
for category in categories:
coco_anno_dict["categories"].append({
"id": num_categories + 1,
"name": category
})
category_to_id[category] = num_categories + 1
num_categories += 1
for file in tqdm(list(src_path.rglob("*instanceIds.png"))):
img_info = {"id": num_images}
num_images += 1
img_info["file_name"] = \
str(file.name).split("_", maxsplit=1)[0] + "/" + \
str(file.name).replace("gtFine", "leftImg8bit").replace("_instanceIds", "")
if foggy:
img_info["file_name"] = \
img_info["file_name"].replace("leftImg8bit", "leftImg8bit_foggy_beta_0.02")
with open(str(file).replace("instanceIds.png", "polygons.json"), "r") as f:
polygon_info = json.load(f)
img_info["width"] = polygon_info["imgWidth"]
img_info["height"] = polygon_info["imgHeight"]
coco_anno_dict["images"].append(img_info)
instances = get_instances_with_polygons(str(file.absolute()))
for category in instances.keys():
if category not in categories:
continue
for instance in instances[category]:
anno_info = {
"id": num_annotations,
"image_id": img_info["id"],
"category_id": category_to_id[category],
"segmentation": [],
"iscrowd": 0,
"area": instance["pixelCount"],
"bbox": polygon_to_bbox(instance["contours"]),
}
num_annotations += 1
coco_anno_dict["annotations"].append(anno_info)
print("# of images:", num_images)
print("# of categories:", num_categories)
print("# of annotations:", num_annotations)
with open(des_path, 'w') as f:
f.write(json.dumps(coco_anno_dict, indent=4))
print(f"Convert successfully to {des_path}")
if __name__ == "__main__":
cityscapes_to_coco(
src_path="cityscapes/gtFine/train",
des_path="cityscapes/label/cityscapes_caronly_train.json",
car_only=True,
)
cityscapes_to_coco(
src_path="cityscapes/gtFine/val",
des_path="cityscapes/label/cityscapes_caronly_val.json",
car_only=True,
)
可视化
import os
import cv2
import json
import matplotlib.pyplot as plt
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
def coco_show(anno_path, image_dir):
coco = COCO(anno_path)
img_ids = coco.getImgIds()
img_ids.sort()
cat_ids = coco.getCatIds()
cname2cid = dict({clsid : coco.loadCats(clsid)[0]['name'] for clsid in cat_ids})
print(cname2cid)
for img_id in img_ids:
img_anno = coco.loadImgs([img_id])[0]
im_fname = img_anno['file_name']
im_w = float(img_anno['width'])
im_h = float(img_anno['height'])
im_path = os.path.join(image_dir,
im_fname) if image_dir else im_fname
ins_anno_ids = coco.getAnnIds(imgIds=[img_id], iscrowd=None)
anns = coco.loadAnns(ins_anno_ids)
img = cv2.imread(im_path)
for ann in anns:
bbox = ann['bbox']
class_id = ann['category_id']
x, y, w, h = bbox
x1, y1 = int(x), int(y)
x2, y2 = int(x + w), int(y + h)
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(img, f'{cname2cid[class_id]}', (x1, y1-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
cv2.imwrite('show_cityscape/'+im_fname, img)
anno_path = "cityscapes/annotations/instances_train2017.json"
image_dir = 'cityscapes/leftImg8bit/train'
coco_show(anno_path, image_dir)
