File size: 5,633 Bytes
ba7e3d3 9886b22 ba7e3d3 ab2c776 ba7e3d3 e20781b ba7e3d3 ab2c776 ba7e3d3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 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 54 55 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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 |
import os
from xml.etree import ElementTree as ET
import datasets
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {medical-staff-people-tracking},
author = {TrainingDataPro},
year = {2023}
}
"""
_DESCRIPTION = """\
The dataset contains a collection of frames extracted from videos captured within a
**hospital environment**. The **bounding boxes** are drawn around the **doctors, nurses,
and other people** who appear in the video footage.
The dataset can be used for **computer vision in healthcare settings** and *the
development of systems that monitor medical staff activities, patient flow, analyze
wait times, and assess the efficiency of hospital processes*.
"""
_NAME = "medical-staff-people-tracking"
_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
_LICENSE = ""
_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
_LABELS = ["nurse", "doctor", "other_people"]
class MedicalStaffPeopleTracking(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="video_01", data_dir=f"{_DATA}video_01.zip"),
datasets.BuilderConfig(name="video_02", data_dir=f"{_DATA}video_02.zip"),
]
DEFAULT_CONFIG_NAME = "video_01"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"name": datasets.Value("string"),
"image": datasets.Image(),
"mask": datasets.Image(),
"shapes": datasets.Sequence(
{
"track_id": datasets.Value("uint32"),
"label": datasets.ClassLabel(
num_classes=len(_LABELS),
names=_LABELS,
),
"type": datasets.Value("string"),
"points": datasets.Sequence(
datasets.Sequence(
datasets.Value("float"),
),
),
"rotation": datasets.Value("float"),
"occluded": datasets.Value("uint8"),
"attributes": datasets.Sequence(
{
"name": datasets.Value("string"),
"text": datasets.Value("string"),
}
),
}
),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data = dl_manager.download_and_extract(self.config.data_dir)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data": data,
},
),
]
@staticmethod
def extract_shapes_from_tracks(
root: ET.Element, file: str, index: int
) -> ET.Element:
img = ET.Element("image")
img.set("name", file)
img.set("id", str(index))
for track in root.iter("track"):
shape = track.find(f".//*[@frame='{index}']")
if not (shape is None):
shape.set("label", track.get("label"))
shape.set("track_id", track.get("id"))
img.append(shape)
return img
@staticmethod
def parse_shape(shape: ET.Element) -> dict:
label = shape.get("label")
track_id = shape.get("track_id")
shape_type = shape.tag
rotation = shape.get("rotation", 0.0)
occluded = shape.get("occluded", 0)
points = None
if shape_type == "points":
points = tuple(map(float, shape.get("points").split(",")))
elif shape_type == "box":
points = [
(float(shape.get("xtl")), float(shape.get("ytl"))),
(float(shape.get("xbr")), float(shape.get("ybr"))),
]
elif shape_type == "polygon":
points = [
tuple(map(float, point.split(",")))
for point in shape.get("points").split(";")
]
attributes = []
for attr in shape:
attr_name = attr.get("name")
attr_text = attr.text
attributes.append({"name": attr_name, "text": attr_text})
shape_data = {
"label": label,
"track_id": track_id,
"type": shape_type,
"points": points,
"rotation": rotation,
"occluded": occluded,
"attributes": attributes,
}
return shape_data
def _generate_examples(self, data):
tree = ET.parse(f"{data}/annotations.xml")
root = tree.getroot()
for idx, file in enumerate(sorted(os.listdir(f"{data}/images"))):
img = self.extract_shapes_from_tracks(root, file, idx)
image_id = img.get("id")
name = img.get("name")
shapes = [self.parse_shape(shape) for shape in img]
print(shapes)
yield idx, {
"id": image_id,
"name": name,
"image": f"{data}/images/{file}",
"mask": f"{data}/boxes/{file}",
"shapes": shapes,
}
|