def test_image_copy(spark: SparkSession, tmpdir):
source_image = os.path.join(tmpdir, "source_image")
with open(source_image, "w") as fobj:
fobj.write("abc")
os.makedirs(os.path.join(tmpdir, "out"))
df = spark.createDataFrame([(Image(source_image), )],
["image"]) # type: pyspark.sql.DataFrame
df = df.withColumn(
"image",
image_copy(col("image"), lit(os.path.join(tmpdir, "out/"))),
)
data = df.collect() # force lazy calculation
out_file = os.path.join(tmpdir, "out", "source_image")
assert Image(out_file) == data[0].image
with open(os.path.join(out_file)) as fobj:
assert fobj.read() == "abc"
def test_image_copy(self):
source_image = os.path.join(self.test_dir, "source_image")
with open(source_image, "w") as fobj:
fobj.write("abc")
os.makedirs(os.path.join(self.test_dir, "out"))
df = self.spark.createDataFrame(
[(Image(source_image), )],
["image"]) # type: pyspark.sql.DataFrame
df = df.withColumn(
"image",
image_copy(col("image"), lit(os.path.join(self.test_dir, "out/"))),
)
data = df.collect() # force lazy calculation
out_file = os.path.join(self.test_dir, "out", "source_image")
self.assertEqual(Image(out_file), data[0].image)
with open(os.path.join(out_file)) as fobj:
self.assertEqual("abc", fobj.read())
def convert(
spark: SparkSession,
dataset_root: str,
limit: int = 0,
asset_dir: Optional[str] = None,
) -> DataFrame:
"""Convert a Coco Dataset into Rikai dataset.
This function expects the COCO datasets are stored in directory with the
following structure:
- dataset
- annotations
- captions_train2017.json
- instances_train2017.json
- ...
- train2017
- val2017
- test2017
Parameters
----------
spark : SparkSession
A live spark session
dataset_root : str
The directory of dataset
limit : int, optional
The number of images of each split to be converted.
asset_dir : str, optional
The asset directory to store images, can be a s3 directory.
Return
------
DataFrame
Returns a Spark DataFrame
"""
train_json = os.path.join(dataset_root, "annotations",
"instances_train2017.json")
val_json = os.path.join(dataset_root, "annotations",
"instances_val2017.json")
categories = load_categories(train_json)
examples = []
for split, anno_file in zip(["train", "val"], [train_json, val_json]):
coco = COCO(annotation_file=anno_file)
# Coco has native dependencies, so we do not distributed them
# to the workers.
image_ids = coco.imgs
if limit > 0:
image_ids = islice(image_ids, limit)
for image_id in image_ids:
ann_id = coco.getAnnIds(imgIds=image_id)
annotations = coco.loadAnns(ann_id)
annos = []
for ann in annotations:
bbox = Box2d(*ann["bbox"])
annos.append({
"category_id":
ann["category_id"],
"category_text":
categories[ann["category_id"]]["name"],
"bbox":
bbox,
"area":
float(ann["area"]),
})
image_payload = coco.loadImgs(ids=image_id)[0]
example = {
"image_id":
image_id,
"annotations":
annos,
"image":
Image(
os.path.abspath(
os.path.join(
os.curdir,
"dataset",
"{}2017".format(split),
image_payload["file_name"],
))),
"split":
split,
}
examples.append(example)
schema = StructType([
StructField("image_id", LongType(), False),
StructField(
"annotations",
ArrayType(
StructType([
StructField("category_id", IntegerType()),
StructField("category_text", StringType()),
StructField("area", FloatType()),
StructField("bbox", Box2dType()),
])),
False,
),
StructField("image", ImageType(), False),
StructField("split", StringType(), False),
])
df = spark.createDataFrame(examples, schema=schema)
if asset_dir:
asset_dir = asset_dir if asset_dir.endswith("/") else asset_dir + "/"
print("ASSET DIR: ", asset_dir)
df = df.withColumn("image", image_copy(col("image"), lit(asset_dir)))
return df
