/
image.py
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/
image.py
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from __future__ import annotations
import io
import typing as t
from typing import TYPE_CHECKING
from urllib.parse import quote
from starlette.requests import Request
from multipart.multipart import parse_options_header
from starlette.responses import Response
from .base import IODescriptor
from ..types import LazyType
from ..utils import LazyLoader
from ..utils.http import set_cookies
from ...exceptions import BadInput
from ...exceptions import InvalidArgument
from ..service.openapi import SUCCESS_DESCRIPTION
from ..service.openapi.specification import Schema
from ..service.openapi.specification import Response as OpenAPIResponse
from ..service.openapi.specification import MediaType
from ..service.openapi.specification import RequestBody
if TYPE_CHECKING:
from types import UnionType
import PIL
import PIL.Image
from bentoml.grpc.v1alpha1 import service_pb2 as pb
from .. import external_typing as ext
from ..context import InferenceApiContext as Context
_Mode = t.Literal[
"1", "CMYK", "F", "HSV", "I", "L", "LAB", "P", "RGB", "RGBA", "RGBX", "YCbCr"
]
else:
from bentoml.grpc.utils import import_generated_stubs
# NOTE: pillow-simd only benefits users who want to do preprocessing
# TODO: add options for users to choose between simd and native mode
_exc = "'Pillow' is required to use the Image IO descriptor. Install it with: 'pip install -U Pillow'."
PIL = LazyLoader("PIL", globals(), "PIL", exc_msg=_exc)
PIL.Image = LazyLoader("PIL.Image", globals(), "PIL.Image", exc_msg=_exc)
pb, _ = import_generated_stubs()
# NOTES: we will keep type in quotation to avoid backward compatibility
# with numpy < 1.20, since we will use the latest stubs from the main branch of numpy.
# that enable a new way to type hint an ndarray.
ImageType = t.Union["PIL.Image.Image", "ext.NpNDArray"]
DEFAULT_PIL_MODE = "RGB"
class Image(IODescriptor[ImageType]):
"""
:obj:`Image` defines API specification for the inputs/outputs of a Service, where either
inputs will be converted to or outputs will be converted from images as specified
in your API function signature.
A sample object detection service:
.. code-block:: python
:caption: `service.py`
from __future__ import annotations
from typing import TYPE_CHECKING
from typing import Any
import bentoml
from bentoml.io import Image
from bentoml.io import NumpyNdarray
if TYPE_CHECKING:
from PIL.Image import Image
from numpy.typing import NDArray
runner = bentoml.tensorflow.get('image-classification:latest').to_runner()
svc = bentoml.Service("vit-object-detection", runners=[runner])
@svc.api(input=Image(), output=NumpyNdarray(dtype="float32"))
async def predict_image(f: Image) -> NDArray[Any]:
assert isinstance(f, Image)
arr = np.array(f) / 255.0
assert arr.shape == (28, 28)
# We are using greyscale image and our PyTorch model expect one
# extra channel dimension
arr = np.expand_dims(arr, (0, 3)).astype("float32") # reshape to [1, 28, 28, 1]
return await runner.async_run(arr)
Users then can then serve this service with :code:`bentoml serve`:
.. code-block:: bash
% bentoml serve ./service.py:svc --reload
Users can then send requests to the newly started services with any client:
.. tab-set::
.. tab-item:: Bash
.. code-block:: bash
# we will run on our input image test.png
# image can get from http://images.cocodataset.org/val2017/000000039769.jpg
% curl -H "Content-Type: multipart/form-data" \\
-F 'fileobj=@test.jpg;type=image/jpeg' \\
http://0.0.0.0:3000/predict_image
# [{"score":0.8610631227493286,"label":"Egyptian cat"},
# {"score":0.08770329505205154,"label":"tabby, tabby cat"},
# {"score":0.03540956228971481,"label":"tiger cat"},
# {"score":0.004140055272728205,"label":"lynx, catamount"},
# {"score":0.0009498853469267488,"label":"Siamese cat, Siamese"}]%
.. tab-item:: Python
.. code-block:: python
:caption: `request.py`
import requests
requests.post(
"http://0.0.0.0:3000/predict_image",
files = {"upload_file": open('test.jpg', 'rb')},
headers = {"content-type": "multipart/form-data"}
).text
Args:
pilmode: Color mode for PIL. Default to ``RGB``.
mime_type: Return MIME type of the :code:`starlette.response.Response`, only available when used as output descriptor.
Returns:
:obj:`Image`: IO Descriptor that either a :code:`PIL.Image.Image` or a :code:`np.ndarray` representing an image.
"""
MIME_EXT_MAPPING: dict[str, str] = {}
_proto_fields = ("file",)
def __init__(
self,
pilmode: _Mode | None = DEFAULT_PIL_MODE,
mime_type: str = "image/jpeg",
):
PIL.Image.init()
self.MIME_EXT_MAPPING.update({v: k for k, v in PIL.Image.MIME.items()})
if mime_type.lower() not in self.MIME_EXT_MAPPING: # pragma: no cover
raise InvalidArgument(
f"Invalid Image mime_type '{mime_type}'. Supported mime types are {', '.join(PIL.Image.MIME.values())}."
) from None
if pilmode is not None and pilmode not in PIL.Image.MODES: # pragma: no cover
raise InvalidArgument(
f"Invalid Image pilmode '{pilmode}'. Supported PIL modes are {', '.join(PIL.Image.MODES)}."
) from None
self._mime_type = mime_type.lower()
self._pilmode: _Mode | None = pilmode
self._format = self.MIME_EXT_MAPPING[mime_type]
def input_type(self) -> UnionType:
return ImageType
def openapi_schema(self) -> Schema:
return Schema(type="string", format="binary")
def openapi_components(self) -> dict[str, t.Any] | None:
pass
def openapi_request_body(self) -> RequestBody:
return RequestBody(
content={self._mime_type: MediaType(schema=self.openapi_schema())},
required=True,
)
def openapi_responses(self) -> OpenAPIResponse:
return OpenAPIResponse(
description=SUCCESS_DESCRIPTION,
content={self._mime_type: MediaType(schema=self.openapi_schema())},
)
async def from_http_request(self, request: Request) -> ImageType:
content_type, _ = parse_options_header(request.headers["content-type"])
mime_type = content_type.decode().lower()
if mime_type == "multipart/form-data":
form = await request.form()
bytes_ = await next(iter(form.values())).read()
elif mime_type.startswith("image/") or mime_type == self._mime_type:
bytes_ = await request.body()
else:
raise BadInput(
f"{self.__class__.__name__} should get 'multipart/form-data', '{self._mime_type}' or 'image/*', got '{content_type}' instead."
)
try:
return PIL.Image.open(io.BytesIO(bytes_))
except PIL.UnidentifiedImageError as e:
raise BadInput(f"Failed reading image file uploaded: {e}") from None
async def to_http_response(
self, obj: ImageType, ctx: Context | None = None
) -> Response:
if LazyType["ext.NpNDArray"]("numpy.ndarray").isinstance(obj):
image = PIL.Image.fromarray(obj, mode=self._pilmode)
elif LazyType[PIL.Image.Image]("PIL.Image.Image").isinstance(obj):
image = obj
else:
raise BadInput(
f"Unsupported Image type received: '{type(obj)}', the Image IO descriptor only supports 'np.ndarray' and 'PIL.Image'."
) from None
filename = f"output.{self._format.lower()}"
ret = io.BytesIO()
image.save(ret, format=self._format)
# rfc2183
content_disposition_filename = quote(filename)
if content_disposition_filename != filename:
content_disposition = "attachment; filename*=utf-8''{}".format(
content_disposition_filename
)
else:
content_disposition = f'attachment; filename="{filename}"'
if ctx is not None:
if "content-disposition" not in ctx.response.headers:
ctx.response.headers["content-disposition"] = content_disposition
res = Response(
ret.getvalue(),
media_type=self._mime_type,
headers=ctx.response.headers, # type: ignore (bad starlette types)
status_code=ctx.response.status_code,
)
set_cookies(res, ctx.response.cookies)
return res
else:
return Response(
ret.getvalue(),
media_type=self._mime_type,
headers={"content-disposition": content_disposition},
)
async def from_proto(self, field: pb.File | bytes) -> ImageType:
from bentoml.grpc.utils import filetype_pb_to_mimetype_map
mapping = filetype_pb_to_mimetype_map()
# check if the request message has the correct field
if isinstance(field, bytes):
content = field
else:
assert isinstance(field, pb.File)
if field.kind:
try:
mime_type = mapping[field.kind]
if mime_type != self._mime_type:
raise BadInput(
f"Inferred mime_type from 'kind' is '{mime_type}', while '{repr(self)}' is expecting '{self._mime_type}'",
)
except KeyError:
raise BadInput(
f"{field.kind} is not a valid File kind. Accepted file kind: {[names for names,_ in pb.File.FileType.items()]}",
) from None
content = field.content
if not content:
raise BadInput("Content is empty!") from None
return PIL.Image.open(io.BytesIO(content))
async def to_proto(self, obj: ImageType) -> pb.File:
from bentoml.grpc.utils import mimetype_to_filetype_pb_map
if LazyType["ext.NpNDArray"]("numpy.ndarray").isinstance(obj):
image = PIL.Image.fromarray(obj, mode=self._pilmode)
elif LazyType["PIL.Image.Image"]("PIL.Image.Image").isinstance(obj):
image = obj
else:
raise BadInput(
f"Unsupported Image type received: '{type(obj)}', the Image IO descriptor only supports 'np.ndarray' and 'PIL.Image'.",
) from None
ret = io.BytesIO()
image.save(ret, format=self._format)
try:
kind = mimetype_to_filetype_pb_map()[self._mime_type]
except KeyError:
raise BadInput(
f"{self._mime_type} doesn't have a corresponding File 'kind'",
) from None
return pb.File(kind=kind, content=ret.getvalue())