Files
visual_critical_discourse_a…/core/database/classes.py
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83 lines
2.4 KiB
Python

from __future__ import annotations
import logging
from base64 import b64decode
from base64 import b64encode
from io import BytesIO
from pathlib import Path
from PIL import Image
from pydantic import BaseModel
from pydantic import field_serializer
from pydantic import field_validator
from core.dto import ModelData
class NoDocumentFoundException(Exception):
pass
class VisualCommunication(BaseModel):
name: str
image: Image.Image
annotation: ModelData | None = None
prediction: ModelData | None = None
class Config:
arbitrary_types_allowed = True
@classmethod
def classname(cls) -> str:
"""Return classname."""
return cls.__name__
@classmethod
def from_file(cls, path: Path) -> VisualCommunication:
"""Instantiate from file."""
name = path.stem
image = Image.open(path)
image.load()
return VisualCommunication(name=name, image=image)
@classmethod
def decode_image(cls, content: str) -> Image.Image:
"""Decode image."""
_, content_data = content.split(',')
return Image.open(BytesIO(b64decode(content_data)))
@field_serializer('image')
def serialize_image(image: Image.Image) -> bytes: # type: ignore
buffer = BytesIO()
image.save(buffer, format='JPEG')
return buffer.getvalue()
@field_validator('image', mode='before')
@classmethod
def convert_to_image(
cls,
image: Image.Image | BytesIO | bytes,
) -> Image.Image:
if isinstance(image, bytes):
image = BytesIO(image)
if isinstance(image, BytesIO):
image = Image.open(image)
return image
def __repr__(self) -> str:
return f"{self.classname()}(name='{self.name}')"
def webencoded_image(self) -> str:
"""Convert image to be displayed on webpage."""
# convert images to bytes string
buffer = BytesIO()
self.image.save(buffer, format='png')
img_enc = b64encode(buffer.getvalue()).decode('utf-8')
return f"data:image/png;base64, {img_enc}"
def generate_random_prediction(self, force: bool = False) -> None:
"""Generate random prediction values."""
if not force and self.prediction is not None:
logging.warning('set force=True to overwrite existing values.')
self.prediction = ModelData.from_random()