image-classification-service/image_prediction/stitching/utils.py

import json
from itertools import chain

from image_prediction.exceptions import InvalidBox
from image_prediction.formatter.formatters.enum import EnumFormatter
from image_prediction.info import Info


def flatten_groups_once(groups):
    return chain.from_iterable(groups)


def make_coord_getter(c):
    return {
        "x1": make_getter(Info.X1),
        "x2": make_getter(Info.X2),
        "y1": make_getter(Info.Y1),
        "y2": make_getter(Info.Y2),
    }[c]


def make_getter(key):
    def getter(pair):
        return pair.metadata[key]

    return getter


def make_length_getter(dim):
    return {
        "width": make_getter(Info.WIDTH),
        "height": make_getter(Info.HEIGHT),
    }[dim]


def validate_box(box):
    validate_box_coords(box)
    validate_box_size(box)
    return box


def validate_box_coords(box):

    x_diff = box[Info.WIDTH] - (box[Info.X2] - box[Info.X1])
    y_diff = box[Info.HEIGHT] - (box[Info.Y2] - box[Info.Y1])

    if x_diff:
        raise InvalidBox(f"Width and x-coordinates differ by {x_diff} units: {format_box(box)}")
    if y_diff:
        raise InvalidBox(f"Width and y-coordinates differ by {y_diff} units: {format_box(box)}")

    return box


def validate_box_size(box):

    if not box[Info.WIDTH]:
        raise InvalidBox(f"Zero width box: {format_box(box)}")

    if not box[Info.HEIGHT]:
        raise InvalidBox(f"Zero height box: {format_box(box)}")

    return box


def format_box(box):
    return json.dumps(EnumFormatter()(box), indent=2)