cv-analysis-service/cv_analysis/server/format.py

from _operator import itemgetter
from functools import partial

import numpy as np

from cv_analysis.utils.structures import Rectangle


def make_formatter(dpi, page_size, rotation):
    rotation = rotation // 90 if rotation not in [0, 1, 2, 3] else rotation

    def format_(key2pixel):
        convert = partial(convert_pixel_to_inch, dpi=dpi)
        x, y, w, h = map(convert, itemgetter("x", "y", "width", "height")(key2pixel))
        x1, y1 = x + w, y + h
        matrix = np.vstack([[x, y], [x1, y1]]).T
        new_matrix = rotate_and_shift(matrix, rotation, page_size)
        x1, x2 = sorted(new_matrix[0, :])
        y1, y2 = sorted(new_matrix[1, :])
        return Rectangle.from_xyxy((x1, y1, x2, y2), discrete=False).json_xywh()

    return format_


def convert_pixel_to_inch(pixel, dpi):
    return pixel / dpi * 72


def rotate(input_matrix, radians):
    rotation_matrix = np.vstack([[np.cos(radians), -np.sin(radians)], [np.sin(radians), np.cos(radians)]])

    return np.dot(rotation_matrix, input_matrix)


def rotate_and_shift(matrix, rotation, size, debug=False):
    """Rotates a matrix against (!) a specified rotation. That is, the rotation is applied negatively. The matrix is
    also shifted to ensure it contains points (columns) in quadrant I.

    Procedure:
        1) Rotate the matrix clockwise according to rotation value
        2) Shift the matrix back into quadrant I
        3) Set x_i and y_i to new lower left and upper right corners, since the corner vectors are no longer at these
            corners due to the rotation

    Args:
        matrix: matrix to transform
        rotation: any of  0, 1, 2, or 3, where 1 = 90 degree CLOCKWISE rotation etc.
        size: the size of the page as a tuple (<width>, <height>)
        debug: Visualizes the transformations for later re-understanding of the code
    """

    def shift_to_quadrant_1(matrix):

        # TODO: generalize
        if rotation == 0:
            back_shift = np.zeros_like(np.eye(2))
        elif rotation == 1:
            back_shift = np.array([[0, 0], [1, 1]]) * size[1]
        elif rotation == 2:
            back_shift = np.array([[1, 1], [1, 1]]) * size
        elif rotation == 3:
            back_shift = np.array([[1, 1], [0, 0]]) * size[0]
        else:
            raise ValueError(f"Unexpected rotation value '{rotation}'. Expected any of 0, 1, 2, or 3.")

        matrix_shifted = matrix + back_shift
        return matrix_shifted

    # PDF rotations are clockwise, hence subtract the radian value of the rotation from 2 pi
    radians = (2 * np.pi) - (np.pi * (rotation / 2))
    matrix_rotated = rotate(matrix, radians)
    matrix_rotated_and_shifted = shift_to_quadrant_1(matrix_rotated)

    if debug:
        __show_matrices(size, radians, matrix, matrix_rotated, matrix_rotated_and_shifted)
    return matrix_rotated_and_shifted


def __show_matrices(size, radians, matrix, matrix_rotated, matrix_rotated_and_shifted):

    import matplotlib.pyplot as plt
    from copy import deepcopy

    m1 = matrix
    m2 = matrix_rotated
    m3 = matrix_rotated_and_shifted

    m1, m2, m3 = map(deepcopy, (m1, m2, m3))

    frame = np.eye(2) * size
    frame_rotated = rotate(frame, radians)

    f1 = frame
    f2 = frame_rotated

    f1 *= 0.005 * 1
    f2 *= 0.005 * 1
    m1 *= 0.005 * 1
    m2 *= 0.005 * 1
    m3 *= 0.005 * 1

    fig, axes = plt.subplots(1, 2, figsize=(8, 4))
    axes = axes.ravel()

    axes[0].quiver([0, 0], [0, 0], f1[0, :], f1[1, :], scale=5, scale_units="inches", color="red")
    axes[1].quiver([0, 0], [0, 0], f2[0, :], f2[1, :], scale=5, scale_units="inches", color="red")
    axes[0].quiver([0, 0], [0, 0], m1[0, :], m1[1, :], scale=5, scale_units="inches")
    axes[1].quiver([0, 0], [0, 0], m2[0, :], m2[1, :], scale=5, scale_units="inches", color="green")
    axes[1].quiver([0, 0], [0, 0], m3[0, :], m3[1, :], scale=5, scale_units="inches", color="blue")

    plt.show()