gistfile1.txt

import rp
# https://gist.github.com/SqrtRyan/c33e4e40ccf74714a20c229a13c717fe
from PIL import Image
import rp
import cv2
import numpy as np
import torch
import torchvision.transforms as T


def approximate_matrix_by_rank(matrix, rank):
    numpy_mode = rp.is_numpy_array(matrix)
    rows, cols = matrix.shape
    if rank > min(rows, cols):
        return matrix
    matrix_torch = (
        matrix
        if rp.is_torch_tensor(matrix)
        else torch.tensor(matrix, dtype=torch.float32).to("mps")
    )
    U, S, V = torch.linalg.svd(matrix_torch)
    S = torch.diag(S[:rank])
    U = U[:, :rank]
    V = V[:rank, :]
    approximated_matrix_torch = U @ S @ V
    approximated_matrix = (
        approximated_matrix_torch.cpu().numpy()
        if numpy_mode
        else approximated_matrix_torch
    )
    return approximated_matrix


def apply_frequency_space_func(matrix, func, *args, **kwargs):
    matrix_torch = torch.tensor(matrix, dtype=torch.float32).to("mps")
    fft_matrix = torch.fft.fft2(matrix_torch)
    fft_matrix = func(fft_matrix, *args, **kwargs)
    output = torch.fft.irfft2(fft_matrix, s=matrix_torch.shape[-2:])
    rp.cv_imshow(rp.full_range(fft_matrix.imag))
    if rp.is_numpy_array(matrix):
        output = output.cpu().numpy()
    return output


def approximate_matrix_by_rank_fourier(matrix, rank):
    # return apply_frequency_space_func(matrix, lambda x:x**1.6/1000)#, rank=1)
    return apply_frequency_space_func(matrix, approximate_matrix_by_rank, rank=rank)


def get_image():
    out = rp.load_image_from_webcam()
    # out=load_image('https://upload.wikimedia.org/wikipedia/en/7/7d/Lenna_%28test_image%29.png',use_cache=True)

    SIZE = 512
    #SIZE = 256
    #SIZE = 128
    out = rp.resize_image_to_fit(out, height=SIZE, width=SIZE)
    # out = resize_image_to_fit(out, height=100, width=100)
    out = rp.as_rgb_image(out)
    #out = as_grayscale_image(out)
    out = rp.as_float_image(out)
    # return create_diagonal_sinusoid(SIZE,frequency=10,theta=gtoc())
    return out


def create_diagonal_sinusoid(size=128, frequency=5, theta=0):
    x = np.arange(size)
    y = np.arange(size)
    X, Y = np.meshgrid(x, y)
    sinusoid = np.sin(
        2 * np.pi * frequency * (np.cos(theta) * X + np.sin(theta) * Y) / size
    )
    return sinusoid.astype(np.float32)


ERRORS = []
ERROR_LEN = 100

image = get_image()
image *= 0
I = 0
while True:
    cam_image = get_image()

    RANK = 5
    # img_fapprox=apply_image_function_per_channel(approximate_matrix_by_rank_fourier, cam_image, RANK)
    img_approx = rp.apply_image_function_per_channel(
        approximate_matrix_by_rank, cam_image, RANK
    )
    # print(((img_fapprox-img_approx)**2).max())

    # display_image(
    # vertically_concatenated_images(
    # cam_image,
    # img_approx,
    # img_fapprox,
    # )
    # )
    # continue

    # image *= 0
    # cam_image=cam_image>0
    # cam_image=cam_image/3
    # cam_image=cam_image+.5-cam_image.max()/2

    delta = cam_image - image
    # delta *= 0.1
    delta *= 0.7
    delta = rp.apply_image_function_per_channel(approximate_matrix_by_rank, delta, RANK)
    image += delta
    error = np.abs(delta).sum()
    print(I, error)
    I += 1

    dimage = rp.tiled_images(
        rp.labeled_images(
            [
                img_approx,
                image,
                delta / 2 + 0.5,
                rp.full_range(delta),
            ],
            ["Rank %i" % RANK, "Approx", "Delta", "Full-Range Delta"],font='Futura'
        ),
        border_thickness=0,
    )
    dimageW = rp.get_image_width(dimage)
    ERRORS.append(error)
    # print(ERRORS)
    # if len(ERRORS) > 2:
    ERRORS = ERRORS[-ERROR_LEN:]
    dimage = rp.vertically_concatenated_images(
        dimage,
        rp.labeled_image(
            rp.cv_line_graph(
                ERRORS,
                width=dimageW,
                height=rp.get_image_height(delta),
                # vertical_bar_x=max_valued_index(ERRORS),
            ),
            "Errors",font='https://github.com/Eyeline-Research/Go-with-the-Flow/raw/refs/heads/website/fonts/Futura.ttc'
        ),
    )
    rp.display_image(dimage)