Spraying Digital Graffiti

graffiti.py

import numpy as np
from skimage.io import imread, imsave
from skimage.filters.edges import sobel
from skimage.color import rgb2gray
from skimage.transform import rescale
from scipy import ndimage
from skimage import measure
from skimage import graph


def main():
    DOWNSCALE_FACTOR = .3
    im1 = rescale(imread("/path/to/wall.jpg"), DOWNSCALE_FACTOR, channel_axis=-1)
    im2 = rescale(imread("/path/to/painted_wall.jpg"), DOWNSCALE_FACTOR, channel_axis=-1)
    mask = rescale(imread("/path/to/binary_mask.png")[..., 0] < 100, DOWNSCALE_FACTOR, order=0)
  
    edgemap = sobel(rgb2gray(im1))
    contours = measure.find_contours(mask)
    contours = [
        fix_contour(contour.round().astype(int), edgemap)
        for contour in contours
    ]
  
    final_mask = np.zeros(edgemap.shape, int)
    for contour in contours:
        final_mask = np.maximum(fill_contour(edgemap.shape, contour), final_mask)
        
    final_mask = final_mask.reshape((*final_mask.shape, 1))
    res = final_mask * im2 + (1 - final_mask) * im1
    imsave("/path/to/result.png", res)

    
def fix_contour(contour, edgemap, weight_coef=1):
    edge_weight = 50
    if np.std(contour, axis=0).mean() < 16:
        edge_weight = 42

    temp = np.zeros(edgemap.shape, bool)
    temp[contour[:, 0], contour[:, 1]] = 1
    dist = ndimage.distance_transform_edt(~temp)
    sample_indices = sample_contour(contour, edgemap)

    cost = dist + edgemap * edge_weight * weight_coef

    total_path = []

    for i in range(sample_indices.shape[0] - 1):
        p, _ = graph.route_through_array(cost, contour[sample_indices[i]], contour[sample_indices[i + 1]], geometric=False)
        p = np.array(p)
        total_path.append(p)

    p, _ = graph.route_through_array(cost, contour[sample_indices[-1]], contour[sample_indices[0]], geometric=False)
    p = np.array(p)
    total_path.append(p)

    p = np.concatenate(total_path)

    return p[:, 0], p[:, 1]
  

def fill_contour(shape, contour):
    res = np.zeros(shape, dtype=bool)
    res[contour] = 1
    res = ndimage.binary_fill_holes(res)

    return res
    
  
def sample_contour(contour, edgemap, N=100):
    max_deviation = 7 if np.std(contour, axis=0).mean() < 16 else 20

    indices = (np.arange(N) * contour.shape[0] / N).round().astype(int)
    for i in range(indices.shape[0]):
        idx_alternatives = np.clip(indices[i] + np.arange(-max_deviation, max_deviation + 1), 0, contour.shape[0] - 1)
        indices[i] = idx_alternatives[edgemap[contour[idx_alternatives][:, 0], contour[idx_alternatives][:, 1]].argmin()]

    return indices


if __name__ == "__main__":
    main()

requirements.txt

numpy
scipy
scikit-image