Rebanada de segmentación de imágenes y coincidencia de transporte óptima

Segmentación de imágenes

Calcular la matriz de distancias según el color

código:

import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'

import cv2
import matplotlib.pyplot as plt
import numpy as np
# import ot
# import ot.plot
from skimage.segmentation import slic, mark_boundaries  # super pixels
from skimage import color


def superpixel_segmentation(image, num_segments):
    """
    Segment an image into an approximative number of superpixels.
    inputs:
        -image: a numpy array to be segmented
        -num_segments: the number of of desired segments in the segmentation
    returns:
        - the number of segments after superpixel segmentation
        - the content of each cluster (center coordinates, mean of color)
    """
    segments = slic(image, n_segments=num_segments, sigma=5)  # segmentation using the k-means clustering, 每个像素赋值为1,2,3,4,5...
    clusters = np.zeros((len(np.unique(segments)), 5))        # 每个seg的 质心 和 平均颜色
    superpixels = color.label2rgb(segments, image, kind='avg')#

    for c in np.unique(segments):
        indexes = np.where(segments == c)
        mean_color = superpixels[indexes[0][0], indexes[1][
            0]]  # just access the first element of superpixel since all elements of superpixel are average color of cluster
        mean_indexes = np.array([int(np.round(np.mean(indexes[0]))), int(np.round(np.mean(indexes[1])))])
        print(c, mean_indexes, mean_color)
        clusters[c-1] = np.concatenate((mean_indexes, mean_color), axis=None)

    return segments, clusters, superpixels


def cluster_weights(segments):
    """
    Compute the weights of each cluster.
    inputs:
        - segments: a 2D array which is an integer mask indicating segment labels
    returns:
        - a list of the weights associated to each cluster given the following formula N_pixel_cluster/N_pixel_image
    """

    size_image = segments.shape[0] * segments.shape[1]
    weights = np.zeros((len(np.unique(segments)),))

    for c in np.unique(segments):
        indexes = np.where(segments == c)
        n_pixels = len(indexes[0])
        weights[c] = n_pixels / size_image

    return weights

def seg_im(file):
    image = cv2.imread(file, 1)[..., ::-1]
    num_segments = 120
    segments, clusters, superpixels = superpixel_segmentation(image, num_segments)
    plt.figure()
    plt.subplot(221)
    plt.imshow(segments)
    plt.subplot(222)
    plt.imshow(np.clip(superpixels,0,255).astype(np.uint8))
    plt.subplot(223)
    plt.imshow(mark_boundaries(image,segments))
    plt.show()

    return segments, clusters, superpixels
if __name__ == "__main__":
    # import os
    #
    # os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

    file = r'D:\code_color\Colorization-optimal-transport-main\examples\fleur-2.jpg'
    image = cv2.imread(file, 1)[..., ::-1]
    num_segments = 120
    segments, clusters, superpixels = superpixel_segmentation(image, num_segments)
    weights1 = cluster_weights(segments)
    plt.figure()
    plt.subplot(221)
    plt.imshow(segments)
    plt.subplot(222)
    plt.imshow(np.clip(superpixels, 0, 255).astype(np.uint8))
    plt.subplot(223)
    plt.imshow(mark_boundaries(image, segments))
    plt.show()

    file2 = r'D:\code_color\Colorization-optimal-transport-main\examples\fleur-4.jpg'
    image2 = cv2.imread(file2, 1)[..., ::-1]
    segments2, clusters2, superpixels2 = superpixel_segmentation(image2, num_segments)
    weights2 = cluster_weights(segments2)
    plt.figure()
    plt.subplot(221)
    plt.imshow(segments2)
    plt.subplot(222)
    plt.imshow(np.clip(superpixels2, 0, 255).astype(np.uint8))
    plt.subplot(223)
    plt.imshow(mark_boundaries(image2, segments2))
    plt.show()


    plt.figure()
    plt.plot(weights1, 'r')
    plt.plot(weights2, 'b')
    plt.show()

    X = clusters
    Y = clusters2
    X_norm = X.copy()
    X_norm[:, 0] = X[:, 0] / 500
    X_norm[:, 1] = X[:, 1] / 500
    X_norm[:, 2:] = X[:, 2:] / 255
    Y_norm = Y.copy()
    Y_norm[:, 0] = Y[:, 0] / 500
    Y_norm[:, 1] = Y[:, 1] / 500
    Y_norm[:, 2:] = Y[:, 2:] / 255

    # import matplotlib.pylab as pl
    # M = ot.dist(X_norm.reshape((-1, 5)), Y_norm.reshape((-1, 5)))
    # pl.figure(2, figsize=(5, 5))
    # ot.plot.plot1D_mat(X_norm, Y_norm, M, 'Cost matrix M')
    # pl.show()
    #
    #
    # # Equivalent to
    # G0 = ot.emd(weights1, weights2, M)
    #
    # pl.figure(3, figsize=(5, 5))
    # ot.plot.plot1D_mat(X_norm, Y_norm, G0, 'OT matrix G0')
    # pl.show()