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| import numpy as np | |
| import matplotlib.pylab as plt | |
| import ot | |
| import ot.plot | |
| from PIL import Image | |
| from sklearn.cluster import KMeans | |
| import gradio as gr | |
| def kclus(data,n_clusters,resize1, resize2): | |
| # img = np.resize(data,(resize2, resize1,3)) | |
| # data = np.array(img,dtype="float64").reshape(-1, 3) | |
| data = Image.fromarray(data) | |
| # print(np.array(data.resize((resize2, resize1)), dtype="float64")) | |
| data = np.array(data.resize((resize2, resize1)), dtype="float64").reshape(-1, 3) | |
| kmeans = KMeans(n_clusters, random_state=0) | |
| cluster_pred = kmeans.fit_predict(data) | |
| # print(cluster_pred.shape) | |
| ans = [0 for i in range(n_clusters)] | |
| for i in range(n_clusters): | |
| x = data[cluster_pred==i] | |
| color_ave = np.sum(x, axis=0)/len(x) | |
| ans[i] = color_ave | |
| # print(ans.shape) | |
| return ans,cluster_pred | |
| def saiteki(source,target,gaso,cluster_pred,resize1,resize2): | |
| xs = np.array(source, dtype="float64") | |
| xt = np.array(target, dtype="float64") | |
| # print(xt.shape) | |
| a, b = np.ones((gaso,)) / gaso, np.ones((gaso,)) / gaso | |
| # print(a.shape,b.shape) | |
| M = ot.dist(xs, xt) | |
| M /= M.max() | |
| G0 = ot.emd(a, b, M) | |
| P = G0 * (gaso) | |
| # print(P.shape) | |
| kansei = np.einsum('id,ji -> jd', xt, P) | |
| # cluster_pred = cluster_pred.reshape(100,100) | |
| kansei = kansei.reshape(gaso,3) | |
| # print(kansei.shape) | |
| new_data = [0 for i in range(len(cluster_pred))] | |
| for i in range(len(cluster_pred)): | |
| new_data[i] = kansei[cluster_pred[i]] | |
| # print(new_data.shape) | |
| new_data = np.array(new_data, dtype="uint8").reshape(resize1,resize2,3) | |
| # plt.figure(1) | |
| # plt.imshow(new_data) | |
| # plt.show() | |
| return new_data | |
| # resize1 = 200 | |
| # resize2 = 350 | |
| # | |
| # im1 = Image.open('data/gohho.jpeg') | |
| # im2 = Image.open('data/yuya_sea.jpeg') | |
| # source = np.array(im1.resize((resize2,resize1)),dtype="float64").reshape(-1,3) | |
| # target = np.array(im2.resize((resize2,resize1)),dtype="float64").reshape(-1,3) | |
| # source = np.array(source, dtype="uint8").reshape(resize1,resize2,3) | |
| # plt.figure(1) | |
| # plt.imshow(sourc) | |
| # plt.show() | |
| # gaso = 200 | |
| # so,cluster_pred1 = kclus(source,gaso) | |
| # re,cluster_pred2 = kclus(target,gaso) | |
| # saiteki(so, re,gaso,cluster_pred1,resize1,resize2) | |
| def sepia(source,target): | |
| resize1 = 200 | |
| resize2 = 300 | |
| gaso = 150 | |
| # a = np.array(source.resize((resize2, resize1)), dtype="float64").reshape(-1, 3) | |
| # b = np.array(target.resize((resize2, resize1)), dtype="float64").reshape(-1, 3) | |
| so, cluster_pred1 = kclus(source, gaso,resize1, resize2) | |
| re, cluster_pred2 = kclus(target, gaso,resize1, resize2) | |
| sepia_img = saiteki(so, re, gaso, cluster_pred1, resize1, resize2) | |
| # plt.figure(1) | |
| # plt.imshow(sepia_img) | |
| # plt.show() | |
| return sepia_img | |
| demo = gr.Interface(fn = sepia, inputs = ["image","image"], outputs = ["image"]) | |
| demo.launch() |