from openTSNE import TSNE import numpy as np import matplotlib import matplotlib.pyplot as plt import random def visualize( x, y, ax=None, title=None, draw_legend=True, draw_centers=False, draw_cluster_labels=False, colors=None, legend_kwargs=None, label_order=None, **kwargs ): if ax is None: _, ax = matplotlib.pyplot.subplots(figsize=(10, 8)) if title is not None: ax.set_title(title) plot_params = {"alpha": kwargs.get("alpha", 0.6), "s": kwargs.get("s", 1)} # Create main plot if label_order is not None: assert all(np.isin(np.unique(y), label_order)) classes = [l for l in label_order if l in np.unique(y)] else: classes = np.unique(y) if colors is None: default_colors = matplotlib.rcParams["axes.prop_cycle"] colors = {k: v["color"] for k, v in zip(classes, default_colors())} point_colors = list(map(colors.get, y)) ax.scatter(x[:, 0], x[:, 1], c=point_colors, rasterized=True, **plot_params) # Plot mediods if draw_centers: centers = [] for yi in classes: mask = yi == y centers.append(np.median(x[mask, :2], axis=0)) centers = np.array(centers) center_colors = list(map(colors.get, classes)) ax.scatter( centers[:, 0], centers[:, 1], c=center_colors, s=48, alpha=1, edgecolor="k" ) # Draw mediod labels if draw_cluster_labels: for idx, label in enumerate(classes): ax.text( centers[idx, 0], centers[idx, 1] + 2.2, label, fontsize=kwargs.get("fontsize", 6), horizontalalignment="center", ) # Hide ticks and axis ax.set_xticks([]), ax.set_yticks([]), ax.axis("off") if draw_legend: legend_handles = [ matplotlib.lines.Line2D( [], [], marker="s", color="w", markerfacecolor=colors[yi], ms=10, alpha=1, linewidth=0, label=yi, markeredgecolor="k", ) for yi in classes ] legend_kwargs_ = dict(loc="best", bbox_to_anchor=(0.05, 0.5), frameon=False, ) if legend_kwargs is not None: legend_kwargs_.update(legend_kwargs) ax.legend(handles=legend_handles, **legend_kwargs_) tsne = TSNE( perplexity=30, metric="euclidean", n_jobs=8, random_state=42, verbose=True, ) # idexp_lm3d_pred_lrs3 = np.load("autio2motion_dream_it_possible.npy") # idx = np.random.choice(np.arange(len(idexp_lm3d_pred_lrs3)), 10000) # idexp_lm3d_pred_lrs3 = idexp_lm3d_pred_lrs3[idx] person_ds = np.load("data/binary/videos/May/trainval_dataset.npy", allow_pickle=True).tolist() person_idexp_mean = person_ds['idexp_lm3d_mean'].reshape([1,204]) person_idexp_std = person_ds['idexp_lm3d_std'].reshape([1,204]) person_idexp_lm3d_train = np.stack([s['idexp_lm3d_normalized'].reshape([204,]) for s in person_ds['train_samples']]) person_idexp_lm3d_val = np.stack([s['idexp_lm3d_normalized'].reshape([204,]) for s in person_ds['val_samples']]) person_idexp_lm3d_train = person_idexp_lm3d_train * person_idexp_std + person_idexp_mean person_idexp_lm3d_val = person_idexp_lm3d_val * person_idexp_std + person_idexp_mean # lrs3_stats = np.load('/home/yezhenhui/datasets/binary/lrs3_0702/stats.npy',allow_pickle=True).tolist() # lrs3_idexp_mean = lrs3_stats['idexp_lm3d_mean'].reshape([1,204]) # lrs3_idexp_std = lrs3_stats['idexp_lm3d_std'].reshape([1,204]) # person_idexp_lm3d_train = (person_idexp_lm3d_train - lrs3_idexp_mean) / lrs3_idexp_std # person_idexp_lm3d_val = (person_idexp_lm3d_val - lrs3_idexp_mean) / lrs3_idexp_std # idexp_lm3d_pred_lrs3 = idexp_lm3d_pred_lrs3 * lrs3_idexp_std + lrs3_idexp_mean idexp_lm3d_pred_vae = np.load("autio2motion_dream_it_possible.npy").reshape([-1,204])[:1000] idexp_lm3d_pred_postnet = np.load("postnet_dream_it_possible.npy").reshape([-1,204])[:1000] idexp_lm3d_pred_lle = np.load("lle_dream_it_possible.npy").reshape([-1,204])[:1000] # idexp_lm3d_pred_postnet = idexp_lm3d_pred_postnet * lrs3_idexp_std + lrs3_idexp_mean idexp_lm3d_all = np.concatenate([person_idexp_lm3d_train,idexp_lm3d_pred_vae, idexp_lm3d_pred_postnet,idexp_lm3d_pred_lle]) idexp_lm3d_all_emb = tsne.fit(idexp_lm3d_all) # array(float64) [B,50]==>[B, 2] # z_p_emb = tsne.fit(z_p) # array(float64) [B,50]==>[B, 2] # y1 = ["pred_lrs3" for _ in range(len(idexp_lm3d_pred_lrs3))] y2 = ["person_train" for _ in range(len(person_idexp_lm3d_train))] y3 = ["vae" for _ in range(len(idexp_lm3d_pred_vae))] y4 = ["postnet" for _ in range(len(idexp_lm3d_pred_postnet))] y5 = ["lle" for _ in range(len(idexp_lm3d_pred_lle))] visualize(idexp_lm3d_all_emb, y2+y3+y4+y5) plt.savefig("0.png") idexp_lm3d_pred_vae = np.load("autio2motion_dream_it_possible.npy").reshape([-1,204])[1000:2000] idexp_lm3d_pred_postnet = np.load("postnet_dream_it_possible.npy").reshape([-1,204])[1000:2000] idexp_lm3d_pred_lle = np.load("lle_dream_it_possible.npy").reshape([-1,204])[1000:2000] # idexp_lm3d_pred_postnet = idexp_lm3d_pred_postnet * lrs3_idexp_std + lrs3_idexp_mean idexp_lm3d_all = np.concatenate([person_idexp_lm3d_train,idexp_lm3d_pred_vae, idexp_lm3d_pred_postnet,idexp_lm3d_pred_lle]) idexp_lm3d_all_emb = tsne.fit(idexp_lm3d_all) # array(float64) [B,50]==>[B, 2] # z_p_emb = tsne.fit(z_p) # array(float64) [B,50]==>[B, 2] # y1 = ["pred_lrs3" for _ in range(len(idexp_lm3d_pred_lrs3))] y2 = ["person_train" for _ in range(len(person_idexp_lm3d_train))] y3 = ["vae" for _ in range(len(idexp_lm3d_pred_vae))] y4 = ["postnet" for _ in range(len(idexp_lm3d_pred_postnet))] y5 = ["lle" for _ in range(len(idexp_lm3d_pred_lle))] visualize(idexp_lm3d_all_emb, y2+y3+y4+y5) plt.savefig("1.png") idexp_lm3d_pred_vae = np.load("autio2motion_dream_it_possible.npy").reshape([-1,204])[2000:2500] idexp_lm3d_pred_postnet = np.load("postnet_dream_it_possible.npy").reshape([-1,204])[2000:2500] idexp_lm3d_pred_lle = np.load("lle_dream_it_possible.npy").reshape([-1,204])[2000:2500] # idexp_lm3d_pred_postnet = idexp_lm3d_pred_postnet * lrs3_idexp_std + lrs3_idexp_mean idexp_lm3d_all = np.concatenate([person_idexp_lm3d_train,idexp_lm3d_pred_vae, idexp_lm3d_pred_postnet,idexp_lm3d_pred_lle]) idexp_lm3d_all_emb = tsne.fit(idexp_lm3d_all) # array(float64) [B,50]==>[B, 2] # z_p_emb = tsne.fit(z_p) # array(float64) [B,50]==>[B, 2] # y1 = ["pred_lrs3" for _ in range(len(idexp_lm3d_pred_lrs3))] y2 = ["person_train" for _ in range(len(person_idexp_lm3d_train))] y3 = ["vae" for _ in range(len(idexp_lm3d_pred_vae))] y4 = ["postnet" for _ in range(len(idexp_lm3d_pred_postnet))] y5 = ["lle" for _ in range(len(idexp_lm3d_pred_lle))] visualize(idexp_lm3d_all_emb, y2+y3+y4+y5) plt.savefig("2.png")