File size: 6,940 Bytes
e34aada |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 |
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") |