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visualization_utils.py
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import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib
from matplotlib.colors import LinearSegmentedColormap
import cv2
import imageio
import os
from sklearn.decomposition import PCA
import cfg
def visualize(input, input_shape):
input_image = input.reshape(input_shape)
plt.matshow(input_image, cmap='hot')
plt.colorbar()
plt.show()
def pca_decomposition(input_data, target_dim):
pca=PCA(n_components=target_dim)
pca.fit(input_data)
pca_data = pca.transform(input_data)
plt.scatter(pca_data[:,0],pca_data[:,1])
plt.savefig(cfg.code_path + "/output/pca_test.jpg", dpi=300)
plt.clf()
return pca_data
def save_visualize(input, input_shape, image_name):
if type(input_shape) == type((1, 1)):
input_image = input.reshape(input_shape)
plt.matshow(input_image, cmap='hot')
# plt.matshow(input_image, cmap='hot', vmin = 0, vmax = 1)
plt.colorbar()
plt.savefig(image_name, dpi=300)
elif type(input_shape) == type(1):
input_image = input.reshape((input_shape, 1))
plt.matshow(input_image, cmap='hot')
# plt.colorbar()
plt.savefig(image_name)
else:
assert 0, "save error."
def save_curve(x, y, image_name):
plt.plot(x,y)
plt.savefig(image_name)
plt.clf()
def save_vis_formatted(train_data):
for event in range(train_data.shape[-1]):
save_dir = cfg.code_path + "/output/"+str(event)
try:
os.mkdir(save_dir)
print(save_dir)
except FileExistsError:
print(save_dir)
for frame in range(train_data.shape[0]):
save_visualize(train_data[frame, :, :, event], (128,128),
os.path.join(save_dir, str(frame)+".jpg"))
def train_result_vis_pca(id_sel):
info = np.load(cfg.code_path + "/expert4_information.npy")
pca=PCA(n_components=2)
pca.fit(info)
pca_data = pca.transform(info)
id = np.load(cfg.code_path + "/expert4_id.npy")
plt.xlim(xmax=2,xmin=-2)
plt.ylim(ymax=2,ymin=-2)
if id_sel > 0:
pca_data_1 = list()
for i in range(0, info.shape[0]):
if id[i] == id_sel:
print(i, id[i], pca_data[i], info[i])
pca_data_1.append(pca_data[i])
pca_data_1 = np.array(pca_data_1)
plt.scatter(pca_data_1[:,0], pca_data_1[:,1])
plt.savefig(cfg.code_path + "/output/pca_test_"+str(id_sel)+".jpg",dpi=300)
plt.clf()
else:
plt.scatter(pca_data[:,0], pca_data[:,1], c=id*30)
plt.savefig(cfg.code_path + "/output/pca_test_all.jpg",dpi=300)
plt.clf()
def train_result_vis_pca_3d(id_sel):
info = np.load(cfg.code_path + "/expert4_information.npy")
pca=PCA(n_components=3)
pca.fit(info)
pca_data = pca.transform(info)
id = np.load(cfg.code_path + "/expert4_id.npy")
# plt.xlim(xmax=2,xmin=-2)
# plt.ylim(ymax=2,ymin=-2)
# plt.zlim(zmax=2,zmin=-2)
pca_data_1 = list()
for i in range(0, info.shape[0]):
if id[i] == id_sel:
print(i, id[i], pca_data[i], info[i])
pca_data_1.append(pca_data[i])
pca_data_1 = np.array(pca_data_1)
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(pca_data[:,0], pca_data[:,1], pca_data[:,2], c=id*30)
ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'})
ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'})
ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'})
plt.savefig(cfg.code_path + "/output/pca_3d_test_"+str(id_sel)+".jpg",dpi=300)
fig.clf()
def save_visualize_gif(input_list, input_shape, image_name):
# fig = plt.figure()
# camera = Camera(fig)
# print("Saving gif", image_name)
# for input in input_list:
# input_image = input.reshape(input_shape)
# plt.matshow(input_image, cmap='hot')
# plt.colorbar()
# plt.show()
# # plt.savefig(image_name)
# camera.snap()
# animation = camera.animate()
# animation.save(image_name)
# # plt.show()
frames = []
print("Saving", image_name)
if type(input_shape) == type((1, 1)):
for input in input_list:
input_image = input.reshape(input_shape)
plt.matshow(input_image, cmap='hot')
# plt.colorbar()
plt.savefig("tmp.png")
frames.append(cv2.imread("tmp.png"))
imageio.mimsave(image_name, frames, fps=20)
elif type(input_shape) == type(1):
for input in input_list:
input_image = input.reshape((input_shape, 1))
plt.matshow(input_image, cmap='hot')
# plt.colorbar()
plt.savefig("tmp.png")
frames.append(cv2.imread("tmp.png"))
imageio.mimsave(image_name, frames, fps=256)
# gif.save(frames, 'random.gif', duration=50)
def save_visualize_img_gif(input_list, image_name):
frames = []
print("Saving", image_name)
for input in input_list:
frames.append(input)
imageio.mimsave(image_name, frames, fps=25)
def save_visualize_3d(input_list, input_shape, image_name):
x = []
y = []
z = []
# c = []
fig=plt.figure(dpi=120)
ax=fig.add_subplot(111,projection='3d')
ax.view_init(elev=10., azim=11)
# colors = matplotlib.cm.rainbow(np.linspace(0, 1, 1024))
for i, input in enumerate(input_list):
input_image = input.reshape(input_shape)
input_max = input.max()
for j in range(input_image.shape[0]):
for k in range(input_image.shape[1]):
if input_image[j][k] != 0:
x.append(j)
z.append(k)
y.append(i)
# c.append(colors[int(input_image[j][k]/ input_max *255)])
#
# print(colors[int(input_image[j][k]/ input_max *255)])
# ax.scatter(x,y,z,c,'filled',cmap='spectral')
ax.scatter(x,y,z,c='b',marker='.',s=20,linewidth=0,alpha=0.8,cmap='spectral')
# plt.matshow(input_image, cmap='hot')
# plt.matshow(input_image, cmap='hot', vmin = 0, vmax = 1)
# plt.colorbar()
plt.savefig(image_name, dpi=300)
def save_visualize_3dsurface(input, input_shape, image_name):
figure = plt.figure()
ax = Axes3D(figure,azim=-75,elev=30)
X = np.arange(0,input_shape[1],1)
Y = np.arange(0,input_shape[0],1)
X,Y = np.meshgrid(X,Y)
ax.plot_surface(X,Y,input,rstride=1,cstride=1,cmap='rainbow')
plt.savefig(image_name, dpi=300)
def show_wave(wave):
dist = 60
channel_num = 20
y_range = dist * channel_num + 50
start_time = int(0 * 30000)
time_scale = 15
i=0
plt.cla()
plt.xlim(i, i + 600)
plt.ylim(-y_range / 2 / dist + channel_num / 2, y_range / 2 / dist + channel_num / 2)
x = np.linspace(i, i + 600, 601).astype(int)
for j in range(0, channel_num):
y = wave[j, x * time_scale + start_time] * 0.4 * 1e6 + int(j - channel_num / 2) * dist + dist / 2
y = y / dist + channel_num / 2
plt.plot(x, y)
i = i + 1
plt.xlabel("Sample Data Point", size=14)
plt.ylabel("Recording Channel", size=14)
plt.title("EEG Motor Movement/Imagery Dataset",
fontdict={'family': 'serif',
'color': 'darkgreen',
'weight': 'bold',
'size': 18})
plt.savefig("tmp.png")
def save_wave(wave, image_name, title):
dist = 60
channel_num = 20
y_range = dist * channel_num + 50
start_time = int(0 * 30000)
time_scale = int(wave.shape[1]/600)
i=0
plt.cla()
plt.xlim(i, i + 600)
plt.ylim(-y_range / 2 / dist + channel_num / 2, y_range / 2 / dist + channel_num / 2)
x = np.linspace(i, i + 600, 601).astype(int)
for j in range(0, channel_num):
y = wave[j, x * time_scale + start_time] * 0.4 * 1e6 + int(j - channel_num / 2) * dist + dist / 2
y = y / dist + channel_num / 2
plt.plot(x, y)
i = i + 1
plt.xlabel("Sample Data Point", size=14)
plt.ylabel("Recording Channel", size=14)
plt.title(title,
fontdict={'family': 'serif',
'color': 'darkgreen',
'weight': 'bold',
'size': 18})
plt.savefig(image_name)