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visualize.py
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import os
import sys
import random
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import kneighbors_graph
from PIL import Image
from matplotlib import gridspec
import matplotlib.image as im
from mpl_toolkits.mplot3d import Axes3D
from sklearn.metrics import silhouette_score
from scipy.spatial import ConvexHull, convex_hull_plot_2d
import networkx as nx
'''
aps_ts = np.load("hands_img_ts.npy")
ts = np.load("hands_all_ts.npy")
print(len(aps_ts))
print(len(ts))
'''
j = 190
#to_be_ignored = [182, 183, 293, 325, 562, 591, 592, 593]
for i in [66, 70, 87, 26, 101]:
#if to_be_ignored.count(i) > 0:
# continue
print('img number: ', i + 1)
xx = '0000000000'
yy = str(i)
file_name = xx[:len(xx) - len(yy)] + yy
#selected_events = selected_events_all[last:event_idx[i]]
#I = Image.open("raw_img/Cars_sequence/"+file_name+".jpeg")
#I = I.rotate(-90, Image.NEAREST)
#I.save("raw_img/Cars_sequence/"+file_name+".jpeg")
#I.close()
#continue
#img = im.imread("raw_img/Street_sequence/"+file_name+".jpeg")
#print(img.shape)
selected_events = np.load("results/"+str(j)+"/selected_events/"+file_name+".npy")
#G = nx.k_nearest_neighbors(selected_events)
#G = kneighbors_graph(selected_events[:20], n_neighbors=3).toarray()
#print(G.toarray())
#G = nx.from_numpy_matrix(np.array(G))
#nx.draw(G)
#nx.draw(G, with_labels=True)
#print(selected_events.dtype)
#print(len(selected_events))
#print(selected_events.shape)
#selected_events = np.load("results/64/selected_events/selected_no_64_0.npy")
#clustering = clustering_all[last:event_idx[i]]
#last = event_idx[i]
clustering = np.load("results/"+str(j)+"/for/clusters/"+file_name+".npy")
for num in range(4):
#clustering = clustering*0
#clustering = np.load("results/64/clusters/cluster_no_64_0.npy")
#clustering = np.ones(clustering.shape)
#print(clustering)
x = np.array(selected_events[:, 0])
#x = 260-x
y = np.array(selected_events[:, 1])
#y = 346-y
z = np.array(selected_events[:, 2])
#points = [x[np.where(clustering == 1, True, False)], y[np.where(clustering == 1, True, False)]]
#points = np.asarray(points)
#print(points.T, points.T.shape)
#points = np.random.default_rng().random((30,2))
#print(points, points.shape)
#hull0 = ConvexHull(points.T)
#print(hull0)
#x1 = np.array(selected_clean_events[:, 0])
#y1 = np.array(selected_clean_events[:, 1])
#z1 = np.array(selected_clean_events[:, 2])
#all_ts.append(np.mean(z)*10000)
#print(len(x), len(x1))
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
#ax0 = fig.add_subplot(131)
#spec = gridspec.GridSpec(ncols=2, nrows=1)
#ax1 = fig.add_subplot(spec[0])
#ax2 = fig.add_subplot(spec[1], aspect=1)
#ax0.plot()
#ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([1, 1, 1, 1]))
if num==0:
col = 'PiYG'
elif num==1:
col = 'rainbow'
elif num==2:
col = 'rainbow'
elif num==3:
col = 'tab10_r'
ax.scatter(y[::], x[::], z[::], marker="o", c=1-clustering[num][::], s=0.5,
cmap=col)
#print(clustering)
#plt.xlabel("x")
#plt.ylabel("y")
#ax.imshow(img, extent=[0, 780, 0, 466])
#ax2.scatter(x, y, marker='.', c=clustering, s=0.1, cmap='gray')
ax.view_init(90, 0)
#ax.tick_params(axis='both', which='major', labelsize=5)
plt.gca().set_axis_off()
plt.subplots_adjust(top=1, bottom=0, right=1, left=0,
hspace=0, wspace=0)
plt.margins(0, 0,0)
#ax2.view_init(90, 0)
#ax.set_xlim([0, 260])
#ax.set_ylim([0, 346])
#plt.axis('off')
#plt.plot(points[:,1], points[:,0])
#plt.plot(selected_events[hull0.vertices,0], selected_events[hull0.vertices,1],'g--',lw=2)
#ax2.set_xlim([0, 260])
#ax2.set_ylim([0, 346])
#plt.show()
fig.savefig("results/"+str(j)+"/for/images/"+file_name+'_'+str(num)+".png", transparent=True)
plt.close()
"""
import networkx as nx
import random
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from sklearn.neighbors import kneighbors_graph
def generate_random_3Dgraph(n_nodes, radius, seed=None):
if seed is not None:
random.seed(seed)
# Generate a dict of positions
pos = {i: (random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1)) for i in range(n_nodes)}
print(pos)
pot = np.load("results/190/selected_events/0000000002.npy")
pot = pot[:,[0,1,2]]
pos = {i: (pot[i][0], pot[i][1], pot[i][2]) for i in range(len(pot[:50]))}
print(pos)
# Create random 3D network
#G = nx.random_k_out_graph(len(pot[:50]), 1, 0.7, self_loops=True, seed=None)
G = nx.random_geometric_graph(len(pot[:50]), radius, pos=pos)
return G
def network_plot_3D(G, angle, save=False):
# Get node positions
pos = nx.get_node_attributes(G, 'pos')
# Get number of nodes
n = G.number_of_nodes()
# Get the maximum number of edges adjacent to a single node
edge_max = max([G.degree(i) for i in range(n)])
# Define color range proportional to number of edges adjacent to a single node
colors = [plt.cm.plasma(G.degree(i) / edge_max) for i in range(n)]
# 3D network plot
with plt.style.context(('ggplot')):
fig = plt.figure()
ax = Axes3D(fig)
# Loop on the pos dictionary to extract the x,y,z coordinates of each node
for key, value in pos.items():
xi = value[0]
yi = value[1]
zi = value[2]
# Scatter plot
ax.scatter(xi, yi, zi, s=20 + 20 * G.degree(0), edgecolors='k', alpha=0.5)
# Loop on the list of edges to get the x,y,z, coordinates of the connected nodes
# Those two points are the extrema of the line to be plotted
for i, j in enumerate(G.edges()):
x = np.array((pos[j[0]][0], pos[j[1]][0]))
y = np.array((pos[j[0]][1], pos[j[1]][1]))
z = np.array((pos[j[0]][2], pos[j[1]][2]))
# Plot the connecting lines
ax.plot(x, y, z, c='black', alpha=0.5)
# Set the initial view
ax.view_init(30, angle)
# Hide the axes
ax.set_axis_off()
if save is not False:
plt.show()
#plt.savefig("C:\scratch\\data\"+str(angle).zfill(3)+".png")
plt.close('all')
else:
plt.show()
return
n=100
G = generate_random_3Dgraph(n_nodes=n, radius=15, seed=1)
#selected_events = np.load("results/190/selected_events/0000000002.npy")
#selected_events = selected_events[:,[0,1,2]]
#G = kneighbors_graph(selected_events, n_neighbors=100)
network_plot_3D(G,0, save=False)
"""