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scene_l1.py
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#!/usr/bin/python3
from models import sensor
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import time
from sklearn.cluster import DBSCAN
import argparse
import os
cwd = os.getcwd()
description_str='Process scene_app using specified parameters'
parser = argparse.ArgumentParser(description=description_str)
parser.add_argument('laser_list',
metavar='L',
type=int,
nargs='+',
help="List of scanners to use. (Available 1, 2, 3, 5, 7, 8)")
parser.add_argument('--sds', action='store_true', help="Save data stats")
args = parser.parse_args()
print("laser_list %s",args.laser_list)
print("sds %s",args.sds)
# Clusters to save
clus_save = range(1,20000, 250)
img=mpimg.imread('image.bmp')
# map_scale = 5.13449
map_scale = 4.1345
max_x = 300
max_y = 225
# d_x = -32.6
d_x = -30.2
# d_y = -31.8
d_y = -24
limits = np.concatenate((
((np.array([0,max_x])/map_scale)+d_x),
((np.array([0,max_y])/map_scale)+d_y)
))
scene = scene.Scene()
lms1 = sensor.Laser(sensor.Laser.SUBTYPE_SINGLELAYER)
lms2 = sensor.Laser(sensor.Laser.SUBTYPE_SINGLELAYER)
lms3 = sensor.Laser(sensor.Laser.SUBTYPE_SINGLELAYER)
lms5 = sensor.Laser(sensor.Laser.SUBTYPE_SINGLELAYER)
lms7 = sensor.Laser(sensor.Laser.SUBTYPE_SINGLELAYER)
lms8 = sensor.Laser(sensor.Laser.SUBTYPE_SINGLELAYER)
laser_sensors = [0, lms1, lms2, lms3, 0, lms5, 0, lms7, lms8]
lms_files = ["", "possi.lms1","possi.lms2","possi.lms3",
"", "possi.lms5", "", "possi.lms7","possi.lms8"]
# MODIFY HERE: Select Laser scanners to use
use_laser = args.laser_list
# use_laser = [1, 2, 3, 5, 7, 8]
# use_laser = [1, 8]
dir_path = "scene_" + "".join([str(x) for x in use_laser])
if not os.path.exists(dir_path):
os.makedirs(dir_path)
sds_filename=dir_path + "/data.log"
scene_filename=dir_path + "/scene_app.log"
if args.sds:
with open(sds_filename, '+w') as out:
data_log="Frame Clusters\n"
out.write(data_log)
#Add laser sensors to Scene
for laser_n in use_laser:
laser_sensors[laser_n].set_src_path(lms_files[laser_n])
scene.add_sensor(laser_sensors[laser_n])
for range_sensor in scene.sensors["range"]:
range_sensor.load()
#lms1.set_src_path("possi.lms3")
#lms1.load()
process_log="Range sensors in the scene_app: %d" % len(scene.sensors["range"]) + "\n"
print("Range sensors in the scene_app: %d" % len(scene.sensors["range"]))
#exit()
theta = np.arange(0, 180.5, 0.5)
theta = theta * np.pi / 180.0
last = 0
xos = []
yos = []
angs = []
# Extract origins
for range_sensor in scene.sensors["range"]:
xos.append(float(range_sensor.dataset["calib_data"]["sx"]))
yos.append(float(range_sensor.dataset["calib_data"]["sy"]))
angs.append(float(range_sensor.dataset["calib_data"]["ang"]))
# MODIFY HERE: Select whether to plot process or not
plot_process = False
if plot_process:
fig = plt.figure(figsize=(30/3,22.5/3))
#ax = fig.add_subplot(111, projection='polar')
ax = fig.add_subplot(111)
print(ax)
#ax.set_rmax(6000)
imgplot = ax.imshow(img, extent=limits, aspect='auto')
print(imgplot)
# xos = [0, 23.7, 13.12, 12.68, -8.62, -2.2]
# yos = [-21.4, 15.6, 26.7, -21.74, 17.1, 22.56]
# angs = [0.150098, -3.344051, -4.174827, -0.059341, 3.679154, -1.933825]
lasers_pts, = ax.plot(xos,yos, '.', markerfacecolor='g', markeredgecolor='k', markersize=10)
plt.ylim(ymin=-25,ymax=32)
plt.xlim(xmin=-30,xmax=40)
fig.show()
#fig.draw()
fig.canvas.draw()
#fig.canvas.manager.show()
background = fig.canvas.copy_from_bbox(ax.bbox)
rdata, = ax.plot(theta)
#print(rdata)
#rdata, = ax.plot(theta, theta, color='b',linestyle='None', marker='.',linewidth=2)
nf = 1
roi={"ymin":-24,"ymax":30,"xmin":-30,"xmax":40}
scene.set_roi(roi)
blob_list = []
blob_count = 1
tstart = time.time()
while not last:
#print("Frame %d" % nf)
data_log = ("%05d " % nf)
#plt.pause(1)
# Preprocessing
data, last, ts = scene.preprocess_data()
# if (nf > 10):
# exit()
if plot_process:
ax.clear()
#rdata.set_xdata(theta)
#rdata.set_ydata(laser_data)
#rdata.set_data(theta, laser_data)
#rdata, = ax.plot(theta, laser_data, color='b',linestyle='None', marker='.',linewidth=2)
#rdata, = ax.plot(x, y,color='b',linestyle='None', marker='.',linewidth=2)
# rdata, = ax.plot(False)
#rdata.set_data(theta, laser_data)
#print(rdata)
#rdata.set_xdata(theta)
#rdata.set_ydata(laser_data)
#rdata.set_linestyle('None')
#rdata.set_color('g')
#rdata.set_marker('.')
#ax.grid(False)
#ax.set_yticklabels([])
plt.ylim(ymin=roi["ymin"],ymax=roi["ymax"])
plt.xlim(xmin=roi["xmin"],xmax=roi["xmax"])
#ax.draw_artist(rdata)
#fig.canvas.blit(ax.bbox)
#fig.canvas.draw()
#ax.set_rmax(2.0)
#plt.ylim(ymin=-10)
#plt.show(block=last)
#fig.canvas.restore_region(background)
# redraw just the points
#ax.draw_artist(rdata)
# fill in the axes rectangle
#fig.canvas.blit(ax.bbox)
#rdata.set_linestyle('None')
#rdata.set_color('b')
#rdata.set_marker('.')
#ax.grid(False)
#ax.set_yticklabels([])
#print("shape x: %s, shape y: %s" % (str(np.shape(x)), str(np.shape(y))))
# data = np.array([x,y]).transpose()
# print(data)
# print("shape data: %s" % (str(np.shape(data))))
# exit()
# data = sensor.apply_roi(data, roi)
#data.transpose()
#print(x.reshape(1,-1))
#print(data)
#print("shape data: %s" % (str(np.shape(data))))
#input()
dbscan_params = {'eps':0.3, 'min_samples':5}
db = DBSCAN(**dbscan_params).fit(data)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_
# print(db.labels_)
# print(db.core_sample_indices_)
# exit()
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
clusters_data = {"raw": data,
"db": db,
"frame": nf,
"ts": ts,
"lasers": use_laser,
}
unique_labels = set(labels)
temp_blob_list = []
for k in unique_labels:
if k != -1:
class_member_mask = (labels == k)
if (sum(class_member_mask) >= db.min_samples):
blob = scene.Blob(data[class_member_mask],
ts,
nf,
blob_count)
blob.get_features()
temp_blob_list.append(blob)
blob_count += 1
blob_list.append(temp_blob_list)
print("blobs %s" % [b.mean for b in blob_list[nf-1]])
if nf > 4:
print(blob_list)
exit()
#print('Estimated number of clusters: %d' % n_clusters_)
data_log += str(n_clusters_) + "\n"
if args.sds:
with open(sds_filename, '+a') as out:
out.write(data_log)
if plot_process:
unique_labels = set(labels)
#colors = plt.cm.Jet(np.linspace(0, 1, len(unique_labels)))
colors = ['b', 'g', 'r', 'm']
colors = colors*50
colors_final = colors[0:len(unique_labels)]
for k, col in zip(unique_labels, colors_final):
if k == -1:
# Black used for noise.
col = 'k'
class_member_mask = (labels == k)
xy = data[class_member_mask & core_samples_mask]
#print("Size data %s" % str(np.shape(data)))
#print("Size xy %s" % str(np.shape(xy)))
#print(xy)
ax.plot(xy[:, 0], xy[:, 1], '.', markerfacecolor=col,
markeredgecolor='k', markersize=10)
xy = data[class_member_mask & ~core_samples_mask]
ax.plot(xy[:, 0], xy[:, 1], '.', markerfacecolor=col,
markeredgecolor='k', markersize=4)
#fig.title('Estimated number of clusters: %d' % n_clusters_)
#plt.show()
#exit()
imgplot = ax.imshow(img, extent=limits, aspect='auto')
#
# xos = [0, 23.7, 13.12, 12.68, -8.62, -2.2]
# yos = [-21.4, 15.6, 26.7, -21.74, 17.1, 22.56]
# angs = [0.150098, -3.344051, -4.174827, -0.059341, 3.679154, -1.933825]
#
ax.plot(xos,yos, '.', marker='v', markerfacecolor='g', markeredgecolor='k', markersize=10)
#print(ax)
#imgplot.set_data(img)
#ax.draw_a rtist(imgplot)
#ax.draw_artist(lasers_pts)
#fig.canvas.blit(ax.bbox)
#fig.canvas.restore_region(background)
fig.canvas.draw()
if nf in clus_save:
import pickle
#dir_path = "scene_" + "".join([str(x) for x in use_laser])
clusters_data = {"raw": data,
"db": db,
"frame": nf,
"ts": ts,
"lasers": use_laser,
}
output_file = dir_path + "/" + format(nf,'05') + ".clus"
fo = open(output_file, "wb")
pickle.dump(clusters_data, fo, pickle.HIGHEST_PROTOCOL)
nf+=1
#input()
if nf == 22000:
break
process_log += 'FPS: %s ' % ((nf-1)/(time.time()-tstart))
#print('FPS:' , (nf-1)/(time.time()-tstart))
print(process_log)
with open(scene_filename, '+w') as out:
out.write(process_log)