# 3.19. Vision QuestΒΆ

In [1]:

import jyro.simulator as jy
import conx as cx
from IPython.display import display
import random
import numpy as np

Using Theano backend.
Conx, version 3.6.0

In [2]:

def make_world(physics):
physics.addBox(0, 0, 5, 5, fill="gray", wallcolor="gray")
physics.addBox(0, 0, 0.5, 0.5, fill="blue", wallcolor="blue")
physics.addBox(0, 5, 0.5, 4.5 , fill="red", wallcolor="red")
physics.addBox(4.5, 4.5, 5, 5, fill="green", wallcolor="green")
physics.addBox(4.5, 0, 5, 0.5, fill="purple", wallcolor="purple")
physics.addBox(2, 1.75, 2.5, 3.25, fill="orange", wallcolor="orange")

def make_robot():
robot = jy.Pioneer("Pioneer", 3, 1, 0)
return robot

robot = make_robot()
robot.mystep = 0
robot.priority = random.choice(["left", "right"])
sim = jy.Simulator(robot, make_world)

if x <= max_x/2 and y <= max_y/2:
return 1
elif x <= max_x/2 and y >= max_y/2:
return 2
elif x >= max_x/2 and y >= max_y/2:
return 3
else:
return 4

SAMPLES = 500

def controller(robot):
if robot.mystep % 200 == 0:
robot.priority = "left" if robot.priority == "right" else "right"
image = robot["camera"].getData()

x, y, h = robot.getPose()

ls = list(robot.targets)
counts = [ls.count(n) for n in [1,2,3,4]]

robot.images.append(image)

sonar = robot["sonar"].getData()
left = min(sonar[0:4])
right = min(sonar[4:8])
clearance = 0.5
noise = random.gauss(0, 0.2)
if robot.priority == "left":
if left < clearance or right < clearance:
robot.move(0, -0.5+noise)
else:
robot.move(0.5+noise, 0)
else:
if left < clearance or right < clearance:
robot.move(0, 0.5+noise)
else:
robot.move(0.5+noise, 0)
robot.mystep += 1

robot.brain = controller
robot.images = []
robot.targets = []


In [3]:

i = 0
while True:
if i % 100 == 0:
print(i, end=" ")
#display(robot["camera"].getImage())
sim.step(run_brain=True)
ls = list(robot.targets)
x = [ls.count(n) for n in [1,2,3,4]]
if min(x) == SAMPLES:
break
i += 1

## Now trim all of them to same length

with open("vision_images.npy", "wb") as fp:
np.save(fp, robot.images)
with open("vision_targets.npy", "wb") as fp:
np.save(fp, robot.targets)
print("done collecting data")

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 done collecting data

In [4]:

!ls -l *.npy

-rw-r--r-- 1 dblank dblank 57600128 Feb 11 23:53 vision_images.npy
-rw-r--r-- 1 dblank dblank    16128 Feb 11 23:53 vision_targets.npy

In [36]:

vision_images = np.load("vision_images.npy")
print(vision_images.shape)
print(vision_targets.shape)

(2000, 40, 60, 3)
(2000,)

In [6]:

ls = list(vision_targets)
x = [ls.count(n) for n in [1,2,3,4]]
print(x)
print(sum(x))

[500, 500, 500, 500]
2000

In [7]:

def vision_network(actf):
net = cx.Network("Vision Controller")
cx.Conv2DLayer("conv1", 10, (5, 5),
activation=actf),
cx.Conv2DLayer("conv2", 10, (5, 5),
activation=actf),
cx.MaxPool2DLayer("pool1",
pool_size=(2,2)),
cx.FlattenLayer("flatten"),
cx.Layer("hidden", 20,
activation=actf),
cx.Layer("output", 4,
activation="softmax"))
net.connect()
net.compile(loss="categorical_crossentropy",
return net

net = vision_network("relu")
net["conv1"].feature = 7
display(net)
net.propagate(vision_images[0])

Out[7]:

[0.2061084508895874,
0.2597593665122986,
0.3951513469219208,
0.13898083567619324]

In [9]:

net.picture(vision_images[19], rotate=True)

Out[9]:

In [14]:

net.propagate_to_features("conv1", vision_images[10], scale=3)

Out[14]:

 Feature 0 Feature 1 Feature 2 Feature 3 Feature 4 Feature 5 Feature 6 Feature 7 Feature 8 Feature 9
In [15]:

net.propagate_to_features("conv1", vision_images[20], scale=3)

Out[15]:

 Feature 0 Feature 1 Feature 2 Feature 3 Feature 4 Feature 5 Feature 6 Feature 7 Feature 8 Feature 9
In [18]:

img = cx.array_to_image(vision_images[0], scale=3.0)
img

Out[18]:

In [21]:

net.picture(vision_images[10], rotate=True)

Out[21]:

In [23]:

net.picture(vision_images[100], rotate=True)

Out[23]:

In [24]:

net.propagate_to_features("conv2", vision_images[100], scale=3.0)

Out[24]:

 Feature 0 Feature 1 Feature 2 Feature 3 Feature 4 Feature 5 Feature 6 Feature 7 Feature 8 Feature 9
In [25]:

ds = net.dataset

In [26]:

ds.clear()

In [37]:

%%time
dataset = []
for i in range(len(vision_images)):
dataset.append([vision_images[i], cx.onehot(vision_targets[i] - 1, 4)])

CPU times: user 213 ms, sys: 84.2 ms, total: 297 ms
Wall time: 309 ms

In [38]:

ds.split(.1)

In [39]:

ds.summary()

_________________________________________________________________
Vision Controller Dataset:
Patterns    Shape                 Range
=================================================================
inputs      (40, 60, 3)           (0.0, 1.0)
targets     (4,)                  (0.0, 1.0)
=================================================================
Total patterns: 2000
Training patterns: 1800
Testing patterns: 200
_________________________________________________________________

In [22]:

#net.delete()
#net.train(5, report_rate=1, plot=True)
#net.save()

In [34]:

if net.saved():
net.plot_results()
else:
net.train(5, report_rate=1, save=True)

In [35]:

net.dashboard()

In [40]:

robot["camera"].getImage().resize((240, 160))

Out[40]:

In [41]:

image = net.propagate_to_image("conv2", vision_images[0], scale=2.0)
image

Out[41]:

In [42]:

net.propagate_to_features("conv2", vision_images[0], scale=3.0)

Out[42]:

 Feature 0 Feature 1 Feature 2 Feature 3 Feature 4 Feature 5 Feature 6 Feature 7 Feature 8 Feature 9
In [43]:

net.propagate(vision_images[10])

Out[43]:

[3.2931123106205717e-12,
0.0011761356145143509,
0.011836284771561623,
0.9869875907897949]

In [45]:

net.propagate(cx.array_to_image(robot["camera"].getData()))

Out[45]:

[0.2795098125934601,
0.5486608743667603,
0.03144071251153946,
0.1403886079788208]

In [32]:

from conx.widgets import CameraWidget

In [33]:

cam = CameraWidget()
cam

In [34]:

image = cam.get_image().resize((60, 40))

In [35]:

net.propagate(image)

Out[35]:

[0.2507583200931549, 0.2764703929424286, 0.1926824152469635, 0.280088871717453]

In [36]:

net.propagate(robot["camera"].getData())

Out[36]:

[0.14169691503047943,
0.39364564418792725,
0.3407711088657379,
0.123886339366436]

In [37]:

net.test()

========================================================
Testing validation dataset with tolerance 0.1...
Total count: 1800
correct: 1191
incorrect: 609
Total percentage correct: 0.6616666666666666

In [46]:

def network_brain(robot):
if robot.mystep % 200 == 0:
robot.priority = "left" if robot.priority == "right" else "right"
inputs = robot["camera"].getData()
outputs = net.propagate(inputs)
print(net.pf(outputs))
sonar = robot["sonar"].getData()
left = min(sonar[0:4])
right = min(sonar[4:8])
clearance = 0.5
noise = random.gauss(0, 0.2)
if robot.priority == "left":
if left < clearance or right < clearance:
robot.move(0, -0.5+noise)
else:
robot.move(0.5+noise, 0)
else:
if left < clearance or right < clearance:
robot.move(0, 0.5+noise)
else:
robot.move(0.5+noise, 0)
robot.mystep += 1

In [48]:

net.visualize = False
robot = make_robot()
robot.brain = network_brain
robot.mystep = 0
robot.priority = random.choice(["left", "right"])
vsim = jy.VSimulator(robot, make_world)