def setupEOgmaNeo():
global _system, _hierarchy, _inputImage, _inputValue
print("Initializing EOgmaNeo::ComputeSystem")
_system = eogmaneo.ComputeSystem(4)
print("Initializing EOgmaNeo::Hierarchy")
lds = eogmaneo.StdVecLayerDesc()
numLayers = 6
layerSize = 36
for l in range(0, numLayers):
ld = eogmaneo.LayerDesc()
ld._width = layerSize
ld._height = layerSize
ld._forwardRadius = 9
ld._backwardRadius = 9
ld._ticksPerUpdate = 2
ld._temporalHorizon = 2
ld._alpha = 0.1
ld._beta = 0.1
ld._gamma = 0.01
lds.push_back(ld)
#layerSize = 16
_hierarchy = eogmaneo.Hierarchy()
_hierarchy.create([(hiddenWidth, hiddenHeight),
(steerChunkSize, steerChunkSize)],
[lineChunkSize, steerChunkSize], [False, True], lds, 41)
_inputValue = eogmaneo.StdVeci(1 * 1)
camera = picamera.PiCamera()
camera.resolution = (camWidth, camHeight)
camera.framerate = 24
#camera.start_preview()
time.sleep(2)
######################
ser = serial.Serial(
'/dev/serial/by-id/usb-Arduino_Srl_Arduino_Uno_556393038343514082D0-if00',
115200)
esystem = eogmaneo.ComputeSystem(4)
ld = 6 * [eogmaneo.LayerDesc()]
for l in range(0, len(ld)):
ld[l]._width = 36
ld[l]._height = 36
ld[l]._chunkSize = 6
if l == 0:
ld[l]._forwardRadius = 9
ld[l]._backwardRadius = 9
else:
ld[l]._forwardRadius = 9
ld[l]._backwardRadius = 9
HIDDEN_HEIGHT = 32
CHUNK_SIZE = 4
RADIUS = 9
print("Building the EOgmaNeo hierarchy and pre-encoder...\n")
# After training an ImageEncoder will output unique sparse chunked representations
# that can be input into an EOgmaNeo hierarchy. Consequently it can take predicted
# sparse chunked representations output from the hierarchy and reconstruct an
# image it has learned.
preEncoder = eogmaneo.ImageEncoder()
preEncoder.create(IMAGE_WIDTH, IMAGE_HEIGHT, HIDDEN_WIDTH, HIDDEN_HEIGHT,
CHUNK_SIZE, int(16), 123)
# Make sure EOgmaNeo uses all CPU cores available
cs = eogmaneo.ComputeSystem(multiprocessing.cpu_count())
# Construct parameter descriptions for all hierarchy encoder-decoder layers
lds = []
for i in range(NUM_LAYERS):
ld = eogmaneo.LayerDesc()
ld._width = HIDDEN_WIDTH
ld._height = HIDDEN_HEIGHT
ld._chunkSize = CHUNK_SIZE
ld._radius = RADIUS
ld._ticksPerUpdate = 2
ld._temporalHorizon = 2
ld._alpha = 0.1
# ----------------------------------------------------------------------------
# EOgmaNeo
# Copyright(c) 2017-2018 Ogma Intelligent Systems Corp. All rights reserved.
#
# This copy of EOgmaNeo is licensed to you under the terms described
# in the EOGMANEO_LICENSE.md file included in this distribution.
# ----------------------------------------------------------------------------
# -*- coding: utf-8 -*-
import numpy as np
import eogmaneo
import matplotlib.pyplot as plt
cs = eogmaneo.ComputeSystem(8)
columnSize = 64
bounds = (-1.0, 1.0) # Range of value
lds = []
for i in range(9):
ld = eogmaneo.LayerDesc()
ld._width = 5
ld._height = 5
ld._columnSize = 32
ld._forwardRadius = ld._backwardRadius = 2
ld._lateralRadius = 2
ld._ticksPerUpdate = 2
