def train(self, readFromFile=True, path="./training3/"):
"""
train the image processor
:return: True if the training succeeds, false otherwise.
"""
self.knn = ColorKnnClassifier()
self.ratioKnns = []
for i in range(7):
self.ratioKnns.append(LetterKnnClassifier())
if readFromFile:
f = open(path, 'r')
for l in f:
l = l.split(",")
x = [float(i) for i in l[1:]]
self.ratioKnns[cc.getColorFromId(int(l[0]))].train.append((int(l[0]), x))
f.close()
f = open("color_data.csv", 'r')
for l in f:
l = l.split(',')
x = [float(i) for i in l[1:]]
self.knn.train.append((int(l[0]), x))
f.close()
else:
fs = listdir(path)
for f in fs:
s = f.split(".")
if len(s) < 2:
continue
s = f.split("_")
if len(s) < 2:
continue
c1 = const.getConstantFromString(s[0])
c2 = cc.getColorFromId(c1)
if c2 is not None:
self.ratioKnns[c2].loadTrainingImage(path + f, c1)
print "LetterClass. " + str(c2) + ":" + str(
cc.getColorFromNumber(c2)) + " trained: " + f + " as " + str(const.getStringFromNumber(c1))
self.knn.trainModel()
for r in self.ratioKnns:
r.trainModel()
print "Completed training"
print "------------------"
__author__ = 'sdiemert'
from os import listdir
from ColorKnnClassifier import ColorKnnClassifier
from LetterKnnClassifier import LetterKnnClassifier
from src.CodeMagnets import constants as const
import src.CodeMagnets.colorConstants as cc
knn = ColorKnnClassifier()
ratioKnns = []
def train(readFromFile=True, path="./training3/"):
"""
train the image processor
:return: True if the training succeeds, false otherwise.
"""
for i in range(7):
ratioKnns.append(LetterKnnClassifier())
print ratioKnns
fs = listdir(path)
for f in fs:
s = f.split(".")
if len(s) < 2:
continue
__author__ = 'sdiemert'
from os import listdir
from ColorKnnClassifier import ColorKnnClassifier
from LetterKnnClassifier import LetterKnnClassifier
from src.CodeMagnets import constants as const
import src.CodeMagnets.colorConstants as cc
knn = ColorKnnClassifier()
ratioKnns = []
def train(readFromFile=True, path="./training3/"):
"""
train the image processor
:return: True if the training succeeds, false otherwise.
"""
for i in range(7):
ratioKnns.append(LetterKnnClassifier())
print ratioKnns
fs = listdir(path)
for f in fs:
s = f.split(".")
if len(s) < 2:
continue
import pprint
from os import listdir
from src.CodeMagnets import ShapeFinder
from ColorKnnClassifier import ColorKnnClassifier
from LetterKnnClassifier import LetterKnnClassifier
import constants as const
import src.CodeMagnets.colorConstants as cc
pp = pprint.PrettyPrinter(indent=4)
print "Starting..."
test_files = ["./training3/example-4-2.jpg"]
knn = ColorKnnClassifier()
ratioKnns = []
for i in range(7):
ratioKnns.append(LetterKnnClassifier())
print ratioKnns
path = "./training3/"
fs = listdir(path)
for f in fs:
s = f.split(".")
import pprint
from os import listdir
from src.CodeMagnets import ShapeFinder
from ColorKnnClassifier import ColorKnnClassifier
from LetterKnnClassifier import LetterKnnClassifier
import constants as const
import src.CodeMagnets.colorConstants as cc
pp = pprint.PrettyPrinter(indent=4)
print "Starting..."
test_files = ["./training3/example-4-2.jpg"]
knn = ColorKnnClassifier()
ratioKnns = []
for i in range(7):
ratioKnns.append(LetterKnnClassifier())
print ratioKnns
path = "./training3/"
fs = listdir(path)
for f in fs:
s = f.split(".")
class CodeMagnetsImageProcessor:
def __init__(self):
self.output = []
self.lineCount = 0
self.colCount = 0
self.threads = []
self.lineLocks = None
def train(self, readFromFile=True, path="./training3/"):
"""
train the image processor
:return: True if the training succeeds, false otherwise.
"""
self.knn = ColorKnnClassifier()
self.ratioKnns = []
for i in range(7):
self.ratioKnns.append(LetterKnnClassifier())
if readFromFile:
f = open(path, 'r')
for l in f:
l = l.split(",")
x = [float(i) for i in l[1:]]
self.ratioKnns[cc.getColorFromId(int(l[0]))].train.append((int(l[0]), x))
f.close()
f = open("color_data.csv", 'r')
for l in f:
l = l.split(',')
x = [float(i) for i in l[1:]]
self.knn.train.append((int(l[0]), x))
f.close()
else:
fs = listdir(path)
for f in fs:
s = f.split(".")
if len(s) < 2:
continue
s = f.split("_")
if len(s) < 2:
continue
c1 = const.getConstantFromString(s[0])
c2 = cc.getColorFromId(c1)
if c2 is not None:
self.ratioKnns[c2].loadTrainingImage(path + f, c1)
print "LetterClass. " + str(c2) + ":" + str(
cc.getColorFromNumber(c2)) + " trained: " + f + " as " + str(const.getStringFromNumber(c1))
self.knn.trainModel()
for r in self.ratioKnns:
r.trainModel()
print "Completed training"
print "------------------"
def process(self, path):
print "Starting Image Processing"
sf = ShapeFinder()
c = sf.getShapeCoordinates(path, show=False)
lines = sf.getShapesByLines(c)
self.output = [[] for i in range(len(lines))]
self.lineLocks = [threading.Lock() for i in range(len(lines))]
self.lineCount = 0
self.threads = []
for l in lines:
self.colCount = 0
for s in l:
z = sf.getSection(s, color=True)
if z is not None:
self.output[self.lineCount].append('')
t = WorkerThread(self.lineCount, self.colCount, z, self)
t.start()
self.threads.append(t)
self.colCount += 1
print ""
self.lineCount += 1
for t in self.threads:
t.join()
print "Done Image Processing"
print "---------------------"
return self.output
def train(self, readFromFile=True, path="./training3/"):
"""
train the image processor
:return: True if the training succeeds, false otherwise.
"""
self.knn = ColorKnnClassifier()
self.ratioKnns = []
for i in range(7):
self.ratioKnns.append(LetterKnnClassifier())
if readFromFile:
f = open(path, 'r')
for l in f:
l = l.split(",")
x = [float(i) for i in l[1:]]
self.ratioKnns[cc.getColorFromId(int(l[0]))].train.append(
(int(l[0]), x))
f.close()
f = open("color_data.csv", 'r')
for l in f:
l = l.split(',')
x = [float(i) for i in l[1:]]
self.knn.train.append((int(l[0]), x))
f.close()
else:
fs = listdir(path)
for f in fs:
s = f.split(".")
if len(s) < 2:
continue
s = f.split("_")
if len(s) < 2:
continue
c1 = const.getConstantFromString(s[0])
c2 = cc.getColorFromId(c1)
if c2 is not None:
self.ratioKnns[c2].loadTrainingImage(path + f, c1)
print "LetterClass. " + str(c2) + ":" + str(
cc.getColorFromNumber(
c2)) + " trained: " + f + " as " + str(
const.getStringFromNumber(c1))
self.knn.trainModel()
for r in self.ratioKnns:
r.trainModel()
print "Completed training"
print "------------------"
class CodeMagnetsImageProcessor:
def __init__(self):
self.output = []
self.lineCount = 0
self.colCount = 0
self.threads = []
self.lineLocks = None
def train(self, readFromFile=True, path="./training3/"):
"""
train the image processor
:return: True if the training succeeds, false otherwise.
"""
self.knn = ColorKnnClassifier()
self.ratioKnns = []
for i in range(7):
self.ratioKnns.append(LetterKnnClassifier())
if readFromFile:
f = open(path, 'r')
for l in f:
l = l.split(",")
x = [float(i) for i in l[1:]]
self.ratioKnns[cc.getColorFromId(int(l[0]))].train.append(
(int(l[0]), x))
f.close()
f = open("color_data.csv", 'r')
for l in f:
l = l.split(',')
x = [float(i) for i in l[1:]]
self.knn.train.append((int(l[0]), x))
f.close()
else:
fs = listdir(path)
for f in fs:
s = f.split(".")
if len(s) < 2:
continue
s = f.split("_")
if len(s) < 2:
continue
c1 = const.getConstantFromString(s[0])
c2 = cc.getColorFromId(c1)
if c2 is not None:
self.ratioKnns[c2].loadTrainingImage(path + f, c1)
print "LetterClass. " + str(c2) + ":" + str(
cc.getColorFromNumber(
c2)) + " trained: " + f + " as " + str(
const.getStringFromNumber(c1))
self.knn.trainModel()
for r in self.ratioKnns:
r.trainModel()
print "Completed training"
print "------------------"
def process(self, path):
print "Starting Image Processing"
sf = ShapeFinder()
c = sf.getShapeCoordinates(path, show=False)
lines = sf.getShapesByLines(c)
self.output = [[] for i in range(len(lines))]
self.lineLocks = [threading.Lock() for i in range(len(lines))]
self.lineCount = 0
self.threads = []
for l in lines:
self.colCount = 0
for s in l:
z = sf.getSection(s, color=True)
if z is not None:
self.output[self.lineCount].append('')
t = WorkerThread(self.lineCount, self.colCount, z, self)
t.start()
self.threads.append(t)
self.colCount += 1
print ""
self.lineCount += 1
for t in self.threads:
t.join()
print "Done Image Processing"
print "---------------------"
return self.output
