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127 changes: 63 additions & 64 deletions Learning To See [Part 12 - Let's Get Greedy].ipynb
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144 changes: 79 additions & 65 deletions Learning To See [Part 15 - Information].ipynb
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302 changes: 144 additions & 158 deletions Learning To See [Part 2 - Rules on Rules on Rules].ipynb
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147 changes: 73 additions & 74 deletions Learning To See [Part 4 - Machine Learning].ipynb
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199 changes: 99 additions & 100 deletions Learning To See [Part 5 - To Learn is to Generalize].ipynb
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207 changes: 119 additions & 88 deletions Learning to See [Part 10 - World Domination].ipynb
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89 changes: 49 additions & 40 deletions Learning to See [Part 6 - It's Definitely Time to Play with Legos].ipynb
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150 changes: 69 additions & 81 deletions Learning to See [Part 9 - Bias Variance Throwdown].ipynb
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68 changes: 34 additions & 34 deletions supportFunctions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,22 +8,22 @@
import matplotlib.pyplot as plt

#Extract 9 by 9 grid and finger/not finger label from imageDict
def extractFeatures(imageDict, whichImage = 'image1bit', dist = 4):
def extractFeatures(imageDict, whichImage = b'image1bit', dist = 4):
img = imageDict[whichImage]

featuresList = []
target = np.zeros(imageDict['numPointsInBox'])
target = np.zeros(imageDict[b'numPointsInBox'])
counter = 0

for i in np.arange(imageDict['boxEdges'][2], imageDict['boxEdges'][3]):
for j in np.arange(imageDict['boxEdges'][0], imageDict['boxEdges'][1]):
for i in np.arange(imageDict[b'boxEdges'][2], imageDict[b'boxEdges'][3]):
for j in np.arange(imageDict[b'boxEdges'][0], imageDict[b'boxEdges'][1]):
f = img[i-dist:i+dist+1, j-dist:j+dist+1]

fVec = f.ravel()
featuresList.append(fVec)

#Check and see if this is a finger pixel or not:
if np.max(np.sum(imageDict['allFingerPoints'] == [i, j], 1)) == 2:
if np.max(np.sum(imageDict[b'allFingerPoints'] == [i, j], 1)) == 2:
target[counter] = 1

counter = counter +1
Expand All @@ -33,7 +33,7 @@ def extractFeatures(imageDict, whichImage = 'image1bit', dist = 4):
return features, target

#Extract indivudual examples from list of imageDicts:
def extractExamplesFromList(imageList, whichImage = 'image1bit', dist = 4):
def extractExamplesFromList(imageList, whichImage = b'image1bit', dist = 4):
allFeaturesList = []
allTargetList = []

Expand Down Expand Up @@ -75,13 +75,13 @@ def make_colormap(seq):
return mcolors.LinearSegmentedColormap('CustomMap', cdict)

def makeGrayScale(imageDict):
im = np.zeros((imageDict['boxHeight'], imageDict['boxWidth'], 3))
im[:,:,0] = 1./255*imageDict['image'][imageDict['boxEdges'][2]:imageDict['boxEdges'][3], \
imageDict['boxEdges'][0]:imageDict['boxEdges'][1]]
im[:,:,1] = 1./255*imageDict['image'][imageDict['boxEdges'][2]:imageDict['boxEdges'][3], \
imageDict['boxEdges'][0]:imageDict['boxEdges'][1]]
im[:,:,2] = 1./255*imageDict['image'][imageDict['boxEdges'][2]:imageDict['boxEdges'][3], \
imageDict['boxEdges'][0]:imageDict['boxEdges'][1]]
im = np.zeros((imageDict[b'boxHeight'], imageDict[b'boxWidth'], 3))
im[:,:,0] = 1./255*imageDict[b'image'][imageDict[b'boxEdges'][2]:imageDict[b'boxEdges'][3], \
imageDict[b'boxEdges'][0]:imageDict[b'boxEdges'][1]]
im[:,:,1] = 1./255*imageDict[b'image'][imageDict[b'boxEdges'][2]:imageDict[b'boxEdges'][3], \
imageDict[b'boxEdges'][0]:imageDict[b'boxEdges'][1]]
im[:,:,2] = 1./255*imageDict[b'image'][imageDict[b'boxEdges'][2]:imageDict[b'boxEdges'][3], \
imageDict[b'boxEdges'][0]:imageDict[b'boxEdges'][1]]

return im

Expand Down Expand Up @@ -115,13 +115,13 @@ def computeConfusionMatrix(y, yHat, verbose = True):
accuracy = float(TP+TN)/len(y)

if verbose:
print 'Confusion Matrix:'
print cm
print 'Recall (TPR) = ' + str(round(recall,3)) + \
' (Portion of fingers that we "caught")'
print 'Precision (PPV) = ' + str(round(precision,3)) + \
'(Portion of predicted finger pixels that were actually finger pixels)'
print 'Accuracy = ' + str(round(accuracy,3))
print ('Confusion Matrix:')
print (cm)
print ('Recall (TPR) = ' + str(round(recall,3)) + \
' (Portion of fingers that we "caught")')
print ('Precision (PPV) = ' + str(round(precision,3)) + \
'(Portion of predicted finger pixels that were actually finger pixels)')
print ('Accuracy = ' + str(round(accuracy,3)))

return cm, accuracy, recall, precision

Expand All @@ -139,7 +139,7 @@ def testRules(rules, exampleIndices, data, fig, X, y, showLegend = True, color =
ax = fig.add_subplot(1,4,i+1)
imageDict = data[exampleIndices[i]]

X1, y1 = extractFeatures(imageDict, whichImage = 'image1bit', dist = 4)
X1, y1 = extractFeatures(imageDict, whichImage = b'image1bit', dist = 4)
im = makeGrayScale(imageDict)

matchingIndices = np.array([], dtype = 'int')
Expand Down Expand Up @@ -170,21 +170,21 @@ def testRules(rules, exampleIndices, data, fig, X, y, showLegend = True, color =
falseNegatives = np.logical_and(y1, np.logical_not(matchVec))

if color == 'Full':
fNImage = falseNegatives.reshape(imageDict['boxHeight'], imageDict['boxWidth'])
fNImage = falseNegatives.reshape(imageDict[b'boxHeight'], imageDict[b'boxWidth'])

#Paint with matches:
im[:,:,0][fNImage==1] = 1
im[:,:,1][fNImage==1] = 1
im[:,:,2][fNImage==1] = 0

tPImage = truePositives.reshape((imageDict['boxHeight'], imageDict['boxWidth']))
tPImage = truePositives.reshape((imageDict[b'boxHeight'], imageDict[b'boxWidth']))

#Paint with matches:
im[:,:,0][tPImage==1] = 0
im[:,:,1][tPImage==1] = 1
im[:,:,2][tPImage==1] = 0

fNImage = falsePositives.reshape((imageDict['boxHeight'], imageDict['boxWidth']))
fNImage = falsePositives.reshape((imageDict[b'boxHeight'], imageDict[b'boxWidth']))

#Paint with matches:
im[:,:,0][fNImage==1] = 1
Expand All @@ -193,7 +193,7 @@ def testRules(rules, exampleIndices, data, fig, X, y, showLegend = True, color =

if color == 'Green':

matchImage = matchVec.reshape((imageDict['boxHeight'], imageDict['boxWidth']))
matchImage = matchVec.reshape((imageDict[b'boxHeight'], imageDict[b'boxWidth']))

#Paint with matches:
im[:,:,0][matchImage==1] = 0
Expand Down Expand Up @@ -247,7 +247,7 @@ def showMatches(rules, exampleIndices, data, fig, verbose = True):

imageDict = data[exampleIndices[i]]

X, y = extractFeatures(imageDict, whichImage = 'image1bit', dist = 4)
X, y = extractFeatures(imageDict, whichImage = b'image1bit', dist = 4)
im = makeGrayScale(imageDict)

matchingIndices = np.array([], dtype = 'int')
Expand All @@ -259,7 +259,7 @@ def showMatches(rules, exampleIndices, data, fig, verbose = True):
matchVec = np.zeros(X.shape[0])
matchVec[matchingIndices] = 1

matchImage = matchVec.reshape((imageDict['boxHeight'], imageDict['boxWidth']))
matchImage = matchVec.reshape((imageDict[b'boxHeight'], imageDict[b'boxWidth']))

#Paint with matches:
im[:,:,0][matchImage==1] = 0
Expand All @@ -279,10 +279,10 @@ def testLogicalRules(exampleIndices, data, fig, X, y, rule):
ax = fig.add_subplot(1,4,i+1)
imageDict = data[exampleIndices[i]]

X1, y1 = extractFeatures(imageDict, whichImage = 'image1bit', dist = 4)
X1, y1 = extractFeatures(imageDict, whichImage = b'image1bit', dist = 4)

im = makeGrayScale(imageDict)
yImage = y1.reshape(imageDict['boxHeight'], imageDict['boxWidth'])
yImage = y1.reshape(imageDict[b'boxHeight'], imageDict[b'boxWidth'])

#Paint with matches:
im[:,:,0][yImage==1] = 1
Expand All @@ -294,14 +294,14 @@ def testLogicalRules(exampleIndices, data, fig, X, y, rule):
truePositives = np.logical_and(y1, yHat)
falsePositives = np.logical_and(np.logical_not(y1), yHat)

tPImage = truePositives.reshape((imageDict['boxHeight'], imageDict['boxWidth']))
tPImage = truePositives.reshape((imageDict[b'boxHeight'], imageDict[b'boxWidth']))

#Paint with matches:
im[:,:,0][tPImage==1] = 0
im[:,:,1][tPImage==1] = 1
im[:,:,2][tPImage==1] = 0

fNImage = falsePositives.reshape((imageDict['boxHeight'], imageDict['boxWidth']))
fNImage = falsePositives.reshape((imageDict[b'boxHeight'], imageDict[b'boxWidth']))

#Paint with matches:
im[:,:,0][fNImage==1] = 1
Expand Down Expand Up @@ -331,7 +331,7 @@ def testLogicalRules(exampleIndices, data, fig, X, y, rule):
# X1, y1 = extractFeatures(imageDict, whichImage = 'image1bit', dist = 4)

# im = makeGrayScale(imageDict)
# yImage = y1.reshape(imageDict['boxHeight'], imageDict['boxWidth'])
# yImage = y1.reshape(imageDict[b'boxHeight'], imageDict[b'boxWidth'])

# #Paint with matches:
# im[:,:,0][yImage==1] = 1
Expand All @@ -350,14 +350,14 @@ def testLogicalRules(exampleIndices, data, fig, X, y, rule):
# truePositives = np.logical_and(y1, matchVec)
# falsePositives = np.logical_and(np.logical_not(y1), matchVec)

# tPImage = truePositives.reshape((imageDict['boxHeight'], imageDict['boxWidth']))
# tPImage = truePositives.reshape((imageDict[b'boxHeight'], imageDict[b'boxWidth']))

# #Paint with matches:
# im[:,:,0][tPImage==1] = 0
# im[:,:,1][tPImage==1] = 1
# im[:,:,2][tPImage==1] = 0

# fNImage = falsePositives.reshape((imageDict['boxHeight'], imageDict['boxWidth']))
# fNImage = falsePositives.reshape((imageDict[b'boxHeight'], imageDict[b'boxWidth']))

# #Paint with matches:
# im[:,:,0][fNImage==1] = 1
Expand Down