def getEben( self ):
i = 20
tr = 500
out = "C:\\Users\\Valerie\\Desktop\\output\\20-500-1"
### END OF NEED?
fh = FileHelper( i, tr, out, self.fPts, False)
trainImg = fh.readInEbenImg()
trainPts = fh.readInOneDude( '000_1_1.pts')
return trainImg, ActiveShape( trainPts )
def process( fPts, fImgs, train, method, maxPx, fSize ):
params ={ "nIters" : 7,
"nTrain" : 500,
"filterPts" : fPts,
"filterImgs" : fImgs,
"trainASM" : train,
"writePCA" : False,
"maxPx" : maxPx,
"fSize" : fSize,
"method" : method
}
params = OrderedDict( sorted( params.items(), key = lambda t : len(t[0]) ) )
##Methods: "SSD", "NCorr", or "grad"
ASMout = getASMfolder( params )
appASMout = getMethodSubFolder( params, ASMout )
fh = FileHelper( params["nIters"], params["nTrain"], ASMout, params["filterPts"], params["filterImgs"] )
if params["filterPts"]:
asm = ActiveShapeModel( [43,35] )
else:
asm = ActiveShapeModel( [36,31] )
if params["trainASM"]:
asm = fh.readInPoints( asm )
asm = ShapeAligner( asm, params["nIters"], ASMout ).alignTrainingSet( )
fh.writeOutASM( asm ) #write out to read in later
else:
asm = fh.readInASM( asm )
### Calculate Principal components
asm.PCA()
if params["trainASM"] or params["writePCA"]:
### Draw PCA stuffs
if not os.path.exists( os.path.join( ASMout, "PCA" ) ) :
os.mkdir( os.path.join( ASMout, "PCA" ) )
wu = WriteUp( asm, fh )
wu.PCAresults()
if not params["trainASM"] :
### Apply ASM to image
img = cv2.imread( "C:\Users\Valerie\Desktop\MicroExpress\CASME2\CASME2_RAW\CASME2-RAW\sub01\EP02_01f\img1.jpg")
img = cv2.cvtColor( img, cv2.COLOR_BGR2GRAY)
appASM = ApplyASM( asm, params["nIters"], params["nTrain"],
appASMout, img, params['method'],
params['maxPx'], params['fSize'], params["filterPts"] )
appASM.applyASM()
def process(fPts, fImgs, train, method, maxPx, fSize):
params = {
"nIters": 7,
"nTrain": 500,
"filterPts": fPts,
"filterImgs": fImgs,
"trainASM": train,
"writePCA": False,
"maxPx": maxPx,
"fSize": fSize,
"method": method
}
params = OrderedDict(sorted(params.items(), key=lambda t: len(t[0])))
##Methods: "SSD", "NCorr", or "grad"
ASMout = getASMfolder(params)
appASMout = getMethodSubFolder(params, ASMout)
fh = FileHelper(params["nIters"], params["nTrain"], ASMout,
params["filterPts"], params["filterImgs"])
if params["filterPts"]:
asm = ActiveShapeModel([43, 35])
else:
asm = ActiveShapeModel([36, 31])
if params["trainASM"]:
asm = fh.readInPoints(asm)
asm = ShapeAligner(asm, params["nIters"], ASMout).alignTrainingSet()
fh.writeOutASM(asm) #write out to read in later
else:
asm = fh.readInASM(asm)
### Calculate Principal components
asm.PCA()
if params["trainASM"] or params["writePCA"]:
### Draw PCA stuffs
if not os.path.exists(os.path.join(ASMout, "PCA")):
os.mkdir(os.path.join(ASMout, "PCA"))
wu = WriteUp(asm, fh)
wu.PCAresults()
if not params["trainASM"]:
### Apply ASM to image
img = cv2.imread(
"C:\Users\Valerie\Desktop\MicroExpress\CASME2\CASME2_RAW\CASME2-RAW\sub01\EP02_01f\img1.jpg"
)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
appASM = ApplyASM(asm, params["nIters"], params["nTrain"], appASMout,
img, params['method'], params['maxPx'],
params['fSize'], params["filterPts"])
appASM.applyASM()
row = rows.flatten()
col = cols.flatten()
return map( lambda x: Vector( x[0], x[1] ), zip( row, col ) )
def coordOffset( pt, n ):
y = pt.y #row
x = pt.x #col
return x - ( n - 1)/2, y - (n-1)/1
#def run( ):
i = 20
tr = 500
out = "C:\\Users\\Valerie\\Desktop\\output\\20-500-1"
fh = FileHelper( i, tr, out )
ebenFace = fh.readInImage()
ebenPoints = fh.readInOneDude( '000_1_1.pts')
ebenShape = ActiveShape( ebenPoints )
#### MATCHING PROCESS
# Read in image
I = cv2.imread( "C:\Users\Valerie\Desktop\MicroExpress\CASME2\CASME2_RAW\CASME2-RAW\sub01\EP02_01f\img1.jpg")
I = cv2.cvtColor( I, cv2.COLOR_BGR2GRAY)
# Align shape
asm = ActiveShapeModel( [36,31] )
asm = fh.readInASM( asm )
asm.PCA()
from helpers.DrawFace import DrawFace from shapes.ActiveShape import ActiveShape from active_shape_models.ActiveShapeModel import ActiveShapeModel from active_shape_models.ApplyASM import ApplyASM from shapes.Vector import Vector from matplotlib import pyplot as plt from matplotlib import gridspec import numpy as np import math from image_processing.TemplateMatcher import TemplateMatcher i = 20 tr = 500 out = "C:\\Users\\Valerie\\Desktop\\output\\ASMTraining-MessingAround\\20-500-1" fh = FileHelper( i, tr, out, False, False ) ebenFace = fh.readInImage() ebenPoints = fh.readInOneDude( '000_1_1.pts') ebenShape = ActiveShape( ebenPoints ) ## draw indices DrawFace( ebenShape, plt).labelIndices() plt.imshow( ebenFace ) plt.set_cmap( "gray" ) plt.gca().axes.xaxis.set_ticks([]) plt.gca().axes.yaxis.set_ticks([]) plt.show() fh = FileHelper( i, tr, out, True, False )
if not os.path.exists( os.path.join( output, study ) ) and (train or align ):
os.mkdir( os.path.join( output, study ) )
output = os.path.join( output, study )
else:
while os.path.exists( os.path.join( output, study )):
i += 1
study = "%d-%d-%d" % ( iters, training, i )
if train or align:
os.mkdir( os.path.join( output, study ) )
else:
study = "%d-%d-%d" % ( iters, training, i )
output = os.path.join( output, study )
fh = FileHelper( iters, training , output )
if train:
asm = ActiveShapeModel( [36,31] )
asm = fh.readInPoints( asm )
### Align Shapes
if align:
asm = ShapeAligner( asm, iters, output ).alignTrainingSet( )
fh.writeOutASM( asm ) #write out to read in later
d = map( lambda x: x.shapeDist( asm.normMeanShape ), asm.allShapes )
d1 = np.mean( d, 1 )
min = np.min( d1 )
i = 0
study = "%d-%d-%d" % (iters, training, i)
if not os.path.exists(os.path.join(output, study)) and (train or align):
os.mkdir(os.path.join(output, study))
output = os.path.join(output, study)
else:
while os.path.exists(os.path.join(output, study)):
i += 1
study = "%d-%d-%d" % (iters, training, i)
if train or align:
os.mkdir(os.path.join(output, study))
else:
study = "%d-%d-%d" % (iters, training, i)
output = os.path.join(output, study)
fh = FileHelper(iters, training, output)
if train:
asm = ActiveShapeModel([36, 31])
asm = fh.readInPoints(asm)
### Align Shapes
if align:
asm = ShapeAligner(asm, iters, output).alignTrainingSet()
fh.writeOutASM(asm) #write out to read in later
d = map(lambda x: x.shapeDist(asm.normMeanShape), asm.allShapes)
d1 = np.mean(d, 1)
min = np.min(d1)
mn = np.mean(d1)