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 applyASM(self): ## Main
SA = ShapeAligner(self.asm, 0, self.out)
XShape, _ = self.initialPosition()
xShape = ActiveShape.createShape(self.x)
modelParams = SA.calcAlignTransBtwn(XShape, xShape,
np.ones(self.asm.n))
# print "params after init"
# print modelParams
#modelParams = SA.calcAlignTransBtwn( XShape , xShape , np.ones( self.asm.n ) )
self.s = modelParams['s']
self.theta = modelParams['theta']
self.Xc = self.genXc(modelParams)
self.X = np.add(xShape.M(self.s, self.theta).flatten(), self.Xc)
# print "Initial Current shape"
# print self.X
i = 0
while np.mean(self.calcdX()) > 0.000001 and i < 150:
print i
f, (ax1, ax2) = plt.subplots(1,
2) #, sharex = True, sharey = True )
xShape = ActiveShape.createShape(self.x)
self.X = np.add(xShape.M(self.s, self.theta).flatten(), self.Xc)
# Calculate point shifts
self.dX = self.calcdX()
# "dX result"
#print self.dX
## X + dX
self.XdX = np.add(self.X, self.dX)
ax1.imshow(self.img)
DrawFace(self.X, ax1).drawContrast()
DrawFace(self.XdX, ax1).drawBold()
DrawFace(self.X, ax2).drawContrast()
DrawFace(self.XdX, ax2).drawBold()
ax1.set_xlim(0, np.shape(self.img)[1])
ax1.set_ylim(np.shape(self.img)[0], 0)
ax2.invert_yaxis()
f.suptitle(
"Original Shape (Contrast) and Gradient Suggested Shape (Bold)"
)
f.savefig(os.path.join(self.out, "deformation-iter-%d.png" % i))
## Find X --> X + dX
XShape = ActiveShape.createShape(self.X)
XdXShape = ActiveShape.createShape(self.XdX)
deltaParams = SA.calcAlignTransBtwn(XdXShape, XShape,
np.ones(self.asm.n))
## Get transformation constrained delta parameters
self.d0 = deltaParams['theta']
self.ds = deltaParams['s']
self.dXc = self.genXc(deltaParams)
## Calculate dx
yShape = ActiveShape.createShape(self.y)
f, (ax1, ax2) = plt.subplots(1, 2)
yt = yShape.M(1 / (self.s * (1 + self.ds)),
-(self.theta + self.d0))
DrawFace(yShape, ax1).drawBold()
DrawFace(yt, ax2).drawBold()
f.suptitle("Transformed contour")
ax1.set_title("Original")
ax2.set_title("Delta'd")
ax1.invert_yaxis()
ax2.invert_yaxis()
f.savefig(os.path.join(self.out, "y-%d.png" % i))
plt.close()
self.dx = np.subtract(yt.flatten(), self.x)
## Calculate db
self.db = np.dot(np.transpose(self.P), self.dx)
## Update
self.theta += self.d0
self.s = self.s * (1 + self.ds)
self.Xc = np.add(self.Xc, self.dXc)
self.b = np.add(self.b, self.db)
f.clear()
plt.close()
i += 1
print "It took you %d iterations" % i
def applyASM(self): ## Main
SA = ShapeAligner(self.asm, 0, self.out)
XShape, _ = self.initialPosition()
xShape = ActiveShape.createShape(self.x)
modelParams = SA.calcAlignTransBtwn(XShape, xShape, np.ones(self.asm.n))
# print "params after init"
# print modelParams
# modelParams = SA.calcAlignTransBtwn( XShape , xShape , np.ones( self.asm.n ) )
self.s = modelParams["s"]
self.theta = modelParams["theta"]
self.Xc = self.genXc(modelParams)
self.X = np.add(xShape.M(self.s, self.theta).flatten(), self.Xc)
# print "Initial Current shape"
# print self.X
i = 0
while np.mean(self.calcdX()) > 0.000001 and i < 150:
print i
f, (ax1, ax2) = plt.subplots(1, 2) # , sharex = True, sharey = True )
xShape = ActiveShape.createShape(self.x)
self.X = np.add(xShape.M(self.s, self.theta).flatten(), self.Xc)
# Calculate point shifts
self.dX = self.calcdX()
# "dX result"
# print self.dX
## X + dX
self.XdX = np.add(self.X, self.dX)
ax1.imshow(self.img)
DrawFace(self.X, ax1).drawContrast()
DrawFace(self.XdX, ax1).drawBold()
DrawFace(self.X, ax2).drawContrast()
DrawFace(self.XdX, ax2).drawBold()
ax1.set_xlim(0, np.shape(self.img)[1])
ax1.set_ylim(np.shape(self.img)[0], 0)
ax2.invert_yaxis()
f.suptitle("Original Shape (Contrast) and Gradient Suggested Shape (Bold)")
f.savefig(os.path.join(self.out, "deformation-iter-%d.png" % i))
## Find X --> X + dX
XShape = ActiveShape.createShape(self.X)
XdXShape = ActiveShape.createShape(self.XdX)
deltaParams = SA.calcAlignTransBtwn(XdXShape, XShape, np.ones(self.asm.n))
## Get transformation constrained delta parameters
self.d0 = deltaParams["theta"]
self.ds = deltaParams["s"]
self.dXc = self.genXc(deltaParams)
## Calculate dx
yShape = ActiveShape.createShape(self.y)
f, (ax1, ax2) = plt.subplots(1, 2)
yt = yShape.M(1 / (self.s * (1 + self.ds)), -(self.theta + self.d0))
DrawFace(yShape, ax1).drawBold()
DrawFace(yt, ax2).drawBold()
f.suptitle("Transformed contour")
ax1.set_title("Original")
ax2.set_title("Delta'd")
ax1.invert_yaxis()
ax2.invert_yaxis()
f.savefig(os.path.join(self.out, "y-%d.png" % i))
plt.close()
self.dx = np.subtract(yt.flatten(), self.x)
## Calculate db
self.db = np.dot(np.transpose(self.P), self.dx)
## Update
self.theta += self.d0
self.s = self.s * (1 + self.ds)
self.Xc = np.add(self.Xc, self.dXc)
self.b = np.add(self.b, self.db)
f.clear()
plt.close()
i += 1
print "It took you %d iterations" % i
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)
q1 = np.percentile(d1, 0.25)
sd = np.std(d1)
max = np.max(d1)
q3 = np.percentile(d1, 0.75)
iqr = q3 - q1
cu1 = mn + 2 * sd
cu2 = mn + 3 * sd
s = 1.2
t = [[-2],[4] ]
rMat = [ [ s * math.cos( r ), - s* math.sin( r ) ] ,
[ s * math.sin( r ), s * math.cos( r ) ]]
def p( x, y ):
return [[x],[y]]
p1 = p( 1,1 )
p2 = p( 3,4 )
p3 = p( 4,2 )
def trans( p, rMat, t ):
return np.dot( rMat, p ) + t
n1 = trans( p1, rMat, t)
n2 = trans( p2, rMat, t)
n3 = trans( p3, rMat, t)
AS1 = ActiveShape( [ (1,1), (3,4), (4,2) ] )
AS2 = ActiveShape( [(-2, 5.697), (-2.849, 9.940), (-0.303, 9.091 ) ])
ASM = ActiveShapeModel( [0,1] )
SA = ShapeAligner( ASM, 100, "" )
tdict = SA.calcAlignTransBtwn( AS2, AS1, np.ones( 3 ) )
