def getGdcTransformFromPixelTransform(imageSize, pixelTransform, refImageGdcTransform):
'''Converts an image-to-image transform chained with a GDC transform
to a pixel to GDC transform for this image.'''
# Convert the image-to-image transform parameters to a class
temp = numpy.array([tform[0:3], tform[3:6], tform[6:9]] )
imageToRefTransform = transform.ProjectiveTransform(temp)
newImageSize = ImageFetcher.miscUtilities.getImageSize(newImagePath)
# Generate a list of point pairs
imagePoints = []
gdcPoints = []
# Loop through an evenly spaced grid of pixels in the new image
# - For each pixel, compute the desired output coordinate
pointPixelSpacing = (newImageSize[0] + newImageSize[1]) / 20 # Results in about 100 points
for r in range(0, newImageSize[0], pointPixelSpacing):
for c in range(0, newImageSize[1], pointPixelSpacing):
# Get pixel in new image and matching pixel in the reference image,
# then pass that into the GDC transform.
thisPixel = numpy.array([float(c), float(r)])
pixelInRefImage = pixelTransform.forward(thisPixel)
gdcCoordinate = refImageGdcTransform.forward(pixelInRefImage)
imagePoints.append(thisPixel)
gdcPoints.append(gdcCoordinate)
# Compute a transform object that converts from the new image to projected coordinates
imageToGdcTransform = transform.getTransform(numpy.asarray(worldPoints),
numpy.asarray(gdcPoints))
return imageToGdcTransform
def _textToTransform(self, text):
'''Converts a database text entry to a transform'''
# Make rows seperated by ;
t = text.replace('[','').replace(']',' ')
parts = [float(x) for x in t.split()]
mat = numpy.array([[parts[0], parts[1], parts[2]],
[parts[3], parts[4], parts[5]],
[parts[6], parts[7], parts[8]]],
dtype='float64')
tform = transform.ProjectiveTransform(mat)
return tform
def convertTransformToGeo(imageToRefImageTransform, newImagePath, refImagePath, refImageGeoTransform=None):
'''Converts an image-to-image homography to the ProjectiveTransform
class used elsewhere in geocam.
Either the reference image must be geo-registered, or the geo transform for it
must be provided.'''
# Convert the image-to-image transform parameters to a class
temp = numpy.array([imageToRefImageTransform[0:3],
imageToRefImageTransform[3:6],
imageToRefImageTransform[6:9]] )
imageToRefTransform = transform.ProjectiveTransform(temp)
newImageSize = IrgGeoFunctions.getImageSize(newImagePath)
refImageSize = IrgGeoFunctions.getImageSize(refImagePath)
# Get a pixel to GDC transform for the reference image
refPixelToGdcTransform = registration_common.getPixelToGdcTransform(
refImagePath, refImageGeoTransform)
# Generate a list of point pairs
imagePoints = []
projPoints = []
gdcPoints = []
print 'transform = \n' + str(imageToRefTransform.matrix)
# Loop through an evenly spaced grid of pixels in the new image
# - For each pixel, compute the desired output coordinate
pointPixelSpacing = (newImageSize[0] + newImageSize[1]) / 20 # Results in about 100 points
for r in range(0, newImageSize[0], pointPixelSpacing):
for c in range(0, newImageSize[1], pointPixelSpacing):
# Get pixel in new image and matching pixel in the reference image
thisPixel = numpy.array([float(c), float(r)])
pixelInRefImage = imageToRefTransform.forward(thisPixel)
# If any pixel transforms outside the reference image our transform
# is probably invalid but continue on skipping this pixel.
if ((not registration_common.isPixelValid(thisPixel, newImageSize)) or
(not registration_common.isPixelValid(pixelInRefImage, refImageSize))):
continue
# Compute the location of this pixel in the projected coordinate system
# used by the transform.py file.
if (not refImageGeoTransform):
# Use the geo information of the reference image
gdcCoordinate = refPixelToGdcTransform.forward(pixelInRefImage)
projectedCoordinate = transform.lonLatToMeters(gdcCoordinate)
else: # Use the user-provided transform
projectedCoordinate = refImageGeoTransform.forward(pixelInRefImage)
gdcCoordinate = transform.metersToLatLon(projectedCoordinate)
imagePoints.append(thisPixel)
projPoints.append(projectedCoordinate)
gdcPoints.append(gdcCoordinate)
#print str(thisPixel) + ' --> ' + str(gdcCoordinate) + ' <--> ' + str(projectedCoordinate)
# Compute a transform object that converts from the new image to projected coordinates
#print 'Converting transform to world coordinates...'
#testImageToProjectedTransform = transform.getTransform(numpy.asarray(worldPoints),
# numpy.asarray(imagePoints))
testImageToProjectedTransform = transform.ProjectiveTransform.fit(numpy.asarray(projPoints),
numpy.asarray(imagePoints))
testImageToGdcTransform = transform.ProjectiveTransform.fit(numpy.asarray(gdcPoints),
numpy.asarray(imagePoints))
#print refPixelToGdcTransform
#print testImageToProjectedTransform
#for i, w in zip(imagePoints, worldPoints):
# print str(i) + ' --> ' + str(w) + ' <--> ' + str(testImageToProjectedTransform.forward(i))
return (testImageToProjectedTransform, testImageToGdcTransform, refPixelToGdcTransform)
def getIdentityTransform():
'''Return an identity transform from the transform.py file'''
return transform.ProjectiveTransform(numpy.matrix([[1,0,0],[0,1,0],[0,0,1]],dtype='float64'))
