def tileBoundsLonLat(zoom, x, y):
corners = ((x, y), (x + 1, y + 1))
pixelCorners = [tileIndexToPixels(*corner) for corner in corners]
mercatorCorners = [
transform.pixelsToMeters(*(pixels + (zoom, )))
for pixels in pixelCorners
]
nw, se = [transform.metersToLatLon(c) for c in mercatorCorners]
west, north = nw
east, south = se
return {'north': north, 'south': south, 'east': east, 'west': west}
def imageMapBounds(imageSize, tform):
w, h = imageSize
imageCorners = cornerPoints([0, 0, w, h])
mercatorCorners = [tform.forward(c) for c in imageCorners]
latLonCorners = [transform.metersToLatLon(c) for c in mercatorCorners]
bounds = Bounds(latLonCorners)
return {
'west': bounds.xmin,
'south': bounds.ymin,
'east': bounds.xmax,
'north': bounds.ymax
}
def parseManualEntryPointPairs(self, manualInfoDict):
'''Unpack the inlier point pairs from an entry in the geocamTiePoint_overlay table'''
imageInliers = []
gdcInliers = []
allPoints = manualInfoDict['points']
for point in allPoints:
if (point[0] == None) or (point[1] == None) or (point[2] == None) or (point[3] == None):
continue # Skip buggy entries!
# The world coordinates in the manual table are stored in projected coordinates
gdcCoord = transform.metersToLatLon((point[0], point[1]))
gdcInliers.append(gdcCoord)
imageInliers.append((point[2], point[3]))
return (imageInliers, gdcInliers)
def getPixelToGdcTransform(imagePath, pixelToProjectedTransform=None):
'''Returns a pixel to GDC transform.
The input image must either be a nicely georegistered image from Earth Engine
or a pixel to projected coordinates transform must be provided.'''
if pixelToProjectedTransform:
# Have image to projected transform, convert it to an image to GDC transform.
# Use the simple file info call (the input file may not have geo information)
(width, height) = IrgGeoFunctions.getImageSize(imagePath)
imagePoints = []
gdcPoints = []
# Loop through a spaced out grid of pixels in the image
pointPixelSpacing = (width +
height) / 20 # Results in about 100 points
for r in range(0, width, pointPixelSpacing):
for c in range(0, height, pointPixelSpacing):
# This pixel --> projected coords --> lonlat coord
thisPixel = numpy.array([float(c), float(r)])
projectedCoordinate = pixelToProjectedTransform.forward(
thisPixel)
gdcCoordinate = transform.metersToLatLon(projectedCoordinate)
imagePoints.append(thisPixel)
gdcPoints.append(gdcCoordinate)
# Solve for a transform with all of these point pairs
pixelToGdcTransform = transform.getTransform(
numpy.asarray(gdcPoints), numpy.asarray(imagePoints))
else: # Using a reference image from EE which will have nice bounds.
# Use the more thorough file info call
stats = IrgGeoFunctions.getImageGeoInfo(imagePath, False)
(width, height) = stats['image_size']
(minLon, maxLon, minLat, maxLat) = stats['lonlat_bounds']
# Make a transform from ref pixel to GDC using metadata on disk
xScale = (maxLon - minLon) / width
yScale = (maxLat - minLat) / height
transformMatrix = numpy.array([[xScale, 0, minLon],
[0, -yScale, maxLat], [0, 0, 1]])
pixelToGdcTransform = transform.LinearTransform(transformMatrix)
return pixelToGdcTransform
def generateGeotiffExport(self, exportName, metaJson, slug):
"""
This generates a geotiff from RPC.
"""
overlay = Overlay.objects.get(alignedQuadTree = self)
imageSizeType = overlay.imageData.sizeType
now = datetime.datetime.utcnow()
timestamp = now.strftime('%Y-%m-%d-%H%M%S-UTC')
# get image width and height
imageWidth = overlay.imageData.width
imageHeight = overlay.imageData.height
# update the center point with current transform and use those values
transformDict = overlay.extras.transform
tform = transform.makeTransform(transformDict)
center_meters = tform.forward([imageWidth / 2, imageHeight / 2])
clon, clat = transform.metersToLatLon(center_meters)
# get the RPC values
T_rpc = rpcModel.fitRpcToModel(self.reversePts,
imageWidth, imageHeight,
clon, clat)
srs = gdalUtil.EPSG_4326
# get original image
imgPath = overlay.getRawImageData().image.url.replace('/data/', settings.DATA_ROOT)
# reproject and tar the output tiff
geotiffExportName = exportName + ('-%s-geotiff_%s' % (imageSizeType, timestamp))
geotiffFolderPath = settings.DATA_ROOT + 'geocamTiePoint/export/' + geotiffExportName
dosys('mkdir %s' % geotiffFolderPath)
fullFilePath = geotiffFolderPath + '/' + geotiffExportName +'.tif'
gdalUtil.reprojectWithRpcMetadata(imgPath, T_rpc.getVrtMetadata(), srs, fullFilePath)
geotiff_writer = quadTree.TarWriter(geotiffExportName)
arcName = geotiffExportName + '.tif'
geotiff_writer.writeData('meta.json', dumps(metaJson))
geotiff_writer.addFile(fullFilePath, geotiffExportName + '/' + arcName) # double check this line (second arg may not be necessary)
self.geotiffExportName = '%s.tar.gz' % geotiffExportName
self.geotiffExport.save(self.geotiffExportName,
ContentFile(geotiff_writer.getData()))
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)