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@@ -0,0 +1,148 @@
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+from os import mkdir
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+from os.path import exists
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+from math import acos
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+from pylab import imsave, array, zeros, inv, dot, norm, svd, floor
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+from xml.dom.minidom import parse
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+from Point import Point
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+from GrayscaleImage import GrayscaleImage
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+
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+class LearningSetGenerator:
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+
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+ def __init__(self, folder_nr, file_nr):
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+ filename = '%04d/00991_%04d%02d' % (folder_nr, folder_nr, file_nr)
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+
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+ self.image = GrayscaleImage('../images/Images/%s.jpg' % filename)
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+ self.read_xml(filename)
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+
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+ # sets the entire license plate of an image
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+ def retrieve_data(self, corners):
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+ x0, y0 = corners[0].to_tuple()
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+ x1, y1 = corners[1].to_tuple()
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+ x2, y2 = corners[2].to_tuple()
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+ x3, y3 = corners[3].to_tuple()
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+
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+ M = int(1.2 * (max(x0, x1, x2, x3) - min(x0, x1, x2, x3)))
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+ N = max(y0, y1, y2, y3) - min(y0, y1, y2, y3)
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+
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+ matrix = array([
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+ [x0, y0, 1, 0, 0, 0, 0, 0, 0],
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+ [ 0, 0, 0, x0, y0, 1, 0, 0, 0],
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+ [x1, y1, 1, 0, 0, 0, -M * x0, -M * y1, -M],
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+ [ 0, 0, 0, x1, y1, 1, 0, 0, 0],
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+ [x2, y2, 1, 0, 0, 0, -M * x2, -M * y2, -M],
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+ [ 0, 0, 0, x2, y2, 1, -N * x2, -N * y2, -N],
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+ [x3, y3, 1, 0, 0, 0, 0, 0, 0],
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+ [ 0, 0, 0, x3, y3, 1, -N * x3, -N * y3, -N]
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+ ])
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+
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+ P = inv(self.get_transformation_matrix(matrix))
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+ data = array([zeros(M, float)] * N)
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+
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+ for i in range(0, M):
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+ for j in range(0, N):
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+ or_coor = dot(P, ([[i],[j],[1]]))
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+ or_coor_h = (or_coor[1][0] / or_coor[2][0],
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+ or_coor[0][0] / or_coor[2][0])
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+
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+ data[j][i] = self.pV(or_coor_h[0], or_coor_h[1])
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+
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+ return data
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+
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+ def get_transformation_matrix(self, matrix):
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+ # Get the vector p and the values that are in there by taking the SVD.
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+ # Since D is diagonal with the eigenvalues sorted from large to small
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+ # on the diagonal, the optimal q in min ||Dq|| is q = [[0]..[1]].
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+ # Therefore, p = Vq means p is the last column in V.
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+ U, D, V = svd(matrix)
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+ p = V[8][:]
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+
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+ return array([
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+ [ p[0], p[1], p[2] ],
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+ [ p[3], p[4], p[5] ],
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+ [ p[6], p[7], p[8] ]
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+ ])
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+
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+ def pV(self, x, y):
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+ image = self.image
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+
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+ #Get the value of a point (interpolated x, y) in the given image
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+ if image.in_bounds(x, y):
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+ x_low = floor(x)
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+ x_high = floor(x + 1)
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+ y_low = floor(y)
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+ y_high = floor(y + 1)
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+ x_y = (x_high - x_low) * (y_high - y_low)
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+
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+ a = x_high - x
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+ b = y_high - y
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+ c = x - x_low
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+ d = y - y_low
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+
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+ return image[x_low, y_low] / x_y * a * b \
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+ + image[x_high, y_low] / x_y * c * b \
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+ + image[x_low , y_high] / x_y * a * d \
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+ + image[x_high, y_high] / x_y * c * d
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+
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+ return 0
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+
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+ def read_xml(self, filename):
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+ dom = parse('../images/Infos/%s.info' % filename)
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+ self.characters = []
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+
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+ version = dom.getElementsByTagName("current-version")[0].firstChild.data
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+ info = dom.getElementsByTagName("info")
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+
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+ for i in info:
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+ if version == i.getElementsByTagName("version")[0].firstChild.data:
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+
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+ self.country = i.getElementsByTagName("identification-letters")[0].firstChild.data
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+ temp = i.getElementsByTagName("characters")
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+
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+ if len(temp):
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+ characters = temp[0].childNodes
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+ else:
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+ self.characters = []
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+ break
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+
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+ for i, character in enumerate(characters):
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+ if character.nodeName == "character":
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+ value = character.getElementsByTagName("char")[0].firstChild.data
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+ corners = self.get_corners(character)
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+
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+ if not len(corners) == 4:
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+ break
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+
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+ image = GrayscaleImage(data = self.retrieve_data(corners))
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+
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+ print value
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+
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+ path = "../images/LearningSet/%s" % value
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+ image_path = "%s/%d_%s.jpg" % (path, i, filename.split('/')[-1])
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+
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+ if not exists(path):
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+ mkdir(path)
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+
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+ if not exists(image_path):
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+ image.save(image_path)
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+
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+ break
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+
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+ def get_corners(self, dom):
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+ nodes = dom.getElementsByTagName("point")
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+
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+ corners = []
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+
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+ for node in nodes:
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+ corners.append(Point(node))
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+
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+ return corners
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+
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+
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+for i in range(1):
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+ for j in range(1):
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+ try:
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+ filename = '%04d/00991_%04d%02d.info' % (i, i, j)
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+ print 'loading file "%s"' % filename
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+ plate = LearningSetGenerator(i, j)
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+ except:
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+ print "failure"
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Taddeus Kroes