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worked on test

Richard Torenvliet %!s(int64=14) %!d(string=hai) anos
pai
achega
c1140af0bc
Modificáronse 2 ficheiros con 12 adicións e 2 borrados
  1. BIN=BIN
      images/test9.png
  2. 12 2
      src/combined_test.py

BIN=BIN
images/test9.png


+ 12 - 2
src/combined_test.py

@@ -3,6 +3,7 @@ from LocalBinaryPatternizer import LocalBinaryPatternizer
 from LetterCropper import LetterCropper
 from LetterCropper import LetterCropper
 from matplotlib.pyplot import imshow, subplot, show, axis
 from matplotlib.pyplot import imshow, subplot, show, axis
 from NormalizedImage import NormalizedImage
 from NormalizedImage import NormalizedImage
+from GaussianFilter import GaussianFilter
 
 
 # Comment added by Richard Torenvliet
 # Comment added by Richard Torenvliet
 # Steps in this test files are
 # Steps in this test files are
@@ -14,11 +15,15 @@ from NormalizedImage import NormalizedImage
 
 
 # Image is now an instance of class GrayscaleImage
 # Image is now an instance of class GrayscaleImage
 # GrayscaleImage has functions like resize, crop etc.
 # GrayscaleImage has functions like resize, crop etc.
-image = GrayscaleImage("../images/test.png")
+image = GrayscaleImage("../images/test9.png")
+filter = GaussianFilter(1.4)
+
+image = filter.get_filtered_copy(image)
 
 
 # Crops image; param threshold is optional: LetterCropper(image, threshold=0.9)
 # Crops image; param threshold is optional: LetterCropper(image, threshold=0.9)
 # image: GrayscaleImage, threshold: float
 # image: GrayscaleImage, threshold: float
-cropper = LetterCropper(image, 0.9)
+
+cropper = LetterCropper(image, 0.7)
 cropped_letter = cropper.get_cropped_letter()
 cropped_letter = cropper.get_cropped_letter()
 
 
 # Show difference in shape
 # Show difference in shape
@@ -29,11 +34,16 @@ print cropped_letter.shape
 norm = NormalizedImage(cropped_letter)
 norm = NormalizedImage(cropped_letter)
 resized = norm.get_normalized_letter()
 resized = norm.get_normalized_letter()
 
 
+print resized.show()
+
+show()
 # Difference is noticable
 # Difference is noticable
 print resized.shape
 print resized.shape
 
 
 lbp = LocalBinaryPatternizer(resized)
 lbp = LocalBinaryPatternizer(resized)
+
 feature_vector = lbp.create_features_vector()
 feature_vector = lbp.create_features_vector()
+print feature_vector
 feature_vector /= 255 # Prepare for displaying -> 0 - 255 -> 0 - 1
 feature_vector /= 255 # Prepare for displaying -> 0 - 255 -> 0 - 1
         
         
 subplot(141)
 subplot(141)