def test_textgrounder(self):
grounder = TextGrounder()
characters = "0" * len(self.segments)
grounder.ground(self.img, self.segments, characters)
self.assertTrue(self.img.is_grounded())
self.assertEquals(reconstruct_chars(self.img.ground.classes),
characters)
def test_textgrounder_wrong_len(self):
grounder = TextGrounder()
characters = "0" * len(self.segments)
with self.assertRaises(ValueError):
grounder.ground(self.img, self.segments, characters[:-4])
self.assertFalse(self.img.is_grounded())
trainingsegmenter = ContourSegmenter(
blur_y=1,
blur_x=1,
min_width=3,
min_height=15,
max_height=50,
min_area=30,
block_size=3,
c=5,
nearline_tolerance=10.0) # tweaked for white font
segments = trainingsegmenter.process(training_image.image)
if verbose:
trainingsegmenter.display()
# grounder = UserGrounder() # interactive version; lets the user review, assign ground truth data
grounder = TextGrounder(
) # non-interactive ground-truth - assumes clean, ordered input
grounder.ground(training_image, segments,
"0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
) # writes out a .box file of image ground truths
ocr.train(training_image)
# Classify given image(s) using training data
test_images = []
dummy_name = args.dir + "\\dummy.jpg"
if os.path.isfile(dummy_name):
os.remove(dummy_name)
if args.file != None and len(args.file) > 0:
for file_to_classify in args.file:
img = find_image_file(file_to_classify)
if img:
segmenter = MinContourSegmenter(blur_y=5, blur_x=5, min_width=5, block_size=17, c=6, max_ratio=4.0)
extractor = SimpleFeatureExtractor(feature_size=10, stretch=False)
classifier = KNNClassifier(k=3 )
ocr = OCR(segmenter, extractor, classifier)
for file_to_train in args.trainfile:
training_image = ImageFile(file_to_train)
if not training_image.isGrounded() or force_train:
#trainingsegmenter = ContourSegmenter(blur_y=1, blur_x=1, min_width=3, min_height=15, max_height=50, min_area=30, block_size=23, c=3) # tweaked for black font
trainingsegmenter = ContourSegmenter(blur_y=1, blur_x=1, min_width=3, min_height=15, max_height=50, min_area=30, block_size=3 , c=5, nearline_tolerance=10.0 ) # tweaked for white font
segments = trainingsegmenter.process(training_image.image)
if verbose:
trainingsegmenter.display()
# grounder = UserGrounder() # interactive version; lets the user review, assign ground truth data
grounder = TextGrounder() # non-interactive ground-truth - assumes clean, ordered input
grounder.ground(training_image, segments, "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ") # writes out a .box file of image ground truths
ocr.train(training_image)
# Classify given image(s) using training data
test_images = []
dummy_name = args.dir + "\\dummy.jpg"
if os.path.isfile(dummy_name):
os.remove(dummy_name)
if args.file != None and len(args.file) > 0:
for file_to_classify in args.file:
img = find_image_file(file_to_classify)
if img:
test_images.append(img)