def test_ocr_digits(self):
# get data from images
img1 = ImageFile('digits1')
img2 = ImageFile('digits2')
ground_truth = img2.ground.classes
img2.remove_ground()
# create OCR
segmenter = ContourSegmenter()
extractor = SimpleFeatureExtractor()
classifier = KNNClassifier()
ocr = OCR(segmenter, extractor, classifier)
# train and test
ocr.train(img1)
chars, classes, _ = ocr.ocr(img2, show_steps=False)
self.assertEqual(list(classes), list(ground_truth))
self.assertEqual(chars, reconstruct_chars(ground_truth))
def _test_ocr(self, train_file, test_file):
# get data from images
ground_truth = test_file.ground.classes
test_file.remove_ground()
# create OCR
segmenter = ContourSegmenter(blur_y=5, blur_x=5)
extractor = SimpleFeatureExtractor()
classifier = KNNClassifier()
ocr = OCR(segmenter, extractor, classifier)
# train and test
ocr.train(train_file)
chars, classes, _ = ocr.ocr(test_file, show_steps=False)
print chars
print reconstruct_chars(ground_truth)
self.assertEqual(chars, reconstruct_chars(ground_truth))
self.assertEqual(list(classes), list(ground_truth))
return result_classes
########NEW FILE########
__FILENAME__ = example
from files import ImageFile
from segmentation import ContourSegmenter, draw_segments
from feature_extraction import SimpleFeatureExtractor
from classification import KNNClassifier
from ocr import OCR, accuracy, show_differences, reconstruct_chars
segmenter= ContourSegmenter( blur_y=5, blur_x=5, block_size=11, c=10)
extractor= SimpleFeatureExtractor( feature_size=10, stretch=False )
classifier= KNNClassifier()
ocr= OCR( segmenter, extractor, classifier )
ocr.train( ImageFile('digits1') )
test_image= ImageFile('digits2')
test_classes, test_segments= ocr.ocr( test_image, show_steps=True )
print "accuracy:", accuracy( test_image.ground.classes, test_classes )
print "OCRed text:\n", reconstruct_chars( test_classes )
show_differences( test_image.image, test_segments, test_image.ground.classes, test_classes)
########NEW FILE########
__FILENAME__ = feature_extraction
import numpy
import cv2
from segmentation import region_from_segment
from opencv_utils import background_color
from files import ImageFile
from segmentation import ContourSegmenter
from feature_extraction import SimpleFeatureExtractor
from classification import KNNClassifier
from ocr import OCR, accuracy, show_differences
segmenter = ContourSegmenter(blur_y=5, blur_x=5, block_size=11, c=10)
extractor = SimpleFeatureExtractor(feature_size=10, stretch=False)
classifier = KNNClassifier()
ocr = OCR(segmenter, extractor, classifier)
ocr.train(ImageFile('digits1'))
test_image = ImageFile('digits2')
test_chars, test_classes, test_segments = ocr.ocr(test_image, show_steps=True)
print("accuracy:", accuracy(test_image.ground.classes, test_classes))
print("OCRed text:\n", test_chars)
from files import ImageFile
from segmentation import ContourSegmenter, draw_segments
from feature_extraction import SimpleFeatureExtractor
from classification import KNNClassifier
from ocr import OCR, accuracy, show_differences, reconstruct_chars
segmenter = ContourSegmenter(blur_y=5, blur_x=5, block_size=11, c=10)
extractor = SimpleFeatureExtractor(feature_size=10, stretch=False)
classifier = KNNClassifier()
ocr = OCR(segmenter, extractor, classifier)
ocr.train(ImageFile('digits1'), ImageFile('alpha'))
test_image = ImageFile('alpha')
test_classes, test_segments = ocr.ocr(test_image, show_steps=True)
print "accuracy:", accuracy(test_image.ground.classes, test_classes)
print "OCRed text:\n", reconstruct_chars(test_classes)
show_differences(test_image.image, test_segments, test_image.ground.classes,
test_classes)
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)
elif args.dir != None:
test_images = get_image_filenames(args.dir)
else:
raise Exception("Need --dir [directory] or [--file <image> ..]")
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)
elif args.dir != None:
test_images = get_image_filenames(args.dir)
else:
raise Exception("Need --dir [directory] or [--file <image> ..]")
