boxfile = 'data/' + imgfile + '.box'
new_image = ImageFile(imgfile)
# delete the box file if it's empty
if (isfile(boxfile)):
if (getsize(boxfile) == 0):
remove(boxfile)
# define what to focus on and ignore in the image
stack = [
segmentation_filters.LargeFilter(),
segmentation_filters.SmallFilter(),
segmentation_filters.LargeAreaFilter(),
segmentation_filters.ContainedFilter()
]
# process image, defining useful-looking segments
segmenter = ContourSegmenter(blur_y=5,
blur_x=5,
block_size=11,
c=10,
filters=stack)
segments = segmenter.process(new_image.image)
# uncomment to watch the segmenter in action
#segmenter.display()
grounder = UserGrounder()
grounder.ground(new_image, segments)
new_image.ground.write()
terse = args.terse
force_train = args.retrain
use_tesseract = args.tesseract
tesslangpath = args.tesslangpath
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)
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"
from files import ImageFile
from grounding import UserGrounder
from segmentation import ContourSegmenter, draw_segments
segmenter= ContourSegmenter( blur_y=5, blur_x=5, block_size=11, c=10)
new_image= ImageFile('digits1')
segments= segmenter.process(new_image.image)
grounder= UserGrounder()
grounder.ground(new_image, segments);
new_image.ground.write()
