Ejemplo n.º 1
0
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()
Ejemplo n.º 2
0
    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)
Ejemplo n.º 3
0
    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"
Ejemplo n.º 4
0
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()