sparkocr.utils.craft#

Classes#

Attributes#

class Craft(output_dir=None, rectify=True, export_extra=True, text_threshold=0.7, link_threshold=0.4, low_text=0.4, cuda=False, long_size=1280, refiner=True, crop_type='poly', weight_path_craft_net: str | None = None, weight_path_refine_net: str | None = None)#

craft_net = None#

crop_type = 'poly'#

cuda = False#

export_extra = True#

link_threshold = 0.4#

long_size = 1280#

low_text = 0.4#

output_dir = None#

rectify = True#

refine_net = None#

refiner = True#

text_threshold = 0.7#

detect_text(image, image_path=None)#

Parameters:

image – path to the image to be processed or numpy array or PIL image

Output:

{

“masks”: lists of predicted masks 2d as bool array, “boxes”: list of coords of points of predicted boxes, “boxes_as_ratios”: list of coords of points of predicted boxes as ratios of image size, “polys_as_ratios”: list of coords of points of predicted polys as ratios of image size, “heatmaps”: visualization of the detected characters/links, “text_crop_paths”: list of paths of the exported text boxes/polys, “times”: elapsed times of the sub modules, in seconds

}

load_craftnet_model(weight_path: str | None = None)#

Loads craftnet model

load_refinenet_model(weight_path: str | None = None)#

Loads refinenet model

unload_craftnet_model()#

Unloads craftnet model

unload_refinenet_model()#

Unloads refinenet model

empty_cuda_cache#

export_detected_regions#

export_extra_results#

get_prediction#

load_craftnet_model#

load_refinenet_model#

read_image#