sbb_pixelwise_segmentation/generate_gt_for_training.py

import click
import json
from gt_gen_utils import *
from tqdm import tqdm

@click.group()
def main():
    pass

@main.command()
@click.option(
    "--dir_xml",
    "-dx",
    help="directory of GT page-xml files",
    type=click.Path(exists=True, file_okay=False),
)
@click.option(
    "--dir_images",
    "-di",
    help="directory of org images. If print space cropping or scaling is needed for labels it would be great to provide the original images to apply the same function on them. So if -ps is not set true or in config files no columns_width key is given this argumnet can be ignored. File stems in this directory should be the same as those in dir_xml.",
    type=click.Path(exists=True, file_okay=False),
)
@click.option(
    "--dir_out_images",
    "-doi",
    help="directory where the output org images after undergoing a process (like print space cropping or scaling) will be written.",
    type=click.Path(exists=True, file_okay=False),
)
@click.option(
    "--dir_out",
    "-do",
    help="directory where ground truth label images would be written",
    type=click.Path(exists=True, file_okay=False),
)

@click.option(
    "--config",
    "-cfg",
    help="config file of prefered layout or use case.",
    type=click.Path(exists=True, dir_okay=False),
)

@click.option(
    "--type_output",
    "-to",
    help="this defines how output should be. A 2d image array or a 3d image array encoded with RGB color. Just pass 2d or 3d. The file will be saved one directory up. 2D image array is 3d but only information of one channel would be enough since all channels have the same values.",
)
@click.option(
    "--printspace",
    "-ps",
    is_flag=True,
    help="if this parameter set to true, generated labels and in the case of provided org images cropping will be imposed and cropped labels and images will be written in output directories.",
)

def pagexml2label(dir_xml,dir_out,type_output,config, printspace, dir_images, dir_out_images):
    if config:
        with open(config) as f:
            config_params = json.load(f)
    else:
        print("passed")
        config_params = None
    gt_list = get_content_of_dir(dir_xml)
    get_images_of_ground_truth(gt_list,dir_xml,dir_out,type_output, config, config_params, printspace, dir_images, dir_out_images)
    
@main.command()
@click.option(
    "--dir_imgs",
    "-dis",
    help="directory of images with high resolution.",
    type=click.Path(exists=True, file_okay=False),
)
@click.option(
    "--dir_out_images",
    "-dois",
    help="directory where degraded images will be written.",
    type=click.Path(exists=True, file_okay=False),
)

@click.option(
    "--dir_out_labels",
    "-dols",
    help="directory where original images will be written as labels.",
    type=click.Path(exists=True, file_okay=False),
)
@click.option(
    "--scales",
    "-scs",
    help="json dictionary where the scales are written.",
    type=click.Path(exists=True, dir_okay=False),
)
def image_enhancement(dir_imgs, dir_out_images, dir_out_labels, scales):
    ls_imgs = os.listdir(dir_imgs)
    with open(scales) as f:
        scale_dict = json.load(f)
    ls_scales = scale_dict['scales']

    for img in tqdm(ls_imgs):
        img_name = img.split('.')[0]
        img_type = img.split('.')[1]
        image = cv2.imread(os.path.join(dir_imgs, img))
        for i, scale in enumerate(ls_scales):
            height_sc = int(image.shape[0]*scale)
            width_sc = int(image.shape[1]*scale)
            
            image_down_scaled = resize_image(image, height_sc, width_sc)
            image_back_to_org_scale = resize_image(image_down_scaled, image.shape[0], image.shape[1])
            
            cv2.imwrite(os.path.join(dir_out_images, img_name+'_'+str(i)+'.'+img_type), image_back_to_org_scale)
            cv2.imwrite(os.path.join(dir_out_labels, img_name+'_'+str(i)+'.'+img_type), image)
    
    
@main.command()
@click.option(
    "--dir_xml",
    "-dx",
    help="directory of GT page-xml files",
    type=click.Path(exists=True, file_okay=False),
)

@click.option(
    "--dir_out_modal_image",
    "-domi",
    help="directory where ground truth images would be written",
    type=click.Path(exists=True, file_okay=False),
)

@click.option(
    "--dir_out_classes",
    "-docl",
    help="directory where ground truth classes would be written",
    type=click.Path(exists=True, file_okay=False),
)

@click.option(
    "--input_height",
    "-ih",
    help="input height",
)
@click.option(
    "--input_width",
    "-iw",
    help="input width",
)
@click.option(
    "--min_area_size",
    "-min",
    help="min area size of regions considered for reading order training.",
)

def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, input_height, input_width, min_area_size):
    xml_files_ind = os.listdir(dir_xml)
    input_height = int(input_height)
    input_width = int(input_width)
    min_area = float(min_area_size)

    indexer_start= 0#55166
    max_area = 1
    #min_area = 0.0001

    for ind_xml in tqdm(xml_files_ind):
        indexer = 0
        #print(ind_xml)
        #print('########################')
        xml_file = os.path.join(dir_xml,ind_xml )
        f_name = ind_xml.split('.')[0]
        _, _, _, file_name, id_paragraph, id_header,co_text_paragraph,co_text_header,tot_region_ref,x_len, y_len,index_tot_regions,img_poly = read_xml(xml_file)
        
        id_all_text = id_paragraph + id_header
        co_text_all = co_text_paragraph + co_text_header
        
        
        _, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, _ = find_new_features_of_contours(co_text_header)
        
        img_header_and_sep = np.zeros((y_len,x_len), dtype='uint8')

        for j in range(len(cy_main)):
            img_header_and_sep[int(y_max_main[j]):int(y_max_main[j])+12,int(x_min_main[j]):int(x_max_main[j]) ] = 1


        texts_corr_order_index  = [index_tot_regions[tot_region_ref.index(i)] for i in id_all_text ]
        texts_corr_order_index_int = [int(x) for x in texts_corr_order_index]
        

        co_text_all, texts_corr_order_index_int = filter_contours_area_of_image(img_poly, co_text_all, texts_corr_order_index_int, max_area, min_area)
        
        arg_array = np.array(range(len(texts_corr_order_index_int)))
        
        labels_con = np.zeros((y_len,x_len,len(arg_array)),dtype='uint8')
        for i in range(len(co_text_all)):
            img_label = np.zeros((y_len,x_len,3),dtype='uint8')
            img_label=cv2.fillPoly(img_label, pts =[co_text_all[i]], color=(1,1,1))
            
            img_label[:,:,0][img_poly[:,:,0]==5] = 2
            img_label[:,:,0][img_header_and_sep[:,:]==1] = 3
            
            labels_con[:,:,i] = img_label[:,:,0]
        
        for i in range(len(texts_corr_order_index_int)):
            for j in range(len(texts_corr_order_index_int)):
                if i!=j:
                    input_multi_visual_modal = np.zeros((input_height,input_width,3)).astype(np.int8)
                    final_f_name = f_name+'_'+str(indexer+indexer_start)
                    order_class_condition = texts_corr_order_index_int[i]-texts_corr_order_index_int[j]
                    if order_class_condition<0:
                        class_type = 1
                    else:
                        class_type = 0

                    input_multi_visual_modal[:,:,0] = resize_image(labels_con[:,:,i], input_height, input_width)
                    input_multi_visual_modal[:,:,1] = resize_image(img_poly[:,:,0], input_height, input_width)
                    input_multi_visual_modal[:,:,2] = resize_image(labels_con[:,:,j], input_height, input_width)

                    np.save(os.path.join(dir_out_classes,final_f_name+'.npy' ), class_type)
                    
                    cv2.imwrite(os.path.join(dir_out_modal_image,final_f_name+'.png' ), input_multi_visual_modal)
                    indexer = indexer+1

    
    
if __name__ == "__main__":
    main()