binarization as a standalone command

qurator/eynollah/cli.py

@@ -2,6 +2,7 @@ import sys
 import click
 from ocrd_utils import initLogging, setOverrideLogLevel
 from qurator.eynollah.eynollah import Eynollah
+from qurator.eynollah.sbb_binarize import SbbBinarizer
 
 @click.group()
 def main():
@@ -48,6 +49,38 @@ def main():
 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)
     
+@main.command()
+@click.option('--patches/--no-patches', default=True, help='by enabling this parameter you let the model to see the image in patches.')
+
+@click.option('--model_dir', '-m', type=click.Path(exists=True, file_okay=False), required=True, help='directory containing models for prediction')
+
+@click.argument('input_image')
+
+@click.argument('output_image')
+@click.option(
+    "--dir_in",
+    "-di",
+    help="directory of images",
+    type=click.Path(exists=True, file_okay=False),
+)
+@click.option(
+    "--dir_out",
+    "-do",
+    help="directory where the binarized images will be written",
+    type=click.Path(exists=True, file_okay=False),
+)
+
+def binarization(patches, model_dir, input_image, output_image, dir_in, dir_out):
+    if not dir_out and (dir_in):
+        print("Error: You used -di but did not set -do")
+        sys.exit(1)
+    elif dir_out and not (dir_in):
+        print("Error: You used -do to write out binarized images but have not set -di")
+        sys.exit(1)
+    SbbBinarizer(model_dir).run(image_path=input_image, use_patches=patches, save=output_image, dir_in=dir_in, dir_out=dir_out)
+    
+    
+    
     
 @main.command()
 @click.option(

qurator/eynollah/eynollah.py

@@ -240,7 +240,6 @@ class Eynollah:
                 pcgts=pcgts)
         self.logger = logger if logger else getLogger('eynollah')
         self.dir_models = dir_models
-
         self.model_dir_of_enhancement = dir_models + "/eynollah-enhancement_20210425"
         self.model_dir_of_binarization = dir_models + "/eynollah-binarization_20210425"
         self.model_dir_of_col_classifier = dir_models + "/eynollah-column-classifier_20210425"
@@ -4769,9 +4768,9 @@ class Eynollah:
                             
                         textline_mask_tot_ea_deskew = resize_image(textline_mask_tot_ea,img_h_new, img_w_new )
                         
-                        slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea_deskew)
+                        slope_deskew, slope_first = 0, 0#self.run_deskew(textline_mask_tot_ea_deskew)
                     else:
-                        slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
+                        slope_deskew, slope_first = 0, 0#self.run_deskew(textline_mask_tot_ea)
                     #print("text region early -2,5 in %.1fs", time.time() - t0)
                     #self.logger.info("Textregion detection took %.1fs ", time.time() - t1t)
                     num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea, img_bin_light = \

qurator/eynollah/sbb_binarize.py

@@ -0,0 +1,383 @@
+"""
+Tool to load model and binarize a given image.
+"""
+
+import sys
+from glob import glob
+from os import environ, devnull
+from os.path import join
+from warnings import catch_warnings, simplefilter
+import os
+
+import numpy as np
+from PIL import Image
+import cv2
+environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+stderr = sys.stderr
+sys.stderr = open(devnull, 'w')
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.python.keras import backend as tensorflow_backend
+sys.stderr = stderr
+
+
+import logging
+
+def resize_image(img_in, input_height, input_width):
+    return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
+
+class SbbBinarizer:
+
+    def __init__(self, model_dir, logger=None):
+        self.model_dir = model_dir
+        self.log = logger if logger else logging.getLogger('SbbBinarizer')
+
+        self.start_new_session()
+
+        self.model_files = glob(self.model_dir+"/*/", recursive = True)
+
+        self.models = []
+        for model_file in self.model_files:
+            self.models.append(self.load_model(model_file))
+
+    def start_new_session(self):
+        config = tf.compat.v1.ConfigProto()
+        config.gpu_options.allow_growth = True
+
+        self.session = tf.compat.v1.Session(config=config)  # tf.InteractiveSession()
+        tensorflow_backend.set_session(self.session)
+
+    def end_session(self):
+        tensorflow_backend.clear_session()
+        self.session.close()
+        del self.session
+
+    def load_model(self, model_name):
+        model = load_model(join(self.model_dir, model_name), compile=False)
+        model_height = model.layers[len(model.layers)-1].output_shape[1]
+        model_width = model.layers[len(model.layers)-1].output_shape[2]
+        n_classes = model.layers[len(model.layers)-1].output_shape[3]
+        return model, model_height, model_width, n_classes
+
+    def predict(self, model_in, img, use_patches, n_batch_inference=5):
+        tensorflow_backend.set_session(self.session)
+        model, model_height, model_width, n_classes = model_in
+        
+        img_org_h = img.shape[0]
+        img_org_w = img.shape[1]
+        
+        if img.shape[0] < model_height and img.shape[1] >= model_width:
+            img_padded = np.zeros(( model_height, img.shape[1], img.shape[2] ))
+            
+            index_start_h =  int( abs( img.shape[0] - model_height) /2.)
+            index_start_w = 0
+            
+            img_padded [ index_start_h: index_start_h+img.shape[0], :, : ] = img[:,:,:]
+            
+        elif img.shape[0] >= model_height and img.shape[1] < model_width:
+            img_padded = np.zeros(( img.shape[0], model_width, img.shape[2] ))
+            
+            index_start_h =  0 
+            index_start_w = int( abs( img.shape[1] - model_width) /2.)
+            
+            img_padded [ :, index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
+            
+            
+        elif img.shape[0] < model_height and img.shape[1] < model_width:
+            img_padded = np.zeros(( model_height, model_width, img.shape[2] ))
+            
+            index_start_h =  int( abs( img.shape[0] - model_height) /2.)
+            index_start_w = int( abs( img.shape[1] - model_width) /2.)
+            
+            img_padded [ index_start_h: index_start_h+img.shape[0], index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
+            
+        else:
+            index_start_h = 0
+            index_start_w  = 0
+            img_padded = np.copy(img)
+            
+            
+        img = np.copy(img_padded)
+        
+            
+
+        if use_patches:
+
+            margin = int(0.1 * model_width)
+
+            width_mid = model_width - 2 * margin
+            height_mid = model_height - 2 * margin
+
+
+            img = img / float(255.0)
+
+            img_h = img.shape[0]
+            img_w = img.shape[1]
+
+            prediction_true = np.zeros((img_h, img_w, 3))
+            mask_true = np.zeros((img_h, img_w))
+            nxf = img_w / float(width_mid)
+            nyf = img_h / float(height_mid)
+
+            if nxf > int(nxf):
+                nxf = int(nxf) + 1
+            else:
+                nxf = int(nxf)
+
+            if nyf > int(nyf):
+                nyf = int(nyf) + 1
+            else:
+                nyf = int(nyf)
+                
+                
+            list_i_s = []
+            list_j_s = []
+            list_x_u = []
+            list_x_d = []
+            list_y_u = []
+            list_y_d = []
+            
+            batch_indexer = 0
+            
+            img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
+
+            for i in range(nxf):
+                for j in range(nyf):
+
+                    if i == 0:
+                        index_x_d = i * width_mid
+                        index_x_u = index_x_d + model_width
+                    elif i > 0:
+                        index_x_d = i * width_mid
+                        index_x_u = index_x_d + model_width
+
+                    if j == 0:
+                        index_y_d = j * height_mid
+                        index_y_u = index_y_d + model_height
+                    elif j > 0:
+                        index_y_d = j * height_mid
+                        index_y_u = index_y_d + model_height
+
+                    if index_x_u > img_w:
+                        index_x_u = img_w
+                        index_x_d = img_w - model_width
+                    if index_y_u > img_h:
+                        index_y_u = img_h
+                        index_y_d = img_h - model_height
+                        
+                        
+                    list_i_s.append(i)
+                    list_j_s.append(j)
+                    list_x_u.append(index_x_u)
+                    list_x_d.append(index_x_d)
+                    list_y_d.append(index_y_d)
+                    list_y_u.append(index_y_u)
+                    
+
+                    img_patch[batch_indexer,:,:,:] = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
+                    
+                    batch_indexer = batch_indexer + 1
+                    
+                    
+
+                    if batch_indexer == n_batch_inference:
+                        
+                        label_p_pred = model.predict(img_patch,verbose=0)
+                        
+                        seg = np.argmax(label_p_pred, axis=3)
+                        
+                        #print(seg.shape, len(seg), len(list_i_s))
+                        
+                        indexer_inside_batch = 0
+                        for i_batch, j_batch in zip(list_i_s, list_j_s):
+                            seg_in = seg[indexer_inside_batch,:,:]
+                            seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
+                            
+                            index_y_u_in = list_y_u[indexer_inside_batch]
+                            index_y_d_in = list_y_d[indexer_inside_batch]
+                            
+                            index_x_u_in = list_x_u[indexer_inside_batch]
+                            index_x_d_in = list_x_d[indexer_inside_batch]
+                            
+                            if i_batch == 0 and j_batch == 0:
+                                seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
+                            elif i_batch == nxf - 1 and j_batch == nyf - 1:
+                                seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
+                            elif i_batch == 0 and j_batch == nyf - 1:
+                                seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
+                            elif i_batch == nxf - 1 and j_batch == 0:
+                                seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
+                                prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
+                            elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
+                                seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
+                            elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
+                                seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
+                            elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
+                                seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
+                            elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
+                                seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
+                            else:
+                                seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
+                                
+                            indexer_inside_batch = indexer_inside_batch +1
+                                
+                        
+                        list_i_s = []
+                        list_j_s = []
+                        list_x_u = []
+                        list_x_d = []
+                        list_y_u = []
+                        list_y_d = []
+                        
+                        batch_indexer = 0
+                        
+                        img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
+                        
+                    elif i==(nxf-1) and j==(nyf-1):
+                        label_p_pred = model.predict(img_patch,verbose=0)
+                        
+                        seg = np.argmax(label_p_pred, axis=3)
+                        
+                        #print(seg.shape, len(seg), len(list_i_s))
+                        
+                        indexer_inside_batch = 0
+                        for i_batch, j_batch in zip(list_i_s, list_j_s):
+                            seg_in = seg[indexer_inside_batch,:,:]
+                            seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
+                            
+                            index_y_u_in = list_y_u[indexer_inside_batch]
+                            index_y_d_in = list_y_d[indexer_inside_batch]
+                            
+                            index_x_u_in = list_x_u[indexer_inside_batch]
+                            index_x_d_in = list_x_d[indexer_inside_batch]
+                            
+                            if i_batch == 0 and j_batch == 0:
+                                seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
+                            elif i_batch == nxf - 1 and j_batch == nyf - 1:
+                                seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
+                            elif i_batch == 0 and j_batch == nyf - 1:
+                                seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
+                            elif i_batch == nxf - 1 and j_batch == 0:
+                                seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
+                                prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
+                            elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
+                                seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
+                            elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
+                                seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
+                            elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
+                                seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
+                            elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
+                                seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
+                            else:
+                                seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
+                                prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
+                                
+                            indexer_inside_batch = indexer_inside_batch +1
+                                
+                        
+                        list_i_s = []
+                        list_j_s = []
+                        list_x_u = []
+                        list_x_d = []
+                        list_y_u = []
+                        list_y_d = []
+                        
+                        batch_indexer = 0
+                        
+                        img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
+            
+            
+            
+            prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
+            prediction_true = prediction_true.astype(np.uint8)
+
+        else:
+            img_h_page = img.shape[0]
+            img_w_page = img.shape[1]
+            img = img / float(255.0)
+            img = resize_image(img, model_height, model_width)
+
+            label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
+
+            seg = np.argmax(label_p_pred, axis=3)[0]
+            seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
+            prediction_true = resize_image(seg_color, img_h_page, img_w_page)
+            prediction_true = prediction_true.astype(np.uint8)
+        return prediction_true[:,:,0]
+
+    def run(self, image=None, image_path=None, save=None, use_patches=False, dir_in=None, dir_out=None):
+        print(dir_in,'dir_in')
+        if not dir_in:
+            if (image is not None and image_path is not None) or \
+                (image is None and image_path is None):
+                raise ValueError("Must pass either a opencv2 image or an image_path")
+            if image_path is not None:
+                image = cv2.imread(image_path)
+            img_last = 0
+            for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
+                self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
+
+                res = self.predict(model, image, use_patches)
+
+                img_fin = np.zeros((res.shape[0], res.shape[1], 3))
+                res[:, :][res[:, :] == 0] = 2
+                res = res - 1
+                res = res * 255
+                img_fin[:, :, 0] = res
+                img_fin[:, :, 1] = res
+                img_fin[:, :, 2] = res
+
+                img_fin = img_fin.astype(np.uint8)
+                img_fin = (res[:, :] == 0) * 255
+                img_last = img_last + img_fin
+
+            kernel = np.ones((5, 5), np.uint8)
+            img_last[:, :][img_last[:, :] > 0] = 255
+            img_last = (img_last[:, :] == 0) * 255
+            if save:
+                cv2.imwrite(save, img_last)
+            return img_last
+        else:
+            ls_imgs  = os.listdir(dir_in)
+            for image_name in ls_imgs:
+                image_stem = image_name.split('.')[0]
+                print(image_name,'image_name')
+                image = cv2.imread(os.path.join(dir_in,image_name) )
+                img_last = 0
+                for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
+                    self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
+
+                    res = self.predict(model, image, use_patches)
+
+                    img_fin = np.zeros((res.shape[0], res.shape[1], 3))
+                    res[:, :][res[:, :] == 0] = 2
+                    res = res - 1
+                    res = res * 255
+                    img_fin[:, :, 0] = res
+                    img_fin[:, :, 1] = res
+                    img_fin[:, :, 2] = res
+
+                    img_fin = img_fin.astype(np.uint8)
+                    img_fin = (res[:, :] == 0) * 255
+                    img_last = img_last + img_fin
+
+                kernel = np.ones((5, 5), np.uint8)
+                img_last[:, :][img_last[:, :] > 0] = 255
+                img_last = (img_last[:, :] == 0) * 255
+                
+                cv2.imwrite(os.path.join(dir_out,image_stem+'.png'), img_last)