Provide OCR as an option to process a directory of XML files, incorporating layout and text line coordinates.

src/eynollah/cli.py

@@ -1,7 +1,7 @@
 import sys
 import click
 from ocrd_utils import initLogging, setOverrideLogLevel
-from eynollah.eynollah import Eynollah
+from eynollah.eynollah import Eynollah, Eynollah_ocr
 from eynollah.sbb_binarize import SbbBinarizer
 
 @click.group()
@@ -305,6 +305,60 @@ def layout(image, out, dir_in, model, save_images, save_layout, save_deskewed, s
     else:
         pcgts = eynollah.run()
         eynollah.writer.write_pagexml(pcgts)
+        
+        
+@main.command()
+@click.option(
+    "--dir_in",
+    "-di",
+    help="directory of images",
+    type=click.Path(exists=True, file_okay=False),
+)
+@click.option(
+    "--out",
+    "-o",
+    help="directory to write output xml data",
+    type=click.Path(exists=True, file_okay=False),
+    required=True,
+)
+@click.option(
+    "--dir_xmls",
+    "-dx",
+    help="directory of xmls",
+    type=click.Path(exists=True, file_okay=False),
+)
+@click.option(
+    "--model",
+    "-m",
+    help="directory of models",
+    type=click.Path(exists=True, file_okay=False),
+    required=True,
+)
+@click.option(
+    "--tr_ocr",
+    "-trocr/-notrocr",
+    is_flag=True,
+    help="if this parameter set to true, transformer ocr will be applied, otherwise cnn_rnn model.",
+)
+@click.option(
+    "--log_level",
+    "-l",
+    type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']),
+    help="Override log level globally to this",
+)
+
+def ocr(dir_in, out, dir_xmls, model, tr_ocr, log_level):
+    if log_level:
+        setOverrideLogLevel(log_level)
+    initLogging()
+    eynollah_ocr = Eynollah_ocr(
+        dir_xmls=dir_xmls,
+        dir_in=dir_in,
+        dir_out=out,
+        dir_models=model,
+        tr_ocr=tr_ocr,
+    )
+    eynollah_ocr.run()
 
 if __name__ == "__main__":
     main()

src/eynollah/eynollah.py

@@ -41,6 +41,9 @@ import matplotlib.pyplot as plt
 # use tf1 compatibility for keras backend
 from tensorflow.compat.v1.keras.backend import set_session
 from tensorflow.keras import layers
+import json
+import xml.etree.ElementTree as ET
+from tensorflow.keras.layers import StringLookup
 
 from .utils.contour import (
     filter_contours_area_of_image,
@@ -2188,18 +2191,18 @@ class Eynollah:
         img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
         
         if not self.dir_in:
-            prediction_textline = self.do_prediction(patches, img, model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3, thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version)
+            ###prediction_textline = self.do_prediction(patches, img, model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3, thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version)
             
-            ##prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, model_textline, n_batch_inference=3)
+            prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, model_textline, n_batch_inference=3)
             
             #if not thresholding_for_artificial_class_in_light_version:
                 #if num_col_classifier==1:
                     #prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
                     #prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
         else:
-            prediction_textline = self.do_prediction(patches, img, self.model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3,thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version)
+            ##prediction_textline = self.do_prediction(patches, img, self.model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3,thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version)
             
-            ###prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, self.model_textline, n_batch_inference=3)
+            prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, self.model_textline, n_batch_inference=3)
             #if not thresholding_for_artificial_class_in_light_version:
                 #if num_col_classifier==1:
                     #prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
@@ -2479,17 +2482,17 @@ class Eynollah:
                 if num_col_classifier == 1 or num_col_classifier == 2:
                     model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np)
                     if self.image_org.shape[0]/self.image_org.shape[1] > 2.5:
-                        ##prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True)
+                        prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True)
-                        prediction_regions_org = self.do_prediction_new_concept(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True)
+                        ###prediction_regions_org = self.do_prediction_new_concept(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True)
                     else:
                         prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3))
-                        ##prediction_regions_page = self.do_prediction_new_concept_scatter_nd(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True)
+                        prediction_regions_page = self.do_prediction_new_concept_scatter_nd(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True)
-                        prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True)
+                        ##prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True)
                         prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
                 else:
                     model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np)
-                    prediction_regions_org = self.do_prediction_new_concept(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
+                    ###prediction_regions_org = self.do_prediction_new_concept(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
-                    ###prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
+                    prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
                 ##model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
                 ##prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
             else:
@@ -5610,3 +5613,394 @@ class Eynollah:
         if self.dir_in:
             self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)
             print("all Job done in %.1fs", time.time() - t0_tot)
+            
+            
+class Eynollah_ocr:
+    def __init__(
+        self,
+        dir_models,
+        dir_xmls=None,
+        dir_in=None,
+        dir_out=None,
+        tr_ocr=False,
+        logger=None,
+    ):
+        self.dir_in = dir_in
+        self.dir_out = dir_out
+        self.dir_xmls = dir_xmls
+        self.dir_models = dir_models
+        self.tr_ocr = tr_ocr
+        if tr_ocr:
+            self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed")
+            self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+            self.model_ocr_dir = dir_models + "/trocr_model_ens_of_3_checkpoints_201124"
+            self.model_ocr = VisionEncoderDecoderModel.from_pretrained(self.model_ocr_dir)
+            self.model_ocr.to(self.device)
+
+        else:
+            self.model_ocr_dir = dir_models + "/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
+            model_ocr = load_model(self.model_ocr_dir , compile=False)
+            
+            self.prediction_model = tf.keras.models.Model(
+                            model_ocr.get_layer(name = "image").input, 
+                            model_ocr.get_layer(name = "dense2").output)
+
+                
+            with open(os.path.join(self.model_ocr_dir, "characters_org.txt"),"r") as config_file:
+                characters = json.load(config_file)
+
+                
+            AUTOTUNE = tf.data.AUTOTUNE
+
+            # Mapping characters to integers.
+            char_to_num = StringLookup(vocabulary=list(characters), mask_token=None)
+
+            # Mapping integers back to original characters.
+            self.num_to_char = StringLookup(
+                vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
+            )
+        
+    def decode_batch_predictions(self, pred, max_len = 128):
+        # input_len is the product of the batch size and the
+        # number of time steps.
+        input_len = np.ones(pred.shape[0]) * pred.shape[1]
+        
+        # Decode CTC predictions using greedy search.
+        # decoded is a tuple with 2 elements.
+        decoded = tf.keras.backend.ctc_decode(pred, 
+                        input_length = input_len, 
+                                    beam_width = 100)
+        # The outputs are in the first element of the tuple.
+        # Additionally, the first element is actually a list,
+        # therefore we take the first element of that list as well.
+        #print(decoded,'decoded')
+        decoded = decoded[0][0][:, :max_len]
+        
+        #print(decoded, decoded.shape,'decoded')
+
+        output = []
+        for d in decoded:
+            # Convert the predicted indices to the corresponding chars.
+            d = tf.strings.reduce_join(self.num_to_char(d))
+            d = d.numpy().decode("utf-8")
+            output.append(d)
+        return output
+        
+        
+    def distortion_free_resize(self, image, img_size):
+        w, h = img_size
+        image = tf.image.resize(image, size=(h, w), preserve_aspect_ratio=True)
+
+        # Check tha amount of padding needed to be done.
+        pad_height = h - tf.shape(image)[0]
+        pad_width = w - tf.shape(image)[1]
+
+        # Only necessary if you want to do same amount of padding on both sides.
+        if pad_height % 2 != 0:
+            height = pad_height // 2
+            pad_height_top = height + 1
+            pad_height_bottom = height
+        else:
+            pad_height_top = pad_height_bottom = pad_height // 2
+
+        if pad_width % 2 != 0:
+            width = pad_width // 2
+            pad_width_left = width + 1
+            pad_width_right = width
+        else:
+            pad_width_left = pad_width_right = pad_width // 2
+
+        image = tf.pad(
+            image,
+            paddings=[
+                [pad_height_top, pad_height_bottom],
+                [pad_width_left, pad_width_right],
+                [0, 0],
+            ],
+        )
+
+        image = tf.transpose(image, (1, 0, 2))
+        image = tf.image.flip_left_right(image)
+        return image
+    
+    def return_start_and_end_of_common_text_of_textline_ocr_without_common_section(self, textline_image):
+        width = np.shape(textline_image)[1]
+        height = np.shape(textline_image)[0]
+        common_window = int(0.06*width)
+
+        width1 = int ( width/2. - common_window )
+        width2 = int ( width/2. + common_window )
+        
+        img_sum = np.sum(textline_image[:,:,0], axis=0)
+        sum_smoothed = gaussian_filter1d(img_sum, 3)
+        
+        peaks_real, _ = find_peaks(sum_smoothed, height=0)
+        
+        if len(peaks_real)>70:
+
+            peaks_real = peaks_real[(peaks_real<width2) & (peaks_real>width1)]
+
+            arg_max = np.argmax(sum_smoothed[peaks_real])
+
+            peaks_final = peaks_real[arg_max]
+            
+            return peaks_final
+        else:
+            return None
+    
+    def return_textlines_split_if_needed(self, textline_image):
+
+        split_point = self.return_start_and_end_of_common_text_of_textline_ocr_without_common_section(textline_image)
+        if split_point:
+            image1 = textline_image[:, :split_point,:]# image.crop((0, 0, width2, height))
+            image2 = textline_image[:, split_point:,:]#image.crop((width1, 0, width, height))
+            return [image1, image2]
+        else:
+            return None
+    
+    def run(self):
+        ls_imgs = os.listdir(self.dir_in)
+        
+        if self.tr_ocr:
+            b_s = 2
+            for ind_img in ls_imgs:
+                t0 = time.time()
+                file_name = ind_img.split('.')[0]
+                dir_img = os.path.join(self.dir_in, ind_img)
+                dir_xml = os.path.join(self.dir_xmls, file_name+'.xml')
+                out_file_ocr = os.path.join(self.dir_out, file_name+'.xml')
+                img = cv2.imread(dir_img)
+
+                ##file_name = Path(dir_xmls).stem
+                tree1 = ET.parse(dir_xml, parser = ET.XMLParser(encoding = 'iso-8859-5'))
+                root1=tree1.getroot()
+                alltags=[elem.tag for elem in root1.iter()]
+                link=alltags[0].split('}')[0]+'}'
+
+                name_space = alltags[0].split('}')[0]
+                name_space = name_space.split('{')[1]
+
+                region_tags=np.unique([x for x in alltags if x.endswith('TextRegion')]) 
+                        
+                    
+                    
+                cropped_lines = []
+                cropped_lines_region_indexer = []
+                cropped_lines_meging_indexing = []
+
+                indexer_text_region = 0
+                for nn in root1.iter(region_tags):
+                    for child_textregion in nn:
+                        if child_textregion.tag.endswith("TextLine"):
+                            
+                            for child_textlines in child_textregion:
+                                if child_textlines.tag.endswith("Coords"):
+                                    cropped_lines_region_indexer.append(indexer_text_region)
+                                    p_h=child_textlines.attrib['points'].split(' ')
+                                    textline_coords =  np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ]  for x in p_h] )
+                                    x,y,w,h = cv2.boundingRect(textline_coords)
+                                    
+                                    h2w_ratio = h/float(w)
+                                    
+                                    img_poly_on_img = np.copy(img)
+                                    mask_poly = np.zeros(img.shape)
+                                    mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1))
+                                    
+                                    mask_poly = mask_poly[y:y+h, x:x+w, :]
+                                    img_crop = img_poly_on_img[y:y+h, x:x+w, :]
+                                    img_crop[mask_poly==0] = 255
+                                    
+                                    if h2w_ratio > 0.05:
+                                        cropped_lines.append(img_crop)
+                                        cropped_lines_meging_indexing.append(0)
+                                    else:
+                                        splited_images = self.return_textlines_split_if_needed(img_crop)
+                                        #print(splited_images)
+                                        if splited_images:
+                                            cropped_lines.append(splited_images[0])
+                                            cropped_lines_meging_indexing.append(1)
+                                            cropped_lines.append(splited_images[1])
+                                            cropped_lines_meging_indexing.append(-1)
+                                        else:
+                                            cropped_lines.append(img_crop)
+                                            cropped_lines_meging_indexing.append(0)
+                    indexer_text_region = indexer_text_region +1
+        
+        
+                extracted_texts = []
+                n_iterations  = math.ceil(len(cropped_lines) / b_s) 
+
+                for i in range(n_iterations):
+                    if i==(n_iterations-1):
+                        n_start = i*b_s
+                        imgs = cropped_lines[n_start:]
+                    else:
+                        n_start = i*b_s
+                        n_end = (i+1)*b_s
+                        imgs = cropped_lines[n_start:n_end]
+                    pixel_values_merged = self.processor(imgs, return_tensors="pt").pixel_values
+                    generated_ids_merged = self.model_ocr.generate(pixel_values_merged.to(self.device))
+                    generated_text_merged = self.processor.batch_decode(generated_ids_merged, skip_special_tokens=True)
+                    
+                    extracted_texts = extracted_texts + generated_text_merged
+
+                extracted_texts_merged = [extracted_texts[ind]  if cropped_lines_meging_indexing[ind]==0 else extracted_texts[ind]+extracted_texts[ind+1] if cropped_lines_meging_indexing[ind]==1 else None for ind in range(len(cropped_lines_meging_indexing))]
+
+                extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None]
+                #print(extracted_texts_merged, len(extracted_texts_merged))
+
+                unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer)
+
+                #print(len(unique_cropped_lines_region_indexer), 'unique_cropped_lines_region_indexer')
+                text_by_textregion = []
+                for ind in unique_cropped_lines_region_indexer:
+                    extracted_texts_merged_un = np.array(extracted_texts_merged)[np.array(cropped_lines_region_indexer)==ind]
+                    
+                    text_by_textregion.append(" ".join(extracted_texts_merged_un))
+                    
+                #print(len(text_by_textregion) , indexer_text_region, "text_by_textregion")
+
+
+                #print(time.time() - t0 ,'elapsed time')
+
+
+                indexer = 0
+                indexer_textregion = 0
+                for nn in root1.iter(region_tags):
+                    text_subelement_textregion = ET.SubElement(nn, 'TextEquiv')
+                    unicode_textregion = ET.SubElement(text_subelement_textregion, 'Unicode')
+
+                    
+                    has_textline = False
+                    for child_textregion in nn:
+                        if child_textregion.tag.endswith("TextLine"):
+                            text_subelement = ET.SubElement(child_textregion, 'TextEquiv')
+                            unicode_textline = ET.SubElement(text_subelement, 'Unicode')
+                            unicode_textline.text = extracted_texts_merged[indexer]
+                            indexer = indexer + 1
+                            has_textline = True
+                    if has_textline:
+                        unicode_textregion.text = text_by_textregion[indexer_textregion]
+                        indexer_textregion = indexer_textregion + 1
+                        
+
+
+                ET.register_namespace("",name_space)
+                tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf8",default_namespace=None)
+                #print("Job done in %.1fs", time.time() - t0)
+        else:
+            max_len = 512
+            padding_token = 299
+            image_width = max_len * 4
+            image_height = 32
+            b_s = 8
+
+
+            img_size=(image_width, image_height)
+            
+            for ind_img in ls_imgs:
+                t0 = time.time()
+                file_name = ind_img.split('.')[0]
+                dir_img = os.path.join(self.dir_in, ind_img)
+                dir_xml = os.path.join(self.dir_xmls, file_name+'.xml')
+                out_file_ocr = os.path.join(self.dir_out, file_name+'.xml')
+                img = cv2.imread(dir_img)
+
+                tree1 = ET.parse(dir_xml, parser = ET.XMLParser(encoding = 'iso-8859-5'))
+                root1=tree1.getroot()
+                alltags=[elem.tag for elem in root1.iter()]
+                link=alltags[0].split('}')[0]+'}'
+
+                name_space = alltags[0].split('}')[0]
+                name_space = name_space.split('{')[1]
+
+                region_tags=np.unique([x for x in alltags if x.endswith('TextRegion')]) 
+                    
+                cropped_lines = []
+                cropped_lines_region_indexer = []
+                cropped_lines_meging_indexing = []
+                
+                tinl = time.time()
+                indexer_text_region = 0
+                for nn in root1.iter(region_tags):
+                    for child_textregion in nn:
+                        if child_textregion.tag.endswith("TextLine"):
+                            
+                            for child_textlines in child_textregion:
+                                if child_textlines.tag.endswith("Coords"):
+                                    cropped_lines_region_indexer.append(indexer_text_region)
+                                    p_h=child_textlines.attrib['points'].split(' ')
+                                    textline_coords =  np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ]  for x in p_h] )
+                                    x,y,w,h = cv2.boundingRect(textline_coords)
+                                    
+                                    h2w_ratio = h/float(w)
+                                    
+                                    img_poly_on_img = np.copy(img)
+                                    mask_poly = np.zeros(img.shape)
+                                    mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1))
+                                    
+                                    mask_poly = mask_poly[y:y+h, x:x+w, :]
+                                    img_crop = img_poly_on_img[y:y+h, x:x+w, :]
+                                    img_crop[mask_poly==0] = 255
+                                    img_crop = tf.reverse(img_crop,axis=[-1])
+                                    img_crop = self.distortion_free_resize(img_crop, img_size)
+                                    img_crop = tf.cast(img_crop, tf.float32) / 255.0
+                                    cropped_lines.append(img_crop)
+
+                    indexer_text_region = indexer_text_region +1
+                    
+                
+                extracted_texts = []
+
+                n_iterations  = math.ceil(len(cropped_lines) / b_s) 
+
+                for i in range(n_iterations):
+                    if i==(n_iterations-1):
+                        n_start = i*b_s
+                        imgs = cropped_lines[n_start:]
+                        imgs = np.array(imgs)
+                        imgs = imgs.reshape(imgs.shape[0], image_width, image_height, 3)
+                    else:
+                        n_start = i*b_s
+                        n_end = (i+1)*b_s
+                        imgs = cropped_lines[n_start:n_end]
+                        imgs = np.array(imgs).reshape(b_s, image_width, image_height, 3)
+                        
+
+                    preds = self.prediction_model.predict(imgs, verbose=0)
+                    pred_texts = self.decode_batch_predictions(preds)
+
+                    for ib in range(imgs.shape[0]):
+                        pred_texts_ib = pred_texts[ib].strip("[UNK]")
+                        extracted_texts.append(pred_texts_ib)
+                
+                unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer)
+                
+                text_by_textregion = []
+                for ind in unique_cropped_lines_region_indexer:
+                    extracted_texts_merged_un = np.array(extracted_texts)[np.array(cropped_lines_region_indexer)==ind]
+                    
+                    text_by_textregion.append(" ".join(extracted_texts_merged_un))
+                    
+                indexer = 0
+                indexer_textregion = 0
+                for nn in root1.iter(region_tags):
+                    text_subelement_textregion = ET.SubElement(nn, 'TextEquiv')
+                    unicode_textregion = ET.SubElement(text_subelement_textregion, 'Unicode')
+
+                    
+                    has_textline = False
+                    for child_textregion in nn:
+                        if child_textregion.tag.endswith("TextLine"):
+                            text_subelement = ET.SubElement(child_textregion, 'TextEquiv')
+                            unicode_textline = ET.SubElement(text_subelement, 'Unicode')
+                            unicode_textline.text = extracted_texts[indexer]
+                            indexer = indexer + 1
+                            has_textline = True
+                    if has_textline:
+                        unicode_textregion.text = text_by_textregion[indexer_textregion]
+                        indexer_textregion = indexer_textregion + 1
+
+                ET.register_namespace("",name_space)
+                tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf8",default_namespace=None)
+                #print("Job done in %.1fs", time.time() - t0)