passing number of columns as an argument

qurator/eynollah/cli.py

@@ -191,6 +191,16 @@ def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, i
     is_flag=True,
     help="if this parameter set to true, this tool will try to do ocr",
 )
+@click.option(
+    "--num_col_upper",
+    "-ncu",
+    help="lower limit of columns in document image",
+)
+@click.option(
+    "--num_col_lower",
+    "-ncl",
+    help="upper limit of columns in document image",
+)
 @click.option(
     "--log_level",
     "-l",
@@ -198,7 +208,7 @@ def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, i
     help="Override log level globally to this",
 )
 
-def layout(image, out, dir_in, model, save_images, save_layout, save_deskewed, save_all, save_page, enable_plotting, allow_enhancement, curved_line, textline_light, full_layout, tables, right2left, input_binary, allow_scaling, headers_off, light_version, reading_order_machine_based, do_ocr, ignore_page_extraction, log_level):
+def layout(image, out, dir_in, model, save_images, save_layout, save_deskewed, save_all, save_page, enable_plotting, allow_enhancement, curved_line, textline_light, full_layout, tables, right2left, input_binary, allow_scaling, headers_off, light_version, reading_order_machine_based, do_ocr, num_col_upper, num_col_lower, ignore_page_extraction, log_level):
     if log_level:
         setOverrideLogLevel(log_level)
     initLogging()
@@ -235,6 +245,8 @@ def layout(image, out, dir_in, model, save_images, save_layout, save_deskewed, s
         ignore_page_extraction=ignore_page_extraction,
         reading_order_machine_based=reading_order_machine_based,
         do_ocr=do_ocr,
+        num_col_upper=num_col_upper,
+        num_col_lower=num_col_lower,
     )
     if dir_in:
         eynollah.run()

qurator/eynollah/eynollah.py

@@ -178,6 +178,8 @@ class Eynollah:
         ignore_page_extraction=False,
         reading_order_machine_based=False,
         do_ocr=False,
+        num_col_upper=None,
+        num_col_lower=None,
         override_dpi=None,
         logger=None,
         pcgts=None,
@@ -212,6 +214,14 @@ class Eynollah:
         self.headers_off = headers_off
         self.ignore_page_extraction = ignore_page_extraction
         self.ocr = do_ocr
+        if num_col_upper:
+            self.num_col_upper = int(num_col_upper)
+        else:
+            self.num_col_upper = num_col_upper
+        if num_col_lower:
+            self.num_col_lower = int(num_col_lower)
+        else:
+            self.num_col_lower = num_col_lower
         self.pcgts = pcgts
         if not dir_in:
             self.plotter = None if not enable_plotting else EynollahPlotter(
@@ -597,36 +607,80 @@ class Eynollah:
         else:
             img = self.imread()
             img_bin = None
-
+        
+        width_early = img.shape[1]
         t1 = time.time()
         _, page_coord = self.early_page_for_num_of_column_classification(img_bin)
         if not self.dir_in:
             model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
         
-        if self.input_binary:
+        if self.num_col_upper and not self.num_col_lower:
-            img_in = np.copy(img)
+            num_col = self.num_col_upper
-            width_early = img_in.shape[1]
+            label_p_pred = [np.ones(6)]
-            img_in = img_in / 255.0
+        elif self.num_col_lower and not self.num_col_upper:
-            img_in = cv2.resize(img_in, (448, 448), interpolation=cv2.INTER_NEAREST)
+            num_col = self.num_col_lower
-            img_in = img_in.reshape(1, 448, 448, 3)
+            label_p_pred = [np.ones(6)]
-        else:
+        
-            img_1ch = self.imread(grayscale=True)
+        elif (not self.num_col_upper and not self.num_col_lower):
-            width_early = img_1ch.shape[1]
+            if self.input_binary:
-            img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
+                img_in = np.copy(img)
+                img_in = img_in / 255.0
+                img_in = cv2.resize(img_in, (448, 448), interpolation=cv2.INTER_NEAREST)
+                img_in = img_in.reshape(1, 448, 448, 3)
+            else:
+                img_1ch = self.imread(grayscale=True)
+                width_early = img_1ch.shape[1]
+                img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
 
-            img_1ch = img_1ch / 255.0
+                img_1ch = img_1ch / 255.0
-            img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
+                img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
-            img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
+                img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
-            img_in[0, :, :, 0] = img_1ch[:, :]
+                img_in[0, :, :, 0] = img_1ch[:, :]
-            img_in[0, :, :, 1] = img_1ch[:, :]
+                img_in[0, :, :, 1] = img_1ch[:, :]
-            img_in[0, :, :, 2] = img_1ch[:, :]
+                img_in[0, :, :, 2] = img_1ch[:, :]
 
 
-        if self.dir_in:
+            if self.dir_in:
-            label_p_pred = self.model_classifier.predict(img_in, verbose=0)
+                label_p_pred = self.model_classifier.predict(img_in, verbose=0)
+            else:
+                label_p_pred = model_num_classifier.predict(img_in, verbose=0)
+            num_col = np.argmax(label_p_pred[0]) + 1
+        elif (self.num_col_upper and self.num_col_lower) and (self.num_col_upper!=self.num_col_lower):
+            if self.input_binary:
+                img_in = np.copy(img)
+                img_in = img_in / 255.0
+                img_in = cv2.resize(img_in, (448, 448), interpolation=cv2.INTER_NEAREST)
+                img_in = img_in.reshape(1, 448, 448, 3)
+            else:
+                img_1ch = self.imread(grayscale=True)
+                width_early = img_1ch.shape[1]
+                img_1ch = img_1ch[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3]]
+
+                img_1ch = img_1ch / 255.0
+                img_1ch = cv2.resize(img_1ch, (448, 448), interpolation=cv2.INTER_NEAREST)
+                img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
+                img_in[0, :, :, 0] = img_1ch[:, :]
+                img_in[0, :, :, 1] = img_1ch[:, :]
+                img_in[0, :, :, 2] = img_1ch[:, :]
+
+
+            if self.dir_in:
+                label_p_pred = self.model_classifier.predict(img_in, verbose=0)
+            else:
+                label_p_pred = model_num_classifier.predict(img_in, verbose=0)
+            num_col = np.argmax(label_p_pred[0]) + 1
+            
+            if num_col > self.num_col_upper:
+                num_col = self.num_col_upper
+                label_p_pred = [np.ones(6)]
+            if num_col < self.num_col_lower:
+                num_col = self.num_col_lower
+                label_p_pred = [np.ones(6)]
+                
         else:
-            label_p_pred = model_num_classifier.predict(img_in, verbose=0)
+            num_col = self.num_col_upper
-        num_col = np.argmax(label_p_pred[0]) + 1
+            label_p_pred = [np.ones(6)]
+                
         
         self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5))