From 341480e9a050a1a728907ddacf98975e7203f468 Mon Sep 17 00:00:00 2001
From: Robert Sachunsky <sachunsky@informatik.uni-leipzig.de>
Date: Wed, 4 Mar 2026 23:41:45 +0100
Subject: [PATCH] do_prediction: if img was too small for model, also upscale
 results

(i.e. resize back to match original size after prediction)
---
 src/eynollah/eynollah.py | 22 ++++++++++++----------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/src/eynollah/eynollah.py b/src/eynollah/eynollah.py
index be4d2c7..2b12f67 100644
--- a/src/eynollah/eynollah.py
+++ b/src/eynollah/eynollah.py
@@ -635,11 +635,13 @@ class Eynollah:
         self.logger.debug("enter do_prediction (patches=%d)", patches)
         img_height_model = model.layers[-1].output_shape[1]
         img_width_model = model.layers[-1].output_shape[2]
+        img_h_page = img.shape[0]
+        img_w_page = img.shape[1]
+
+        img = img / 255.
+        img = img.astype(np.float16)
 
         if not patches:
-            img_h_page = img.shape[0]
-            img_w_page = img.shape[1]
-            img = img / 255.0
             img = resize_image(img, img_height_model, img_width_model)
 
             label_p_pred = model.predict(img[np.newaxis], verbose=0)[0]
@@ -658,17 +660,15 @@ class Eynollah:
 
             return resize_image(seg, img_h_page, img_w_page).astype(np.uint8)
 
-        if img.shape[0] < img_height_model:
+        if img_h_page < img_height_model:
             img = resize_image(img, img_height_model, img.shape[1])
-        if img.shape[1] < img_width_model:
+        if img_w_page < img_width_model:
             img = resize_image(img, img.shape[0], img_width_model)
 
         self.logger.debug("Patch size: %sx%s", img_height_model, img_width_model)
         margin = int(marginal_of_patch_percent * img_height_model)
         width_mid = img_width_model - 2 * margin
         height_mid = img_height_model - 2 * margin
-        img = img / 255.
-        #img = img.astype(np.float16)
         img_h = img.shape[0]
         img_w = img.shape[1]
         prediction = np.zeros((img_h, img_w), dtype=np.uint8)
@@ -808,6 +808,10 @@ class Eynollah:
                            only=True,
                            skeletonize=True,
                            dilate=3)
+
+        if img_h != img_h_page or img_w != img_w_page:
+            prediction = resize_image(prediction, img_h_page, img_w_page)
+
         gc.collect()
         return prediction
 
@@ -1073,12 +1077,10 @@ class Eynollah:
         model_region = self.model_zoo.get("region_fl") if patches else self.model_zoo.get("region_fl_np")
 
         thresholding_for_heading = True
+        img = otsu_copy_binary(img).astype(np.uint8)
         if not patches:
-            img = otsu_copy_binary(img).astype(np.uint8)
-            prediction_regions = None
             thresholding_for_heading = False
         elif cols:
-            img = otsu_copy_binary(img).astype(np.uint8)
             if cols == 1:
                 img = resize_image(img, int(img_height_h * 1000 / float(img_width_h)), 1000).astype(np.uint8)
             elif cols == 2: