hybrid cnn & transformer model is integrated

requirements.txt

@@ -2,4 +2,4 @@ numpy
 setuptools >= 41
 opencv-python-headless
 ocrd >= 2.22.3
-tensorflow >= 2.4.0
+tensorflow == 2.4.*

sbb_binarize/cli.py

@@ -1,7 +1,7 @@
 """
 sbb_binarize CLI
 """
-
+import click
 from click import command, option, argument, version_option, types
 from .sbb_binarize import SbbBinarizer
 

sbb_binarize/sbb_binarize.py

@@ -17,14 +17,72 @@ 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
+from tensorflow.keras import layers
+import tensorflow.keras.losses
+from tensorflow.keras.layers import *
 sys.stderr = stderr
 
 
 import logging
 
+
+projection_dim = 64
+patch_size = 1
+num_patches =14*14
+
 def resize_image(img_in, input_height, input_width):
     return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
 
+
+class Patches(layers.Layer):
+    def __init__(self, **kwargs):
+        super(Patches, self).__init__()
+        self.patch_size = patch_size
+
+    def call(self, images):
+        batch_size = tf.shape(images)[0]
+        patches = tf.image.extract_patches(
+            images=images,
+            sizes=[1, self.patch_size, self.patch_size, 1],
+            strides=[1, self.patch_size, self.patch_size, 1],
+            rates=[1, 1, 1, 1],
+            padding="VALID",
+        )
+        patch_dims = patches.shape[-1]
+        patches = tf.reshape(patches, [batch_size, -1, patch_dims])
+        return patches
+    def get_config(self):
+
+        config = super().get_config().copy()
+        config.update({
+            'patch_size': self.patch_size,
+        })
+        return config
+    
+    
+class PatchEncoder(layers.Layer):
+    def __init__(self, **kwargs):
+        super(PatchEncoder, self).__init__()
+        self.num_patches = num_patches
+        self.projection = layers.Dense(units=projection_dim)
+        self.position_embedding = layers.Embedding(
+            input_dim=num_patches, output_dim=projection_dim
+        )
+
+    def call(self, patch):
+        positions = tf.range(start=0, limit=self.num_patches, delta=1)
+        encoded = self.projection(patch) + self.position_embedding(positions)
+        return encoded
+    def get_config(self):
+
+        config = super().get_config().copy()
+        config.update({
+            'num_patches': self.num_patches,
+            'projection': self.projection,
+            'position_embedding': self.position_embedding,
+        })
+        return config
+
 class SbbBinarizer:
 
     def __init__(self, model_dir, logger=None):
@@ -52,7 +110,10 @@ class SbbBinarizer:
         del self.session
 
     def load_model(self, model_name):
+        try:
             model = load_model(join(self.model_dir, model_name), compile=False)
+        except:
+            model = load_model(join(self.model_dir, model_name) , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
         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]
@@ -154,11 +215,46 @@ class SbbBinarizer:
 
                     img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
                     
+                    h_res = int( img_patch.shape[0]/1.05)
+                    w_res = int( img_patch.shape[1]/1.05)
+                    
+                    img_patch_resize = resize_image(img_patch, h_res, w_res)
+                    
+                    img_patch_resized_padded =np.ones((img_patch.shape[0],img_patch.shape[1],img_patch.shape[2])).astype(float)#self.do_padding()
+                    
+                    h_start=int( abs(img_patch.shape[0]-img_patch_resize.shape[0])/2. )
+                    
+                    w_start=int( abs(img_patch.shape[1]-img_patch_resize.shape[1])/2. )
+                    
+                    img_patch_resized_padded[h_start:h_start+img_patch_resize.shape[0],w_start:w_start+img_patch_resize.shape[1],:]=np.copy(img_patch_resize[:,:,:])
+                    
+                    label_p_pred_padded = model.predict(img_patch_resized_padded.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
+
                     label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
 
+                    #seg = np.argmax(label_p_pred, axis=3)[0]
+
+                    #label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
+
                     seg = np.argmax(label_p_pred, axis=3)[0]
                     
-                    seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
+                    
+                    seg_padded = np.argmax(label_p_pred_padded, axis=3)[0]
+                    
+                    seg_padded_take_core = seg_padded[h_start:h_start+img_patch_resize.shape[0],w_start:w_start+img_patch_resize.shape[1]]
+                    
+                    seg_padded_take_core_org_size= resize_image(seg_padded_take_core, img_patch.shape[0], img_patch.shape[1])
+                    
+                    #print(seg_padded_take_core_org_size,'sag padded')
+                    #print(seg,'sag')
+                    
+                    seg_tot  = seg_padded_take_core_org_size+0#seg
+                    
+                    seg_tot[seg_tot>1]=1
+
+                    seg_color = np.repeat(seg_tot[:, :, np.newaxis], 3, axis=2)
+
+                    #seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
 
                     if i == 0 and j == 0:
                         seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]