Update app.py

app.py

@@ -8,6 +8,18 @@ from huggingface_hub import from_pretrained_keras
 def resize_image(img_in,input_height,input_width):
     return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST)
 
+def otsu_copy_binary(img):
+    img_r=np.zeros((img.shape[0],img.shape[1],3))
+    img1=img[:,:,0]
+
+    retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+
+    img_r[:,:,0]=threshold1
+    img_r[:,:,1]=threshold1
+    img_r[:,:,2]=threshold1
+
+    return img_r
+
 def visualize_model_output(prediction, img):
     unique_classes = np.unique(prediction[:,:,0])
     rgb_colors = {'0' : [255, 255, 255],
@@ -48,7 +60,21 @@ def visualize_model_output(prediction, img):
     added_image = cv2.addWeighted(img,0.5,output,0.1,0)
     return added_image
 
+def return_num_columns(img):
+    model_classifier = from_pretrained_keras("SBB/eynollah-column-classifier")
+    img_1ch = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
+    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[:, :]
+              
+    label_p_pred = model.predict(img_in, verbose=0)
+    num_col = np.argmax(label_p_pred[0]) + 1
+    return num_col
+    
 def do_prediction(model_name, img):
     img_org = np.copy(img)
     model = from_pretrained_keras(model_name)
@@ -77,12 +103,52 @@ def do_prediction(model_name, img):
             img_width_model=model.layers[len(model.layers)-1].output_shape[2]
             n_classes=model.layers[len(model.layers)-1].output_shape[3]
 
+
+
+            img_org = np.copy(img)
+            img_height_h = img_org.shape[0]
+            img_width_h = img_org.shape[1]
+    
+            #model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
+    
+            
+            
+            if num_col_classifier == 1:
+                img_w_new = 1000
+                img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
+                
+            elif num_col_classifier == 2:
+                img_w_new = 1500
+                img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
+                
+            elif num_col_classifier == 3:
+                img_w_new = 2000
+                img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
+                
+            elif num_col_classifier == 4:
+                img_w_new = 2500
+                img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
+            elif num_col_classifier == 5:
+                img_w_new = 3000
+                img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
+            else:
+                img_w_new = 4000
+                img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
+            img_resized = resize_image(img,img_h_new, img_w_new )
+
+            img = otsu_copy_binary(img_resized)
+
+            
+
             if img.shape[0] < img_height_model:
                 img = resize_image(img, img_height_model, img.shape[1])
 
             if img.shape[1] < img_width_model:
                 img = resize_image(img, img.shape[0], img_width_model)
 
+
+            
+
             marginal_of_patch_percent = 0.1
             margin = int(marginal_of_patch_percent * img_height_model)
             width_mid = img_width_model - 2 * margin