Update app.py

app.py

@@ -9,8 +9,160 @@ def resize_image(img_in,input_height,input_width):
     return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST)
 
 
-def greet(image):
+def do_prediction(image):
     model = from_pretrained_keras("vahidrezanezhad/sbb_binarization")
+
+    img_height_model=model.layers[len(model.layers)-1].output_shape[1]
+    img_width_model=model.layers[len(model.layers)-1].output_shape[2]
+    n_classes=model.layers[len(model.layers)-1].output_shape[3]
+
+    kernel = np.ones((5,5),np.uint8)
+    margin = int(0.1 * img_width_model)
+
+    width_mid = img_width_model - 2 * margin
+    height_mid = img_height_model - 2 * margin
+
+    img = img / float(255.0)
+
+    img_h = img.shape[0]
+    img_w = img.shape[1]
+
+    prediction_true = np.zeros((img_h, img_w, 3))
+    mask_true = np.zeros((img_h, img_w))
+    nxf = img_w / float(width_mid)
+    nyf = img_h / float(height_mid)
+
+    if nxf > int(nxf):
+        nxf = int(nxf) + 1
+    else:
+        nxf = int(nxf)
+
+    if nyf > int(nyf):
+        nyf = int(nyf) + 1
+    else:
+        nyf = int(nyf)
+
+    for i in range(nxf):
+        for j in range(nyf):
+
+            if i == 0:
+                index_x_d = i * width_mid
+                index_x_u = index_x_d + img_width_model
+            elif i > 0:
+                index_x_d = i * width_mid
+                index_x_u = index_x_d + img_width_model
+
+            if j == 0:
+                index_y_d = j * height_mid
+                index_y_u = index_y_d + img_height_model
+            elif j > 0:
+                index_y_d = j * height_mid
+                index_y_u = index_y_d + img_height_model
+
+            if index_x_u > img_w:
+                index_x_u = img_w
+                index_x_d = img_w - img_width_model
+            if index_y_u > img_h:
+                index_y_u = img_h
+                index_y_d = img_h - img_height_model
+                
+            
+
+            img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
+
+            label_p_pred = model.predict(
+                img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]), verbose=0)
+
+            seg = np.argmax(label_p_pred, 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, :]
+                seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]
+
+                mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
+                prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin,
+                :] = seg_color
+                
+            elif i==nxf-1 and j==nyf-1:
+                seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
+                seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
+
+                mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg
+                prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0,
+                :] = seg_color
+                
+            elif i==0 and j==nyf-1:
+                seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
+                seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]
+
+                mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg
+                prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin,
+                :] = seg_color
+                
+            elif i==nxf-1 and j==0:
+                seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
+                seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]
+
+                mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
+                prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0,
+                :] = seg_color
+                
+            elif i==0 and j!=0 and j!=nyf-1:
+                seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
+                seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]
+
+                mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
+                prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin,
+                :] = seg_color
+                
+            elif i==nxf-1 and j!=0 and j!=nyf-1:
+                seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
+                seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]
+
+                mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
+                prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0,
+                :] = seg_color
+                
+            elif i!=0 and i!=nxf-1 and j==0:
+                seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
+                seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
+
+                mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
+                prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin,
+                :] = seg_color
+                
+            elif i!=0 and i!=nxf-1 and j==nyf-1:
+                seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
+                seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]
+
+                mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg
+                prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin,
+                :] = seg_color
+
+            else:
+                seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
+                seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]
+
+                mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
+                prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin,
+                :] = seg_color
+
+    prediction_true = prediction_true.astype(np.uint8)
+
+    y_predi=cv2.resize( y_predi, ( img.shape[1],img.shape[0]) ,interpolation=cv2.INTER_NEAREST)
+    #return y_predi
+            
+            
+            
+    print(y_predi.shape, np.unique(y_predi))  
+                
+
+
+    '''
+    img = img / float(255.0)
     image = resize_image(image, 224,448)
     prediction = model.predict(image.reshape(1,224,448,image.shape[2]))
     prediction = tf.squeeze(tf.round(prediction))
@@ -20,9 +172,8 @@ def greet(image):
     prediction = np.repeat(prediction[:, :, np.newaxis]*255, 3, axis=2)
     print(prediction.shape)
     
-    
-    #print(model.summary())
-    return prediction
+    '''
+    return y_predi
 
-iface = gr.Interface(fn=greet, inputs=gr.Image(), outputs=gr.Image())
+iface = gr.Interface(fn=do_prediction, inputs=gr.Image(), outputs=gr.Image())
 iface.launch()