Update app.py

app.py

@@ -16,150 +16,77 @@ def do_prediction(img):
     img_width_model=model.layers[len(model.layers)-1].output_shape[2]
     n_classes=model.layers[len(model.layers)-1].output_shape[3]
 
-    print(img_height_model, img_width_model, n_classes,'didi')
+    if img.shape[0] < img_height_model:
+        img = resize_image(img, img_height_model, img.shape[1])
 
-    kernel = np.ones((5,5),np.uint8)
-    margin = int(0.1 * img_width_model)
+    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
     height_mid = img_height_model - 2 * margin
-
     img = img / float(255.0)
-
+    img = img.astype(np.float16)
     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)
+    nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
+    nyf = int(nyf) + 1 if nyf > int(nyf) else 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:
+            else:
                 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:
+            else:
                 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)
-
+            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
 
+            if i == 0 and j == 0:
+                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:
+                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:
+                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:
+                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:
+                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:
+                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:
+                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:
+                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[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))  
+    print(prediction_true.shape, np.unique(prediction_true))  
                 
 
 
@@ -175,7 +102,7 @@ def do_prediction(img):
     print(prediction.shape)
     
     '''
-    return y_predi
+    return prediction_true
 
 iface = gr.Interface(fn=do_prediction, inputs=gr.Image(), outputs=gr.Image())
 iface.launch()