Update app.py

app.py

@@ -89,12 +89,14 @@ def test(gpu_id, net, img_list, group_size, img_size):
         _,pred_mask=net(group_img*1)
         pred_mask=(pred_mask.detach().squeeze()*255)#.numpy().astype(np.uint8)
         #pred_mask=[F.interpolate(pred_mask[i].reshape(1,1,pred_mask[i].shape[-2],pred_mask[i].shape[-1]),size=(size,size),mode='bilinear').squeeze().numpy().astype(np.uint8) for i in range(5)]
-        pred_mask=[crf_refine(((group_img[i]-group_img[i].min())/(group_img[i].max()-group_img[i].min())*255).permute(1,2,0).contiguous().numpy().astype(np.uint8),pred_mask[i].numpy().astype(np.uint8)) for i in range(5)]
+        img_resize=[((group_img[i]-group_img[i].min())/(group_img[i].max()-group_img[i].min())*255).permute(1,2,0).contiguous().numpy().astype(np.uint8) 
+                    for i in range(5)]
+        pred_mask=[crf_refine(img_resize,pred_mask[i].numpy().astype(np.uint8)) for i in range(5)]
         #for i in range(5):
         #    print(img_list[i].shape,pred_mask[i].shape)
         #pred_mask=[crf_refine(img_list[i],pred_mask[i]) for i in range(5)]
         print(pred_mask[0].shape)
-        result = [((torch.from_numpy(pred_mask[i]).unsqueeze(2).repeat(1,1,3)).numpy()) for i in range(5)]
+        result = [torch.cat([img_resize[i],torch.from_numpy(pred_mask[i]).unsqueeze(2).repeat(1,1,3)],dim=1).numpy() for i in range(5)]
         #w, h = 224,224#Image.open(image_list[i][j]).size
         #result = result.resize((w, h), Image.BILINEAR)
         #result.convert('L').save('0.png')