Upload app.py

app.py

@@ -83,8 +83,7 @@ def single_scale_test(image):
         
     foreground = im_org * matte_org + np.full(im_org.shape, 255) * (1 - matte_org)         # 计算前景，获得抠像
 #        fg_name = im_name.split('.')[0] + '_fg.png'
-    Image.fromarray(((foreground).astype('uint8')), mode='RGB').save(os.path.join('MODNet/output-img', 'fg_name.png'))
-    output = Image.open(os.path.join('MODNet/output-img', 'fg_name.png'))
+    output = Image.fromarray(((foreground).astype('uint8')), mode='RGB')
     image = np.asarray(output)
 
     model = RCF().cuda()
@@ -104,11 +103,13 @@ def single_scale_test(image):
     #filename = osp.splitext(test_list[idx])[0]
     #torchvision.utils.save_image(1 - all_res, osp.join('RCFPyTorch0/results/RCF', 'result.jpg'))
     fuse_res = torch.squeeze(results[1].detach()).cpu().numpy()
-    fuse_res = ((1 - fuse_res) * 255).astype(np.uint8)
+    fuse_res = ((1 - fuse_res) * 255).astype('uint8')
     cv2.imwrite(osp.join("RCFPyTorch0/results/RCF", 'result_ss.png'), fuse_res)
     #print('\rRunning single-scale test [%d/%d]' % (idx + 1, len(test_loader)), end='')
     #print('Running single-scale test done')
     output = Image.open(os.path.join('RCFPyTorch0/results/RCF', 'result_ss.png'))
+    #output = Image.fromarray(((fuse_res).astype('uint8')), mode='RGB')
+    print(type(output))
     return output
 
 parser = argparse.ArgumentParser(description='PyTorch Testing')