Update app.py

app.py

@@ -10,25 +10,28 @@ import numpy as np
 os.mkdir("data")
 os.mkdir("dataout")
 model = hub.Module(name='U2Net')
-def infer(img):
+def infer(img,mask,option):
   basewidth = 600
   wpercent = (basewidth/float(img.size[0]))
   hsize = int((float(img.size[1])*float(wpercent)))
   img = img.resize((basewidth,hsize), Image.ANTIALIAS)
   img.save("./data/data.png")
-  result = model.Segmentation(
-      images=[cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)],
-      paths=None,
-      batch_size=1,
-      input_size=320,
-      output_dir='output',
-      visualization=True)
-  im = Image.fromarray(result[0]['mask'])
+  if option == "automatic (U2net)"
+      result = model.Segmentation(
+          images=[cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)],
+          paths=None,
+          batch_size=1,
+          input_size=320,
+          output_dir='output',
+          visualization=True)
+      im = Image.fromarray(result[0]['mask'])
+  else:
+      im = mask
   im.save("./data/data_mask.png")
   os.system('python predict.py model.path=/home/user/app/big-lama/ indir=/home/user/app/data/ outdir=/home/user/app/dataout/ device=cpu')
   return "./dataout/data_mask.png",im
-inputs = gr.inputs.Image(type='pil', label="Original Image")
-outputs = [gr.outputs.Image(type="file",label="output"),gr.outputs.Image(type="pil",label="Mask from U^2Net")]
+inputs = [gr.inputs.Image(type='pil', label="Original Image"),gr.inputs.Image(type='pil',source="canvas", label="Mask",optional=True),gr.inputs.Radio(choices=["automatic (U2net)","manual"], type="value", default="manual", label="Masking option")]
+outputs = [gr.outputs.Image(type="file",label="output"),gr.outputs.Image(type="pil",label="Mask")]
 title = "LaMa Image Inpainting"
 description = "Gradio demo for LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Masks are generated by U^2net"
 article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.07161' target='_blank'>Resolution-robust Large Mask Inpainting with Fourier Convolutions</a> | <a href='https://github.com/saic-mdal/lama' target='_blank'>Github Repo</a></p>"