add script

.gitignore

@@ -0,0 +1 @@
+.venv

README.md

@@ -3,7 +3,7 @@ title: Kspace Reconstruction Masks
 emoji: 🏆
 colorFrom: red
 colorTo: gray
-sdk: streamlit
+sdk: gradio
 sdk_version: 1.36.0
 app_file: app.py
 pinned: false

app.py

@@ -0,0 +1,58 @@
+import gradio as gr
+
+from fastmri.data.subsample import create_mask_for_mask_type
+from fastmri.data.transforms import apply_mask, to_tensor, center_crop
+from pytorch_msssim import ssim
+
+
+
+# st.title('FastMRI Kspace Reconstruction Masks')
+# st.write('This app allows you to visualize the masks and their effects on the kspace data.')
+
+
+def main_func(
+    mask_name: str,
+    mask_center_fractions: int,
+    accelerations: int,
+    seed: int,
+    input_image: str,
+):
+    
+    file_dict = {
+        "knee 1": "knee_singlecoil_train/file1000002.h5",
+        "knee 2": "knee_singlecoil_train/file1000003.h5",
+        "brain 1": "brain_axial_train/file1000002.h5",
+        "prostate 1": "prostate_t1_tse_train/file1000002.h5",
+        "prostate 2": "prostate_t2_tse_train/file1000002.h5",
+    }
+    input_file = file_dict[input_image]
+    
+    mask_func = create_mask_for_mask_type(
+        mask_name, center_fractions=[mask_center_fractions], accelerations=[accelerations]
+    )
+    mask = 
+    masked_kspace, mask = mask(input_image, return_mask=True)
+    return masked_kspace, mask
+
+demo = gr.Interface(
+    fn=main_func,
+    inputs=[
+        gr.inputs.Radio(['random', 'equispaced'], label="Mask Type"),
+        gr.inputs.Slider(minimum=0.04, maximum=0.4, default=0.08, label="Center Fraction"),
+        gr.inputs.Number(default=4, label="Acceleration"),
+        gr.inputs.Number(default=0, label="Seed"),
+        gr.inputs.Radio(["knee 1", "knee 2", "brain 1", "prostate 1", "prostate 2"], label="Input Image")
+    ],
+    outputs=[
+        gr.outputs.Image(type="mask", label="Mask"),
+        gr.outputs.Image(type="kspace", label="Masked Kspace"),
+        gr.outputs.Image(type="kspace", label="Reconstructed Image"),
+        gr.outputs.Image(type="kspace", label="Original Image"),
+
+        gr.outputs.Dataframe()
+    ],
+    title="FastMRI Kspace Reconstruction Masks",
+    description="This app allows you to visualize the masks and their effects on the kspace data."
+)
+
+demo.launch()

requirements.txt

@@ -1 +1,5 @@
 fastmri
+scikit-image
+pandas
+numpy
+torch