changed images

app.py

@@ -7,41 +7,64 @@ from pathlib import Path
 # -----------------------------------------------------------------------------
 def main():
     st.title("SatVision Few-Shot Comparison")
-    
-    selected_option = st.select_slider(
-        "## Number of training samples",
-        options=[10, 100, 500, 1000, 5000])
 
-    st.markdown('Move slider to select how many training ' + \
-                'samples the models were trained on')
+    st.write("")
 
-    images = load_images(selected_option, Path('./images/images'))
+    selected_option = st.selectbox(
+        "Number of training samples", [10, 100, 500, 1000, 5000]
+    )
+    st.markdown(
+        "Move slider to select how many training "
+        + "samples the models were trained on"
+    )
 
-    labels = load_labels(selected_option, Path('./images/labels'))
+    images = load_images(selected_option, Path("./images/images"))
 
-    preds = load_predictions(selected_option, Path('./images/predictions'))
+    labels = load_labels(selected_option, Path("./images/labels"))
 
-    zipped_st_images = zip(images, preds['svb'], preds['unet'], labels)
+    preds = load_predictions(selected_option, Path("./images/predictions"))
 
-    grid = make_grid(4, 4)
+    zipped_st_images = zip(images, preds["svb"], preds["unet"], labels)
+
+    st.write("")
+
+    titleCol0, titleCol1, titleCol2, titleCol3 = st.columns(4)
 
-    for i, (image_data, svb_data, unet_data, label_data) in \
-        enumerate(zipped_st_images):
+    titleCol0.markdown(f"### MOD09GA [3-2-1] Image Chip")
+    titleCol1.markdown(f"### SatVision-B Prediction")
+    titleCol2.markdown(f"### UNet (CNN) Prediction")
+    titleCol3.markdown(f"### MCD12Q1 LandCover Target")
 
-        if i == 0:
+    st.write("")
+
+    grid = make_grid(4, 4)
 
-            grid[0][0].markdown(f'## MOD09GA 3-2-1 Image Chip')
-            grid[0][1].markdown(f'## SatVision-B Prediction')
-            grid[0][2].markdown(f'## UNet (CNN) Prediction')
-            grid[0][3].markdown(f'## MCD12Q1 LandCover Target')
+    for i, (image_data, svb_data, unet_data, label_data) in enumerate(zipped_st_images):
+        # if i == 0:
+
+        #    grid[0][0].markdown(f'## MOD09GA 3-2-1 Image Chip')
+        #    grid[0][1].markdown(f'## SatVision-B Prediction')
+        #    grid[0][2].markdown(f'## UNet (CNN) Prediction')
+        #    grid[0][3].markdown(f'## MCD12Q1 LandCover Target')
 
         grid[i][0].image(image_data[0], image_data[1], use_column_width=True)
         grid[i][1].image(svb_data[0], svb_data[1], use_column_width=True)
         grid[i][2].image(unet_data[0], unet_data[1], use_column_width=True)
         grid[i][3].image(label_data[0], label_data[1], use_column_width=True)
 
-    st.text("Additional Information:")
-    st.text("This is a placeholder for additional information about the images.")
+    st.markdown("### Few-Shot Learning with SatVision-Base")
+    description = (
+        "Pre-trained vision transformers (we use SwinV2) offers a "
+        + "good advantage when looking to apply a model to a task with very little"
+        + " labeled training data. We pre-trained SatVision-Base on 26 million "
+        + " MODIS Surface Reflectance image patches. This allows the "
+        + " SatVision-Base models to learn relevant features and representations"
+        + " from a diverse range of scenes. This knowledge can be transferred to a"
+        + " few-shot learning task, enabling the model to leverage its"
+        + " understanding of spatial patterns, textures, and contextual information"
+    )
+    st.markdown(description)
+
 
 # -----------------------------------------------------------------------------
 # load_images
@@ -53,23 +76,25 @@ def load_images(selected_option: str, image_dir: Path):
 
     image_paths = find_images(selected_option, image_dir)
 
-    images = [(str(path), f"MOD09GA 3-2-1 H18v04 2019 Example {i}") for \
-              i, path in enumerate(image_paths, 1)]
+    images = [
+        (str(path), f"MOD09GA 3-2-1 H18v04 2019 Example {i}")
+        for i, path in enumerate(image_paths, 1)
+    ]
 
     return images
 
+
 # -----------------------------------------------------------------------------
 # find_images
 # -----------------------------------------------------------------------------
 def find_images(selected_option: str, image_dir: Path):
-
-    images_regex = f'ft_demo_{selected_option}_*_img.png'
+    images_regex = f"ft_demo_{selected_option}_*_img.png"
 
     images_matching_regex = sorted(image_dir.glob(images_regex))
 
     assert len(images_matching_regex) == 3, "Should be 3 images matching regex"
 
-    assert '1071' in str(images_matching_regex[0]), 'Should be 1071'
+    assert "1071" in str(images_matching_regex[0]), "Should be 1071"
 
     return images_matching_regex
 
@@ -80,8 +105,10 @@ def find_images(selected_option: str, image_dir: Path):
 def load_labels(selected_option, label_dir: Path):
     label_paths = find_labels(selected_option, label_dir)
 
-    labels = [(str(path), f"MCD12Q1 LandCover Target Example {i}") for \
-              i, path in enumerate(label_paths, 1)]
+    labels = [
+        (str(path), f"MCD12Q1 LandCover Target Example {i}")
+        for i, path in enumerate(label_paths, 1)
+    ]
 
     return labels
 
@@ -90,15 +117,13 @@ def load_labels(selected_option, label_dir: Path):
 # find_labels
 # -----------------------------------------------------------------------------
 def find_labels(selected_option: str, label_dir: Path):
-
-    labels_regex = f'ft_demo_{selected_option}_*_label.png'
+    labels_regex = f"ft_demo_{selected_option}_*_label.png"
 
     labels_matching_regex = sorted(label_dir.glob(labels_regex))
 
-    assert len(labels_matching_regex) == 3, \
-        "Should be 3 label images matching regex"
+    assert len(labels_matching_regex) == 3, "Should be 3 label images matching regex"
 
-    assert '1071' in str(labels_matching_regex[0]), 'Should be 1071'
+    assert "1071" in str(labels_matching_regex[0]), "Should be 1071"
 
     return labels_matching_regex
 
@@ -107,17 +132,21 @@ def find_labels(selected_option: str, label_dir: Path):
 # load_predictions
 # -----------------------------------------------------------------------------
 def load_predictions(selected_option: str, pred_dir: Path):
-    svb_pred_paths = find_preds(selected_option, pred_dir, 'svb')
+    svb_pred_paths = find_preds(selected_option, pred_dir, "svb")
+
+    unet_pred_paths = find_preds(selected_option, pred_dir, "cnn")
 
-    unet_pred_paths = find_preds(selected_option, pred_dir, 'cnn')
+    svb_preds = [
+        (str(path), f"SatVision-B Prediction Example {i}")
+        for i, path in enumerate(svb_pred_paths, 1)
+    ]
 
-    svb_preds = [(str(path), f"SatVision-B Prediction Example {i}") for \
-              i, path in enumerate(svb_pred_paths, 1)]
+    unet_preds = [
+        (str(path), f"Unet Prediction Example {i}")
+        for i, path in enumerate(unet_pred_paths, 1)
+    ]
 
-    unet_preds = [(str(path), f"Unet Prediction Example {i}") for \
-              i, path in enumerate(unet_pred_paths, 1)]
-    
-    prediction_dict = {'svb': svb_preds, 'unet': unet_preds}
+    prediction_dict = {"svb": svb_preds, "unet": unet_preds}
 
     return prediction_dict
 
@@ -126,24 +155,23 @@ def load_predictions(selected_option: str, pred_dir: Path):
 # find_preds
 # -----------------------------------------------------------------------------
 def find_preds(selected_option: int, pred_dir: Path, model: str):
-
-    if model == 'cnn':
-
-        pred_regex = f'ft_cnn_demo_{selected_option}_*_pred.png'
+    if model == "cnn":
+        pred_regex = f"ft_cnn_demo_{selected_option}_*_pred.png"
 
     else:
-        pred_regex = f'ft_demo_{selected_option}_*_pred.png'   
+        pred_regex = f"ft_demo_{selected_option}_*_pred.png"
 
     model_specific_dir = pred_dir / str(selected_option) / model
 
-    assert model_specific_dir.exists(), f'{model_specific_dir} does not exist'
+    assert model_specific_dir.exists(), f"{model_specific_dir} does not exist"
 
     preds_matching_regex = sorted(model_specific_dir.glob(pred_regex))
 
-    assert len(preds_matching_regex) == 3, \
-        "Should be 3 prediction images matching regex"
+    assert (
+        len(preds_matching_regex) == 3
+    ), "Should be 3 prediction images matching regex"
 
-    assert '1071' in str(preds_matching_regex[0]), 'Should be 1071'
+    assert "1071" in str(preds_matching_regex[0]), "Should be 1071"
 
     return preds_matching_regex
 
@@ -151,20 +179,18 @@ def find_preds(selected_option: int, pred_dir: Path, model: str):
 # -----------------------------------------------------------------------------
 # make_grid
 # -----------------------------------------------------------------------------
-def make_grid(cols,rows):
-
-    grid = [0]*cols
+def make_grid(cols, rows):
+    grid = [0] * cols
 
     for i in range(cols):
-
         with st.container():
-
-            grid[i] = st.columns(rows, gap='large')
+            grid[i] = st.columns(rows, gap="large")
 
     return grid
 
+
 # -----------------------------------------------------------------------------
 # Main execution
 # -----------------------------------------------------------------------------
 if __name__ == "__main__":
-    main()
+    main()