diff --git a/app.py b/app.py index ac0d0103e9cded39b47321c46f6f0f47fd63f6b0..dc8d96d91b871685555ae0606320c304b8b96783 100644 --- a/app.py +++ b/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() \ No newline at end of file + main() diff --git a/images/images/ft_demo_1000_1071_img.png b/images/images/ft_demo_1000_1071_img.png index f496fb9a3deda816411a0ba230a5b1bdaa47eb88..2153cdd405819a8588e9243af40526498ad0125d 100644 Binary files a/images/images/ft_demo_1000_1071_img.png and b/images/images/ft_demo_1000_1071_img.png differ diff --git a/images/images/ft_demo_1000_1076_img.png b/images/images/ft_demo_1000_1076_img.png index 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