diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..ac0d0103e9cded39b47321c46f6f0f47fd63f6b0 --- /dev/null +++ b/app.py @@ -0,0 +1,170 @@ +import streamlit as st +from pathlib import Path + + +# ----------------------------------------------------------------------------- +# main +# ----------------------------------------------------------------------------- +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') + + images = load_images(selected_option, Path('./images/images')) + + labels = load_labels(selected_option, Path('./images/labels')) + + preds = load_predictions(selected_option, Path('./images/predictions')) + + zipped_st_images = zip(images, preds['svb'], preds['unet'], labels) + + grid = make_grid(4, 4) + + 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.") + +# ----------------------------------------------------------------------------- +# load_images +# ----------------------------------------------------------------------------- +def load_images(selected_option: str, image_dir: Path): + """ + Given a selected option and image dir, return streamlit image objects. + """ + + 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)] + + return images + +# ----------------------------------------------------------------------------- +# find_images +# ----------------------------------------------------------------------------- +def find_images(selected_option: str, image_dir: Path): + + 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' + + return images_matching_regex + + +# ----------------------------------------------------------------------------- +# load_labels +# ----------------------------------------------------------------------------- +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)] + + return labels + + +# ----------------------------------------------------------------------------- +# find_labels +# ----------------------------------------------------------------------------- +def find_labels(selected_option: str, label_dir: Path): + + 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 '1071' in str(labels_matching_regex[0]), 'Should be 1071' + + return labels_matching_regex + + +# ----------------------------------------------------------------------------- +# load_predictions +# ----------------------------------------------------------------------------- +def load_predictions(selected_option: str, pred_dir: Path): + svb_pred_paths = find_preds(selected_option, pred_dir, 'svb') + + 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)] + + 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} + + return prediction_dict + + +# ----------------------------------------------------------------------------- +# 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' + + else: + 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' + + preds_matching_regex = sorted(model_specific_dir.glob(pred_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' + + return preds_matching_regex + + +# ----------------------------------------------------------------------------- +# make_grid +# ----------------------------------------------------------------------------- +def make_grid(cols,rows): + + grid = [0]*cols + + for i in range(cols): + + with st.container(): + + grid[i] = st.columns(rows, gap='large') + + return grid + +# ----------------------------------------------------------------------------- +# Main execution +# ----------------------------------------------------------------------------- +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/images/images/ft_demo_1000_1071_img.png b/images/images/ft_demo_1000_1071_img.png new file mode 100644 index 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