app.py

import os

import streamlit as st

from utils import get_configs, get_display_names, get_path_for_viz

# st.header("EVREAL  - Event-based Video Reconstruction Evaluation and Analysis Library")
#
# paper_link = "https://arxiv.org/abs/2305.00434"
# code_link = "https://github.com/ercanburak/EVREAL"
# page_link = "https://ercanburak.github.io/evreal.html"
# instructions_video = "https://www.youtube.com/watch?v="
#
# st.markdown("Paper: " + paper_link, unsafe_allow_html=True)
# st.markdown("Code: " + paper_link, unsafe_allow_html=True)
# st.markdown("Page: " + paper_link, unsafe_allow_html=True)
# st.markdown("Please see this video for instructions on how to use this tool: " + instructions_video, unsafe_allow_html=True)

st.title("Result Analysis Tool")

data_base_path = "/home/bercan/ebv/evreal_data"

dataset_cfg_path = os.path.join("cfg", "dataset")
model_cfg_path = os.path.join("cfg", "model")
metric_cfg_path = os.path.join("cfg", "metric")
viz_cfg_path = os.path.join("cfg", "viz")

datasets = get_configs(dataset_cfg_path)
models = get_configs(model_cfg_path)
metrics = get_configs(metric_cfg_path)
visualizations = get_configs(viz_cfg_path)

dataset_display_names = get_display_names(datasets)
model_display_names = get_display_names(models)
metric_display_names = get_display_names(metrics)
viz_display_names = get_display_names(visualizations)

assert len(set(dataset_display_names)) == len(dataset_display_names), "Dataset display names are not unique"
assert len(set(model_display_names)) == len(model_display_names), "Model display names are not unique"
assert len(set(metric_display_names)) == len(metric_display_names), "Metric display names are not unique"
assert len(set(viz_display_names)) == len(viz_display_names), "Viz display names are not unique"

selected_model_names = st.multiselect('Select multiple methods to compare', model_display_names)
selected_models = [model for model in models if model['display_name'] in selected_model_names]

col1, col2 = st.columns(2)

with col1:
    selected_dataset_name = st.selectbox('Select dataset', options=dataset_display_names)
    selected_dataset = [dataset for dataset in datasets if dataset['display_name'] == selected_dataset_name][0]

with col2:
    selected_sequence = st.selectbox('Select sequence', options=selected_dataset["sequences"].keys())

usable_metrics = [metric for metric in metrics if metric['no_ref'] == selected_dataset['no_ref']]
usable_metric_display_names = get_display_names(usable_metrics)

selected_metric_names = st.multiselect('Select metrics to display', usable_metric_display_names)
selected_metrics = [metric for metric in usable_metrics if metric['display_name'] in selected_metric_names]

if not selected_dataset['has_frames']:
    usable_viz = [viz for viz in visualizations if viz['gt_type'] != 'frame']
else:
    usable_viz = visualizations
usable_viz_display_names = get_display_names(usable_viz)

selected_viz = st.multiselect('Select other visualizations to display', usable_viz_display_names)
selected_visualizations = [viz for viz in visualizations if viz['display_name'] in selected_viz]

if not st.button('Get Results'):
    st.stop()

gt_only_viz = [viz for viz in selected_visualizations if viz['viz_type'] == 'gt_only']
model_only_viz = [viz for viz in selected_visualizations if viz['viz_type'] == 'model_only']
both_viz = [viz for viz in selected_visualizations if viz['viz_type'] == 'both']

recon_viz = {"name": "recon", "display_name": "Reconstruction", "viz_type": "both", "gt_type": "frame"}
ground_truth = {"name": "gt", "display_name": "Ground Truth", "model_id": "groundtruth"}

model_viz = [recon_viz] + both_viz + selected_metrics + model_only_viz
num_model_rows = len(model_viz) + 1

gt_viz = []
if selected_dataset['has_frames']:
    gt_viz.append(recon_viz)
    gt_viz.extend([viz for viz in both_viz if viz['gt_type'] == 'frame'])
    gt_viz.extend([viz for viz in gt_only_viz if viz['gt_type'] == 'frame'])

gt_viz.extend([viz for viz in both_viz if viz['gt_type'] == 'event'])
gt_viz.extend([viz for viz in gt_viz if viz['gt_type'] == 'event'])

num_gt_rows = len(gt_viz) + 1
num_rows = max(num_model_rows, num_gt_rows)

num_model_columns = len(selected_models) + 1

for row_idx in range(num_rows):
    row_visualizations = []
    for col_idx in range(num_model_columns):
        if row_idx == 0 and col_idx == 0:
            print("meta")
            pass
        elif row_idx == 0:
            # model names
            print(selected_models[col_idx - 1]['display_name'])
            pass
        elif col_idx == 0:
            # metric names
            print(model_viz[row_idx - 1]['display_name'])
            pass
        else:
            video_path = get_path_for_viz(data_base_path, selected_dataset, selected_sequence, selected_models[col_idx - 1], model_viz[row_idx - 1])
            print(video_path)
            pass