import os import subprocess import streamlit as st from utils import get_configs, get_display_names, get_path_for_viz, get_video_height, get_text_str # 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" font_path = "font/Ubuntu-B.ttf" 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) 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_only_viz if viz['gt_type'] == 'event']) num_gt_rows = len(gt_viz) num_rows = max(num_model_rows, num_gt_rows) if len(gt_viz) > 0: selected_models.append(ground_truth) padding = 2 font_size = 20 num_cols = len(selected_models) crop_str = "crop=trunc(iw/2)*2:trunc(ih/2)*2" pad_str = "pad=ceil(iw/2)*2+{}:ceil(ih/2)*2+{}:{}:{}".format(padding*2, padding*2, padding, padding) num_elements = num_rows * num_cols w = selected_dataset["width"] h = selected_dataset["height"] input_filter_parts = [] xstack_input_parts = [] layout_parts = [] video_paths = [] row_heights = [""]*num_rows gt_viz_indices = [] if len(model_viz) > 1: left_pad = font_size*max([len(viz['display_name']) for viz in model_viz[1:]]) + padding*2 else: left_pad = 0 for row_idx in range(num_rows): for col_idx in range(num_cols): vid_idx = len(video_paths) cur_model = selected_models[col_idx] if cur_model['name'] == "gt": if row_idx < len(gt_viz): video_path = get_path_for_viz(data_base_path, selected_dataset, selected_sequence, cur_model, gt_viz[row_idx]) if not os.path.isfile(video_path): raise ValueError("Video path does not exist: " + video_path) gt_viz_indices.append(vid_idx) else: continue else: if row_idx < len(model_viz): video_path = get_path_for_viz(data_base_path, selected_dataset, selected_sequence, cur_model, model_viz[row_idx]) if not os.path.isfile(video_path): raise ValueError("Video path does not exist: " + video_path) else: continue if row_heights[row_idx] == "": row_heights[row_idx] = "h{}".format(vid_idx) if row_idx == 0: pad_height = font_size+padding*2 pad_txt_str = ",pad={}:{}:0:{}:white".format(w+padding*2, h+font_size+padding*4, pad_height) text_str = get_text_str(pad_height, w, cur_model['display_name'], font_path, font_size) pad_txt_str = pad_txt_str + "," + text_str elif row_idx > 0 and col_idx == 0: pad_txt_str = ",pad={}:ih:{}:0:white".format(w + left_pad + padding*2, left_pad) text_str = get_text_str("h", left_pad, model_viz[row_idx]['display_name'], font_path, font_size) pad_txt_str = pad_txt_str + "," + text_str else: pad_txt_str = "" input_filter_part = "[{}:v]scale={}:-1,{}{}[v{}]".format(vid_idx, w, pad_str, pad_txt_str, vid_idx) input_filter_parts.append(input_filter_part) xstack_input_part = "[v{}]".format(vid_idx) xstack_input_parts.append(xstack_input_part) video_paths.append(video_path) if row_idx == 0 or col_idx > 0: layout_w_parts = [str(left_pad)] + ["w{}".format(i) for i in range(col_idx)] layout_w = "+".join(layout_w_parts) else: layout_w = "+".join(["w{}".format(i) for i in range(col_idx)]) if col_idx > 0 else "0" if cur_model['name'] == "gt": layout_h = "+".join(["h{}".format(i) for i in gt_viz_indices[:-1]]) if row_idx > 0 else "0" else: layout_h = "+".join(row_heights[:row_idx]) if row_idx > 0 else "0" layout_part = layout_w + "_" + layout_h layout_parts.append(layout_part) inputs_str = " ".join(["-i " + video_path for video_path in video_paths]) num_inputs = len(video_paths) input_scaling_str = ";".join(input_filter_parts) xstack_input_str = "".join(xstack_input_parts) layout_str = "|".join(layout_parts) # opt = "-c:v libx264 -preset veryslow -crf 18 -c:a copy" opt = "" opt_fill = ":fill=black" # opt_fill = "" ffmpeg_command_str = "ffmpeg -y " + inputs_str + " -filter_complex \"" + input_scaling_str + ";" + xstack_input_str + "xstack=inputs=" + str(num_inputs) + ":layout=" + layout_str + opt_fill + "\"" + opt + " output.mp4" print(ffmpeg_command_str) ret = subprocess.call(ffmpeg_command_str, shell=True) if ret != 0: st.error("Error while generating video.") st.stop() video_file = open('output.mp4', 'rb') video_bytes = video_file.read() st.video(video_bytes)