import logging import time import pandas import gradio as gr import seaborn as sns import matplotlib.pyplot as plt import numpy as np import pandas as pd from config import * from videomatch import index_hashes_for_video, get_decent_distance, \ get_video_indices, compare_videos, get_change_points logging.basicConfig() logging.getLogger().setLevel(logging.INFO) def plot_comparison(lims, D, I, hash_vectors, MIN_DISTANCE = 3): sns.set_theme() x = [(lims[i+1]-lims[i]) * [i] for i in range(hash_vectors.shape[0])] x = [i/FPS for j in x for i in j] y = [i/FPS for i in I] # Create figure and dataframe to plot with sns fig = plt.figure() # plt.tight_layout() df = pd.DataFrame(zip(x, y), columns = ['X', 'Y']) g = sns.scatterplot(data=df, x='X', y='Y', s=2*(1-D/(MIN_DISTANCE+1)), alpha=1-D/MIN_DISTANCE) # Set x-labels to be more readable x_locs, x_labels = plt.xticks() # Get original locations and labels for x ticks x_labels = [time.strftime('%H:%M:%S', time.gmtime(x)) for x in x_locs] plt.xticks(x_locs, x_labels) plt.xticks(rotation=90) plt.xlabel('Time in source video (H:M:S)') plt.xlim(0, None) # Set y-labels to be more readable y_locs, y_labels = plt.yticks() # Get original locations and labels for x ticks y_labels = [time.strftime('%H:%M:%S', time.gmtime(y)) for y in y_locs] plt.yticks(y_locs, y_labels) plt.ylabel('Time in target video (H:M:S)') # Adjust padding to fit gradio plt.subplots_adjust(bottom=0.25, left=0.20) return fig def plot_multi_comparison(df, change_points): """ From the dataframe plot the current set of plots, where the bottom right is most indicative """ fig, ax_arr = plt.subplots(3, 2, figsize=(12, 6), dpi=100, sharex=True) sns.scatterplot(data = df, x='time', y='SOURCE_S', ax=ax_arr[0,0]) sns.lineplot(data = df, x='time', y='SOURCE_LIP_S', ax=ax_arr[0,1]) sns.scatterplot(data = df, x='time', y='OFFSET', ax=ax_arr[1,0]) sns.lineplot(data = df, x='time', y='OFFSET_LIP', ax=ax_arr[1,1]) # Plot change point as lines sns.lineplot(data = df, x='time', y='OFFSET_LIP', ax=ax_arr[2,1]) for x in change_points: cp_time = x.start_time plt.vlines(x=cp_time, ymin=np.min(df['OFFSET_LIP']), ymax=np.max(df['OFFSET_LIP']), colors='red', lw=2) rand_y_pos = np.random.uniform(low=np.min(df['OFFSET_LIP']), high=np.max(df['OFFSET_LIP']), size=None) plt.text(x=cp_time, y=rand_y_pos, s=str(np.round(x.confidence, 2)), color='r', rotation=-0.0, fontsize=14) plt.xticks(rotation=90) return fig def get_videomatch_df(url, target, min_distance=MIN_DISTANCE, vanilla_df=False): distance = get_decent_distance(url, target, MIN_DISTANCE, MAX_DISTANCE) video_index, hash_vectors, target_indices = get_video_indices(url, target, MIN_DISTANCE = distance) lims, D, I, hash_vectors = compare_videos(hash_vectors, target_indices, MIN_DISTANCE = distance) target = [(lims[i+1]-lims[i]) * [i] for i in range(hash_vectors.shape[0])] target_s = [i/FPS for j in target for i in j] source_s = [i/FPS for i in I] # Make df df = pd.DataFrame(zip(target_s, source_s, D, I), columns = ['TARGET_S', 'SOURCE_S', 'DISTANCE', 'INDICES']) if vanilla_df: return df # Minimum distance dataframe ---- # Group by X so for every second/x there will be 1 value of Y in the end # index_min_distance = df.groupby('TARGET_S')['DISTANCE'].idxmin() # df_min = df.loc[index_min_distance] # df_min # ------------------------------- df['TARGET_WEIGHT'] = 1 - df['DISTANCE']/distance # Higher value means a better match df['SOURCE_WEIGHTED_VALUE'] = df['SOURCE_S'] * df['TARGET_WEIGHT'] # Multiply the weight (which indicates a better match) with the value for Y and aggregate to get a less noisy estimate of Y # Group by X so for every second/x there will be 1 value of Y in the end grouped_X = df.groupby('TARGET_S').agg({'SOURCE_WEIGHTED_VALUE' : 'sum', 'TARGET_WEIGHT' : 'sum'}) grouped_X['FINAL_SOURCE_VALUE'] = grouped_X['SOURCE_WEIGHTED_VALUE'] / grouped_X['TARGET_WEIGHT'] # Remake the dataframe df = grouped_X.reset_index() df = df.drop(columns=['SOURCE_WEIGHTED_VALUE', 'TARGET_WEIGHT']) df = df.rename({'FINAL_SOURCE_VALUE' : 'SOURCE_S'}, axis='columns') # Add NAN to "missing" x values (base it off hash vector, not target_s) step_size = 1/FPS x_complete = np.round(np.arange(start=0.0, stop = max(df['TARGET_S'])+step_size, step = step_size), 1) # More robust df['TARGET_S'] = np.round(df['TARGET_S'], 1) df_complete = pd.DataFrame(x_complete, columns=['TARGET_S']) # Merge dataframes to get NAN values for every missing SOURCE_S df = df_complete.merge(df, on='TARGET_S', how='left') # Interpolate between frames since there are missing values df['SOURCE_LIP_S'] = df['SOURCE_S'].interpolate(method='linear', limit_direction='both', axis=0) # Add timeshift col and timeshift col with Linearly Interpolated Values df['TIMESHIFT'] = df['SOURCE_S'].shift(1) - df['SOURCE_S'] df['TIMESHIFT_LIP'] = df['SOURCE_LIP_S'].shift(1) - df['SOURCE_LIP_S'] # Add Offset col that assumes the video is played at the same speed as the other to do a "timeshift" df['OFFSET'] = df['SOURCE_S'] - df['TARGET_S'] - np.min(df['SOURCE_S']) df['OFFSET_LIP'] = df['SOURCE_LIP_S'] - df['TARGET_S'] - np.min(df['SOURCE_LIP_S']) # Add time column for plotting df['time'] = pd.to_datetime(df["TARGET_S"], unit='s') # Needs a datetime as input return df def get_comparison(url, target, MIN_DISTANCE = 4): """ Function for Gradio to combine all helper functions""" video_index, hash_vectors, target_indices = get_video_indices(url, target, MIN_DISTANCE = MIN_DISTANCE) lims, D, I, hash_vectors = compare_videos(hash_vectors, target_indices, MIN_DISTANCE = MIN_DISTANCE) fig = plot_comparison(lims, D, I, hash_vectors, MIN_DISTANCE = MIN_DISTANCE) return fig def get_auto_comparison(url, target, smoothing_window_size=10, method="CUSUM"): """ Function for Gradio to combine all helper functions""" distance = get_decent_distance(url, target, MIN_DISTANCE, MAX_DISTANCE) if distance == None: return None raise gr.Error("No matches found!") video_index, hash_vectors, target_indices = get_video_indices(url, target, MIN_DISTANCE = distance) lims, D, I, hash_vectors = compare_videos(hash_vectors, target_indices, MIN_DISTANCE = distance) # fig = plot_comparison(lims, D, I, hash_vectors, MIN_DISTANCE = distance) df = get_videomatch_df(url, target, min_distance=MIN_DISTANCE, vanilla_df=False) change_points = get_change_points(df, smoothing_window_size=smoothing_window_size, method=method) fig = plot_multi_comparison(df, change_points) return fig video_urls = ["https://www.dropbox.com/s/8c89a9aba0w8gjg/Ploumen.mp4?dl=1", "https://www.dropbox.com/s/rzmicviu1fe740t/Bram%20van%20Ojik%20krijgt%20reprimande.mp4?dl=1", "https://www.dropbox.com/s/wcot34ldmb84071/Baudet%20ontmaskert%20Omtzigt_%20u%20bent%20door%20de%20mand%20gevallen%21.mp4?dl=1", "https://drive.google.com/uc?id=1XW0niHR1k09vPNv1cp6NvdGXe7FHJc1D&export=download", "https://www.dropbox.com/s/4ognq8lshcujk43/Plenaire_zaal_20200923132426_Omtzigt.mp4?dl=1"] index_iface = gr.Interface(fn=lambda url: index_hashes_for_video(url).ntotal, inputs="text", outputs="text", examples=video_urls, cache_examples=True) compare_iface = gr.Interface(fn=get_comparison, inputs=["text", "text", gr.Slider(2, 30, 4, step=2)], outputs="plot", examples=[[x, video_urls[-1]] for x in video_urls[:-1]]) auto_compare_iface = gr.Interface(fn=get_auto_comparison, inputs=["text", "text", gr.Slider(1, 50, 10, step=1), gr.Dropdown(choices=["CUSUM", "Robust"], value="CUSUM")], outputs="plot", examples=[[x, video_urls[-1]] for x in video_urls[:-1]]) iface = gr.TabbedInterface([auto_compare_iface, compare_iface, index_iface,], ["AutoCompare", "Compare", "Index"]) if __name__ == "__main__": import matplotlib matplotlib.use('SVG') # To be able to plot in gradio iface.launch(show_error=True) #iface.launch(auth=("test", "test"), share=True, debug=True)