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import time
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats as st
from config import FPS
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 change_points_to_segments(df, change_points):
""" Convert change points from kats detector to segment indicators """
return [pd.to_datetime(0.0, unit='s').to_datetime64()] + [cp.start_time for cp in change_points] + [pd.to_datetime(df.iloc[-1]['TARGET_S'], unit='s').to_datetime64()]
def add_seconds_to_datetime64(datetime64, seconds, subtract=False):
"""Add or substract a number of seconds to a np.datetime64 object """
s, m = divmod(seconds, 1.0)
if subtract:
return datetime64 - np.timedelta64(int(s), 's') - np.timedelta64(int(m * 1000), 'ms')
return datetime64 + np.timedelta64(int(s), 's') + np.timedelta64(int(m * 1000), 'ms')
def plot_segment_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(2, 1, figsize=(16, 6), dpi=100, sharex=True)
sns.scatterplot(data = df, x='time', y='SOURCE_S', ax=ax_arr[0])
# sns.lineplot(data = df, x='time', y='SOURCE_LIP_S', ax=ax_arr[0,1])
# Plot change point as lines
# sns.lineplot(data = df, x='time', y='OFFSET_LIP', ax=ax_arr[1,0])
sns.lineplot(data = df, x='time', y='OFFSET_LIP', ax=ax_arr[1])
timestamps = change_points_to_segments(df, change_points)
# To plot the detected segment lines
for x in timestamps:
plt.vlines(x=x, ymin=np.min(df['OFFSET_LIP']), ymax=np.max(df['OFFSET_LIP']), colors='black', lw=2, alpha=0.5)
rand_y_pos = np.random.uniform(low=np.min(df['OFFSET_LIP']), high=np.max(df['OFFSET_LIP']), size=None)
# To get each detected segment and their mean?
threshold_diff = 1.5 # Average diff threshold
# threshold = 3.0 # s diff threshold
for start_time, end_time in zip(timestamps[:-1], timestamps[1:]):
add_offset = np.min(df['SOURCE_S'])
# Cut out the segment between the segment lines
segment = df[(df['time'] > start_time) & (df['time'] < end_time)] # Not offset LIP
segment_no_nan = segment[~np.isnan(segment['OFFSET'])] # Remove NaNs
segment_offsets = segment_no_nan['OFFSET'] # np.round(segment_no_nan['OFFSET'], 1)
# segment_offsets = np.round(segment_no_nan['OFFSET'], 0)
# Calculate mean/median/mode
# seg_sum_stat = np.mean(segment_offsets)
# seg_sum_stat = np.median(segment_offsets)
seg_sum_stat = st.mode(segment_offsets)[0][0]
# Get average difference from mean/median/mode of the segment to see if it is a "straight line" or not
average_diff = np.mean(np.abs(segment_offsets - seg_sum_stat))
# If the time where the segment comes from (origin time) is close to the start_time, it's a "good match", so no editing
noisy = False if average_diff < threshold_diff else True
origin_time = add_seconds_to_datetime64(start_time, seg_sum_stat + add_offset)
# Plot green for a confident prediction (straight line), red otherwise
if not noisy:
# Plot estimated straight line
plt.hlines(y=seg_sum_stat, xmin=start_time, xmax=end_time, color='green', lw=3, alpha=0.5)
plt.text(x=start_time, y=seg_sum_stat, s=str(np.round(average_diff, 1)), color='green', rotation=-0.0, fontsize=14)
else:
# Plot estimated straight line
plt.hlines(y=seg_sum_stat, xmin=start_time, xmax=end_time, color='red', lw=3, alpha=0.5)
plt.text(x=start_time, y=seg_sum_stat, s=str(np.round(average_diff, 1)), color='red', rotation=-0.0, fontsize=14)
# print(f"DIFF={average_diff:.1f} SUMSTAT={seg_sum_stat:.1f} {start_time} -> {end_time} comes from video X, from {origin_time}")
# Return figure
plt.xticks(rotation=90)
return fig |