import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# from matplotlib.patches import patch
import os
import sys
import cv2
import glob
import argparse
from IPython import embed
from tqdm import tqdm
from thunderfish.powerspectrum import decibel
import pathlib
import pandas as pd
def main(folder, dt):
video_path = glob.glob(os.path.join(folder, '2022*.mp4'))[0]
create_video_path = os.path.join(folder, 'rise_video')
if not os.path.exists(create_video_path):
os.mkdir(create_video_path)
video = cv2.VideoCapture(video_path) # was 'cap'
# fish_freqs = np.load(os.path.join(folder, 'analysis', 'fish_freq_interp.npy'))
fish_freqs = np.load(os.path.join(folder, 'analysis', 'fish_freq.npy'))
# rise_idx = np.load(os.path.join(folder, 'analysis', 'rise_idx.npy'))
meta = pd.read_csv(pathlib.Path(folder).parent / 'meta.csv', sep=',', encoding='utf-7', index_col=0)
filename = pathlib.Path(folder).name
win_id = meta.loc[filename, 'Win_ID']
lose_id = meta.loc[filename, 'Lose_ID']
rise_bboxes = pd.read_csv(pathlib.Path(folder) / "risedetector_bboxes.csv", sep=',')
chirp_bboxes = pd.read_csv(pathlib.Path(folder) / "chirpdetector_bboxes.csv", sep=',')
rise_times = rise_bboxes['t0'][rise_bboxes['id'] == lose_id].to_numpy()
chirp_times = chirp_bboxes['chirp_times'][(chirp_bboxes['assigned_track'] == lose_id)].to_numpy()
# ToDo: rise and chipt times to times idxs!!!
# embed()
# quit()
frame_times = np.load(os.path.join(folder, 'analysis', 'frame_times.npy'))
times = np.load(os.path.join(folder, 'times.npy'))
fill_freqs = np.load(os.path.join(folder, 'fill_freqs.npy'))
fill_times = np.load(os.path.join(folder, 'fill_times.npy'))
fill_spec_shape = np.load(os.path.join(folder, 'fill_spec_shape.npy'))
fill_spec = np.memmap(os.path.join(folder, 'fill_spec.npy'), dtype='float', mode='r',
shape=(fill_spec_shape[0], fill_spec_shape[1]), order='F')
for rise_time in rise_bboxes['t0'][rise_bboxes['id'] == lose_id].to_numpy():
relevant_chirps = chirp_times[((chirp_times - rise_time) > 0 ) &
((chirp_times - rise_time) < dt * 3)]
if len(relevant_chirps) == 0:
continue
rel_chirp_time = relevant_chirps - rise_time
HH = int((rise_time / 3600) // 1)
MM = int((rise_time - HH * 3600) // 60)
SS = int(rise_time - HH * 3600 - MM * 60)
frames_oi = np.arange(len(frame_times))[((frame_times - rise_time) >= -dt) & ((frame_times - rise_time) <= 3*dt)]
idxs_oi = np.arange(len(times))[((times - rise_time) >= -dt) & ((times - rise_time) <= 3*dt)]
fig = plt.figure(figsize=(16 * 2 / 2.54, 9 * 2 / 2.54))
fig.patch.set_facecolor('black')
gs = gridspec.GridSpec(6, 2, left=0.075, bottom=0.05, right=.99, top=0.95, width_ratios=(1.5, 3), hspace=.3,
wspace=0.05)
ax = []
ax.append(fig.add_subplot(gs[:, 1]))
ax.append(fig.add_subplot(gs[1:3, 0]))
ax.append(fig.add_subplot(gs[3:5, 0]))
y00, y01 = np.nanmin(fish_freqs[0][idxs_oi]), np.nanmax(fish_freqs[0][idxs_oi])
y10, y11 = np.nanmin(fish_freqs[1][idxs_oi]), np.nanmax(fish_freqs[1][idxs_oi])
if y01 - y00 < 20:
y01 = y00 + 20
if y11 - y10 < 20:
y11 = y10 + 20
freq_span1 = (y01) - (y00)
freq_span2 = (y11) - (y10)
yspan = freq_span1 if freq_span1 > freq_span2 else freq_span2
ax[1].plot(times[idxs_oi] - rise_time, fish_freqs[0][idxs_oi], marker='.', markersize=4, color='darkorange', lw=2,
alpha=0.4)
ax[2].plot(times[idxs_oi] - rise_time, fish_freqs[1][idxs_oi], marker='.', markersize=4, color='forestgreen',
lw=2, alpha=0.4)
ax[1].plot([0, 0], [y00 - yspan * 0.2, y00 + yspan * 1.3], '--', color='white')
ax[2].plot([0, 0], [y10 - yspan * 0.2, y10 + yspan * 1.3], '--', color='white')
for ct in rel_chirp_time:
ax[2].plot([ct, ct], [y10 - yspan * 0.2, y10 + yspan * 1.3], '--', color='tab:orange')
ax[1].set_xticks([-30, -15, 0, 15, 30])
ax[2].set_xticks([-30, -15, 0, 15, 30])
plt.setp(ax[1].get_xticklabels(), visible=False)
# spectrograms
f_mask1 = np.arange(len(fill_freqs))[(fill_freqs >= y00 - yspan * 0.2) & (fill_freqs <= y00 + yspan * 1.3)]
f_mask2 = np.arange(len(fill_freqs))[(fill_freqs >= y10 - yspan * 0.2) & (fill_freqs <= y10 + yspan * 1.3)]
t_mask = np.arange(len(fill_times))[(fill_times >= rise_time - dt * 4) & (fill_times <= rise_time + dt * 4)]
ax[1].imshow(decibel(fill_spec[f_mask1[0]:f_mask1[-1], t_mask[0]:t_mask[-1]][::-1]),
extent=[-dt * 4, dt * 4, y00 - yspan * 0.2, y00 + yspan * 1.3],
aspect='auto', vmin=-100, vmax=-50, cmap='afmhot', interpolation='gaussian')
ax[2].imshow(decibel(fill_spec[f_mask2[0]:f_mask2[-1], t_mask[0]:t_mask[-1]][::-1]),
extent=[-dt * 4, dt * 4, y10 - yspan * 0.2, y10 + yspan * 1.3],
aspect='auto', vmin=-100, vmax=-50, cmap='afmhot', interpolation='gaussian')
ax[1].set_ylim(y00 - yspan * 0.1, y00 + yspan * 1.2)
# ax[1].set_xlim(-dt * 3, dt * 3)
ax[1].set_xlim(-dt, dt * 3)
ax[2].set_ylim(y10 - yspan * 0.1, y10 + yspan * 1.2)
# ax[2].set_xlim(-dt * 3, dt * 3)
ax[2].set_xlim(-dt, dt * 3)
ax[0].set_xticks([])
ax[0].set_yticks([])
ax[1].tick_params(labelsize=12, color='white', labelcolor='white')
ax[2].tick_params(labelsize=12, color='white', labelcolor='white')
# embed()
# quit()
for a in ax[1:]:
a.spines['left'].set_edgecolor('white')
a.spines['bottom'].set_edgecolor('white')
# for spine in ax[1].spines.values():
# spine.set_edgecolor('white')
# for spine in ax[2].spines.values():
# spine.set_edgecolor('white')
ax[2].set_xlabel('time [s]', fontsize=14, color='white')
fig.text(0.02, 0.5, 'frequency [Hz]', fontsize=14, va='center', rotation='vertical', color='white')
# embed()
# quit()
rise_dot_counter = 0
rise_passed = False
chirp_dot_counter = np.zeros(len(relevant_chirps), dtype=int)
chirp_passed = np.zeros(len(relevant_chirps), dtype=bool)
chirp_dot_handls = []
chirp_active = False
# plt.show()
# embed()
# quit()
for i in tqdm(np.arange(len(frames_oi))):
# break
video.set(cv2.CAP_PROP_POS_FRAMES, int(frames_oi[i]))
ret, frame = video.read()
if i == 0:
img = ax[0].imshow(frame)
line1, = ax[1].plot([frame_times[frames_oi[i]] - rise_time, frame_times[frames_oi[i]] - rise_time],
[y00 - yspan * 0.15, y00 + yspan * 1.3],
color='white', lw=1)
line2, = ax[2].plot([frame_times[frames_oi[i]] - rise_time, frame_times[frames_oi[i]] - rise_time],
[y10 - yspan * 0.15, y10 + yspan * 1.3],
color='white', lw=1)
else:
img.set_data(frame)
line1.set_data([frame_times[frames_oi[i]] - rise_time, frame_times[frames_oi[i]] - rise_time],
[y00 - yspan * 0.15, y00 + yspan * 1.3])
line2.set_data([frame_times[frames_oi[i]] - rise_time, frame_times[frames_oi[i]] - rise_time],
[y10 - yspan * 0.15, y10 + yspan * 1.3])
if frame_times[frames_oi[i]] - rise_time > 0:
if rise_passed == False:
rise_dot, = ax[0].plot(0.05, 0.95, 'o', color='white', transform=ax[0].transAxes, markersize=20)
rise_passed = True
for pos in ['left', 'bottom', 'right', 'top']:
ax[0].spines[pos].set_edgecolor('white')
ax[0].spines[pos].set_edgecolor('white')
if rise_passed == True:
if rise_dot_counter < 6:
rise_dot_counter += 1
elif rise_dot_counter == 6:
rise_dot.remove()
if not chirp_active:
for pos in ['left', 'bottom', 'right', 'top']:
ax[0].spines[pos].set_edgecolor('k')
ax[0].spines[pos].set_edgecolor('k')
rise_dot_counter += 1
else:
pass
for enu, chirp_time in enumerate(relevant_chirps):
if frame_times[frames_oi[i]] - chirp_time > 0:
if chirp_passed[enu] == False:
chirp_dot, = ax[0].plot(0.05, 0.95, 'o', color='tab:orange', transform=ax[0].transAxes,
markersize=20)
chirp_dot_handls.append(chirp_dot)
for pos in ['left', 'bottom', 'right', 'top']:
ax[0].spines[pos].set_edgecolor('tab:orange')
ax[0].spines[pos].set_edgecolor('tab:orange')
chirp_passed[enu] = True
chirp_active = True
if chirp_passed[enu] == True:
if chirp_dot_counter[enu] < 6:
chirp_dot_counter[enu] += 1
elif chirp_dot_counter[enu] == 6:
chirp_dot_handls[enu].remove()
for pos in ['left', 'bottom', 'right', 'top']:
ax[0].spines[pos].set_edgecolor('k')
ax[0].spines[pos].set_edgecolor('k')
chirp_dot_counter[enu] += 1
else:
pass
label = (os.path.join(create_video_path,
'frame%4.f.jpg' % len(glob.glob(os.path.join(create_video_path, '*.jpg'))))).replace(' ',
'0')
plt.savefig(label, dpi=300)
# for fish_nr in np.arange(2)[::-1]:
# for idx_oi in tqdm(np.array(rise_idx[fish_nr][~np.isnan(rise_idx[fish_nr])], dtype=int)):
# time_oi = times[idx_oi]
#
# HH = int((time_oi / 3600) // 1)
# MM = int((time_oi - HH * 3600) // 60)
# SS = int(time_oi - HH * 3600 - MM * 60)
#
# frames_oi = np.arange(len(frame_times))[np.abs(frame_times - time_oi) <= dt]
# idxs_oi = np.arange(len(times))[np.abs(times - time_oi) <= dt*3]
#
# fig = plt.figure(figsize=(16*2/2.54, 9*2/2.54))
# gs = gridspec.GridSpec(6, 2, left=0.075, bottom=0.05, right=1, top=0.95, width_ratios=(1.5, 3), hspace=.3, wspace=0.05)
# ax = []
# ax.append(fig.add_subplot(gs[:, 1]))
# ax.append(fig.add_subplot(gs[1:3, 0]))
# ax.append(fig.add_subplot(gs[3:5, 0]))
#
#
# y00, y01 = np.nanmin(fish_freqs[0][idxs_oi]), np.nanmax(fish_freqs[0][idxs_oi])
# y10, y11 = np.nanmin(fish_freqs[1][idxs_oi]), np.nanmax(fish_freqs[1][idxs_oi])
#
# if y01 - y00 < 20:
# y01 = y00 + 20
# if y11 - y10 < 20:
# y11 = y10 + 20
# freq_span1 = (y01) - (y00)
# freq_span2 = (y11) - (y10)
#
# yspan = freq_span1 if freq_span1 > freq_span2 else freq_span2
#
# ax[1].plot(times[idxs_oi] - time_oi, fish_freqs[0][idxs_oi], marker='.', markersize=4, color='darkorange', lw=2, alpha=0.4)
# ax[2].plot(times[idxs_oi] - time_oi, fish_freqs[1][idxs_oi], marker='.', markersize=4,color='forestgreen', lw=2, alpha=0.4)
# ax[1].plot([0, 0], [y00 - yspan * 0.2, y00 + yspan * 1.3], '--', color='k')
# ax[2].plot([0, 0], [y10 - yspan * 0.2, y10 + yspan * 1.3], '--', color='k')
#
# ax[1].set_xticks([-30, -15, 0, 15, 30])
# ax[2].set_xticks([-30, -15, 0, 15, 30])
# plt.setp(ax[1].get_xticklabels(), visible=False)
#
# # spectrograms
# f_mask1 = np.arange(len(fill_freqs))[(fill_freqs >= y00 - yspan * 0.2) & (fill_freqs <= y00 + yspan * 1.3)]
# f_mask2 = np.arange(len(fill_freqs))[(fill_freqs >= y10 - yspan * 0.2) & (fill_freqs <= y10 + yspan * 1.3)]
# t_mask = np.arange(len(fill_times))[(fill_times >= time_oi-dt*4) & (fill_times <= time_oi+dt*4)]
#
# ax[1].imshow(decibel(fill_spec[f_mask1[0]:f_mask1[-1], t_mask[0]:t_mask[-1]][::-1]),
# extent=[-dt*4, dt*4, y00 - yspan * 0.2, y00 + yspan * 1.3],
# aspect='auto',vmin = -100, vmax = -50, alpha=0.7, cmap='jet', interpolation='gaussian')
# ax[2].imshow(decibel(fill_spec[f_mask2[0]:f_mask2[-1], t_mask[0]:t_mask[-1]][::-1]),
# extent=[-dt*4, dt*4, y10 - yspan * 0.2, y10 + yspan * 1.3],
# aspect='auto',vmin = -100, vmax = -50, alpha=0.7, cmap='jet', interpolation='gaussian')
#
# ax[1].set_ylim(y00 - yspan * 0.1, y00 + yspan * 1.2)
# ax[1].set_xlim(-dt*3, dt*3)
# ax[2].set_ylim(y10 - yspan * 0.1, y10 + yspan * 1.2)
# ax[2].set_xlim(-dt*3, dt*3)
#
# ax[0].set_xticks([])
# ax[0].set_yticks([])
#
# ax[1].tick_params(labelsize=12)
# ax[2].tick_params(labelsize=12)
#
# ax[2].set_xlabel('time [s]', fontsize=14)
# fig.text(0.02, 0.5, 'frequency [Hz]', fontsize=14, va='center', rotation='vertical')
#
# # plt.ion()
# for i in tqdm(np.arange(len(frames_oi))):
# # break
# video.set(cv2.CAP_PROP_POS_FRAMES, int(frames_oi[i]))
# ret, frame = video.read()
#
# if i == 250:
# dot, = ax[0].plot(0.05, 0.95, 'o', color='firebrick', transform = ax[0].transAxes, markersize=20)
# if i == 280:
# dot.remove()
#
# if i == 0:
# img = ax[0].imshow(frame)
# line1, = ax[1].plot([frame_times[frames_oi[i]] - time_oi, frame_times[frames_oi[i]] - time_oi],
# [y00 - yspan * 0.15, y00 + yspan * 1.3],
# color='k', lw=1)
# line2, = ax[2].plot([frame_times[frames_oi[i]] - time_oi, frame_times[frames_oi[i]] - time_oi],
# [y10 - yspan * 0.15, y10 + yspan * 1.3],
# color='k', lw=1)
# else:
# img.set_data(frame)
# line1.set_data([frame_times[frames_oi[i]] - time_oi, frame_times[frames_oi[i]] - time_oi],
# [y00 - yspan * 0.15, y00 + yspan * 1.3])
# line2.set_data([frame_times[frames_oi[i]] - time_oi, frame_times[frames_oi[i]] - time_oi],
# [y10 - yspan * 0.15, y10 + yspan * 1.3])
#
# label = (os.path.join(create_video_path, 'frame%4.f.jpg' % len(glob.glob(os.path.join(create_video_path, '*.jpg'))))).replace(' ', '0')
# plt.savefig(label, dpi=300)
# # plt.pause(0.001)
# quit()
win_lose_str = 'lose'
# video_name = ("./rise_video/%s_%2.f:%2.f:%2.f.mp4" % (win_lose_str, HH, MM, SS)).replace(' ', '0')
# command = "ffmpeg -r 25 -i './rise_video/frame%4d.jpg' -vf 'pad=ceil(iw/2)*2:ceil(ih/2)*2' -vcodec libx264 -y -an"
video_name = os.path.join(create_video_path, ("%s_%2.f:%2.f:%2.f.mp4" % (win_lose_str, HH, MM, SS)).replace(' ', '0'))
command1 = "ffmpeg -r 25 -i"
frames_path = '"%s"' % os.path.join(create_video_path, "frame%4d.jpg")
command2 = "-vf 'pad=ceil(iw/2)*2:ceil(ih/2)*2' -vcodec libx264 -y -an"
os.system(' '.join([command1, frames_path, command2, video_name]))
os.system(' '.join(['rm', os.path.join(create_video_path, '*.jpg')]))
# os.system(' '.join([command, video_name]))
# os.system('rm ./rise_video/*.jpg')
plt.close()
embed()
quit()
###############################
fig, ax = plt.subplots()
for i, c in enumerate(['firebrick', 'cornflowerblue']):
ax.plot(times, fish_freqs[i], marker='.', color=c)
r_idx = np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int)
ax.plot(times[r_idx], fish_freqs[i][r_idx], 'o', color='k')
pass
##############################
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Generate videos around events.')
parser.add_argument('file', type=str, help='folder/dataset to generate videos from.')
parser.add_argument('-t', type=float, default=10, help='video duration before and after event.')
# parser.add_argument("-c", action="store_true", help="check if LED pos is correct")
# parser.add_argument('-x', type=int, nargs=2, default=[1272, 1282], help='x-borders of LED detect area (in pixels)')
# parser.add_argument('-y', type=int, nargs=2, default=[1500, 1516], help='y-borders of LED area (in pixels)')
args = parser.parse_args()
main(args.file, args.t)