165 lines
8.3 KiB
Python
165 lines
8.3 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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import os
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import sys
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import cv2
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import glob
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import argparse
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from IPython import embed
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from tqdm import tqdm
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from thunderfish.powerspectrum import decibel
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def main(folder, dt):
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video_path = glob.glob(os.path.join(folder, '2022*.mp4'))[0]
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create_video_path = os.path.join(folder, 'rise_video')
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if not os.path.exists(create_video_path):
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os.mkdir(create_video_path)
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video = cv2.VideoCapture(video_path) # was 'cap'
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# fish_freqs = np.load(os.path.join(folder, 'analysis', 'fish_freq_interp.npy'))
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fish_freqs = np.load(os.path.join(folder, 'analysis', 'fish_freq.npy'))
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rise_idx = np.load(os.path.join(folder, 'analysis', 'rise_idx.npy'))
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frame_times = np.load(os.path.join(folder, 'analysis', 'frame_times.npy'))
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times = np.load(os.path.join(folder, 'times.npy'))
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fill_freqs = np.load(os.path.join(folder, 'fill_freqs.npy'))
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fill_times = np.load(os.path.join(folder, 'fill_times.npy'))
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fill_spec_shape = np.load(os.path.join(folder, 'fill_spec_shape.npy'))
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fill_spec = np.memmap(os.path.join(folder, 'fill_spec.npy'), dtype='float', mode='r',
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shape=(fill_spec_shape[0], fill_spec_shape[1]), order='F')
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#######################################
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for fish_nr in np.arange(2)[::-1]:
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for idx_oi in tqdm(np.array(rise_idx[fish_nr][~np.isnan(rise_idx[fish_nr])], dtype=int)):
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time_oi = times[idx_oi]
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HH = int((time_oi / 3600) // 1)
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MM = int((time_oi - HH * 3600) // 60)
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SS = int(time_oi - HH * 3600 - MM * 60)
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frames_oi = np.arange(len(frame_times))[np.abs(frame_times - time_oi) <= dt]
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idxs_oi = np.arange(len(times))[np.abs(times - time_oi) <= dt*3]
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fig = plt.figure(figsize=(16*2/2.54, 9*2/2.54))
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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)
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ax = []
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ax.append(fig.add_subplot(gs[:, 1]))
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ax.append(fig.add_subplot(gs[1:3, 0]))
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ax.append(fig.add_subplot(gs[3:5, 0]))
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y00, y01 = np.nanmin(fish_freqs[0][idxs_oi]), np.nanmax(fish_freqs[0][idxs_oi])
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y10, y11 = np.nanmin(fish_freqs[1][idxs_oi]), np.nanmax(fish_freqs[1][idxs_oi])
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if y01 - y00 < 20:
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y01 = y00 + 20
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if y11 - y10 < 20:
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y11 = y10 + 20
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freq_span1 = (y01) - (y00)
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freq_span2 = (y11) - (y10)
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yspan = freq_span1 if freq_span1 > freq_span2 else freq_span2
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ax[1].plot(times[idxs_oi] - time_oi, fish_freqs[0][idxs_oi], marker='.', markersize=4, color='darkorange', lw=2, alpha=0.4)
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ax[2].plot(times[idxs_oi] - time_oi, fish_freqs[1][idxs_oi], marker='.', markersize=4,color='forestgreen', lw=2, alpha=0.4)
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ax[1].plot([0, 0], [y00 - yspan * 0.2, y00 + yspan * 1.3], '--', color='k')
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ax[2].plot([0, 0], [y10 - yspan * 0.2, y10 + yspan * 1.3], '--', color='k')
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ax[1].set_xticks([-30, -15, 0, 15, 30])
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ax[2].set_xticks([-30, -15, 0, 15, 30])
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plt.setp(ax[1].get_xticklabels(), visible=False)
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# spectrograms
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f_mask1 = np.arange(len(fill_freqs))[(fill_freqs >= y00 - yspan * 0.2) & (fill_freqs <= y00 + yspan * 1.3)]
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f_mask2 = np.arange(len(fill_freqs))[(fill_freqs >= y10 - yspan * 0.2) & (fill_freqs <= y10 + yspan * 1.3)]
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t_mask = np.arange(len(fill_times))[(fill_times >= time_oi-dt*4) & (fill_times <= time_oi+dt*4)]
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ax[1].imshow(decibel(fill_spec[f_mask1[0]:f_mask1[-1], t_mask[0]:t_mask[-1]][::-1]),
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extent=[-dt*4, dt*4, y00 - yspan * 0.2, y00 + yspan * 1.3],
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aspect='auto',vmin = -100, vmax = -50, alpha=0.7, cmap='jet', interpolation='gaussian')
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ax[2].imshow(decibel(fill_spec[f_mask2[0]:f_mask2[-1], t_mask[0]:t_mask[-1]][::-1]),
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extent=[-dt*4, dt*4, y10 - yspan * 0.2, y10 + yspan * 1.3],
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aspect='auto',vmin = -100, vmax = -50, alpha=0.7, cmap='jet', interpolation='gaussian')
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ax[1].set_ylim(y00 - yspan * 0.1, y00 + yspan * 1.2)
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ax[1].set_xlim(-dt*3, dt*3)
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ax[2].set_ylim(y10 - yspan * 0.1, y10 + yspan * 1.2)
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ax[2].set_xlim(-dt*3, dt*3)
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ax[0].set_xticks([])
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ax[0].set_yticks([])
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ax[1].tick_params(labelsize=12)
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ax[2].tick_params(labelsize=12)
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ax[2].set_xlabel('time [s]', fontsize=14)
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fig.text(0.02, 0.5, 'frequency [Hz]', fontsize=14, va='center', rotation='vertical')
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# plt.ion()
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for i in tqdm(np.arange(len(frames_oi))):
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break
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video.set(cv2.CAP_PROP_POS_FRAMES, int(frames_oi[i]))
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ret, frame = video.read()
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if i == 250:
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dot, = ax[0].plot(0.05, 0.95, 'o', color='firebrick', transform = ax[0].transAxes, markersize=20)
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if i == 280:
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dot.remove()
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if i == 0:
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img = ax[0].imshow(frame)
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line1, = ax[1].plot([frame_times[frames_oi[i]] - time_oi, frame_times[frames_oi[i]] - time_oi],
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[y00 - yspan * 0.15, y00 + yspan * 1.3],
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color='k', lw=1)
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line2, = ax[2].plot([frame_times[frames_oi[i]] - time_oi, frame_times[frames_oi[i]] - time_oi],
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[y10 - yspan * 0.15, y10 + yspan * 1.3],
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color='k', lw=1)
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else:
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img.set_data(frame)
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line1.set_data([frame_times[frames_oi[i]] - time_oi, frame_times[frames_oi[i]] - time_oi],
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[y00 - yspan * 0.15, y00 + yspan * 1.3])
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line2.set_data([frame_times[frames_oi[i]] - time_oi, frame_times[frames_oi[i]] - time_oi],
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[y10 - yspan * 0.15, y10 + yspan * 1.3])
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label = (os.path.join(create_video_path, 'frame%4.f.jpg' % len(glob.glob(os.path.join(create_video_path, '*.jpg'))))).replace(' ', '0')
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plt.savefig(label, dpi=300)
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# plt.pause(0.001)
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# quit()
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win_lose_str = 'lose' if fish_nr == 1 else 'win'
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# video_name = ("./rise_video/%s_%2.f:%2.f:%2.f.mp4" % (win_lose_str, HH, MM, SS)).replace(' ', '0')
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# command = "ffmpeg -r 25 -i './rise_video/frame%4d.jpg' -vf 'pad=ceil(iw/2)*2:ceil(ih/2)*2' -vcodec libx264 -y -an"
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video_name = os.path.join(create_video_path, ("%s_%2.f:%2.f:%2.f.mp4" % (win_lose_str, HH, MM, SS)).replace(' ', '0'))
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command1 = "ffmpeg -r 25 -i"
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frames_path = '"%s"' % os.path.join(create_video_path, "frame%4d.jpg")
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command2 = "-vf 'pad=ceil(iw/2)*2:ceil(ih/2)*2' -vcodec libx264 -y -an"
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os.system(' '.join([command1, frames_path, command2, video_name]))
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os.system(' '.join(['rm', os.path.join(create_video_path, '*.jpg')]))
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# os.system(' '.join([command, video_name]))
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# os.system('rm ./rise_video/*.jpg')
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plt.close()
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embed()
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quit()
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###############################
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fig, ax = plt.subplots()
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for i, c in enumerate(['firebrick', 'cornflowerblue']):
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ax.plot(times, fish_freqs[i], marker='.', color=c)
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r_idx = np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int)
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ax.plot(times[r_idx], fish_freqs[i][r_idx], 'o', color='k')
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pass
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##############################
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Generate videos around events.')
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parser.add_argument('file', type=str, help='folder/dataset to generate videos from.')
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parser.add_argument('-t', type=float, default=10, help='video duration before and after event.')
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# parser.add_argument("-c", action="store_true", help="check if LED pos is correct")
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# parser.add_argument('-x', type=int, nargs=2, default=[1272, 1282], help='x-borders of LED detect area (in pixels)')
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# parser.add_argument('-y', type=int, nargs=2, default=[1500, 1516], help='y-borders of LED area (in pixels)')
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args = parser.parse_args()
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main(args.file, args.t) |