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gui_playback.py
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gui_playback.py
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import tkinter as tk
import numpy as np
import xdf
import sys
import cv2
from PIL import Image, ImageTk
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
from matplotlib.widgets import Slider
from matplotlib import colors as mcolors
from pylsl import StreamInfo, StreamOutlet
matplotlib.use('TkAgg')
# how many samples should be drawn at once
SAMPLE_FREQ = 1000
TICK_FREQ = 10
VIDEO_WIDTH = 160 #320
VIDEO_HEIGHT = 120 #240
# video capture size and channels
WC_WIDTH = 160
WC_HEIGHT = 120
WC_CHNS = 3
# video capture sample intervals in ms
HIGH_FRAMERATE = 33
LOW_FRAMERATE = 1000
# initial conditions, capture at high fram rate
WEBCAM_SAMPLE_RATE = HIGH_FRAMERATE
DURATION_HIGH_FRAMERATE_SEC = 30.0
DURATION_HIGH_FRAMERATE = (1.0 / WEBCAM_SAMPLE_RATE) * DURATION_HIGH_FRAMERATE_SEC * 1000
# Muse constants
MUSE_SAMPLE_RATE = 10
CHNS_MUSE = 22
# HRV constants
CHNS_HRV = 2
HRV_SAMPLE_RATE = 10
# Shadowsuit constants
mocap_channels = 32
sample_size = 8
def fig2rgb_array(fig):
fig.canvas.draw()
buf = fig.canvas.tostring_rgb()
ncols, nrows = fig.canvas.get_width_height()
return np.fromstring(buf, dtype=np.uint8).reshape(nrows, ncols, 3)
# find nearest item
def nearest(items, pivot):
return min(items, key=lambda x: abs(x - pivot))
# get the time in in the format 'hh:mm:ss'
def convert_time(seconds_total):
seconds = int(seconds_total % 60)
minutes = int(seconds_total / 60) % 60
hours = int(seconds_total / (60 * 60))
return '{}:{}:{}'.format(hours, minutes, seconds)
# create vectorized function so it can be applied across array elements
convert_time_v = np.vectorize(convert_time)
def nothing(x):
pass
# retrieve a particular data stream from xdf file given index
def retrieve_stream_data(stream, i):
samples = stream[0][i]["time_series"]
samples = np.array(samples)
timestamps = stream[0][i]["time_stamps"]
return samples, timestamps
# fill subplot with data stream content
def create_wave_plot(ax, samples, timestamps, channels):
num_samples = len(samples)
num_channels = len(samples[0])
print(timestamps)
# get only some samples since it will be too slow if there are too many data points
samples_index = np.linspace(0, num_samples, num=SAMPLE_FREQ, endpoint=False).astype(int)
samples_loc = np.array(timestamps[samples_index.tolist()]) - timestamps[0]
samples_used = samples[samples_index.tolist()]
# convert the time to more familiar format
timestamp_index = np.linspace(0, num_samples, num=TICK_FREQ, endpoint=False).astype(int)
timestamp_loc = np.array(timestamps[timestamp_index.tolist()]) - timestamps[0]
timestamp_labels = convert_time_v(timestamp_loc)
ticklocs = []
# set the time stamps
ax.set_xlim(samples_loc[0], samples_loc[-1])
ax.set_xticks(timestamp_loc)
ax.set_xticklabels(timestamp_labels)
# calculate the height for each channel to vary in
dmin = samples.min()
dmax = samples.max()
dr = (dmax - dmin) * 0.7 # Crowd them a bit.
y0 = dmin
y1 = (num_channels - 1) * dr + dmax
ax.set_ylim(y0, y1)
segs = []
for i in range(num_channels):
segs.append(np.hstack((samples_loc[:, np.newaxis], samples_used[:, i, np.newaxis])))
ticklocs.append(i * dr)
offsets = np.zeros((num_channels, 2), dtype=float)
offsets[:, 1] = ticklocs
colors = [mcolors.to_rgba(c)
for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]
# create the lines
lines = LineCollection(segs, transOffset=None, offsets=offsets, linewidths=.5, colors=colors)
ax.add_collection(lines)
# Set the yticks to use axes coordinates on the y axis
ax.set_yticks(ticklocs)
ax.set_yticklabels(channels)
# places the track line onto the graphs
def place_trackline(ax, trackline, pos):
if (trackline is not None):
trackline.remove()
trackline = ax.axvline(x=pos, linewidth=0.5, color='k')
return trackline
# buffers avi video into memory
def buffer_video(video_path):
cap = cv2.VideoCapture(video_path)
frame_buffer = []
while(cap.isOpened()):
ret, frame = cap.read()
if (frame is None):
break
frame_buffer.append(frame)
cap.release()
return frame_buffer
# the app class that contains the code to run the GUI
class App():
def __init__(self, file_path):
# create frames for the gui
self.root = tk.Tk()
# quit button
# self.quit_button = tk.Button(self.root, text="Quit", command=self.root.destroy).pack()
# self.quit_button = tk.Button(self.root, text="Quit", command=sys.exit()).pack()
# the video frame
self.frame_video = tk.Frame(self.root)
self.frame_video.pack(side=tk.LEFT)
# the plots frame
self.frame_wave_plots = tk.Frame(self.root)
self.frame_wave_plots.pack(side=tk.RIGHT)
# the sliders frame
self.frame_slider = tk.Frame(self.root)
self.frame_slider.pack(side=tk.TOP)
# retrieve xdf data
stream = xdf.load_xdf(file_path)
self.data_all = []
# for astream in stream:
# print(astream)
# print(stream[1])
# exit()
# fill the above list with necessay data to build the graphs
for sub_stream in stream[0]:
print(sub_stream['info']['name'])
print(sub_stream['time_stamps'])
# buffer video or plot data
if (sub_stream['info']['name'][0] == 'Webcam'):
self.data_video = sub_stream['time_series']
continue
self.data_all.append((
sub_stream['time_series'],
sub_stream['time_stamps'],
sub_stream['info']['desc'][0]['channels'][0].keys(),
sub_stream['info']['name'][0])) # Record device name in the data
# exit()
# create plots
self.fig, self.axes = plt.subplots(len(self.data_all), 1, figsize=(15, 10))
if len(self.data_all) > 1:
self.axes = self.axes.ravel()
elif len(self.data_all) == 1:
self.axes = [self.axes]
# add the fig to the gui
self.w_canvas = FigureCanvasTkAgg(self.fig, self.frame_wave_plots)
self.w_canvas.get_tk_widget().pack()
# fill the plots with data
for i, sub_stream in enumerate(self.data_all):
print(i, sub_stream)
create_wave_plot(self.axes[i], sub_stream[0], sub_stream[1], sub_stream[2])
# create the slider plots
self.slider_scale_ax = self.fig.add_axes([0.1, 0.04, 0.8, 0.02])
self.slider_scale = Slider(self.slider_scale_ax, 'scale', 0.0, 1.0, valinit=1.0)
self.slider_scale.on_changed(self.handle_slider_scale)
self.slider_window_ax = self.fig.add_axes([0.1, 0.02, 0.8, 0.02])
self.slider_window = Slider(self.slider_window_ax, 'window', 0.0, 1.0, valinit=0.0)
self.slider_window.on_changed(self.handle_slider_window)
self.scale = 1.0
self.window_start = 0.0
# place initial tracklines
self.tracklines = []
for ax in self.axes:
self.tracklines.append(place_trackline(ax, None, 0))
'''
Create video objects
'''
# buffer video
self.frame_buffer = np.uint8(np.array(self.data_video).reshape(-1, VIDEO_HEIGHT, VIDEO_WIDTH, 3))
print("VIDEO LEN " + str(len(self.frame_buffer)))
# initilize video vars
self.len_buffer = len(self.frame_buffer)
self.frame_index = 0
self.play = False
self.frame_delay = 100
# create video widget
self.w_video = tk.Label(self.frame_video)
self.w_video.pack(side=tk.TOP)
# create frame within video frame to hold buttons and progress slider
self.frame_video_buttons = tk.Frame(self.frame_video)
self.frame_video_buttons.pack(side=tk.TOP)
self.frame_video_progress = tk.Frame(self.frame_video)
self.frame_video_progress.pack(side=tk.TOP)
# create buttons for video control
self.w_video_play = tk.Button(self.frame_video_buttons, text='Play', command=self.handle_video_play)
self.w_video_play.pack(side=tk.LEFT)
self.w_video_pause = tk.Button(self.frame_video_buttons, text='Pause', command=self.handle_video_pause)
self.w_video_pause.pack(side=tk.LEFT)
self.w_video_restart = tk.Button(self.frame_video_buttons, text='Restart', command=self.handle_video_restart)
self.w_video_restart.pack(side=tk.LEFT)
# create progress slider for video
self.w_video_progress = tk.Scale(self.frame_video_progress, from_=0, to=100, orient=tk.HORIZONTAL, command=self.handle_progress_change, length=400)
self.w_video_progress.pack(side=tk.LEFT)
'''
Set up lsl stream before frame update
'''
# Outstream muse
# Setup outlet stream infos
stream_info_muse = StreamInfo('Muse', 'EEG', CHNS_MUSE, MUSE_SAMPLE_RATE, 'float32', 'museid_1')
channels = stream_info_muse.desc().append_child("channels")
channel_list = ["ar0", "ar1", "ar2", "ar3",
"br0", "br1", "br2", "br3",
"gr0", "gr1", "gr2", "gr3",
"tr0", "tr1", "tr2", "tr3",
"dr0", "dr1", "dr2", "dr3",
"mellow", "concentration"]
for c in channel_list:
channels.append_child(c)
# Create outlets
self.outlet_muse = StreamOutlet(stream_info_muse)
# Outstream hrv
# Setup outlet stream infos
stream_info_hrv = StreamInfo('HRV', 'EEG', CHNS_HRV, HRV_SAMPLE_RATE, 'float32', 'hrvid_1')
channels = stream_info_hrv.desc().append_child("channels")
channels.append_child('hr')
channels.append_child('rr')
# Create outlets
self.outlet_hrv = StreamOutlet(stream_info_hrv)
# Outstream the shadowsuit
stream_info_mocap = StreamInfo('ShadowSuit', 'MOCAP', mocap_channels * sample_size, 200)
channels = stream_info_mocap.desc().append_child("channels")
channel_list = ["lq0", "lq1", "lq2", "lq3",
"c0", "c1", "c2", "c3"]
for c in channel_list:
channels.append_child(c)
# Create outlets
self.outlet_mocap = StreamOutlet(stream_info_mocap)
# frame counts to keep track of which frame is updating
self.web_framecount = 0
# Outstream the webcam
stream_info_webcam = StreamInfo('Webcam', 'Experiment', WC_WIDTH *
WC_HEIGHT * WC_CHNS, WEBCAM_SAMPLE_RATE, 'int32', 'webcamid_1')
# Create webcam outstream
self.outlet_webcam = StreamOutlet(stream_info_webcam)
'''
End lsl stream update
'''
# set first frame
self.update_frame()
# start video frame update coroutine
self.update_video_frame()
## COROUTINE UPDATES
# updates the current video frame to the next
def update_video_frame(self):
if self.play and self.frame_index < self.len_buffer - 1:
self.frame_index += 1
self.update_frame()
else:
pass
# here's where the coroutine recalls itself to execute periodically
self.job_update_video_frame = self.root.after(self.frame_delay, self.update_video_frame)
## GUI HANDLERS
def handle_video_play(self):
self.play = True
def handle_video_pause(self):
self.play = False
def handle_video_restart(self):
self.frame_index = 0
self.update_frame()
def handle_progress_change(self, event):
# cancel the update frame job since we are changing the video's location
if (self.job_update_video_frame):
self.root.after_cancel(self.job_update_video_frame)
# make sure the index doesnt go out of bounds
self.frame_index = np.clip(int(self.len_buffer * self.w_video_progress.get() / 100), 0, self.len_buffer - 1)
# now update the frame
self.update_frame()
# start the coroutine again
self.update_video_frame()
def handle_slider_scale(self, val):
# get the new value
self.scale = self.slider_scale.val
# after the sliders have changed, we want to modify the plot's x and y size
for i, sub_stream in enumerate(self.data_all):
len_sub_stream = len(sub_stream[0])
# get the new parameters for the x axis
window_size = np.clip(int(len_sub_stream * self.scale), 1, None)
start_index = np.clip(int(len_sub_stream * self.window_start), 0, len_sub_stream - window_size)
end_index = np.clip(start_index + window_size - 1, window_size - 1, len_sub_stream - 1)
# finding the index of data in the streams to plot
if (end_index - start_index + 1 <= SAMPLE_FREQ):
sample_index = np.arange(start_index, end_index + 1)
else:
sample_index = np.linspace(start_index, end_index, num=SAMPLE_FREQ, endpoint=True).astype(int)
sample_locations = np.array(sub_stream[1][sample_index.tolist()]) - sub_stream[1][0]
# getting the respective timestamps
timestamp_index = np.linspace(start_index, end_index, num=TICK_FREQ, endpoint=True).astype(int)
timestamp_ticks = np.array(sub_stream[1][timestamp_index.tolist()]) - sub_stream[1][0]
timestamp_labels = convert_time_v(timestamp_ticks)
# set the new labels down
self.axes[i].set_xlim(sample_locations[0], sample_locations[-1])
self.axes[i].set_xticks(timestamp_ticks)
self.axes[i].set_xticklabels(timestamp_labels)
self.fig.canvas.draw_idle()
def handle_slider_window(self, val):
self.window_start = self.slider_window.val
for i, sub_stream in enumerate(self.data_all):
len_sub_stream = len(sub_stream[0])
window_size = np.clip(int(len_sub_stream * self.scale), 1, None)
start_index = np.clip(int(len_sub_stream * self.window_start), 0, len_sub_stream - window_size)
end_index = np.clip(start_index + window_size - 1, window_size - 1, len_sub_stream - 1)
# finding the index of data in the streams to plot
if (end_index - start_index + 1 <= SAMPLE_FREQ):
sample_index = np.arange(start_index, end_index + 1)
else:
sample_index = np.linspace(start_index, end_index, num=SAMPLE_FREQ, endpoint=True).astype(int)
sample_locations = np.array(sub_stream[1][sample_index.tolist()]) - sub_stream[1][0]
# getting the respective timestamps
timestamp_index = np.linspace(start_index, end_index, num=TICK_FREQ, endpoint=True).astype(int)
timestamp_ticks = np.array(sub_stream[1][timestamp_index.tolist()]) - sub_stream[1][0]
timestamp_labels = convert_time_v(timestamp_ticks)
# set the new labels down
self.axes[i].set_xlim(sample_locations[0], sample_locations[-1])
self.axes[i].set_xticks(timestamp_ticks)
self.axes[i].set_xticklabels(timestamp_labels)
self.fig.canvas.draw_idle()
## HELPERS
def output_current_data(self, video_time):
# Output list containing information about nearest values
output = []
# after the sliders have changed, we want to modify the plot's x and y size
for i, sub_stream in enumerate(self.data_all):
len_sub_stream = len(sub_stream[0])
# get the new parameters for the x axis
window_size = np.clip(int(len_sub_stream * self.scale), 1, None)
start_index = np.clip(int(len_sub_stream * self.window_start), 0, len_sub_stream - window_size)
end_index = np.clip(start_index + window_size - 1, window_size - 1, len_sub_stream - 1)
# finding the index of data in the streams to plot
if (end_index - start_index + 1 <= SAMPLE_FREQ):
sample_index = np.arange(start_index, end_index + 1)
else:
sample_index = np.linspace(start_index, end_index, num=SAMPLE_FREQ, endpoint=True).astype(int)
sample_locations = np.array(sub_stream[1][sample_index.tolist()]) - sub_stream[1][0]
# find sample closest to current video frame
nearest_time = nearest(sample_locations, video_time)
nearest_index = sample_locations.tolist().index(nearest_time)
nearest_value = sub_stream[0][nearest_index]
# output substream values at nearest time
print(video_time, nearest_time, nearest_index, nearest_value)
output.append((nearest_value, sub_stream[3]))
return output
def update_frame(self):
# handle video and progress bar update
frame = self.frame_buffer[self.frame_index]
outstream_frame = frame
frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
frame = ImageTk.PhotoImage(image=frame)
self.video_current_frame = frame
self.w_video.configure(image=self.video_current_frame)
self.w_video.image = self.video_current_frame
self.w_video_progress.set(100 * self.frame_index / self.len_buffer)
# handle plot line update
line_loc = self.data_all[0][1][int(self.frame_index / self.len_buffer * len(self.data_all[0][1]))] - self.data_all[0][1][0]
for i in range(len(self.axes)):
self.tracklines[i] = place_trackline(self.axes[i], self.tracklines[i], line_loc)
# print(self.frame_index)
self.output = self.output_current_data(line_loc)
self.fig.canvas.draw_idle()
'''
Put data to LSL inlets every time when the frame is updated
'''
self.outstream_webcam(outstream_frame)
self.outstream_shadowsuit(self.output)
self.outstream_muse(self.output)
self.outstream_hrv(self.output)
'''
End stream
'''
return frame
def outstream_webcam(self, frame):
# Check whether the channel count is the same as sample length
# if not, return an error
if len(frame.flatten()) != self.outlet_webcam.channel_count:
raise ValueError('The number of channels from Webcam data is ' +
'not the same as expected. There should be ' +
self.outlet_webcam.channel_count + 'channels, ' +
'but the actual value is', len(new_sample))
self.outlet_webcam.push_sample(frame.flatten())
def outstream_muse(self, output):
# find the muse data from output list
new_sample = []
found = False
for device in self.output:
if device[1] == 'Muse':
new_sample = device[0]
found = True
break
if found is False:
return
# Check whether the channel count is the same as sample length
# if not, return an error
if len(new_sample) != self.outlet_muse.channel_count:
raise ValueError('The number of channels from Muse data is ' +
'not the same as expected. There should be 22 channels, ' +
'but the actual value is', len(new_sample))
# push the found data into the outstream
self.outlet_muse.push_sample(new_sample)
def outstream_hrv(self, output):
# find the hrv data from output list
new_sample = []
found = False
for device in self.output:
if device[1] == 'Polar':
new_sample = device[0]
found = True
break
if found is False:
return
# Check whether the channel count is the same as sample length
# if not, return an error
if len(new_sample) != self.outlet_hrv.channel_count:
raise ValueError('The number of channels from Polar data is ' +
'not the same as expected. There should be 2 channels, ' +
'but the actual value is', len(new_sample))
# push the found data into the outstream
self.outlet_hrv.push_sample(new_sample)
def outstream_shadowsuit(self, output):
# find the shadowsuit data from output list
new_sample = []
found = False
for device in self.output:
if device[1] == "ShadowSuit":
new_sample = device[0]
found = True
break
if found is False:
return
# Check whether the channel count is the same as sample length
# if not, return an error
if len(new_sample) != self.outlet_mocap.channel_count:
raise ValueError('The number of channels from Shadow Suit data is ' +
'not the same as expected. There should be 8 channels, ' +
'but the actual value is', len(new_sample))
# push the found data into the outstream
self.outlet_mocap.push_sample(new_sample)
if __name__ == "__main__":
newApp = App(sys.argv[1])
tk.mainloop()