def CLIPS_start_to_end_trial(sessions_subset):
try:
for session in sessions_subset:
start_idx = []
end_idx = []
#create folder to allocate the clips
script_dir = os.path.join(hardrive_path + session)
csv_dir_path = os.path.join(hardrive_path, session + '/events/')
results_dir = os.path.join(script_dir +'/Clips')
csv_path = 'Trial_idx.csv'
if not os.path.isdir(results_dir):
os.makedirs(results_dir)
trial_end_path = os.path.join(hardrive_path, session + '/events/' + 'TrialEnd.csv')
trial_start_path = os.path.join(hardrive_path, session + '/events/' + 'TrialStart.csv')
video_csv_path = os.path.join(hardrive_path, session + '/Video.csv')
video_path = os.path.join(hardrive_path, session + '/Video.avi')
start = behaviour.timestamp_CSV_to_pandas(trial_start_path)
end = behaviour.timestamp_CSV_to_pandas(trial_end_path)
video_time = behaviour.timestamp_CSV_to_pandas(video_csv_path)
start_closest = behaviour.closest_timestamps_to_events(video_time, start)
end_closest = behaviour.closest_timestamps_to_events(video_time, end)
if len(start_closest)>len(end_closest):
start_closest = start_closest[:-1]
start_idx.append(start_closest)
end_idx.append(end_closest)
np.savetxt(csv_dir_path + csv_path , np.vstack((start_idx,end_idx)).T,delimiter=',', fmt='%s')
#video stuff
inputVid = cv2.VideoCapture(video_path)
inputWidth = int(inputVid.get(cv2.CAP_PROP_FRAME_WIDTH))
inputHeight = int(inputVid.get(cv2.CAP_PROP_FRAME_HEIGHT))
# Open Output movie file, then specify compression, frames per second and size
fourcc = cv2.VideoWriter_fourcc('F','M','P','4')
fps = 120
offset = 360 #3''
# Specify clip parameters
startFrame = np.array(start_closest) - offset
endFrame = np.array(end_closest) + offset
numFrames = endFrame - startFrame
# Set starting read position
start = 0
for e in range(len(numFrames[:])):
start_frame = start + e
inputVid.set(cv2.CAP_PROP_POS_FRAMES, startFrame[start_frame])
if e < 10:
outputFilename= results_dir +'/Clip0%d.avi' %start_frame
outputVid = cv2.VideoWriter(outputFilename, fourcc, fps, (inputWidth, inputHeight))
else:
outputFilename= results_dir +'/Clip%d.avi' %start_frame
outputVid = cv2.VideoWriter(outputFilename, fourcc, fps, (inputWidth, inputHeight))
for i in range(numFrames[start_frame]):
ret, im = inputVid.read()
outputVid.write(im)
print (i)
outputVid.release()
inputVid.release()
except Exception:
print('error'+ session)
pass
video_csv = os.path.join(session_path + '/Video.csv') trial_start_path = os.path.join(session_path + '/events/TrialStart.csv') video_time = behaviour.timestamp_CSV_to_pandas(video_csv) touch_time = behaviour.timestamp_CSV_to_pandas(touch_path) reward_time = behaviour.timestamp_CSV_to_pandas(reward_path) ball_time = behaviour.timestamp_CSV_to_pandas(ball_on_path) trial_time = behaviour.timestamp_CSV_to_pandas(trial_start_path) touching_light = behaviour.closest_timestamps_to_events(video_time, touch_time) reward = behaviour.closest_timestamps_to_events(video_time, reward_time) ball_on = behaviour.closest_timestamps_to_events(video_time, ball_time) start = behaviour.closest_timestamps_to_events(video_time, trial_time) events_list = [touching_light,reward]#ball_noticed] # Average around event events = events_list[0] mua_event0_avg = np.mean(mua_zeroed[:, :, events], 2) events = events_list[1] mua_event1_avg = np.mean(mua_zeroed[:, :, events], 2) #events = events_list[2]
#os.sys.path.append('/home/kampff/Repos/Pac-Rat/libraries')
os.sys.path.append('D:/Repos/Pac-Rat/libraries')
import behaviour_library as behaviour
motion_stimulus_path = 'F:/Videogame_Assay/AK_33.2/2018_04_06-15_13/motions.csv'
motion_stimulus = np.genfromtxt(motion_stimulus_path, delimiter=',', usecols=1)
video_path = 'F:/Videogame_Assay/AK_33.2/2018_04_06-15_13/Video.csv'
ball_path = 'F:/Videogame_Assay/AK_33.2/2018_04_06-15_13/events/BallOn.csv'
touch_path = 'F:/Videogame_Assay/AK_33.2/2018_04_06-15_13/events/RatTouchBall.csv'
video = behaviour.timestamp_CSV_to_pandas(video_path)
ball = behaviour.timestamp_CSV_to_pandas(ball_path)
touch = behaviour.timestamp_CSV_to_pandas(touch_path)
ball_idx = behaviour.closest_timestamps_to_events(video, ball)
touch_idx = behaviour.closest_timestamps_to_events(video, touch)
# stimulus
events_list = [touch_idx, ball_idx]
event = events_list[0]
#extract motion chunk around ball on
before = 0
after = 60
n = len(ball_idx)
motion_around_event = [[] for _ in range(n)]