data_dirs = {
't_data': [('./data/Translation/Y1/', 'trans_1'),
('./data/Translation/Y2/', 'trans_2'),
('./data/Translation/Y3/', 'trans_3'),
('./data/Translation/Y4/', 'trans_4')],
'p_data': [('./data/Pitch/d1_-40/', 'pitch_1'),
('./data/Pitch/d2_-37/', 'pitch_2'),
('./data/Pitch/d3_-34/', 'pitch_3'),
('./data/Pitch/d4_-31/', 'pitch_4')],
'y_data': [('./data/Yaw/d1_44/', 'yaw_1'), ('./data/Yaw/d2_41/', 'yaw_2'),
('./data/Yaw/d3_38/', 'yaw_3'), ('./data/Yaw/d4_35/', 'yaw_4')],
'v_data': [('./data/RV_Data2/', 'vid')]
}
if frame_1 == 0 and (data_file != 'V' and data_file != 'v'):
data_set_1 = dat.Dataset(data_dirs[which_data][frame_1])
frame_text = "Translation Image"
if frame_2 != 0:
data_set_2 = dat.Dataset(data_dirs[which_data][frame_2])
for i in range(len(data_set_1.data)):
#while True:
frame_1 = data_set_1.next_entry()
frame_2 = data_set_2.next_entry()
# if data_file == 'P' or data_file == 'p'
frame_1.amplitude[frame_1.amplitude == 65533] = 0
frame_2.amplitude[frame_2.amplitude == 65533] = 0
frame_1.amplitude = gaussian_filter(frame_1.amplitude,
sigma=sigma_val)
frame_1.x = gaussian_filter(frame_1.x, sigma=sigma_val)
frame_1.y = gaussian_filter(frame_1.y, sigma=sigma_val)
'trans': [('./data/Translation/Y1/', 'trans_1'),
('./data/Translation/Y2/', 'trans_2'),
('./data/Translation/Y3/', 'trans_3'),
('./data/Translation/Y4/', 'trans_4')],
'pitch': [('./data/Pitch/d1_-40/', 'pitch_1'),
('./data/Pitch/d2_-37/', 'pitch_2'),
('./data/Pitch/d3_-34/', 'pitch_3'),
('./data/Pitch/d4_-31/', 'pitch_4')],
'yaw': [('./data/Yaw/d1_44/', 'yaw_1'), ('./data/Yaw/d2_41/', 'yaw_2'),
('./data/Yaw/d3_38/', 'yaw_3'), ('./data/Yaw/d4_35/', 'yaw_4')],
'video': [('./data/RV_Data2/', 'vid')]
}
if T >= 0 or P >= 0 or Y >= 0:
if T >= 0:
data_set = data.Dataset(data_dirs['trans'][T])
frame_text = "Translation Image"
elif P >= 0:
data_set = data.Dataset(data_dirs['pitch'][P])
frame_text = "Pitch Image"
elif Y >= 0:
data_set = data.Dataset(data_dirs['yaw'][Y])
frame_text = "Yaw Image"
# ####image display
img = np.zeros_like(data_set.data[0].amplitude)
img = np.float32(img)
if avg == 1:
frame_text += ": averaged"
i = 0
for frame in data_set.data:
# print("Previous threshold value: ", ransac_thresh)
# ransac_thresh = float(input("Enter threshold value: "))
all_pitch = []
all_yaw = []
all_roll = []
all_x = []
all_y = []
all_z = []
data_dirs = {'v_data': [('./data/RV_Data2/', 'vid')]}
if data_file == 'V' or data_file == 'v':
frame_text = "Video Run"
data_set_1 = data.Dataset(data_dirs[which_data][loc_1])
for frame_1 in data_set_1.data:
frame_1.x = -1 * frame_1.x
temp_y1 = frame_1.y
frame_1.y = frame_1.z
frame_1.z = temp_y1
frame_1.amplitude[frame_1.amplitude > 250] = 0 # adjust maximum
frame_1.amplitude[frame_1.amplitude == 0] = np.max(frame_1.amplitude)
frame_1.amplitude = gaussian_filter(frame_1.amplitude, sigma=sigma_val) # start gaussian filter
frame_1.x = gaussian_filter(frame_1.x, sigma=sigma_val)
frame_1.y = gaussian_filter(frame_1.y, sigma=sigma_val)
frame_1.z = gaussian_filter(frame_1.z, sigma=sigma_val)
# print("Dataset Length: ", len(data_set_1.data))