print("Defining Converter ...")
header = Header()
header.w = pca_data[0].shape[1] # input image width
header.h = pca_data[0].shape[0] # input image height, trimmed
header.sf = 4000000 # magic number, sorry!
probe = Probe()
probe.radius = 10000 # based on '10' in transducer model number
probe.numElements = 128 # based on '128' in transducer model number
probe.pitch = 185 # based on Ultrasonix C9-5/10 transducer
conv = Converter(header, probe)
print("Defining region of interest ...")
# get mean frame and apply mask
mean_frame = pca_data.mean(axis=0)
conv_mean = np.flipud(conv.convert(np.flipud(mean_frame)))
plt.title("Mean frame, Spkr {:}".format(subject))
plt.imshow(conv_mean, cmap="Greys_r")
file_ending_mean = "subj{:}_mean.pdf".format(subject)
savepath_mean = os.path.join(root, d, file_ending_mean)
plt.savefig(savepath_mean)
roi_upper = 600
roi_lower = 300
roi_left = 20
roi_right = 100
# TODO give mask converted shape
while True:
mask = us.roi(mean_frame,
upper=roi_upper,
lower=roi_lower,
start_extr = frame_indices[0]
end_extr = frame_indices[-1]
# extract and convert v.t1 - v.t2 range
for idx in range(start_extr, (end_extr)):
# extract frame using RawReader
unconv_frame = rdr.get_frame(idx)
# trim junk pixels off of top
trimmed_frame = unconv_frame[junk:, :]
if args.flop:
trimmed_frame = np.fliplr(trimmed_frame)
# convert to fan shape
conv_frame = conv.convert(np.flipud(trimmed_frame))
ready_frame = np.flipud(conv_frame)
# create frame handle and save to copy dir
fh = basename + "." + "{0:05d}".format(idx) + ".bmp"
out_img = Image.fromarray(ready_frame)
out_img.save(os.path.join(copy_dir, fh))
frame_exp = os.path.join(copy_dir, basename + ".%05d.bmp")
print(frame_exp)
framerate = 25 # TODO tweak/automatically get
out_fh = basename + '.avi'
out_path = os.path.join(copy_dir, out_fh)
avi_args = [
'ffmpeg', '-y', '-i', frame_exp, '-framerate',
str(framerate), '-vcodec', 'huffyuv', out_path