def __init__(self, video_path, is_color, slider):
super().__init__()
self.is_color = is_color
self.is_paused = False
self.current_frame = 2 # cap2.oni запускается только со 2-го кадра
self.slider: QSlider = slider
self.dev = openni2.Device.open_file(video_path.encode('utf-8'))
self.pbs = openni2.PlaybackSupport(self.dev)
self.image_stream = openni2.VideoStream(
self.dev, SENSOR_COLOR if is_color else SENSOR_DEPTH)
self.image_stream.start()
def openDevice(video_path):
try:
if sys.platform == "win32":
libpath = "lib/Windows"
else:
libpath = "lib/Linux"
openni2.initialize(libpath)
dev = openni2.Device.open_file(video_path)
pbs = openni2.PlaybackSupport(dev)
pbs.set_repeat_enabled(True)
pbs.set_speed(-1.0)
return dev, pbs
except Exception as ex:
print(ex)
raise Exception("Initialization Error")
def open_device(self):
if self.is_open:
self.stop_video()
path = self.browse_folder()
if path:
self.device = self.device.open_file(path)
self.depth_stream = self.device.create_depth_stream()
self.color_stream = self.device.create_color_stream()
self.num_depth_frames = self.depth_stream.get_number_of_frames()
self.num_color_frames = self.color_stream.get_number_of_frames()
self.playback_support = openni2.PlaybackSupport(self.device)
self.horizontalSlider.setRange(2, self.num_depth_frames)
self.is_open = True
self.play_button.setEnabled(True)
self.stop_button.setEnabled(True)
self.next_button.setEnabled(True)
self.prev_button.setEnabled(True)
def oni_converter(file_path):
''' Convert oni file to color and depth avi files.
avi files will be saved in working directory
as color.avi and depth.avi
Parameters
----------
file_path : str
full path to oni file
'''
count = 0
result = None
PATH_TO_OPENNI2_SO_FILE = './OpenNI-Linux-x64-2.2/Redist'
t1_start = process_time()
openni2.initialize(PATH_TO_OPENNI2_SO_FILE)
dev = openni2.Device.open_file(file_path.encode('utf-8'))
c_stream, d_stream = dev.create_color_stream(), dev.create_depth_stream()
openni2.PlaybackSupport(dev).set_speed(ctypes.c_float(0.0))
d_stream.start()
c_stream.start()
c_images, d_images = [], []
n_c_frames = openni2.PlaybackSupport(
dev).get_number_of_frames(c_stream) - 1
n_d_frames = openni2.PlaybackSupport(dev).get_number_of_frames(d_stream)
if n_c_frames != n_d_frames:
print('Разное количество кадров в color и depth потоках!\n'
f'color - {n_c_frames}, depth - {n_d_frames}')
sys.exit()
for i in tqdm(range(n_c_frames)):
# process depth stream
d_frame = np.fromstring(d_stream.read_frame().get_buffer_as_uint16(),
dtype=np.uint16).reshape(480, 640)
# Correct the range. Depth images are 12bits
d_img = np.uint8(d_frame.astype(float) * 255 / 2**12 - 1)
d_img = cv2.cvtColor(d_img, cv2.COLOR_GRAY2RGB)
d_img = 255 - d_img
d_images.append(d_img)
if i == 0:
continue
# process color stream
c_frame = c_stream.read_frame()
c_frame_data = c_frame.get_buffer_as_uint8()
c_img_bgr = np.frombuffer(c_frame_data, dtype=np.uint8)
c_img_bgr.shape = (480, 640, 3)
c_img_rgb = cv2.cvtColor(c_img_bgr, cv2.COLOR_BGR2RGB)
c_images.append(c_img_rgb)
count += 1
# yield count
openni2.unload()
c_out = cv2.VideoWriter(
'color.avi', cv2.VideoWriter_fourcc(*'DIVX'), 30, (640, 480)) # надо добавить fps как переменную
d_out = cv2.VideoWriter(
'depth.avi', cv2.VideoWriter_fourcc(*'DIVX'), 30, (640, 480)) # надо добавить fps как переменную
for i in tqdm(range(len(c_images))):
c_out.write(c_images[i])
d_out.write(d_images[i])
count += 1
# yield count
c_out.release()
d_out.release()
t1_stop = process_time()
print(f"Process duration, seconds:, {round(t1_stop-t1_start, 3)}")
return "ONI file has been processed successfully."
def play(name):
filename = os.path.join('data', name)
recorder_exposure = open(filename + '_exposure.txt', 'r')
frame_exposure = open(filename + '_frame_exp.txt', 'w')
dev = openni2.Device.open_file((filename + '.oni').encode('utf-8'))
pc = openni2.PlaybackSupport(dev)
pc.set_speed(-1.0)
pc.set_repeat_enabled(False)
print(dev.get_device_info())
depth_stream = dev.create_depth_stream()
depth_mode = depth_stream.get_video_mode()
depth_stream.start()
print(depth_mode)
color_stream = dev.create_color_stream()
color_mode = color_stream.get_video_mode()
color_stream.start()
print(color_mode)
print('Replay Start')
is_end = False
g = np.loadtxt(filename + '_curve.txt')
img_cnt = 0
exposure = 64
exposure_next = int(recorder_exposure.readline())
last_median = [0, 0]
for t in range(pc.get_number_of_frames(color_stream)):
_, _, is_end, _, image_color = show_image(depth_stream, color_stream)
if is_end:
break
curr_median = [
np.percentile(image_color, 25) + 5,
np.percentile(image_color, 75) + 5
]
if img_cnt > 1 and \
((exposure_next > exposure or exposure == 0) and
(curr_median[1] / last_median[1] > 1.2 or
curr_median[0] / last_median[0] > 1.2)) or \
((exposure_next < exposure or exposure == 0) and
(last_median[1] / curr_median[1] > 1.2 or
last_median[0] / curr_median[0] > 1.2)):
img_cnt = 0
exposure = exposure_next
exposure_next = recorder_exposure.readline()
if exposure_next == '':
is_end = True
else:
exposure_next = int(exposure_next)
print(exposure)
frame_exposure.write(str(exposure) + '\n')
frame_exposure.flush()
last_median[0] = curr_median[0]
last_median[1] = curr_median[1]
print('\t', img_cnt)
img_cnt += 1
if exposure > 0:
image_color = np.exp(g[image_color]) / exposure * 10
cv2.imshow('linear', image_color)
cv2.waitKey(1)
recorder_exposure.close()
frame_exposure.close()
depth_stream.stop()
color_stream.stop()
