def __init__(self, w=640, h=480, depth=True, frame_rate=30):
'''
Initializing the Python RealSense Control Flow:
w: Int (default = 640, can also be 1280)
h: Int (default = 480, can also be 720)
depth: Bool (default = True)
frame_rate: Int (default = 30)
RGB and Depth formats are: bgr8, z16
Note: In this class, variables should not be directly changed.
'''
self.width = w
self.height = h
self.depth_on = depth
self._pipeline = rs.pipeline()
self._config = rs.config()
self._config.enable_stream(rs.stream.color, w, h, rs.format.bgr8,
frame_rate)
self._intrinsic = None
if depth:
self.align = rs.align(rs.stream.color)
self._preset = 0
# Presets (for D415):
# 0: Custom
# 1: Default
# 2: Hand
# 3: High Accuracy
# 4: High Density
# 5: Medium Density
# depth interpolation
self.interpolation = cv2.INTER_NEAREST # use nearest neighbor
# self.interpolation = cv2.INTER_LINEAR # linear
# self.interpolation = cv2.INTER_CUBIC # cubic
# beautify depth image for viewing
self._config.enable_stream(rs.stream.depth, w, h, rs.format.z16,
frame_rate)
self.colorizer = rs.colorizer()
# initialize filters
self.decimation = rs.decimation_filter()
self.decimation.set_option(rs.option.filter_magnitude, 4)
self.depth_to_disparity = rs.disparity_transform(True)
self.spatial = rs.spatial_filter()
self.spatial.set_option(rs.option.filter_magnitude, 5)
self.spatial.set_option(rs.option.filter_smooth_alpha, 0.5)
self.spatial.set_option(rs.option.filter_smooth_delta, 20)
self.temporal = rs.temporal_filter()
self.disparity_to_depth = rs.disparity_transform(False)
print(
"Initialized RealSense Camera\nw: {}, h: {}, depth: {}, frame_rate: {}"
.format(w, h, depth, frame_rate))
def get_depth_filter_list(decimate=True,
d2d=True,
spatial=True,
temporal=True):
filters = []
if decimate:
dec_filt = rs.decimation_filter()
dec_filt.set_option(rs.option.filter_magnitude, 2)
filters.append(dec_filt)
if d2d:
depth2disparity = rs.disparity_transform()
filters.append(depth2disparity)
if spatial:
spat = rs.spatial_filter()
spat.set_option(rs.option.holes_fill, FILL_ALL_ZERO_PIXELS)
filters.append(spat)
if temporal:
temp = rs.temporal_filter()
filters.append(temp)
if d2d:
disparity2depth = rs.disparity_transform(False)
filters.append(disparity2depth)
return filters
def filtering(self, frame):
'''Filter setting'''
# Decimation #
decimation = rs.decimation_filter()
decimation.set_option(rs.option.filter_magnitude, 1)
# Spatial #
spatial = rs.spatial_filter()
# spatial.set_option(rs.option.filter_magnitude, 5)
spatial.set_option(rs.option.filter_smooth_alpha, 0.6)
spatial.set_option(rs.option.filter_smooth_delta, 8)
# spatial.set_option(rs.option.holes_fill, 3)
# Temporal #
temporal = rs.temporal_filter()
temporal.set_option(rs.option.filter_smooth_alpha, 0.5)
temporal.set_option(rs.option.filter_smooth_delta, 20)
# Hole #
hole_filling = rs.hole_filling_filter()
##
depth_to_disparity = rs.disparity_transform(True)
disparity_to_depth = rs.disparity_transform(False)
'''Appling filter'''
frame = decimation.process(frame)
frame = depth_to_disparity.process(frame)
frame = spatial.process(frame)
frame = temporal.process(frame)
frame = disparity_to_depth.process(frame)
frame = hole_filling.process(frame)
return frame
def __init__(self):
ctx = rs.context()
self.devices = ctx.query_devices()
self.configs = list()
self.filters = list()
for device in self.devices:
config = rs.config()
config.enable_device(device.get_info(rs.camera_info.serial_number))
config.enable_stream(rs.stream.depth, IMG_WIDTH, IMG_HEIGHT,
rs.format.z16, 30)
config.enable_stream(rs.stream.color, IMG_WIDTH, IMG_HEIGHT,
rs.format.bgr8, 30)
self.configs.append(config)
align = rs.align(rs.stream.color)
spatial = rs.spatial_filter()
spatial.set_option(rs.option.filter_magnitude, 5)
spatial.set_option(rs.option.filter_smooth_alpha, 1)
spatial.set_option(rs.option.filter_smooth_delta, 50)
spatial.set_option(ts.option.holes_fill, 3)
temporal = rs.temporal_filter()
hole_filling = rs.hole_filling_filter()
depth_to_disparity = rs.disparity_transform(True)
disparity_to_depth = rs.disparity_transform(False)
decimate = rs.decimation_filter()
self.filters.append({
'align': align,
'spatial': spatial,
'temporal': temporal,
'hole': hole_filling,
'disparity': depth_to_disparity,
'depth': disparity_to_depth,
'decimate': decimate
})
def _filter_depth_frame(depth_frame):
"""
滤波器，用于获取坐标前的深度图像处理
:param depth_frame: 深度帧
:return: 滤波后的深度帧
"""
dec = rs.decimation_filter()
dec.set_option(rs.option.filter_magnitude, 1)
depth_frame_pro = dec.process(depth_frame)
depth2disparity = rs.disparity_transform()
depth_frame_pro = depth2disparity.process(depth_frame_pro)
spat = rs.spatial_filter()
# 启用空洞填充,5为填充所有零像素
spat.set_option(rs.option.holes_fill, 5)
depth_frame_pro = spat.process(depth_frame_pro)
temp = rs.temporal_filter()
depth_frame_pro = temp.process(depth_frame_pro)
disparity2depth = rs.disparity_transform(False)
depth_frame_pro = disparity2depth.process(depth_frame_pro)
# depth_image_pro = np.asanyarray(depth_frame_pro.get_data())
# depth_colormap_pro = cv2.applyColorMap(cv2.convertScaleAbs(depth_image_pro, alpha=0.03), cv2.COLORMAP_JET)
return depth_frame_pro
def __init__(self):
self.filters = [
rs.decimation_filter(RESCALE),
rs.disparity_transform(True),
rs.hole_filling_filter(1),
rs.spatial_filter(0.5, 8, 2, 2),
rs.temporal_filter(0.5, 20, 1),
rs.disparity_transform(False)
]
def depth_filter(depth_frame):
depth_to_disparity = rs.disparity_transform(True)
disparity_to_depth = rs.disparity_transform(False)
hole_filling = rs.hole_filling_filter(2)
spatial = rs.spatial_filter()
depth_frame = depth_to_disparity.process(depth_frame)
depth_frame = spatial.process(depth_frame)
# frame = temporal.process(frame)
depth_frame = disparity_to_depth.process(depth_frame)
depth_frame = hole_filling.process(depth_frame)
return depth_frame
def __init__(self, debugFlag=False, debugPath=''):
self.debugFlag = debugFlag
# Decimation - reduces depth frame density
self.decimateFilter = rs.decimation_filter()
self.thresholdFilter = rs.threshold_filter(min_dist = 1.8, max_dist = 3)
# Converts from depth representation to disparity representation and vice - versa in depth frames
self.depth_to_disparity = rs.disparity_transform(True)
# Spatial - edge-preserving spatial smoothing
self.spatial_filter = rs.spatial_filter()
# Temporal - reduces temporal noise
self.temporalFilter = rs.temporal_filter()
self.disparity_to_depth = rs.disparity_transform(False)
def filtering(self):
depth_to_disparity = rs.disparity_transform(True)
disparity_to_depth = rs.disparity_transform(False)
spatial = rs.spatial_filter()
temporal = rs.temporal_filter()
hole_filling = rs.hole_filling_filter()
for frame in self.depth_frams:
frame = depth_to_disparity.process(frame)
frame = spatial.process(frame)
frame = temporal.process(frame)
frame = disparity_to_depth.process(frame)
frame = hole_filling.process(frame)
self.aligned_depth_frame = frame.get_data()
self.colorized_depth = np.asanyarray(
self.colorizer.colorize(frame).get_data())
def disparity(self, frame):
"""
converts the depth frame to disparity
"""
disparity = rs.disparity_transform(True)
depthFrame = disparity.process(frame)
return depthFrame
def prepare_filters(self):
# prepare post-processing filters
decimate = rs.decimation_filter()
decimate.set_option(rs.option.filter_magnitude, 2 ** 3)
spatial = rs.spatial_filter()
spatial.set_option(rs.option.filter_magnitude, 5)
spatial.set_option(rs.option.filter_smooth_alpha, 1)
spatial.set_option(rs.option.filter_smooth_delta, 50)
spatial.set_option(rs.option.holes_fill, 3)
colorizer = rs.colorizer()
self.filters = [rs.disparity_transform(),
rs.decimation_filter(),
rs.spatial_filter(),
rs.temporal_filter(),
rs.disparity_transform(False)]
def post_processing(frame,
enable_spatial=True,
enable_temporal=True,
enable_hole=True,
spatial_params=[(rs.option.filter_magnitude, 5),
(rs.option.filter_smooth_alpha, 1),
(rs.option.filter_smooth_delta, 50),
(rs.option.holes_fill, 3)],
temporal_params=[],
hole_params=[]):
"""Filters to cleanup depth maps.
"""
# Filters and settings
depth_to_disparity = rs.disparity_transform(True)
disparity_to_depth = rs.disparity_transform(False)
# Depth to disparity before spatial and temporal filters
frame = depth_to_disparity.process(frame)
# Spatial filter
if enable_spatial:
# Settings
spatial = rs.spatial_filter()
for spatial_param in spatial_params:
spatial.set_option(spatial_param[0], spatial_param[1])
# Apply on frame
frame = spatial.process(frame)
# Temporal filter
if enable_temporal:
temporal = rs.temporal_filter()
for temporal_param in temporal_params:
temporal.set_option(temporal_param[0], temporal_param[1])
frame = temporal.process(frame)
# Back to depth
frame = disparity_to_depth.process(frame)
# Hole filling
if enable_hole:
hole_filling = rs.hole_filling_filter()
for hole_param in hole_params:
hole_filling.set_option(hole_param[0], hole_param[1])
frame = hole_filling.process(frame)
return frame
def create_filters(self):
spatial = rs.spatial_filter()
spatial.set_option(rs.option.filter_magnitude, 5)
spatial.set_option(rs.option.filter_smooth_alpha, 1)
spatial.set_option(rs.option.filter_smooth_delta, 50)
spatial.set_option(rs.option.holes_fill, 3)
temporal = rs.temporal_filter()
hole_filling = rs.hole_filling_filter()
disparity_to_depth = rs.disparity_transform(False)
depth_to_disparity = rs.disparity_transform(True)
filters = {
"S": spatial,
"T": temporal,
"H": hole_filling,
"DZ": disparity_to_depth,
"ZD": depth_to_disparity
}
return filters
def start_pipe(self, align=True, usb3=True):
if not self.pipelineStarted:
if align:
print('Etablissement de la connection caméra')
# Create a config and configure the pipeline to stream
# different resolutions of color and depth streams
self.pipeline = rs.pipeline()
# Create a config and configure the pipeline to stream
# different resolutions of color and depth streams
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16,
30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8,
30)
# Start streaming
self.profile = self.pipeline.start(config)
align_to = rs.stream.color
self.align = rs.align(align_to)
time.sleep(1)
# self.pipeline = rs.pipeline()
# config = rs.config()
#
# if usb3:
# config.enable_stream(rs.stream.depth, 640, 360, rs.format.z16, 30)
# config.enable_stream(rs.stream.color, 640, 480, rs.format.rgb8, 30)
#
# else:
# self.profile = config.resolve(self.pipeline) # does not start streaming
#
# self.profile = self.pipeline.start(config)
# self.pipelineStarted = True
# # Align the two streams
# align_to = rs.stream.color
# self.align = rs.align(align_to)
self.pipelineStarted = True
# Get depth scale
depth_sensor = self.profile.get_device().first_depth_sensor()
self.depth_scale = depth_sensor.get_depth_scale()
# Création des filtres
self.hole_filling = rs.hole_filling_filter()
self.temporal_filter = rs.temporal_filter()
self.spatial_filter = rs.spatial_filter()
self.depth_to_disparity = rs.disparity_transform(False)
# Get Intrinsic parameters
self.get_intrinsic()
print('Caméra Ouverte')
def get_filter(filter_name: str, *args) -> rs2.filter_interface:
"""
Basically a factory for filters (Maybe change to Dict 'cause OOP)
:param filter_name: The filter name
:param args: The arguments for the filter
:return: A filter which corresponds to the filter name with it's arguments
"""
if filter_name == 'decimation_filter':
return rs2.decimation_filter(*args)
elif filter_name == 'threshold_filter':
return rs2.threshold_filter(*args)
elif filter_name == 'disparity_transform':
return rs2.disparity_transform(*args)
elif filter_name == 'spatial_filter':
return rs2.spatial_filter(*args)
elif filter_name == 'temporal_filter':
return rs2.temporal_filter(*args)
elif filter_name == 'hole_filling_filter':
return rs2.hole_filling_filter(*args)
else:
raise Exception(f'The filter \'{filter_name}\' does not exist!')
def encode_by_colorization(depth_frame,
min_depth,
max_depth,
use_disparity=False):
"""Encoded given realsense depth_frame as colorized image.
Depth limit are in meters.
Returns color image as numpy array.
"""
filtered = threshold(depth_frame, min_depth, max_depth)
color_filter = rs.colorizer()
color_filter.set_option(rs.option.histogram_equalization_enabled, 0)
color_filter.set_option(rs.option.color_scheme, 9.0)
color_filter.set_option(rs.option.min_distance, min_depth)
color_filter.set_option(rs.option.max_distance, max_depth)
if use_disparity:
filtered = rs.disparity_transform(True).process(filtered)
colorized = color_filter.process(filtered)
arr = np.asanyarray(colorized.get_data()).copy()
return arr
min_depth = 0.3
max_depth = 4.0
# Create colorizer object
colorizer = rs.colorizer()
colorizer.set_option(rs.option.color_scheme, 9)
colorizer.set_option(rs.option.histogram_equalization_enabled, 0)
colorizer.set_option(rs.option.min_distance, min_depth)
colorizer.set_option(rs.option.max_distance, max_depth)
# Filter
thr_filter = rs.threshold_filter()
thr_filter.set_option(rs.option.min_distance, min_depth)
thr_filter.set_option(rs.option.max_distance, max_depth)
# Disparity
dp_filter = rs.disparity_transform()
#https://dev.intelrealsense.com/docs/depth-image-compression-by-colorization-for-intel-realsense-depth-cameras#section-6references
#https://github.com/TetsuriSonoda/rs-colorize/blob/master/rs-colorize/rs-colorize.cpp
start = timer()
cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
cv2.moveWindow("RealSense", 0, 0)
depth_sensor = profile.get_device().first_depth_sensor()
intrinsics = True
try:
while True:
# Wait for a coherent pair of frames: depth and color
def main():
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 1280, 720, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.rgb8, 30)
# Start streaming
#queue =rs.frame_queue(50)
cfg = pipeline.start(config) #, queue)
align = rs.align(rs.stream.color)
profile = cfg.get_stream(rs.stream.depth)
vis = o3d.visualization.Visualizer()
vis.create_window('PCD', width=1280, height=720)
pointcloud = o3d.geometry.PointCloud()
geom_added = False
intrinsics = profile.as_video_stream_profile().get_intrinsics()
print(intrinsics)
depth_sensor = cfg.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
print("Depth Scale is: ", depth_scale)
# clipping_distance_in_meters meters away
clipping_distance_in_meters = 3 # 1 meter
clipping_distance = clipping_distance_in_meters / depth_scale
# filters
# decimation filter
decimation = rs.decimation_filter()
decimation.set_option(rs.option.filter_magnitude, 4)
# spatial filter
spatial = rs.spatial_filter()
spatial.set_option(rs.option.filter_magnitude, 5)
spatial.set_option(rs.option.filter_smooth_alpha, 1)
spatial.set_option(rs.option.filter_smooth_delta, 50)
temporal = rs.temporal_filter()
hole_filling = rs.hole_filling_filter()
depth_to_disparity = rs.disparity_transform(True)
disparity_to_depth = rs.disparity_transform(False)
while True:
dt0 = datetime.now()
frames = pipeline.wait_for_frames()
#frames = queue.wait_for_frame().as_frameset()
frames = align.process(frames)
profile = frames.get_profile()
depth_frame = frames.get_depth_frame()
# depth_frame = decimation.process(depth_frame)
depth_frame = depth_to_disparity.process(depth_frame)
depth_frame = spatial.process(depth_frame)
depth_frame = temporal.process(depth_frame)
depth_frame = disparity_to_depth.process(depth_frame)
#depth_frame = hole_filling.process(depth_frame)
# We will be removing the background of objects more than
color_frame = frames.get_color_frame()
if not depth_frame or not color_frame:
continue
# Convert images to numpy arrays
depth_image = np.asanyarray(depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
img_depth = o3d.geometry.Image(depth_image)
img_color = o3d.geometry.Image(color_image)
rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth(
img_color,
img_depth,
depth_trunc=clipping_distance_in_meters,
convert_rgb_to_intensity=False)
pinhole_camera_intrinsic = o3d.camera.PinholeCameraIntrinsic(
intrinsics.width, intrinsics.height, intrinsics.fx, intrinsics.fy,
intrinsics.ppx, intrinsics.ppy)
pcd = o3d.geometry.PointCloud.create_from_rgbd_image(
rgbd, pinhole_camera_intrinsic)
pcd.transform([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0],
[0, 0, 0, 1]])
pointcloud.points = pcd.points
pointcloud.colors = pcd.colors
#clusteringDepthImage(pointcloud)
if geom_added == False:
vis.add_geometry(pointcloud)
geom_added = True
vis.update_geometry(pointcloud)
vis.poll_events()
vis.update_renderer()
# cv2.imshow('bgr', color_image)
# key = cv2.waitKey(1)
# if key == ord('q'):
# break
process_time = datetime.now() - dt0
print("FPS: " + str(1 / process_time.total_seconds()))
pipeline.stop()
cv2.destroyAllWindows()
vis.destroy_window()
del vis
def image_process():
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 360, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
profile = pipeline.start(config)
depth_sensor = profile.get_device().first_depth_sensor()
depth_sensor.set_option(rs.option.visual_preset, 4)
# 创建align对象
align_to = rs.stream.color
align = rs.align(align_to)
try:
while True:
frames = pipeline.wait_for_frames()
# 对齐
align_frames = align.process(frames)
depth_frame = align_frames.get_depth_frame()
color_frame = align_frames.get_color_frame()
if not depth_frame or not color_frame:
continue
# color_image = np.asanyarray(color_frame.get_data())
# cv2.imshow('rgb', color_image)
depth_image = np.asanyarray(depth_frame.get_data())
depth_colormap = cv2.applyColorMap(
cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
dec = rs.decimation_filter()
dec.set_option(rs.option.filter_magnitude, 1)
depth_frame_pro = dec.process(depth_frame)
depth2disparity = rs.disparity_transform()
depth_frame_pro = depth2disparity.process(depth_frame_pro)
spat = rs.spatial_filter()
# 启用空洞填充,5为填充所有零像素
spat.set_option(rs.option.holes_fill, 5)
depth_frame_pro = spat.process(depth_frame_pro)
temp = rs.temporal_filter()
depth_frame_pro = temp.process(depth_frame_pro)
disparity2depth = rs.disparity_transform(False)
depth_frame_pro = disparity2depth.process(depth_frame_pro)
depth_image_pro = np.asanyarray(depth_frame_pro.get_data())
depth_colormap_pro = cv2.applyColorMap(
cv2.convertScaleAbs(depth_image_pro, alpha=0.03),
cv2.COLORMAP_JET)
images = np.hstack((depth_colormap, depth_colormap_pro))
cv2.imshow('depth_images', images)
key = cv2.waitKey(1)
if key & 0xFF == ord('q') or key == 27:
cv2.destroyAllWindows()
break
finally:
pipeline.stop()
return depth_image_pro
def capture_filter_thread(color_np_queue, mask_np_queue):
global depth_scale, isRunning
print('> Capture Thread Started')
# Create a context object. This object owns the handles to all connected realsense devices
pipeline = rs.pipeline()
try:
# Config realsense
config = rs.config()
config.enable_stream(rs.stream.depth, IMG_WIDTH, IMG_HEIGHT)
config.enable_stream(rs.stream.color, IMG_WIDTH, IMG_HEIGHT)
# Start streaming
profile = pipeline.start(config)
# Create an align object
# rs.align allows us to perform alignment of depth frames to others frames
# The "align_to" is the stream type to which we plan to align depth frames
align_to = rs.stream.color
align = rs.align(align_to)
# Get the depth sensor's depth scale
depth_sensor = profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
# print('Depth Scale is: ', depth_scale)
'''''' '''''' '''''' '''''' '''
Create Filters
''' '''''' '''''' '''''' ''''''
# Decimation Filter - change resolution size!
# decimation = rs.decimation_filter()
# decimation.set_option(rs.option.filter_magnitude, 0.5)
# Spatial Filter
spatial = rs.spatial_filter()
spatial.set_option(rs.option.filter_magnitude, 5)
spatial.set_option(rs.option.filter_smooth_alpha, 1)
spatial.set_option(rs.option.filter_smooth_delta, 50)
spatial.set_option(rs.option.holes_fill, 3)
# Temporal Filter
temporal = rs.temporal_filter()
# Hole Filling Filter
hole_filling = rs.hole_filling_filter()
# Disparity Transform
depth_to_disparity = rs.disparity_transform(True)
disparity_to_depth = rs.disparity_transform(False)
frameset = None
filtered_depth_frame = None
# Skip 5 first frames to give the Auto-Exposure time to adjust
for x in range(5):
pipeline.wait_for_frames()
while isRunning:
print('---> Capture One Frame')
frameset = pipeline.wait_for_frames()
# Validate that both frames are valid
if not frameset:
continue
# put aligned frameset into queue, queue will be blocked if queue is full. Timeout = 5 to throw exception
alignment = align.process(frameset)
for x in range(SLIDING_WINDOW):
# get frame
frameset = alignment
filtered_depth_frame = frameset.get_depth_frame()
# Filtering
filtered_depth_frame = depth_to_disparity.process(
filtered_depth_frame)
filtered_depth_frame = spatial.process(filtered_depth_frame)
filtered_depth_frame = temporal.process(filtered_depth_frame)
filtered_depth_frame = disparity_to_depth.process(
filtered_depth_frame)
filtered_depth_frame = hole_filling.process(
filtered_depth_frame)
color_np_queue.put(
np.asanyarray(frameset.get_color_frame().get_data()))
mask_np_queue.put(np.asanyarray(filtered_depth_frame.get_data()))
del frameset
gc.collect()
time.sleep(CAPTURE_CYCLE)
except:
traceback.print_exc()
rs.log_to_console(rs.log_severity.debug)
finally:
pipeline.stop()
print('> Capture Thread Stopped')
def save_thread(color_np_queue, mask_np_queue):
global frameset_queue, isRunning
print('> Filter & Save Thread Started')
try:
# visualising the data
#colorizer = rs.colorizer()
sequence = 0
'''''' '''''' '''''' '''''' '''
Create Filters
''' '''''' '''''' '''''' ''''''
# Decimation Filter - change resolution size!
# decimation = rs.decimation_filter()
# decimation.set_option(rs.option.filter_magnitude, 0.5)
# Spatial Filter
spatial = rs.spatial_filter()
spatial.set_option(rs.option.filter_magnitude, 5)
spatial.set_option(rs.option.filter_smooth_alpha, 1)
spatial.set_option(rs.option.filter_smooth_delta, 50)
spatial.set_option(rs.option.holes_fill, 3)
# Temporal Filter
temporal = rs.temporal_filter()
# Hole Filling Filter
hole_filling = rs.hole_filling_filter()
# Disparity Transform
depth_to_disparity = rs.disparity_transform(True)
disparity_to_depth = rs.disparity_transform(False)
frameset = None
filtered_depth_frame = None
while isRunning:
time.sleep(0.2)
while not frameset_queue.empty():
time.sleep(0.2)
print('---> Wait for Frames')
# filter frames
for x in range(SLIDING_WINDOW):
# print('----> Get Sliding Window')
if frameset_queue.empty():
break
# get depth frame
frameset = frameset_queue.get(False)
filtered_depth_frame = frameset.get_depth_frame()
# Filtering
# filtered_depth_frame = decimation.process(filtered_depth_frame)
filtered_depth_frame = depth_to_disparity.process(
filtered_depth_frame)
filtered_depth_frame = spatial.process(
filtered_depth_frame)
filtered_depth_frame = temporal.process(
filtered_depth_frame)
filtered_depth_frame = disparity_to_depth.process(
filtered_depth_frame)
filtered_depth_frame = hole_filling.process(
filtered_depth_frame)
print('---> Got One')
# Save Images
color_np = np.asanyarray(frameset.get_color_frame().get_data())
saveImage(color_np, 'IMAGE_', sequence)
# save depth image
#saveImage(np.asanyarray(frameset.get_depth_frame().get_data()), 'DEPTH_', sequence)
filtered_depth_np = np.asanyarray(
filtered_depth_frame.get_data())
# save filtered depth frame
#saveImage(filtered_depth_np, 'FILTERED_DEPTH_', sequence)
# save colorized filtered depth frame
#saveImage(np.asanyarray(colorizer.colorize(filtered_depth_frame).get_data()), 'COLORIZED_FILTERED_DEPTH_', sequence)
# save mask from clipped filtered depth frame
saveImage(createMask(filtered_depth_np, CLIPPING_DISTANCE),
'IMAGE_', sequence, True)
# save clipped filtered color frame
#replace_color = 0
#filtered_depth_image_3d = np.dstack((filtered_depth_np, filtered_depth_np, filtered_depth_np))
#clipped_filtered_color_np = np.where((filtered_depth_image_3d > (CLIPPING_DISTANCE / depth_scale)) | (filtered_depth_image_3d <= 0), replace_color, color_np)
#saveImage(clipped_filtered_color_np, 'CLIPPED_FILTERED_COLOR_', sequence)
sequence += 1
except:
traceback.print_exc()
finally:
print('> Filter & Save Thread Stopped')
def print_hi():
# License: Apache 2.0. See LICENSE file in root directory.
# Copyright(c) 2015-2017 Intel Corporation. All Rights Reserved.
"""
OpenGL Pointcloud viewer with http://pyglet.org
Usage:
------
Mouse:
Drag with left button to rotate around pivot (thick small axes),
with right button to translate and the wheel to zoom.
Keyboard:
[p] Pause
[r] Reset View
[d] Cycle through decimation values
[z] Toggle point scaling
[x] Toggle point distance attenuation
[c] Toggle color source
[l] Toggle lighting
[f] Toggle depth post-processing
[s] Save PNG (./out.png)
[e] Export points to ply (./out.ply)
[q/ESC] Quit
Notes:
------
Using deprecated OpenGL (FFP lighting, matrix stack...) however, draw calls
are kept low with pyglet.graphics.* which uses glDrawArrays internally.
Normals calculation is done with numpy on CPU which is rather slow, should really
be done with shaders but was omitted for several reasons - brevity, for lowering
dependencies (pyglet doesn't ship with shader support & recommends pyshaders)
and for reference.
"""
# https://stackoverflow.com/a/6802723
def rotation_matrix(axis, theta):
"""
Return the rotation matrix associated with counterclockwise rotation about
the given axis by theta radians.
"""
axis = np.asarray(axis)
axis = axis / math.sqrt(np.dot(axis, axis))
a = math.cos(theta / 2.0)
b, c, d = -axis * math.sin(theta / 2.0)
aa, bb, cc, dd = a * a, b * b, c * c, d * d
bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d
return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],
[2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],
[2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]])
class AppState:
def __init__(self, *args, **kwargs):
self.pitch, self.yaw = math.radians(-10), math.radians(-15)
self.translation = np.array([0, 0, 1], np.float32)
self.distance = 2
self.mouse_btns = [False, False, False]
self.paused = False
self.decimate = 0
self.scale = True
self.attenuation = False
self.color = True
self.lighting = False
self.postprocessing = False
def reset(self):
self.pitch, self.yaw, self.distance = 0, 0, 2
self.translation[:] = 0, 0, 1
@property
def rotation(self):
Rx = rotation_matrix((1, 0, 0), math.radians(-self.pitch))
Ry = rotation_matrix((0, 1, 0), math.radians(-self.yaw))
return np.dot(Ry, Rx).astype(np.float32)
state = AppState()
# Configure streams
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
# other_stream, other_format = rs.stream.infrared, rs.format.y8
other_stream, other_format = rs.stream.color, rs.format.rgb8
config.enable_stream(other_stream, 640, 480, other_format, 30)
# Start streaming
pipeline.start(config)
profile = pipeline.get_active_profile()
depth_sensor = profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
depth_profile = rs.video_stream_profile(profile.get_stream(
rs.stream.depth))
depth_intrinsics = depth_profile.get_intrinsics()
w, h = depth_intrinsics.width, depth_intrinsics.height
# Processing blocks
pc = rs.pointcloud()
decimate = rs.decimation_filter()
decimate.set_option(rs.option.filter_magnitude, 2**state.decimate)
colorizer = rs.colorizer()
filters = [
rs.disparity_transform(),
rs.spatial_filter(),
rs.temporal_filter(),
rs.disparity_transform(False)
]
# pyglet
window = pyglet.window.Window(
config=gl.Config(
double_buffer=True,
samples=8 # MSAA
),
resizable=True,
vsync=True)
keys = pyglet.window.key.KeyStateHandler()
window.push_handlers(keys)
def convert_fmt(fmt):
"""rs.format to pyglet format string"""
return {
rs.format.rgb8: 'RGB',
rs.format.bgr8: 'BGR',
rs.format.rgba8: 'RGBA',
rs.format.bgra8: 'BGRA',
rs.format.y8: 'L',
}[fmt]
# Create a VertexList to hold pointcloud data
# Will pre-allocates memory according to the attributes below
vertex_list = pyglet.graphics.vertex_list(w * h, 'v3f/stream',
't2f/stream', 'n3f/stream')
# Create and allocate memory for our color data
other_profile = rs.video_stream_profile(profile.get_stream(other_stream))
image_data = pyglet.image.ImageData(w, h,
convert_fmt(other_profile.format()),
(gl.GLubyte * (w * h * 3))())
if (pyglet.version.startswith('1.')
and not pyglet.version.startswith('1.4')):
# pyglet.clock.ClockDisplay has be removed in 1.4
fps_display = pyglet.clock.ClockDisplay()
else:
fps_display = pyglet.window.FPSDisplay(window)
@window.event
def on_mouse_drag(x, y, dx, dy, buttons, modifiers):
w, h = map(float, window.get_size())
if buttons & pyglet.window.mouse.LEFT:
state.yaw -= dx * 0.5
state.pitch -= dy * 0.5
if buttons & pyglet.window.mouse.RIGHT:
dp = np.array((dx / w, -dy / h, 0), np.float32)
state.translation += np.dot(state.rotation, dp)
if buttons & pyglet.window.mouse.MIDDLE:
dz = dy * 0.01
state.translation -= (0, 0, dz)
state.distance -= dz
def handle_mouse_btns(x, y, button, modifiers):
state.mouse_btns[0] ^= (button & pyglet.window.mouse.LEFT)
state.mouse_btns[1] ^= (button & pyglet.window.mouse.RIGHT)
state.mouse_btns[2] ^= (button & pyglet.window.mouse.MIDDLE)
window.on_mouse_press = window.on_mouse_release = handle_mouse_btns
@window.event
def on_mouse_scroll(x, y, scroll_x, scroll_y):
dz = scroll_y * 0.1
state.translation -= (0, 0, dz)
state.distance -= dz
def on_key_press(symbol, modifiers):
if symbol == pyglet.window.key.R:
state.reset()
if symbol == pyglet.window.key.P:
state.paused ^= True
if symbol == pyglet.window.key.D:
state.decimate = (state.decimate + 1) % 3
decimate.set_option(rs.option.filter_magnitude, 2**state.decimate)
if symbol == pyglet.window.key.C:
state.color ^= True
if symbol == pyglet.window.key.Z:
state.scale ^= True
if symbol == pyglet.window.key.X:
state.attenuation ^= True
if symbol == pyglet.window.key.L:
state.lighting ^= True
if symbol == pyglet.window.key.F:
state.postprocessing ^= True
if symbol == pyglet.window.key.S:
pyglet.image.get_buffer_manager().get_color_buffer().save(
'out.png')
if symbol == pyglet.window.key.Q:
window.close()
window.push_handlers(on_key_press)
def axes(size=1, width=1):
"""draw 3d axes"""
gl.glLineWidth(width)
pyglet.graphics.draw(
6, gl.GL_LINES,
('v3f',
(0, 0, 0, size, 0, 0, 0, 0, 0, 0, size, 0, 0, 0, 0, 0, 0, size)),
('c3f', (
1,
0,
0,
1,
0,
0,
0,
1,
0,
0,
1,
0,
0,
0,
1,
0,
0,
1,
)))
def frustum(intrinsics):
"""draw camera's frustum"""
w, h = intrinsics.width, intrinsics.height
batch = pyglet.graphics.Batch()
for d in range(1, 6, 2):
def get_point(x, y):
p = rs.rs2_deproject_pixel_to_point(intrinsics, [x, y], d)
batch.add(2, gl.GL_LINES, None, ('v3f', [0, 0, 0] + p))
return p
top_left = get_point(0, 0)
top_right = get_point(w, 0)
bottom_right = get_point(w, h)
bottom_left = get_point(0, h)
batch.add(2, gl.GL_LINES, None, ('v3f', top_left + top_right))
batch.add(2, gl.GL_LINES, None, ('v3f', top_right + bottom_right))
batch.add(2, gl.GL_LINES, None,
('v3f', bottom_right + bottom_left))
batch.add(2, gl.GL_LINES, None, ('v3f', bottom_left + top_left))
batch.draw()
def grid(size=1, n=10, width=1):
"""draw a grid on xz plane"""
gl.glLineWidth(width)
s = size / float(n)
s2 = 0.5 * size
batch = pyglet.graphics.Batch()
for i in range(0, n + 1):
x = -s2 + i * s
batch.add(2, gl.GL_LINES, None, ('v3f', (x, 0, -s2, x, 0, s2)))
for i in range(0, n + 1):
z = -s2 + i * s
batch.add(2, gl.GL_LINES, None, ('v3f', (-s2, 0, z, s2, 0, z)))
batch.draw()
@window.event
def on_draw():
window.clear()
gl.glEnable(gl.GL_DEPTH_TEST)
gl.glEnable(gl.GL_LINE_SMOOTH)
width, height = window.get_size()
gl.glViewport(0, 0, width, height)
gl.glMatrixMode(gl.GL_PROJECTION)
gl.glLoadIdentity()
gl.gluPerspective(60, width / float(height), 0.01, 20)
gl.glMatrixMode(gl.GL_TEXTURE)
gl.glLoadIdentity()
# texcoords are [0..1] and relative to top-left pixel corner, add 0.5 to center
gl.glTranslatef(0.5 / image_data.width, 0.5 / image_data.height, 0)
image_texture = image_data.get_texture()
# texture size may be increased by pyglet to a power of 2
tw, th = image_texture.owner.width, image_texture.owner.height
gl.glScalef(image_data.width / float(tw),
image_data.height / float(th), 1)
gl.glMatrixMode(gl.GL_MODELVIEW)
gl.glLoadIdentity()
gl.gluLookAt(0, 0, 0, 0, 0, 1, 0, -1, 0)
gl.glTranslatef(0, 0, state.distance)
gl.glRotated(state.pitch, 1, 0, 0)
gl.glRotated(state.yaw, 0, 1, 0)
if any(state.mouse_btns):
axes(0.1, 4)
gl.glTranslatef(0, 0, -state.distance)
gl.glTranslatef(*state.translation)
gl.glColor3f(0.5, 0.5, 0.5)
gl.glPushMatrix()
gl.glTranslatef(0, 0.5, 0.5)
grid()
gl.glPopMatrix()
psz = max(window.get_size()) / float(max(w, h)) if state.scale else 1
gl.glPointSize(psz)
distance = (0, 0, 1) if state.attenuation else (1, 0, 0)
gl.glPointParameterfv(gl.GL_POINT_DISTANCE_ATTENUATION,
(gl.GLfloat * 3)(*distance))
if state.lighting:
ldir = [0.5, 0.5, 0.5] # world-space lighting
ldir = np.dot(state.rotation, (0, 0, 1)) # MeshLab style lighting
ldir = list(ldir) + [0] # w=0, directional light
gl.glLightfv(gl.GL_LIGHT0, gl.GL_POSITION, (gl.GLfloat * 4)(*ldir))
gl.glLightfv(gl.GL_LIGHT0, gl.GL_DIFFUSE,
(gl.GLfloat * 3)(1.0, 1.0, 1.0))
gl.glLightfv(gl.GL_LIGHT0, gl.GL_AMBIENT,
(gl.GLfloat * 3)(0.75, 0.75, 0.75))
gl.glEnable(gl.GL_LIGHT0)
gl.glEnable(gl.GL_NORMALIZE)
gl.glEnable(gl.GL_LIGHTING)
gl.glColor3f(1, 1, 1)
texture = image_data.get_texture()
gl.glEnable(texture.target)
gl.glBindTexture(texture.target, texture.id)
gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER,
gl.GL_NEAREST)
# comment this to get round points with MSAA on
gl.glEnable(gl.GL_POINT_SPRITE)
if not state.scale and not state.attenuation:
gl.glDisable(gl.GL_MULTISAMPLE) # for true 1px points with MSAA on
vertex_list.draw(gl.GL_POINTS)
gl.glDisable(texture.target)
if not state.scale and not state.attenuation:
gl.glEnable(gl.GL_MULTISAMPLE)
gl.glDisable(gl.GL_LIGHTING)
gl.glColor3f(0.25, 0.25, 0.25)
frustum(depth_intrinsics)
axes()
gl.glMatrixMode(gl.GL_PROJECTION)
gl.glLoadIdentity()
gl.glOrtho(0, width, 0, height, -1, 1)
gl.glMatrixMode(gl.GL_MODELVIEW)
gl.glLoadIdentity()
gl.glMatrixMode(gl.GL_TEXTURE)
gl.glLoadIdentity()
gl.glDisable(gl.GL_DEPTH_TEST)
fps_display.draw()
def run(dt):
global w, h
window.set_caption("RealSense (%dx%d) %dFPS (%.2fms) %s" %
(w, h, 0 if dt == 0 else 1.0 / dt, dt * 1000,
"PAUSED" if state.paused else ""))
if state.paused:
return
success, frames = pipeline.try_wait_for_frames(timeout_ms=0)
if not success:
return
depth_frame = frames.get_depth_frame().as_video_frame()
other_frame = frames.first(other_stream).as_video_frame()
depth_frame = decimate.process(depth_frame)
if state.postprocessing:
for f in filters:
depth_frame = f.process(depth_frame)
# Grab new intrinsics (may be changed by decimation)
depth_intrinsics = rs.video_stream_profile(
depth_frame.profile).get_intrinsics()
w, h = depth_intrinsics.width, depth_intrinsics.height
color_image = np.asanyarray(other_frame.get_data())
colorized_depth = colorizer.colorize(depth_frame)
depth_colormap = np.asanyarray(colorized_depth.get_data())
if state.color:
mapped_frame, color_source = other_frame, color_image
else:
mapped_frame, color_source = colorized_depth, depth_colormap
points = pc.calculate(depth_frame)
pc.map_to(mapped_frame)
# handle color source or size change
fmt = convert_fmt(mapped_frame.profile.format())
global image_data
if (image_data.format, image_data.pitch) != (fmt,
color_source.strides[0]):
empty = (gl.GLubyte * (w * h * 3))()
image_data = pyglet.image.ImageData(w, h, fmt, empty)
# copy image data to pyglet
image_data.set_data(fmt, color_source.strides[0],
color_source.ctypes.data)
verts = np.asarray(points.get_vertices(2)).reshape(h, w, 3)
texcoords = np.asarray(points.get_texture_coordinates(2))
if len(vertex_list.vertices) != verts.size:
vertex_list.resize(verts.size // 3)
# need to reassign after resizing
vertex_list.vertices = verts.ravel()
vertex_list.tex_coords = texcoords.ravel()
# copy our data to pre-allocated buffers, this is faster than assigning...
# pyglet will take care of uploading to GPU
def copy(dst, src):
"""copy numpy array to pyglet array"""
# timeit was mostly inconclusive, favoring slice assignment for safety
np.array(dst, copy=False)[:] = src.ravel()
# ctypes.memmove(dst, src.ctypes.data, src.nbytes)
copy(vertex_list.vertices, verts)
copy(vertex_list.tex_coords, texcoords)
if state.lighting:
# compute normals
dy, dx = np.gradient(verts, axis=(0, 1))
n = np.cross(dx, dy)
# can use this, np.linalg.norm or similar to normalize, but OpenGL can do this for us, see GL_NORMALIZE above
# norm = np.sqrt((n*n).sum(axis=2, keepdims=True))
# np.divide(n, norm, out=n, where=norm != 0)
# import cv2
# n = cv2.bilateralFilter(n, 5, 1, 1)
copy(vertex_list.normals, n)
if keys[pyglet.window.key.E]:
points.export_to_ply('./out.ply', mapped_frame)
pyglet.clock.schedule(run)
try:
pyglet.app.run()
finally:
pipeline.stop()
pointcloud = PointCloud()
i = 0
while True:
dt0 = datetime.now()
pointcloud.clear()
frames = pipeline.wait_for_frames()
aligned_frames = align.process(frames)
color_frame = aligned_frames.get_color_frame()
color_image = np.asanyarray(color_frame.get_data())
depth_frame = aligned_frames.get_depth_frame()
depth_frame = rs.decimation_filter(1).process(depth_frame)
depth_frame = rs.disparity_transform(True).process(depth_frame)
depth_frame = rs.spatial_filter().process(depth_frame)
depth_frame = rs.temporal_filter().process(depth_frame)
depth_frame = rs.disparity_transform(False).process(depth_frame)
# depth_frame = rs.hole_filling_filter().process(depth_frame)
depth_image = np.asanyarray(depth_frame.get_data())
color_image1 = cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR)
cv2.namedWindow('color image', cv2.WINDOW_AUTOSIZE)
cv2.imshow('color image', cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR))
cv2.namedWindow('depth image', cv2.WINDOW_AUTOSIZE)
cv2.imshow('depth image', depth_image)
depth = Image(depth_image)
color = Image(color_image)
profile = pipeline.get_active_profile()
depth_sensor = profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
depth_profile = rs.video_stream_profile(profile.get_stream(rs.stream.depth))
depth_intrinsics = depth_profile.get_intrinsics()
w, h = depth_intrinsics.width, depth_intrinsics.height
# Processing blocks
pc = rs.pointcloud()
decimate = rs.decimation_filter()
decimate.set_option(rs.option.filter_magnitude, 2**state.decimate)
colorizer = rs.colorizer()
filters = [
rs.disparity_transform(),
rs.spatial_filter(),
rs.temporal_filter(),
rs.disparity_transform(False)
]
# pyglet
window = pyglet.window.Window(
config=gl.Config(
double_buffer=True,
samples=8 # MSAA
),
resizable=True,
vsync=True)
keys = pyglet.window.key.KeyStateHandler()
window.push_handlers(keys)
pipeline.start(config)
profile = pipeline.get_active_profile()
depth_sensor = profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
depth_profile = rs.video_stream_profile(profile.get_stream(rs.stream.depth))
depth_intrinsics = depth_profile.get_intrinsics()
w, h = depth_intrinsics.width, depth_intrinsics.height
# Processing blocks
pc = rs.pointcloud()
decimate = rs.decimation_filter()
decimate.set_option(rs.option.filter_magnitude, 2 ** state.decimate)
colorizer = rs.colorizer()
filters = [rs.disparity_transform(),
rs.spatial_filter(),
rs.temporal_filter(),
rs.disparity_transform(False)]
# pyglet
window = pyglet.window.Window(
config=gl.Config(
double_buffer=True,
samples=8 # MSAA
),
resizable=True, vsync=True)
keys = pyglet.window.key.KeyStateHandler()
window.push_handlers(keys)
def main():
global colour_image
global depth_image
# open3d visualiser
visualiser = open3d.visualization.Visualizer()
point_cloud = open3d.geometry.PointCloud()
point_cloud.points = open3d.utility.Vector3dVector(
np.array([[i, i, i] for i in range(-5, 5)]))
visualiser.create_window()
visualiser.add_geometry(point_cloud)
# Create windows
cv2.namedWindow("Colour", cv2.WINDOW_NORMAL)
cv2.namedWindow("Depth", cv2.WINDOW_NORMAL)
# Camera config
config = rs.config()
config.enable_stream(rs.stream.depth, rs.format.z16, 30)
config.enable_stream(rs.stream.color, rs.format.bgr8, 30)
# Start camera pipeline
pipeline = rs.pipeline()
pipeline.start(config)
# Depth and colour alignment
align_to = rs.stream.color
align = rs.align(align_to)
# Filters
spatial = rs.spatial_filter()
temporal = rs.temporal_filter(0.4, 20, 5)
disparity = rs.disparity_transform(True)
coloriser = rs.colorizer()
# Get stream profile and camera intrinsics
profile = pipeline.get_active_profile()
depth_profile = rs.video_stream_profile(profile.get_stream(
rs.stream.depth))
intrinsics = depth_profile.get_intrinsics()
# Create camera intrinsics for open3d
intrinsic = open3d.camera.PinholeCameraIntrinsic(
intrinsics.width, intrinsics.height, intrinsics.fx, intrinsics.fy,
intrinsics.width // 2, intrinsics.height // 2)
while True:
# Obtain and align frames
current_frame = pipeline.wait_for_frames()
current_frame = align.process(current_frame)
depth_frame = temporal.process(current_frame.get_depth_frame())
# Get colour and depth frames
#depth_image = np.asanyarray(spatial.process(current_frame.get_depth_frame()).get_data())
depth_image = np.asanyarray(depth_frame.get_data())
colour_image = np.asanyarray(
current_frame.get_color_frame().get_data())
# Create rgbd image
#rgbd = open3d.geometry.create_rgbd_image_from_color_and_depth(open3d.geometry.Image(cv2.cvtColor(colour_image, cv2.COLOR_BGR2RGB)), open3d.geometry.Image(depth_image), convert_rgb_to_intensity=False)
# Create point cloud
#pcd = open3d.geometry.create_point_cloud_from_rgbd_image(rgbd, intrinsic)
# Update point cloud for visualiser
#point_cloud.points = pcd.points
#point_cloud.colors = pcd.colors
# Update visualiser
#visualiser.update_geometry()
#visualiser.poll_events()
#visualiser.update_renderer()
depth_image = np.asanyarray(coloriser.colorize(depth_frame).get_data())
cv2.imshow("Depth", depth_image)
cv2.imshow("Colour", colour_image)
cv2.waitKey(1)
USE_PRESET_FILE = True
PRESET_FILE = "../cfg/d4xx-default.json"
RTSP_STREAMING_ENABLE = True
RTSP_PORT = "8554"
RTSP_MOUNT_POINT = "/d4xx"
# List of filters to be applied, in this order.
# https://github.com/IntelRealSense/librealsense/blob/master/doc/post-processing-filters.md
filters = [[True, "Decimation Filter",
rs.decimation_filter()],
[True, "Threshold Filter",
rs.threshold_filter()],
[True, "Depth to Disparity",
rs.disparity_transform(True)],
[True, "Spatial Filter",
rs.spatial_filter()],
[True, "Temporal Filter",
rs.temporal_filter()],
[False, "Hole Filling Filter",
rs.hole_filling_filter()],
[True, "Disparity to Depth",
rs.disparity_transform(False)]]
#
# The filters can be tuned with opencv_depth_filtering.py script, and save the default values to here
# Individual filters have different options so one have to apply the values accordingly
#
# decimation_magnitude = 8
align = rs.align(align_to) # Setting up filters dec_filter = rs.decimation_filter() dec_filter.set_option(rs.option.filter_magnitude, 4) spa_filter = rs.spatial_filter() spa_filter.set_option(rs.option.filter_magnitude, 5) spa_filter.set_option(rs.option.filter_smooth_alpha, 1) spa_filter.set_option(rs.option.filter_smooth_delta, 50) spa_filter.set_option(rs.option.holes_fill, 3) tmp_filter = rs.temporal_filter() hol_fillin = rs.hole_filling_filter() dep_to_dis = rs.disparity_transform(True) dis_to_dep = rs.disparity_transform(False) # Setting up indices d1, d2 = int(480 / 4), int(640 / 4) idx, jdx = np.indices((d1, d2)) idx_back = np.clip(idx - 1, 0, idx.max()).flatten() idx_front = np.clip(idx + 1, 0, idx.max()).flatten() jdx_back = np.clip(jdx - 1, 0, jdx.max()).flatten() jdx_front = np.clip(jdx + 1, 0, jdx.max()).flatten() idx = idx.flatten() jdx = jdx.flatten() # rand_idx = np.random.choice(np.arange(idx.shape[0]), size=d1*d2, replace=False) f1 = (idx_front * d2 + jdx) # [rand_idx] f2 = (idx_back * d2 + jdx) # [rand_idx]
print("Depth Scale is: " , depth_scale)
# We will be removing the background of objects more than
# clipping_distance_in_meters meters away
clipping_distance_in_meters = 4 #1 meter
clipping_distance = clipping_distance_in_meters / depth_scale
# Create an align object
# rs.align allows us to perform alignment of depth frames to others frames
# The "align_to" is the stream type to which we plan to align depth frames.
align_to = rs.stream.color
align = rs.align(align_to)
decimate = rs.decimation_filter(magnitude=2)
spatial = rs.spatial_filter(smooth_alpha=0.5, smooth_delta=28, magnitude=2, hole_fill=1)
temporal = rs.temporal_filter(smooth_alpha=0.05, smooth_delta=28, persistence_control=2)
depth_to_disparity = rs.disparity_transform(True)
disparity_to_depth = rs.disparity_transform(False)
# Streaming loop
try:
while True:
# Get frameset of color and depth
success, frames = pipeline.try_wait_for_frames(timeout_ms=20)
if not success:
continue
# frames.get_depth_frame() is a 640x360 depth image
# Align the depth frame to color frame
aligned_frames = align.process(frames)
# Get aligned frames
def create_pipeline(config: dict):
"""Sets up the pipeline to extract depth and rgb frames
Arguments:
config {dict} -- A dictionary mapping for configuration. see default.yaml
Returns:
tuple -- pipeline, process modules, filters, t265 device (optional)
"""
# Create pipeline and config for D4XX,L5XX
pipeline = rs.pipeline()
rs_config = rs.config()
# IF t265 is enabled, need to handle seperately
t265_dev = None
t265_sensor = None
t265_pipeline = rs.pipeline()
t265_config = rs.config()
if config['playback']['enabled']:
# Load recorded bag file
rs.config.enable_device_from_file(
rs_config, config['playback']['file'],
config['playback'].get('repeat', False))
# This code is only activated if the user points to a T265 Recorded Bag File
if config['tracking']['enabled']:
rs.config.enable_device_from_file(
t265_config, config['tracking']['playback']['file'],
config['playback'].get('repeat', False))
t265_config.enable_stream(rs.stream.pose)
t265_pipeline.start(t265_config)
profile_temp = t265_pipeline.get_active_profile()
t265_dev = profile_temp.get_device()
t265_playback = t265_dev.as_playback()
t265_playback.set_real_time(False)
else:
# Ensure device is connected
ctx = rs.context()
devices = ctx.query_devices()
if len(devices) == 0:
logging.error("No connected Intel Realsense Device!")
sys.exit(1)
if config['advanced']:
logging.info(
"Attempting to enter advanced mode and upload JSON settings file"
)
load_setting_file(ctx, devices, config['advanced'])
if config['tracking']['enabled']:
# Cycle through connected devices and print them
for dev in devices:
dev_name = dev.get_info(rs.camera_info.name)
print("Found {}".format(dev_name))
if "Intel RealSense D4" in dev_name:
pass
elif "Intel RealSense T265" in dev_name:
t265_dev = dev
elif "Intel RealSense L515" in dev_name:
pass
if config['tracking']['enabled']:
if len(devices) != 2:
logging.error("Need 2 connected Intel Realsense Devices!")
sys.exit(1)
if t265_dev is None:
logging.error("Need Intel Realsense T265 Device!")
sys.exit(1)
if t265_dev:
# Unable to open as a pipeline, must use sensors
t265_sensor = t265_dev.query_sensors()[0]
profiles = t265_sensor.get_stream_profiles()
pose_profile = [
profile for profile in profiles
if profile.stream_name() == 'Pose'
][0]
t265_sensor.open(pose_profile)
t265_sensor.start(callback_pose)
logging.info("Started streaming Pose")
rs_config.enable_stream(rs.stream.depth, config['depth']['width'],
config['depth']['height'], rs.format.z16,
config['depth']['framerate'])
# other_stream, other_format = rs.stream.infrared, rs.format.y8
rs_config.enable_stream(rs.stream.color, config['color']['width'],
config['color']['height'], rs.format.rgb8,
config['color']['framerate'])
# Start streaming
pipeline.start(rs_config)
profile = pipeline.get_active_profile()
depth_sensor = profile.get_device().first_depth_sensor()
color_sensor = profile.get_device().first_color_sensor()
depth_scale = depth_sensor.get_depth_scale()
# depth_sensor.set_option(rs.option.global_time_enabled, 1.0)
# color_sensor.set_option(rs.option.global_time_enabled, 1.0)
if config['playback']['enabled']:
dev = profile.get_device()
playback = dev.as_playback()
playback.set_real_time(False)
# Processing blocks
filters = []
decimate = None
align = rs.align(rs.stream.color)
depth_to_disparity = rs.disparity_transform(True)
disparity_to_depth = rs.disparity_transform(False)
# Decimation
if config.get("filters").get("decimation"):
filt = config.get("filters").get("decimation")
if filt.get('active', True):
filt.pop('active', None) # Remove active key before passing params
decimate = rs.decimation_filter(**filt)
# Spatial
if config.get("filters").get("spatial"):
filt = config.get("filters").get("spatial")
if filt.get('active', True):
filt.pop('active', None) # Remove active key before passing params
my_filter = rs.spatial_filter(**filt)
filters.append(my_filter)
# Temporal
if config.get("filters").get("temporal"):
filt = config.get("filters").get("temporal")
if filt.get('active', True):
filt.pop('active', None) # Remove active key before passing params
my_filter = rs.temporal_filter(**filt)
filters.append(my_filter)
process_modules = (align, depth_to_disparity, disparity_to_depth, decimate)
intrinsics = get_intrinsics(pipeline, rs.stream.color)
proj_mat = create_projection_matrix(intrinsics)
sensor_meta = dict(depth_scale=depth_scale)
config['sensor_meta'] = sensor_meta
# Note that sensor must be saved so that it is not garbage collected
t265_device = dict(pipeline=t265_pipeline, sensor=t265_sensor)
return pipeline, process_modules, filters, proj_mat, t265_device
def get_pointcloud_frame(bagfile_dir, filter = False):
# -------------------------------------------- #
# Read the bag file and save point cloud to .ply file
# n_img: number of matrix you want to save
# interval: number of frames between saved matrix
# -------------------------------------------- #
input = bagfile_dir
try:
# Declare pointcloud object, for calculating pointclouds and texture mappings
pc = rs.pointcloud()
# We want the points object to be persistent so we can display the last cloud when a frame drops
points = rs.points()
# Create pipeline
pipeline = rs.pipeline()
# Create a config object
config = rs.config()
# Tell config that we will use a recorded device from filem to be used by the pipeline through playback.
rs.config.enable_device_from_file(config, input)
# Configure the pipeline to stream the depth and color stream
config.enable_stream(rs.stream.depth, 1280, 720, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 1280, 720, rs.format.rgb8, 30)
# Declare filters
dec_filter = rs.decimation_filter() # Decimation - reduces depth frame density
spat_filter = rs.spatial_filter() # Spatial - edge-preserving spatial smoothing
temp_filter = rs.temporal_filter() # Temporal - reduces temporal noise
fill_filter = rs.hole_filling_filter() # Hole Filling filter
disp_filter = rs.disparity_transform() # Disparity Transform filter
# Start streaming from file
profile = pipeline.start(config)
depth_sensor = profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
print("Depth Scale is: " , depth_scale)
while(True):
# Get frameset of depth
frames = pipeline.wait_for_frames()
# Get depth frame
depth_frame = frames.get_depth_frame()
color_frame = frames.get_color_frame()
if (filter):
# Perform filtering
filtered = dec_filter.process(depth_frame)
filtered = spat_filter.process(filtered)
filtered = fill_filter.process(filtered)
depth_frame = temp_filter.process(filtered)
color_frame = dec_filter.process(color_frame)
# Tell pointcloud object to map to this color frame
pc.map_to(color_frame)
# Generate the pointcloud and texture mappings
points = pc.calculate(depth_frame)
# yield
points_array = np.asanyarray(points.get_vertices())
yield np.asanyarray(depth_frame.get_data()), points_array
finally:
pass
return