def realsense_init(self):
# Configure depth and color streams
self.pipeline = rs.pipeline()
config = rs.config()
realsense_ctx = rs.context()
connected_devices = []
for i in range(len(realsense_ctx.devices)):
detected_camera = realsense_ctx.devices[i].get_info(
rs.camera_info.serial_number)
connected_devices.append(detected_camera)
# choose device if more than 1 connected:
if len(connected_devices) > 1:
print("choose device by pressing the number:")
for i in range(len(realsense_ctx.devices)):
print("[%s]: %s @ %s" % (i, realsense_ctx.devices[i].get_info(
rs.camera_info.name), realsense_ctx.devices[i].get_info(
rs.camera_info.physical_port)))
idx = int(input(">>> "))
device_product_line = connected_devices[idx]
self.UDP_PORT = 5000 + idx
print("sending at UDP %s:%s" % (self.UDP_IP, self.UDP_PORT))
else:
device_product_line = connected_devices[0]
self.UDP_PORT = 5000
config.enable_device(device_product_line)
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
# Start streaming
try:
# setup for USB 3
try:
config.enable_stream(rs.stream.color, 1920, 1080,
rs.format.bgr8, 30)
print("trying to stream 1920x1080...", end=" ")
self.pipeline.start(config)
except Exception:
print("no success.")
config.enable_stream(rs.stream.color, 1280, 720,
rs.format.bgr8, 30)
print("trying to stream 1280x720...", end=" ")
self.pipeline.start(config)
except Exception:
# setup for USB 2
print("no success.")
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
print("streaming in 640x480...", end=" ")
self.pipeline.start(config)
# set sensitivity parameters:
self.device = pipeline_profile.get_device().first_color_sensor()
self.device.set_option(rs.option.exposure, self.exposure)
self.device.set_option(rs.option.gain, self.gain)
print("success!")
print("Realsense initialization complete.")
def make_realsense():
# Configure depth and color streams
pipeline = rs.pipeline()
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
found_rgb = False
for s in device.sensors:
if s.get_info(rs.camera_info.name) == 'RGB Camera':
found_rgb = True
break
if not found_rgb:
print("The demo requires Depth camera with Color sensor")
exit(0)
config.enable_stream(rs.stream.depth, 640, 360, rs.format.z16, 60)
if device_product_line == 'L500':
config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
else:
config.enable_stream(rs.stream.color, 640, 360, rs.format.bgr8, 60)
# Start streaming
pipeline.start(config)
return pipeline
def create_intelrs_pipeline():
""" Creates the pipeline for the Intel RS camera stream
Args:
run_inferance_on_image: function to perform object detection with an image
cap: stream from pc/laptop webcam camera
show_video: conditional to show the video from the live feed
Returns:
pipeline: simplifies the user interaction with the device
"""
pipeline = rs.pipeline()
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
if device_product_line == 'L500':
config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
else:
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
# Start streaming
pipeline.start(config)
return pipeline
def cameraConfig(threshold_distance):
# Configure depth and color streams
pipeline = rs.pipeline()
colorizer = rs.colorizer()
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
depth_sensor = pipeline_profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
if device_product_line == 'L500':
config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
else:
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
# Start streaming
clipping_distance_in_meters = threshold_distance
clipping_distance = clipping_distance_in_meters / depth_scale
align_to = rs.stream.color
return pipeline, align_to, config, clipping_distance
def initRealSense(self, resolution=(640, 480), fps=60):
# Configure depth and color streams
self.pipeline = rs.pipeline()
self.config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = self.config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
self.device_product_line = str(
device.get_info(rs.camera_info.product_line))
# Enable stream for RealSense
res_x = resolution[0]
res_y = resolution[1]
self.config.enable_stream(rs.stream.depth, res_x, res_y, rs.format.z16,
fps)
self.config.enable_stream(rs.stream.color, res_x, res_y,
rs.format.bgr8, fps)
self.pipeline.start(self.config)
# Depth jetmap init
self.colorizer = rs.colorizer()
# Create an align object
align_to = rs.stream.color
self.align = rs.align(align_to)
def init_reasense(self):
# Configure depth and color streams
self.pipeline = rs.pipeline()
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
config.enable_stream(rs.stream.depth, 1280, 720, rs.format.z16,
self.fps)
if device_product_line == 'L500':
config.enable_stream(rs.stream.color, 1920, 1080, rs.format.bgr8,
self.fps)
else:
config.enable_stream(rs.stream.color, 1920, 1080, rs.format.bgr8,
self.fps)
self.pc = rs.pointcloud()
# Start streaming
self.profile = self.pipeline.start(config)
depth_sensor = self.profile.get_device().first_depth_sensor()
self.depth_scale = depth_sensor.get_depth_scale()
align_to = rs.stream.color
self.align = rs.align(align_to)
def __init__(self):
self.isStreaming = False
# Camera configuration
# Configure depth and color streams
self.pipeline = rs.pipeline()
self.config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = self.config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
self.config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
if device_product_line == 'L500':
self.config.enable_stream(rs.stream.color, 960, 540,
rs.format.bgr8, 30)
else:
self.config.enable_stream(rs.stream.color, 640, 480,
rs.format.bgr8, 30)
# 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
self.align = rs.align(align_to)
def __init__(self):
self.pipeline = rs.pipeline()
self.config = rs.config()
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = self.config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
self.config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
if device_product_line == 'L500':
self.config.enable_stream(rs.stream.color, 960, 540,
rs.format.bgr8, 30)
else:
self.config.enable_stream(rs.stream.color, 640, 480,
rs.format.bgr8, 30)
profile = self.pipeline.start(self.config)
depth_sensor = profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
print("Depth Scale is: ", depth_scale)
clipping_distance_in_meters = 1
clipping_distance = clipping_distance_in_meters / depth_scale
align_to = rs.stream.color
self.align = rs.align(align_to)
def __init__(self):
bag = r'/home/vdr/Desktop/RealSense/test2.bag'
self.pipeline = rs.pipeline()
config = rs.config()
#From a bag file
config.enable_device_from_file(bag, False)
config.enable_all_streams()
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
playback = device.as_playback()
playback.set_real_time(False)
''' #For real time capturing
device_product_line = str(device.get_info(rs.camera_info.product_line))
self.depth_sensor = pipeline_profile.get_device().first_depth_sensor()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
if device_product_line == 'L500':
config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
else:
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
'''
self.pipeline.start(config)
def __init__(self, width=640, height=480, fps=30):
super().__init__()
self.width = width
self.height = height
self.fps = fps
# Configure depth and color streams
self.pipeline = realsense.pipeline()
self.config = realsense.config()
# Get device product line for setting a supporting resolution
self.pipeline_wrapper = realsense.pipeline_wrapper(self.pipeline)
self.pipeline_profile = self.config.resolve(self.pipeline_wrapper)
def main():
rospy.init_node("ImageCapturerIntel")
# FPS.
RATE = int(rospy.get_param('~RATE', 15))
# Default webcam.
CAMERA = int(rospy.get_param('~CAMERAID', 0))
rospy.loginfo("INTEL" + str(CAMERA))
image_publisher = rospy.Publisher("camaras/" + str(CAMERA) + "/",
CompressedImage,
queue_size=RATE)
rospy.loginfo("*Node " + "ImageCapturerIntel-" + str(CAMERA) + " started*")
pipeline = rs.pipeline()
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
if device_product_line == 'L500':
config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
else:
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
# Start streaming
pipeline.start(config)
while (True):
# Wait for a coherent pair of frames: depth and color
frames = pipeline.wait_for_frames()
color_frame = frames.get_color_frame()
if not color_frame:
continue
# Convert images to numpy arrays
color_image = np.asanyarray(color_frame.get_data())
if VERBOSE:
cv2.imshow('imgIntel', color_image) # display the captured image
if cv2.waitKey(1) & 0xFF == ord('q'):
break
msg = CompressedImage()
msg.header.stamp = rospy.Time.now()
msg.format = "jpeg"
msg.data = np.array(cv2.imencode('.jpg', color_image)[1]).tostring()
image_publisher.publish(msg)
pipeline.stop()
def __init__(self, model_name):
# Configure depth and color streams
self.pipeline = rs.pipeline()
self.config = rs.config()
self.model_name = model_name
# Get device product line for setting a supporting resolution
self.pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
self.pipeline_profile = self.config.resolve(self.pipeline_wrapper)
self.device = self.pipeline_profile.get_device()
self.device_product_line = str(
self.device.get_info(rs.camera_info.product_line))
self.final_color_frames = []
self.final_depth_frames = []
print(f'[INFO] Device: {self.device}')
def __init__(self, IMG_SIZE):
self.pipeline = rs.pipeline()
self.config = rs.config()
# Get device product line for setting a supporting resolution
self.pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
self.pipeline_profile = self.config.resolve(self.pipeline_wrapper)
self.device = self.pipeline_profile.get_device()
self.device_product_line = str(self.device.get_info(rs.camera_info.product_line))
self.config.enable_stream(rs.stream.depth, IMG_SIZE[0], IMG_SIZE[1], rs.format.z16, 30)
if self.device_product_line == 'L500':
self.config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
else:
self.config.enable_stream(rs.stream.color, IMG_SIZE[0], IMG_SIZE[1], rs.format.bgr8, 30)
def __init__(self):
# Configure depth and color streams
self.pipeline = rs.pipeline()
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
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.pipeline.start(config)
def __init__(self, model_name):
self.model = DepthNet()
self.preview_size = (720, 720)
self.project_model = "test2"
self.model.load_state_dict(torch.load(f'{self.project_model}.net'))
self.model.eval()
# Configure depth and color streams
self.pipeline = rs.pipeline()
self.config = rs.config()
self.model_name = model_name
# Get device product line for setting a supporting resolution
self.pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
self.pipeline_profile = self.config.resolve(self.pipeline_wrapper)
self.device = self.pipeline_profile.get_device()
self.device_product_line = str(
self.device.get_info(rs.camera_info.product_line))
print(f'[INFO] Device: {self.device}')
self.config.enable_stream(rs.stream.depth, 1280, 720, rs.format.z16,
30)
if self.device_product_line == 'L500':
self.config.enable_stream(rs.stream.color, 960, 540,
rs.format.bgr8, 30)
else:
self.config.enable_stream(rs.stream.color, 1280, 720,
rs.format.bgr8, 30)
# Start streaming
self.pipeline.start(self.config)
print("[INFO] Stream Started...")
# Aligning depth to color image
self.align = rs.align(rs.stream.color)
cv2.namedWindow(f'Project: {self.model_name} Preview')
while True:
self.deliver_preview_frame(self.preview_size)
key = cv2.waitKey(1)
if key & 0xFF == ord('q') or key == 27:
cv2.destroyAllWindows()
print("[INFO] Stream Stopped")
return
def _init_device(self):
"""
Open the pipeline for adquiring frames
Returns:
"""
# Threading
self._lock = threading.Lock()
self._thread = None
self._thread_status = 'stopped' # status: 'stopped', 'running'
self.pipeline = rs.pipeline()
self.config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = self.config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
self.config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
self.config.enable_stream(rs.stream.infrared, 640, 480, rs.format.y8, 30)
if device_product_line == 'L500':
self.config.enable_stream(rs.stream.color, 960, 540, rs.format.rgb8, 30)
else:
self.config.enable_stream(rs.stream.color, 640, 480, rs.format.rgb8, 30)
# Start streaming
self.profile = self.pipeline.start(self.config)
self.depth_scale = self.profile.get_device().first_depth_sensor().get_depth_scale()
self._color = np.zeros((self.color_height, self.color_width, 3))
self._depth = np.zeros((self.depth_height, self.depth_width))
self._ir = np.zeros((self.depth_height, self.depth_width))
self._run()
logger.info("Searching first frame")
while True:
if not np.all(self._depth == 0):
logger.info("First frame found")
break
def __init__(self):
self.pipeline = rs.pipeline()
config = rs.config()
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
if device_product_line == 'L500':
config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
else:
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
depth_profile = rs.video_stream_profile(
pipeline_profile.get_stream(rs.stream.depth))
self.depth_intrinsics = depth_profile.get_intrinsics()
self.profile = self.pipeline.start(config)
pass
def __init__(self):
# Configure depth and color streams
self.pipeline = rs.pipeline()
self.config = rs.config()
# Get device product line for setting a supporting resolution
self.pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
self.pipeline_profile = self.config.resolve(self.pipeline_wrapper)
self.device = self.pipeline_profile.get_device()
self.depth_scale = self.device.first_depth_sensor().get_depth_scale()
# this is used to align the depth and color images since they are captured
# from slightly different viewports
self.aligner = rs.align(rs.stream.color)
self.device_product_line = str(
self.device.get_info(rs.camera_info.product_line))
self.config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
self.config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8,
30)
# Start streaming
self.pipeline.start(self.config)
def __init__(self):
# Configure depth and color streams
self.pipeline = rs.pipeline()
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
# realsense align 맞추기 추가
# 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
self.align = rs.align(align_to)
# Start streaming
self.pipeline.start(config)
def __init__(self):
self.pipeline = rs.pipeline()
self.config = rs.config()
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = self.config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
#For real time capturing
device_product_line = str(device.get_info(rs.camera_info.product_line))
self.depth_sensor = pipeline_profile.get_device().first_depth_sensor()
self.config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
if device_product_line == 'L500':
self.config.enable_stream(rs.stream.color, 960, 540,
rs.format.bgr8, 30)
else:
self.config.enable_stream(rs.stream.color, 640, 480,
rs.format.bgr8, 30)
self.config.enable_record_to_file('test2.bag')
self.pipeline.start(self.config)
return image
if __name__ == '__main__':
"""
test everything
"""
args = sys.argv
pipeline = rs.pipeline()
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
found_rgb = False
for s in device.sensors:
if s.get_info(rs.camera_info.name) == 'RGB Camera':
found_rgb = True
break
if not found_rgb:
print("The demo requires Depth camera with Color sensor")
exit(0)
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
def main():
# start node
rospy.init_node("kinect_find_xyz")
rospy.loginfo("kinect_find_xyz node initialized")
# create Point32 publisher
pointPub = rospy.Publisher(pointPubTopic, PointStamped, queue_size=1)
# create moving average filters
WINDOW_SIZE = 4
moving_avg_x = MovingWindowAverage(WINDOW_SIZE)
moving_avg_y = MovingWindowAverage(WINDOW_SIZE)
moving_avg_z = MovingWindowAverage(WINDOW_SIZE)
# Create a pipeline
pipeline = rs.pipeline()
# Create a config and configure the pipeline to stream
# different resolutions of color and depth streams
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
# enable depth and color streams
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
profile = pipeline.start(config)
# Getting the depth sensor's depth scale (see rs-align example for explanation)
depth_sensor = profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_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)
while (not rospy.is_shutdown()):
# grab frames from intel RealSense
frames = pipeline.wait_for_frames()
# Align the depth frame to color frame
aligned_frames = align.process(frames)
# Get aligned frames
aligned_depth_frame = aligned_frames.get_depth_frame(
) # aligned_depth_frame is a 640x480 depth image
color_frame = aligned_frames.get_color_frame()
# get depth frame intrinsics
depth_intrin = aligned_depth_frame.profile.as_video_stream_profile(
).intrinsics
# Validate that both frames are valid
if not aligned_depth_frame or not color_frame:
continue
# convert to numpy arrays
numpyRGB = np.asanyarray(color_frame.get_data())
numpyDepth = np.asanyarray(aligned_depth_frame.get_data())
# find min enclosing circle
status, circle = findMinEnclosingCircle(numpyRGB)
if (status):
# draw circles if they are found
(x, y), radius = circle
cv2.circle(numpyRGB, (int(x), int(y)), int(radius), (0, 255, 0), 2)
# find depth of detected circle
reading = circle_to_realworld(aligned_depth_frame, circle,
depth_intrin)
average_x = moving_avg_x.addAndProc(reading[0])
average_y = moving_avg_y.addAndProc(reading[1])
average_z = moving_avg_z.addAndProc(reading[2])
# invert sign of y-point to align with standard cartesian
# views
average_y = -1 * average_y
# create PointStamped message and publish
pointMessage = PointStamped()
pointMessage.header.stamp = rospy.Time.now()
pointMessage.point.x = average_x
pointMessage.point.y = average_y
pointMessage.point.z = average_z
pointPub.publish(pointMessage)
# publish color image
imgMsg = imgBridge.cv2_to_imgmsg(numpyRGB, "bgr8")
imgPub.publish(imgMsg)
# display images
depth_colormap = cv2.applyColorMap(
cv2.convertScaleAbs(numpyDepth, alpha=0.03), cv2.COLORMAP_JET)
cv2.imshow('Depth', depth_colormap)
cv2.imshow('Video', numpyRGB)
if (cv2.waitKey(1) & 0xFF == ord('q')):
break
def main(argv):
size, file_cnt = read_parameters(argv)
pipeline = rs.pipeline()
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
auto_calibrated_device = rs.auto_calibrated_device(device)
if not auto_calibrated_device:
print("The connected device does not support auto calibration")
return
config.enable_stream(rs.stream.depth, 256, 144, rs.format.z16, 90)
conf = pipeline.start(config)
calib_dev = rs.auto_calibrated_device(conf.get_device())
def on_chip_calib_cb(progress):
print(". ")
while True:
try:
input = raw_input(
"Please select what the operation you want to do\nc - on chip calibration\nt - tare calibration\ng - get the active calibration\nw - write new calibration\ne - exit\n"
)
if input == 'c':
print("Starting on chip calibration")
new_calib, health = calib_dev.run_on_chip_calibration(
file_cnt, on_chip_calib_cb, 5000)
print("Calibration completed")
print("health factor = ", health)
if input == 't':
print("Starting tare calibration")
ground_truth = float(
raw_input("Please enter ground truth in mm\n"))
new_calib, health = calib_dev.run_tare_calibration(
ground_truth, file_cnt, on_chip_calib_cb, 5000)
print("Calibration completed")
print("health factor = ", health)
if input == 'g':
calib = calib_dev.get_calibration_table()
print("Calibration", calib)
if input == 'w':
print("Writing the new calibration")
calib_dev.set_calibration_table(new_calib)
calib_dev.write_calibration()
if input == 'e':
pipeline.stop()
return
print("Done\n")
except Exception as e:
print(e)
except:
print("A different Error")
def main(args):
if args.t: # read tracking datas
Pubs = JointPub()
tracking_datas = []
with open("/home/pku-hr6/yyf_ws/data/arm_running.txt", 'r') as rf:
lines = rf.readlines()
for line in lines:
datas = line.split(" ")
datas = [float(data) for data in datas]
print(datas)
tracking_datas.append(datas)
else:
rospy.init_node('data_saver')
data_nums = args.n
file_names = args.file
datas = []
save_files = os.path.join("./data", file_names + '.txt')
# Configure depth and color streams
pipeline = rs.pipeline()
config = rs.config()
pc = rs.pointcloud()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
# device_product_line is SR300
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
pipeline.start(config)
# get scale of depth sensor
depth_sensor = pipeline_profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
# clipping_distance_in_meters meters away
clipping_distance_in_meters = 0.5 # 0.5 meter
clipping_distance = clipping_distance_in_meters / depth_scale
# creat align map
align_to = rs.stream.color
align = rs.align(align_to)
l_h = 170
l_s = 42
l_v = 41
u_h = 185
u_s = 255
u_v = 255
l_b = np.array([l_h, l_s, l_v])
u_b = np.array([u_h, u_s, u_v])
count = 0
with open(save_files, "w") as wf:
# while count < data_nums:
while len(datas) < data_nums:
# while True:
# Wait for a coherent pair of frames: depth and color
if args.t:
if count >= len(tracking_datas) - 1:
break
frames = pipeline.wait_for_frames()
aligned_frame = align.process(frames)
depth_frame = aligned_frame.get_depth_frame()
color_frame = aligned_frame.get_color_frame()
if not depth_frame or not color_frame:
continue
depth_image = np.asanyarray(depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
# remove background
grey_color = 153
depth_image_3d = np.dstack(
(depth_image, depth_image,
depth_image)) # depth img is 1 channel, color is 3 channels
bg_rmvd = np.where(
(depth_image_3d > clipping_distance) | (depth_image_3d <= 0),
grey_color, color_image)
# get final img
depth_colormap = cv2.applyColorMap(
cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
hsv_map = cv2.cvtColor(bg_rmvd, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_map, l_b, u_b)
res = cv2.bitwise_and(bg_rmvd, bg_rmvd, mask=mask)
img = np.hstack((bg_rmvd, depth_colormap))
# get object from mask map and calculate position
mask_index = np.nonzero(mask)
valid_p = False
depth_point = np.array([])
if not mask_index[0].shape[0] == 0:
valid_p = True
x_index = int(np.median(mask_index[1]))
y_index = int(np.median(mask_index[0]))
x_min = x_index - 20
x_max = x_index + 20
y_min = y_index - 20
y_max = y_index + 20
# Intrinsics & Extrinsics
depth_intrin = depth_frame.profile.as_video_stream_profile(
).intrinsics
# print(depth_intrin)
# 640x480 p[314.696 243.657] f[615.932 615.932]
depth_pixel = [x_index, y_index]
dist2obj = depth_frame.get_distance(x_index, y_index)
depth_point = rs.rs2_deproject_pixel_to_point(
depth_intrin, depth_pixel, dist2obj)
depth_point = [float(dep) * 100
for dep in depth_point] # using cm
depth_point = [
depth_point[2], -depth_point[0], -depth_point[1]
] #[x,y,z](in base) = [z,-x,-y](in camera)
depth_point = [
depth_point[0] + 1.8, depth_point[1], depth_point[2] + 5.3
]
txt = "({:.2f},{:.2f},{:.2f})".format(depth_point[0],
depth_point[1],
depth_point[2])
# print(txt)
cv2.rectangle(res, (x_min, y_min), (x_max, y_max), (255, 0, 0),
2)
cv2.putText(res, txt, (x_index, y_index),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 1)
cv2.imshow('Align Example', img)
cv2.imshow("mask", mask)
cv2.imshow("res", res)
# print(depth_point)
# msg = rospy.wait_for_message('/motor_states/pan_tilt_port',MotorStateList,timeout=10)
# # print(len(msg.motor_states))
# joints,valid_q = get_joint_angle(msg)
# if valid_p and valid_q:
# depth_point = np.array(depth_point)
# # print(joints)
# # print(depth_point)
# if args.only_q:
# data = joints
# else:
# data = np.append(depth_point, joints)
# print(data)
# datas.append(data)
# if args.t: # pubulish joint
# data = tracking_datas[count]
# Pubs.publish_jointsD(data)
# count = count + 1
if cv2.waitKey(100) & 0xFF == ord('q'): # quit
break
for data in datas:
for i in range(len(data)):
wf.write(str(round(data[i], 2)))
if i != len(data) - 1:
wf.write(" ")
else:
wf.write("\n")
def print_hi(name):
# Configure depth and color streams
pipeline = rs.pipeline()
colorizer = rs.colorizer()
threshold_distance = 0.4
tr1 = rs.threshold_filter(min_dist=0.15, max_dist=threshold_distance)
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
depth_sensor = pipeline_profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
if device_product_line == 'L500':
config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
else:
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
# Start streaming
clipping_distance_in_meters = 0.9
clipping_distance = clipping_distance_in_meters / depth_scale
align_to = rs.stream.color
align = rs.align(align_to)
pipeline.start(config)
#thresholds for detect skins
lower = np.array([0, 48, 80], dtype="uint8")
upper = np.array([20, 255, 255], dtype="uint8")
try:
while True:
frames = pipeline.wait_for_frames()
# Align the depth frame to color frame
aligned_frames = align.process(frames)
# Get aligned frames
aligned_depth_frame = aligned_frames.get_depth_frame(
) # aligned_depth_frame is a 640x480 depth image
color_frame = aligned_frames.get_color_frame()
# Validate that both frames are valid
if not aligned_depth_frame or not color_frame:
continue
depth_image = np.asanyarray(aligned_depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
# Remove background - Set pixels further than clipping_distance to grey
grey_color = 153
depth_image_3d = np.dstack(
(depth_image, depth_image,
depth_image)) # depth image is 1 channel, color is 3 channels
bg_removed = np.where(
(depth_image_3d > clipping_distance) | (depth_image_3d <= 0),
grey_color, color_image)
# Render images
depth_colormap = cv2.applyColorMap(
cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
images = np.hstack((bg_removed, depth_colormap))
# cv2.namedWindow('Background Removed', cv2.WINDOW_AUTOSIZE)
cv2.imshow('Color+depth', images)
# images[270:, :] = [0, 0, 0]
# images[0:240,:] = [0,0,0]
# images[:, 0:50] = [0, 0, 0]
# images[:, 630:] = [0, 0, 0]
img_hsv = cv2.cvtColor(bg_removed, cv2.COLOR_BGR2HSV)
## Gen lower mask (0-5) and upper mask (175-180) of RED
lower_red = np.array([94, 80, 2], dtype="uint8")
upper_red = np.array([126, 255, 255], dtype="uint8")
mask = cv2.inRange(img_hsv, lower_red, upper_red)
bluepen = cv2.bitwise_and(bg_removed, bg_removed, mask=mask)
cv2.imshow('BluePen', bluepen)
bgModel = cv2.createBackgroundSubtractorMOG2(0, 50)
fgmask = bgModel.apply(bluepen)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
img = cv2.bitwise_and(bluepen, bluepen, mask=fgmask)
# Skin detect and thresholding
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower = np.array([94, 80, 2], dtype="uint8")
upper = np.array([126, 255, 255], dtype="uint8")
skinMask = cv2.inRange(hsv, lower, upper)
kernel = np.ones((3, 3), np.uint8)
skinMask = cv2.erode(skinMask, kernel, iterations=2)
skinMask = cv2.dilate(skinMask, kernel, iterations=1)
cv2.imshow('Threshold Hands', skinMask)
contours, hierarchy = cv2.findContours(skinMask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
length = len(contours)
maxArea = -1
drawing = np.zeros(img.shape, np.uint8)
if length > 0:
for i in xrange(length):
temp = contours[i]
area = cv2.contourArea(temp)
if area > maxArea:
maxArea = area
ci = i
res = contours[ci]
hull = cv2.convexHull(res)
# drawing = np.zeros(img.shape, np.uint8)
cx, cy = centroid(res)
print(aligned_depth_frame.get_distance(cx, cy))
cv2.drawContours(drawing, [res], 0, (0, 255, 0), 2)
else:
drawing = np.zeros(img.shape, np.uint8)
cv2.imshow('DRAWING', drawing)
# bluepen_gray = cv2.cvtColor(bluepen, cv2.COLOR_BGR2HSV)
# contours, hierarchy = cv2.findContours(bluepen_gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# length = len(contours)
# drawing = np.zeros(bluepen_gray.shape, np.uint8)
# maxArea = -1
# if length > 0:
# for i in xrange(length):
# temp = contours[i]
# area = cv2.contourArea(temp)
# if area > maxArea:
# maxArea = area
# ci = i
# res1 = contours[ci]
#
# hull = cv2.convexHull(res1)
#
# cv2.drawContours(drawing, [res1], 0, (0, 255, 0), 2)
# cv2.drawContours(drawing, [hull], 0, (0, 0, 255), 3)
# cv2.imshow('',drawing)
# length = len(contours)
# print(length)
# drawing = np.zeros(res.shape, np.uint8)
# maxArea = -1
# if length > 0:
# for i in xrange(length):
# temp = contours[i]
# area = cv2.contourArea(temp)
# if area > maxArea:
# maxArea = area
# ci = i
# res = contours[ci]
#
# hull = cv2.convexHull(res)
#
#
# cv2.drawContours(drawing, [res], 0, (0, 255, 0), 2)
# cv2.drawContours(drawing, [hull], 0, (0, 0, 255), 3)
# cv2.imshow('Only contour for calibration', drawing)
# cv2.imshow('BluePen', res)
# converted = cv2.cvtColor(images, cv2.COLOR_BGR2HSV)
# skinMask = cv2.inRange(converted, lower, upper)
# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
# skinMask = cv2.erode(skinMask, kernel, iterations=2)
# skinMask = cv2.dilate(skinMask, kernel, iterations=1)
#
# skinMask = cv2.GaussianBlur(skinMask, (5, 5), 0)
# skin = cv2.bitwise_and(images, images, mask=skinMask)
cv2.waitKey(1)
# img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# ## Gen lower mask (0-5) and upper mask (175-180) of RED
# mask1 = cv2.inRange(img_hsv, (0, 50, 20), (5, 255, 255))
# mask2 = cv2.inRange(img_hsv, (175, 50, 20), (180, 255, 255))
#
# ## Merge the mask and crop the red regions
# mask = cv2.bitwise_or(mask1, mask2)
# croped = cv2.bitwise_and(img, img, mask=mask)
finally:
# Stop streaming
pipeline.stop()
# threshold_filter = rs.threshold_filter()
# threshold_filter.set_option(rs.option.max_distance, 1)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +
'haarcascade_frontalface_default.xml')
def marker_pose(self):
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = self.config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
self.config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8,
30)
self.pipeline.start(self.config)
# self.cap.set(3, 1280)
# self.cap.set(4, 720)
# set dictionary size depending on the aruco marker selected
self.param.adaptiveThreshConstant = 7
# setup matrix from imu to cam
self.imu_cam[0][1] = -1.0
self.imu_cam[0][3] = 0.06
self.imu_cam[1][0] = -1.0
self.imu_cam[1][3] = 0.04
self.imu_cam[2][2] = -1.0
self.imu_cam[2][3] = -0.08
self.imu_cam[3][3] = 1.0
# create vector tvec1, tvec2
tvec1 = np.zeros((4, 1), dtype=np.float)
tvec1[3][0] = 1.0
tvec2 = np.zeros((4, 1), dtype=np.float)
# tvec2[3][0] = 1.0
# define the ids of marker
# a = 0 # the index of first marker need to detect
self.ids_target[0] = 11
self.ids_target[1] = 15
# markerLength=0.4
while not rospy.is_shutdown():
frames = self.pipeline.wait_for_frames()
# ret, frame = self.cap.read()
color_frame = frames.get_color_frame()
if not color_frame:
continue
frame = np.asanyarray(color_frame.get_data())
# operations on the frame
gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
# lists of ids and the corners belonging to each id
corners, ids, rejectedImgPoints = aruco.detectMarkers(
gray, self.dict, parameters=self.param)
if np.all(ids is not None):
for i in range(0, ids.size):
if ids[i][0] == self.ids_target[0]:
self.corners = corners[i]
markerLength = 0.4
ret1 = aruco.estimatePoseSingleMarkers(
corners=self.corners,
markerLength=markerLength,
cameraMatrix=self.mtx,
distCoeffs=self.dist)
rvec, tvec = ret1[0][0, 0, :], ret1[1][0, 0, :]
(rvec - tvec).any(
) # get rid of that nasty numpy value array error
tvec1[0][0] = tvec[0]
tvec1[1][0] = tvec[1]
tvec1[2][0] = tvec[2]
## marker in the body (UAV frane)
fly_pos = PS()
tvec2 = np.matmul(self.imu_cam, tvec1)
fly_pos.pose.position.x = -tvec2[0][0]
fly_pos.pose.position.y = -tvec2[1][0]
fly_pos.pose.position.z = -tvec2[2][0]
# publish marker in body frame
self.fly_pos_pub.publish(fly_pos)
# if (self.local_pos[3] > -0.1 and self.local_pos[3] < 0.1):
marker_pos = PS()
tvec2 = np.matmul(self.imu_cam, tvec1)
marker_pos.pose.position.x = tvec2[0][0]
marker_pos.pose.position.y = tvec2[1][0]
marker_pos.pose.position.z = tvec2[2][0]
self.aruco_marker_pos_pub.publish(marker_pos)
# -- Draw the detected marker and put a reference frame over it
# aruco.drawDetectedMarkers(frame, corners, ids)
aruco.drawAxis(frame, self.mtx, self.dist, rvec, tvec,
0.1)
str_position0 = "Marker Position in Camera frame: x=%f y=%f z=%f" % (
tvec2[0][0], tvec2[1][0], tvec2[2][0])
cv2.putText(frame, str_position0, (0, 50), self.font,
0.7, (0, 255, 0), 1, cv2.LINE_AA)
self.rate.sleep()
cv2.imshow("frame", frame)
cv2.waitKey(1)
type=int,
default=720,
help='Image height expected by OpenCVB')
parser.add_argument('--pipeName',
default='',
help='The name of the input pipe for image data.')
args = parser.parse_args()
pipeName = '//./pipe/' + args.pipeName
pipeOut = open(pipeName, 'wb')
pipeIn = open(pipeName + 'in', 'rb')
rsPipeline = rs.pipeline()
rsConfig = rs.config()
rsPipeline_wrapper = rs.pipeline_wrapper(rsPipeline)
rsPipeline_profile = rsConfig.resolve(rsPipeline_wrapper)
device = rsPipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
rsConfig.enable_stream(rs.stream.depth, args.Width, args.Height, rs.format.z16,
30)
rsConfig.enable_stream(rs.stream.color, args.Width, args.Height,
rs.format.bgr8, 30)
rsConfig.enable_stream(rs.stream.infrared, 1, args.Width, args.Height,
rs.format.y8, 30)
rsConfig.enable_stream(rs.stream.infrared, 2, args.Width, args.Height,
rs.format.y8, 30)
#rsConfig.enable_stream(rs.stream.gyro)
#rsConfig.enable_stream(rs.stream.accel)
import pyrealsense2 as rs
import numpy as np
rs.pipeline()
rs.pipeline.try_wait_for_frames()
rs.pipeline.wait_for_frames()
rs.pipeline_profile()
rs.pipeline_profile.get_device()
rs.pipeline_profile.get_stream()
rs.pipeline_profile.get_streams()
rs.pipeline_wrapper()
rs.playback()
rs.playback_status()
rs.points()
rs.pose()
rs.pose_frame()
rs.pose_stream_profile()
rs.pipeline
rs.pipeline.get_active_profile()
rs.pipeline.poll_for_frames()
rs.pipeline.start()
rs.pipeline.stop()
rs.pipeline.wait_for_frames()
rs.pipeline.try_wait_for_frames()
pipe = rs.pipeline()
config = rs.config()
config.enable_stream
config.enable_all_streams
config.enable_device
def main():
xcent = 0
ycent = 0
xstart = 0
xend = 0
ystart = 0
yend = 0
StanSmith = 0
# Configure depth and color streams
pipeline = rs.pipeline()
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
config.enable_stream(rs.stream.color, args.cam_width, args.cam_height,
rs.format.bgr8, 30)
# Start streaming
pipeline.start(config)
with tf.Session() as sess:
model_cfg, model_outputs = posenet.load_model(args.model, sess)
output_stride = model_cfg['output_stride']
# ROS exstra
pub = rospy.Publisher('body_pose', Transform, queue_size=10)
rospy.init_node('talker', anonymous=True)
rate = rospy.Rate(100) # 10hz
count_stand = 0
count_sit = 0
count_unknown = 0
pose = "unknown"
color1 = (255, 255, 255)
queSize = 1
q1 = []
for i in range(queSize):
q1.append(0)
start = time.time()
frame_count = 0
while not rospy.is_shutdown():
frames = pipeline.wait_for_frames()
color_frame = frames.get_color_frame()
# Convert images to numpy arrays
color_image = np.asanyarray(color_frame.get_data())
input_image, display_image, output_scale = posenet.read_cap(
color_image,
scale_factor=args.scale_factor,
output_stride=output_stride)
heatmaps_result, offsets_result, displacement_fwd_result, displacement_bwd_result = sess.run(
model_outputs, feed_dict={'image:0': input_image})
pose_scores, keypoint_scores, keypoint_coords = posenet.decode_multi.decode_multiple_poses(
heatmaps_result.squeeze(axis=0),
offsets_result.squeeze(axis=0),
displacement_fwd_result.squeeze(axis=0),
displacement_bwd_result.squeeze(axis=0),
output_stride=output_stride,
max_pose_detections=10,
min_pose_score=amin_pose_score)
keypoint_coords *= output_scale
if not args.notxt:
print()
print("Results for image:")
for pi in range(len(pose_scores)):
if pose_scores[pi] == 0.:
break
print('Pose #%d, score = %f' % (pi, pose_scores[pi]))
poses = [keypoint_scores[pi, :], keypoint_coords[pi, :, :]]
left_index = [5, 11, 13]
right_index = [6, 12, 14]
left_side = []
right_side = []
for i in left_index:
left_side.append([
poses[0][i],
round(poses[1][i][0]),
round(poses[1][i][1])
])
for i in right_index:
right_side.append([
poses[0][i],
round(poses[1][i][0]),
round(poses[1][i][1])
])
if check_side(right_side):
if stand_sit(right_side) == 1:
q1.pop(0)
q1.append(1)
else:
q1.pop(0)
q1.append(-1)
elif check_side(left_side):
if stand_sit(left_side) == 1:
q1.pop(0)
q1.append(1)
else:
q1.pop(0)
q1.append(-1)
else:
q1.pop(0)
q1.append(0)
print(q1)
mean_pose = statistics.mean(q1)
if mean_pose > 0:
pose = "stand"
color1 = (255, 0, 0)
count_stand = count_stand + 1
StanSmith = 1
elif mean_pose < 0:
pose = "sit"
color1 = (0, 255, 0)
count_sit = count_sit + 1
StanSmith = 2
else:
pose = "unknown"
color1 = (0, 0, 255)
count_unknown = count_unknown + 1
StanSmith = 0
print("meanpose: %f" % mean_pose)
print(pose)
lSs = poses[0][5] # lShoulder score
rSs = poses[0][6] # rShoulder score
lHs = poses[0][11] # lHip score
rHs = poses[0][12] # rHip score
lSc = poses[1][5] # lShoulder coordinates
rSc = poses[1][6] # rShoulder coordinates
lHc = poses[1][11] # lHip coordinates
rHc = poses[1][12] # rHip coordinates
if lSs > amin_part_score: # Check if left shoulder has been spotted
lS = 1
else:
lS = 0
if rSs > amin_part_score: # Check if right shoulder has been spotted
rS = 1
else:
rS = 0
if lHs > amin_part_score: # Check if left hip has been spotted
lH = 1
else:
lH = 0
if rHs > amin_part_score: # Check if right hip has been spotted
rH = 1
else:
rH = 0
if lS != 0 or rS != 0: # At least one shoulder visible
if lS == 1 and rS == 1: # Both shoulders visible
if lH == 1 and rH == 1: # Both shoulders, both hips
print("Both shoulders, both hips")
ycent = (lSc[0] + rSc[0] + lHc[0] + rHc[0]) / 4
xcent = (lSc[1] + rSc[1] + lHc[1] + rHc[1]) / 4
# Box creator
buffer = min(abs(lSc[1] - rSc[1]),
abs(lHc[1] - rHc[1])) / 2
buffer *= 0.80
xstart = xcent - buffer
xend = xcent + buffer
ystart = ycent - buffer
yend = ycent + buffer
elif lH == 0 and rH == 0:
print("Only shoulders")
# Do coordinates from both shoulders only
ycent = (lSc[0] + rSc[0]) / 2
xcent = (lSc[1] + rSc[1]) / 2
buffer = abs(lSc[1] - rSc[1]) / 2
buffer *= 0.40
xstart = xcent - buffer
xend = xcent + buffer
ystart = ycent - buffer
yend = ycent + buffer
elif lH == 1:
print("Both shoulders, left hip")
# Do triangle, left hip both shoulders
ycent = (lSc[0] + rSc[0] + lHc[0]) / 3
xcent = (lSc[1] + rSc[1] + lHc[1]) / 3
buffer = abs(lSc[1] - rSc[1]) / 2
buffer *= 0.80
xstart = xcent - buffer
xend = xcent + buffer
ystart = ycent - buffer
yend = ycent + buffer
else:
print("Both shoulders, right hip")
# Do triangle right hip both shoulders
ycent = (lSc[0] + rSc[0] + rHc[0]) / 3
xcent = (lSc[1] + rSc[1] + rHc[1]) / 3
buffer = abs(lSc[1] - rSc[1]) / 2
buffer *= 0.80
xstart = xcent - buffer
xend = xcent + buffer
ystart = ycent - buffer
yend = ycent + buffer
else: # Only one shoulder visible
if lS == 1: # left Shoulder
if lH == 1 and rH == 1:
print("Left shoulder, both hips")
# Do lower triangle, left shoulder
ycent = (lSc[0] + lHc[0] + rHc[0]) / 3
xcent = (lSc[1] + lHc[1] + rHc[1]) / 3
buffer = abs(lHc[1] - rHc[1]) / 2
buffer *= 0.75
xstart = xcent - buffer
xend = xcent + buffer
ystart = ycent - buffer
yend = ycent + buffer
elif rH == 1:
print("Left shoulder, right hip")
# Do center between left shoulder and right hip
ycent = (lSc[0] + rHc[0]) / 2
xcent = (lSc[1] + rHc[1]) / 2
buffer = abs(lSc[1] - rHc[1]) / 2
buffer *= 0.80
xstart = xcent - buffer
xend = xcent + buffer
ystart = ycent - buffer
yend = ycent + buffer
else: # right shoulder
if lH == 1 and rH == 1:
print("Right shoulder, both hips")
# Do lower triangle, right shoulder
ycent = (rSc[0] + lHc[0] + rHc[0]) / 3
xcent = (rSc[1] + lHc[1] + rHc[1]) / 3
buffer = abs(lHc[1] - rHc[1]) / 2
buffer *= 0.75
xstart = xcent - buffer
xend = xcent + buffer
ystart = ycent - buffer
yend = ycent + buffer
elif lH == 1:
print("Right shoulder, left hip")
# Do center between right shoulder and left hip
ycent = (rSc[0] + lHc[0]) / 2
xcent = (rSc[1] + lHc[1]) / 2
buffer = abs(lHc[1] - rSc[1]) / 2
buffer *= 0.80
xstart = xcent - buffer
xend = xcent + buffer
ystart = ycent - buffer
yend = ycent + buffer
else:
print('no torso spotted')
xcent = 0
ycent = 0
cv2.circle(display_image, (round(xcent), round(ycent)), 5,
color1, 2) # Draw a circle
cv2.rectangle(display_image, (round(xend), round(yend)),
(round(xstart), round(ystart)), color1, 2)
# ROS HERE PLS
rosmsg = geometry_msgs.msg.Transform()
rosmsg.translation.x = pi
rosmsg.translation.y = StanSmith
rosmsg.translation.z = 0
rosmsg.rotation.x = round(xstart)
rosmsg.rotation.y = round(ystart)
rosmsg.rotation.z = round(xend)
rosmsg.rotation.w = round(yend)
print(pi)
rospy.loginfo(rosmsg)
pub.publish(rosmsg)
rate.sleep()
# TODO this isn't particularly fast, use GL for drawing and display someday...
overlay_image = posenet.draw_skel_and_kp(
display_image,
pose_scores,
keypoint_scores,
keypoint_coords,
min_pose_score=amin_pose_score,
min_part_score=amin_part_score)
#cv2.putText(overlay_image, "Unknown: %f" % count_unknown, (50,50), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
#cv2.putText(overlay_image, "Sitting: %f" % count_sit, (50,100), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
#cv2.putText(overlay_image, "Standing: %f" % count_stand, (50,150), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
cv2.imshow('posenet', overlay_image)
frame_count += 1
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print("Unknown: %f" % count_unknown)
print("Sitting: %f" % count_sit)
print("Standing: %f" % count_stand)
print('Average FPS: ', frame_count / (time.time() - start))
def marker_pose(self):
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(self.pipeline)
pipeline_profile = self.config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
self.config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8,
15)
self.pipeline.start(self.config)
# self.cap.set(3, 1280)
# self.cap.set(4, 720)
# set dictionary size depending on the aruco marker selected
self.param.adaptiveThreshConstant = 7
# setup matrix from imu to cam
self.imu_cam[0][1] = -1.0
self.imu_cam[0][3] = 0.06
self.imu_cam[1][0] = -1.0
self.imu_cam[1][3] = 0.04
self.imu_cam[2][2] = -1.0
self.imu_cam[2][3] = -0.08
self.imu_cam[3][3] = 1.0
# create vector tvec1, tvec2
tvec1 = np.zeros((4, 1), dtype=np.float)
tvec1[3][0] = 1.0
tvec2 = np.zeros((4, 1), dtype=np.float)
# tvec2[3][0] = 1.0
# define the ids of marker
# a = 0 # the index of first marker need to detect
self.ids_target[0] = 11
self.ids_target[1] = 15
# markerLength=0.4
while not rospy.is_shutdown():
frames = self.pipeline.wait_for_frames()
# ret, frame = self.cap.read()
color_frame = frames.get_color_frame()
if not color_frame:
continue
frame = np.asanyarray(color_frame.get_data())
# operations on the frame
gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
# lists of ids and the corners belonging to each id
corners, ids, rejectedImgPoints = aruco.detectMarkers(
gray, self.dict, parameters=self.param)
if np.all(ids is not None):
ids_marker = True
self.check_marker_detection.publish(ids_marker)
for i in range(0, ids.size):
if self.altitude > 3.0:
if ids[i][0] == self.ids_target[0]:
self.corners = corners[i]
markerLength = 0.4
ret1 = aruco.estimatePoseSingleMarkers(
corners=self.corners,
markerLength=markerLength,
cameraMatrix=self.mtx,
distCoeffs=self.dist)
rvec, tvec = ret1[0][0, 0, :], ret1[1][0, 0, :]
# -- Draw the detected marker and put a reference frame over it
aruco.drawDetectedMarkers(frame, corners, ids)
aruco.drawAxis(frame, self.mtx, self.dist, rvec,
tvec, 0.1)
str_position0 = "Marker Position in Camera frame: x=%f y=%f z=%f" % (
tvec[0], tvec[1], tvec[2])
cv2.putText(frame, str_position0, (0, 50),
self.font, 0.7, (0, 255, 0), 1,
cv2.LINE_AA)
(rvec - tvec).any(
) # get rid of that nasty numpy value array error
tvec1[0][0] = tvec[0]
tvec1[1][0] = tvec[1]
tvec1[2][0] = tvec[2]
self.altitude = tvec[2]
alpha = 5.0
self.beta[0] = abs(
np.rad2deg(np.arctan(tvec[0] / tvec[2])))
self.beta[1] = abs(
np.rad2deg(np.arctan(tvec[1] / tvec[2])))
# decide move or decend
check_move = self.check_angle(alpha)
self.check_move_position.publish(check_move)
# marker in the body (UAV frane)
marker_pos = PS()
tvec2 = np.matmul(self.imu_cam, tvec1)
marker_pos.pose.position.x = tvec2[0][0]
marker_pos.pose.position.y = tvec2[1][0]
marker_pos.pose.position.z = tvec2[2][0]
# publish marker in body frame
self.aruco_marker_pos_pub.publish(marker_pos)
# if (self.local_pos[3] > -0.1 and self.local_pos[3] < 0.1):
## marker in the global frame
fly_pos = PS()
fly_pos.pose.position.x = tvec2[0][
0] + self.local_pos[0]
fly_pos.pose.position.y = tvec2[1][
0] + self.local_pos[1]
fly_pos.pose.position.z = tvec2[2][
0] + self.local_pos[2]
# publish marker in body frame
self.fly_pos_pub.publish(fly_pos)
self.rate.sleep()
# publish target position in world frame
# target_pos = PS()
# target_pos.pose.position.x = self.local_pos[0] + tvec2[0][0]
# target_pos.pose.position.y = self.local_pos[1] + tvec2[1][0]
# self.target_position.publish(target_pos)
# if (self.altitude < 4):
# a = 1
# markerLength = 0.08
# break
# check_err = np.linalg.norm([tvec2[0][0], tvec2[1][0]])
# self.check_error_pos.publish(check_err)
# self.rate.sleep()
#str_position0 = "Marker Position in Camera frame: x=%f y=%f " % (tvec2[0][0], tvec2[1][0])
# cv2.putText(frame, str_position0, (0, 50), self.font, 1, (0, 255, 0), 2, cv2.LINE_AA)
# else:
# # # change form
# rotMat = tr.eulerAnglesToRotationMatrix([0, 0, self.local_pos[3]])
# rotMat = np.matmul(rotMat, self.imu_cam)
# # rotMat = rotMat[0:3, 0:3]
# tvec2 = np.matmul(rotMat, tvec1)
# # publish marker position in uav frame
# marker_pos = PS()
# marker_pos.pose.position.x = tvec2[0][0]
# marker_pos.pose.position.y = tvec2[1][0]
# marker_pos.pose.position.z = tvec2[2][0]
# self.aruco_marker_pos_pub.publish(marker_pos)
# # publish target position in world frame
# target_pos = PS()
# target_pos.pose.position.x = self.local_pos[0] + tvec2[0][0]
# target_pos.pose.position.y = self.local_pos[1] + tvec2[1][0]
# self.target_position.publish(target_pos)
# #str_position0 = "Marker Position in Camera frame: x=%f y=%f " % (tvec2[0][0], tvec2[1][0])
# #cv2.putText(frame, str_position0, (0, 50), self.font, 1, (0, 255, 0), 2, cv2.LINE_AA)
# check_err = np.linalg.norm([tvec2[0][0], tvec2[1][0]])
# self.check_error_pos.publish(check_err)
# self.rate.sleep()
else:
if ids[i][0] == self.ids_target[1]:
# get corner at index i responsible id at index 1
self.corners = corners[i]
markerLength = 0.08
ret1 = aruco.estimatePoseSingleMarkers(
corners=self.corners,
markerLength=markerLength,
cameraMatrix=self.mtx,
distCoeffs=self.dist)
rvec, tvec = ret1[0][0, 0, :], ret1[1][0, 0, :]
# -- Draw the detected marker and put a reference frame over it
aruco.drawDetectedMarkers(frame, corners, ids)
aruco.drawAxis(frame, self.mtx, self.dist, rvec,
tvec, 0.1)
str_position0 = "Marker Position in Camera frame: x=%f y=%f z=%f" % (
tvec[0], tvec[1], tvec[2])
cv2.putText(frame, str_position0, (0, 50),
self.font, 0.7, (0, 255, 0), 1,
cv2.LINE_AA)
(rvec - tvec).any(
) # get rid of that nasty numpy value array error
tvec1[0][0] = tvec[0]
tvec1[1][0] = tvec[1]
tvec1[2][0] = tvec[2]
self.altitude = tvec[2]
alpha = 3.0
self.beta[0] = abs(
np.rad2deg(np.arctan(tvec[0] / tvec[2])))
self.beta[1] = abs(
np.rad2deg(np.arctan(tvec[1] / tvec[2])))
# decide move or decend
check_move = self.check_angle(alpha)
self.check_move_position.publish(check_move)
# marker in the body (UAV frane)
marker_pos = PS()
tvec2 = np.matmul(self.imu_cam, tvec1)
marker_pos.pose.position.x = tvec2[0][0]
marker_pos.pose.position.y = tvec2[1][0]
marker_pos.pose.position.z = tvec2[2][0]
# publish marker in body frame
self.aruco_marker_pos_pub.publish(marker_pos)
# if (self.local_pos[3] > -0.1 and self.local_pos[3] < 0.1):
## marker in the global frame
fly_pos = PS()
fly_pos.pose.position.x = tvec2[0][
0] + self.local_pos[0]
fly_pos.pose.position.y = tvec2[1][
0] + self.local_pos[1]
fly_pos.pose.position.z = tvec2[2][
0] + self.local_pos[2]
# publish marker in body frame
self.fly_pos_pub.publish(fly_pos)
self.rate.sleep()
else:
ids_marker = False
self.check_marker_detection.publish(ids_marker)
cv2.imshow("frame", frame)
cv2.waitKey(1)
