def run(self):
numb_frame = self.image_stream.get_number_of_frames()
self.slider.setRange(2, numb_frame)
while True:
if self.current_frame > numb_frame:
self.current_frame = 2
if self.current_frame < 2:
self.current_frame = 2
self.slider.setValue(self.current_frame)
self.pbs.seek(self.image_stream,
self.current_frame) # с какого кадра стартовать
frame_image = self.image_stream.read_frame()
self.build_frame(frame_image)
time.sleep(0.016)
if not self.is_paused:
self.current_frame += 1
else:
continue
self.image_stream.stop()
openni2.unload()
def main():
print("pyOniRecorder by Rocco Pietrini v1 \n ")
readSettings()
try:
if sys.platform == "win32":
libpath = "lib/Windows"
else:
libpath = "lib/Linux"
print("library path is: ",
os.path.join(os.path.dirname(__file__), libpath))
openni2.initialize(os.path.join(os.path.dirname(__file__), libpath))
print("OpenNI2 initialized \n")
except Exception as ex:
print("ERROR OpenNI2 not initialized", ex, " check library path..\n")
return
try:
dev = openni2.Device.open_any()
except Exception as ex:
print("ERROR Unable to open the device: ", ex,
" device disconnected? \n")
return
write_files(dev)
try:
openni2.unload()
print("Device unloaded \n")
except Exception as ex:
print("Device not unloaded: ", ex, "\n")
def endtread(self):
##image relise
cv2.destroyAllWindows() #destory windown
#self.cam_astras.rgb_stream.stop()#stop 3D rbg_stream
#self.cam_astras.depth_stream.stop()#stop 3D depth stream
openni2.unload()
print("camera closed")
def colorStream(self, pathDir:str):
color_stream = self.dev.create_color_stream()
color_stream.start()
color_stream.set_video_mode( c_api.OniVideoMode(pixelFormat = c_api.OniPixelFormat.ONI_PIXEL_FORMAT_RGB888, resolutionX = 640,
resolutionY = 480, fps = 30))
while True:
frame = color_stream.read_frame()
frame_data = frame.get_buffer_as_uint8()
img = np.frombuffer(frame_data, dtype=np.uint8)
img.shape = (307200, 3)
b = img[:, 0].reshape(480, 640)
g = img[:, 1].reshape(480, 640)
r = img[:, 2].reshape(480, 640)
rgb = (r[..., np.newaxis], g[..., np.newaxis], b[..., np.newaxis])
img = np.concatenate(rgb, axis=-1)
cv2.imshow("Color Image", img)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
if key == ord("c"):
filename = pathDir + str(datetime.datetime.now()) + ".png"
cv2.imwrite(filename, img)
openni2.unload()
cv2.destroyAllWindows()
def __del__(self):
# close all the streams
for stream in self.streams:
stream.close()
# unload openni2
openni2.unload()
def depthStream(self):
depth_stream = self.dev.create_depth_stream()
depth_stream.start()
depth_stream.set_video_mode( c_api.OniVideoMode(pixelFormat=c_api.OniPixelFormat.ONI_PIXEL_FORMAT_DEPTH_100_UM,
resolutionX=640, resolutionY=480, fps=30))
while True:
# Grab a new depth frame
frame = depth_stream.read_frame()
frame_data = frame.get_buffer_as_uint16()
# Put the depth frame into a numpy array and reshape it
img = np.frombuffer(frame_data, dtype=np.uint16)
img.shape = (1, 480, 640)
img = np.concatenate((img, img, img), axis=0)
img = np.swapaxes(img, 0, 2)
img = np.swapaxes(img, 0, 1)
# Display the reshaped depth frame using OpenCV
cv2.imshow("Depth Image", img)
key = cv2.waitKey(1) & 0xFF
# If the 'c' key is pressed, break the while loop
if key == ord("q"):
break
# Close all windows and unload the depth device
openni2.unload()
cv2.destroyAllWindows()
def __del__(self):
"""Delete the Kinect interface."""
# close all the streams
self.rgb_stream.close()
self.depth_stream.close()
# unload openni2
openni2.unload()
def update():
colors = ((1.0, 1.0, 1.0, 1.0))
# Grab a new depth frame
depth_frame = depth_stream.read_frame()
depth_frame_data = depth_frame.get_buffer_as_uint16()
# Grab a new color frame
color_frame = color_stream.read_frame()
color_frame_data = color_frame.get_buffer_as_uint8()
depth_img = np.frombuffer(depth_frame_data, dtype=np.uint16)
depth_img.shape = (1, 480, 640)
depth_img = np.concatenate((depth_img, depth_img, depth_img), axis=0)
depth_img = np.swapaxes(depth_img, 0, 2)
depth_img = np.swapaxes(depth_img, 0, 1)
#cloud = points.astype(np.float32)/1000
depth_image = np.ndarray((depth_frame.height,depth_frame.width),dtype=np.uint16,buffer=depth_frame_data)
x, y, z, cloud = point_cloud(depth_image)
x = cloud[:,:,0].flatten()
y = cloud[:,:,1].flatten()
z = cloud[:,:,2].flatten()
N = max(x.shape)
pos = np.empty((N,3))
pos[:, 0] = x
pos[:, 1] = z
pos[:, 2] = y
#size = np.empty((pos.shape[0]))
#color = np.empty((pos.shape[0], 4))
#for i in range(pos.shape[0]):
# size[i] = 0.1
# color[i] = (1,0,0,1)
#d = np.ndarray((depth_frame.height, depth_frame.width),dtype=np.uint16,buffer=points)#/10000
out = pos
color_img = np.frombuffer(color_frame_data, dtype=np.uint8)
color_img.shape = (480, 640, 3)
color_img = color_img[...,::-1]
cv_img = cv2.convertScaleAbs(depth_img, alpha=(255.0/65535.0))
cv2.imshow("Image", color_img)
sp2.setData(pos=out, size=2, color=colors, pxMode=True)
key = cv2.waitKey(1) & 0xFF
if (key == 27 or key == ord('q') or key == ord('x') or key == ord("c")):
depth_stream.stop()
color_stream.stop()
openni2.unload()
cv2.destroyAllWindows()
sys.exit(0)
def main(args):
device = getOrbbec()
# 创建深度流
depth_stream = device.create_depth_stream()
depth_stream.set_mirroring_enabled(args.mirroring)
depth_stream.set_video_mode(
c_api.OniVideoMode(
resolutionX=args.width,
resolutionY=args.height,
fps=args.fps,
pixelFormat=c_api.OniPixelFormat.ONI_PIXEL_FORMAT_DEPTH_1_MM))
# 获取uvc
cap = cv2.VideoCapture(0)
# 设置 镜像 帧同步
device.set_image_registration_mode(True)
device.set_depth_color_sync_enabled(True)
depth_stream.start()
while True:
# 读取帧
frame_depth = depth_stream.read_frame()
frame_depth_data = frame_depth.get_buffer_as_uint16()
# 读取帧的深度信息 depth_array 也是可以用在后端处理的 numpy格式的
depth_array = np.ndarray((frame_depth.height, frame_depth.width),
dtype=np.uint16,
buffer=frame_depth_data)
# 变换格式用于 opencv 显示
depth_uint8 = 1 - 250 / (depth_array)
depth_uint8[depth_uint8 > 1] = 1
depth_uint8[depth_uint8 < 0] = 0
cv2.imshow('depth', depth_uint8)
# 读取 彩色图
_, color_array = cap.read()
cv2.imshow('color', color_array)
# 对彩色图 color_array 做处理
# 对深度图 depth_array 做处理
# 键盘监听
if cv2.waitKey(1) == ord('q'):
# 关闭窗口 和 相机
depth_stream.stop()
cap.release()
cv2.destroyAllWindows()
break
# 检测设备是否关闭（没什么用）
try:
openni2.unload()
print("Device unloaded \n")
except Exception as ex:
print("Device not unloaded: ", ex, "\n")
def startApp():
openni2.initialize(
) # can also accept the path of the OpenNI redistribution
dev = openni2.Device.open_any()
print(dev.get_device_info())
depth_stream = dev.create_depth_stream()
depth_stream.start()
punkty = []
def getFrame(readFrame):
frame_data = readFrame.get_buffer_as_uint16()
img = np.frombuffer(frame_data, dtype=np.uint16)
img.shape = (480, 640)
return img
while (True):
img = getFrame(depth_stream.read_frame())
img = np.ma.masked_equal(img, 0.0,
copy=True) #moze nie bedzie potrzebny
indexClosestToCamera = img.argmin()
j, i = np.unravel_index(indexClosestToCamera, img.shape)
point = (i, j)
dlX = 350
dlY = 300
xStart = 120
yStart = 120
czyWGranicachPionowych = lambda p: xStart <= p[0] < (xStart + dlX)
czyWGranicachPoziomych = lambda p: yStart <= p[1] < (yStart + dlY)
if czyWGranicachPionowych(point) and czyWGranicachPoziomych(point):
pixelValueNearestToCamera = img.min()
print(
str(j) + " " + str(i) + "->" + str(pixelValueNearestToCamera))
# if 1700 > pixelValueNearestToCamera > 1200:
cv2.circle(img, (i, j), 30, (0, 0, 0), 5)
punkty.append((i, j))
if pixelValueNearestToCamera > 1400 and len(punkty) > 30:
result = loadArtificialNeuralNetwork(punkty)
print(result)
break
# cv2.line(img,(xStart, yStart), (xStart+dlX, yStart), (0,0,0), 5)
# cv2.line(img,(xStart+dlX, yStart), (xStart+dlX, yStart+dlY), (0,0,0), 5)
# cv2.line(img,(xStart+dlX, yStart+dlY), (xStart, yStart+dlY), (0,0,0), 5)
# cv2.line(img,(xStart, yStart+dlY), (xStart, yStart), (0,0,0), 5)
cv2.imshow("Malgorzata Niewiadomska Inzynieria Biomedyczna", img)
if cv2.waitKey(1) & 0xFF == ord('z'):
break
depth_stream.stop()
openni2.unload()
print(punkty)
def __exit__(self, ):
if self.video_stream is not None:
self.video_stream.stop()
else:
self.video_stream = None
if self.depth_stream is not None:
self.depth_stream.stop()
else:
self.depth_stream = None
openni2.unload()
def quit_player(self):
reply = QtWidgets.QMessageBox.question(
self, 'Message', 'Are you sure to quit?',
QtWidgets.QMessageBox.Yes | QtWidgets.QMessageBox.No,
QtWidgets.QMessageBox.No)
if reply == QtWidgets.QMessageBox.Yes:
if self.is_streaming:
self.close_streaming()
openni2.unload()
self.close()
def stop(self):
""" Stop the sensor """
# check that everything is running
if not self._running or self._device is None:
logging.warning('Primesense not running. Aborting stop')
return False
# stop streams
if self._depth_stream:
self._depth_stream.stop()
if self._color_stream:
self._color_stream.stop()
self._running = False
# Unload openni2
openni2.unload()
return True
def main():
p = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description="")
p.add_argument('--v',
dest='video_path',
action='store',
required=True,
help='path Video')
p.add_argument('--d',
dest='dst',
action='store',
default='img',
help='Destination Folder')
p.add_argument('--i',
dest='interval',
action='store',
default=1,
help='Interval')
args = p.parse_args()
interval = int(args.interval)
dst = args.dst
print(args.video_path)
#i=args.video_path.rfind('/')
#videoname=args.video_path[-i:]
splittedpath, videoname = os.path.split(args.video_path)
print("Videoname: " + videoname)
videoname = os.path.splitext(videoname)[0] + "_"
if not os.path.exists(dst):
os.mkdir(dst)
print("Directory ", dst, " Created ")
try:
dev, pbs = openDevice(args.video_path.encode('utf-8'))
pbs.set_repeat_enabled(True)
if dev.has_sensor(openni2.SENSOR_COLOR):
print("Color Stream found")
processColor(dev, pbs, interval, dst, videoname)
if dev.has_sensor(openni2.SENSOR_DEPTH):
print("Depth Stream found")
processDepth(dev, pbs, interval, dst, videoname)
print("Done!")
except Exception as ex:
print(ex)
openni2.unload()
def update():
colors = ((1.0, 1.0, 1.0, 1.0))
# Grab a new depth frame
depth_frame = depth_stream.read_frame()
depth_frame_data = depth_frame.get_buffer_as_uint16()
# Grab a new color frame
color_frame = color_stream.read_frame()
color_frame_data = color_frame.get_buffer_as_uint8()
points = np.frombuffer(depth_frame_data, dtype=np.uint16)
points.shape = (1, 480, 640)
points = np.concatenate((points,points,points), axis=0)
points = np.swapaxes(points, 0, 2)
points = np.swapaxes(points, 0, 1)
# (x,y,z)
d = np.ndarray((depth_frame.height, depth_frame.width),dtype=np.uint16,buffer=depth_frame_data)#/10000
for row in range(depth_frame.height)
for col in range(depth_frame.width)
X = row
Y = col
Z = d[row][col]
#out = pos
cv_img = cv2.convertScaleAbs(points, alpha=(255.0/65535.0))
cv2.imshow("Image", cv_img)
sp2.setData(pos=out, size=1, color=colors, pxMode=True)
key = cv2.waitKey(1) & 0xFF
if key == ord("c"):
depth_stream.stop()
color_stream.stop()
openni2.unload()
cv2.destroyAllWindows()
sys.exit(0)
def main():
cap = cv2.VideoCapture(0)
(ret, color_frame) = cap.read()
openni2.initialize()
device = openni2.Device.open_any()
device.set_depth_color_sync_enabled(True)
depth_stream = device.create_depth_stream()
depth_stream.start()
while True:
print("#################")
frame = depth_stream.read_frame()
ret, color_frame = cap.read()
frame_data = frame.get_buffer_as_uint8()
position = openni2.convert_depth_to_world(depth_stream, 100, 100,
frame_data[10000])
print position
cv2.imshow("image", color_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
depth_stream.stop()
openni2.unload()
human_nodes = {node.name: node for node in bodypartslist}
try:
while True:
frame = userTracker.read_frame()
#logger.info("%s humans detected" % len(frame.users))
humanparts = process_frame(frame)
if humanparts is not None:
for name, transformation in humanparts.items():
node = human_nodes[name]
node.transformation = transformation
nodes.update(node)
except KeyboardInterrupt:
logger.info("Quitting...")
## Clean up
for _, node in human_nodes.items():
nodes.remove(node)
nodes.remove(camera)
nite2.unload()
openni2.unload()
logger.info("Bye!")
human_nodes = {node.name:node for node in bodypartslist}
try:
while True:
frame = userTracker.read_frame()
#logger.info("%s humans detected" % len(frame.users))
humanparts = process_frame(frame)
if humanparts is not None:
for name, transformation in humanparts.items():
node = human_nodes[name]
node.transformation = transformation
nodes.update(node)
except KeyboardInterrupt:
logger.info("Quitting...")
## Clean up
for _, node in human_nodes.items():
nodes.remove(node)
nodes.remove(camera)
nite2.unload()
openni2.unload()
logger.info("Bye!")
def __del__(self):
openni2.unload()
def pegarMovimentos():
global player1HandRightx
global player1HandRighty
global player1HandLeftx
global player1HandLefty
global player2HandRightx
global player2HandRighty
global player2HandLefty
global player2HandLeftx
global joints
global leoteste
#openni2.initialize("/home/leonardo/Downloads/OpenNI-Linux-x64-2.2/Redist")
openni2.initialize("/home/leonardo/Tcc/OpenNI-Linux-x64-2.2/Redist")
#nite2.initialize("/home/leonardo/Downloads/NiTE-Linux-x64-2.2/Redist")
nite2.initialize("/home/leonardo/Tcc/NiTE-2.0.0/Redist")
dev = openni2.Device.open_any()
device_info = dev.get_device_info()
try:
userTracker = nite2.UserTracker(dev)
except utils.NiteError as ne:
print "entrou em exept"
print(
"Unable to start the NiTE human tracker. Check the error messages in the console. Model data (s.dat, h.dat...) might be inaccessible."
)
print(ne)
sys.exit(-1)
print "antes do while"
while True:
frame = userTracker.read_frame()
depth_frame = frame.get_depth_frame()
if frame.users:
i = 1
for user in frame.users:
user.id = i
i += 1
if user.skeleton.state == nite2.SkeletonState.NITE_SKELETON_TRACKED:
print user.id
else:
print user.id
print user.is_new()
print user.is_visible()
print("Skeleton state: " + str(user.skeleton.state))
#print 'leo' + str(user.id)
#if user.id == 0 and leoteste==0:
# print 'e zero'
# leoteste=+1
userTracker.start_skeleton_tracking(user.id)
print("Skeleton state: " + str(user.skeleton.state))
# userTracker.start_skeleton_tracking(2)
#if user.is_new():
# print("New human detected! ID: %d Calibrating...", user.id)
# userTracker.start_skeleton_tracking(1)
#elif user.skeleton.state == nite2.SkeletonState.NITE_SKELETON_TRACKED:
#else:
#print user.skeleton.joints
#print user.id
#print str(user.is_visible())
preenchermovimento(user.id, user.skeleton.joints)
else:
print("No users")
nite2.unload()
openni2.unload()
def update():
#######################################################################
global alpha, distPt, inspectPt, calc_plane, plane_first, plane_second, mgrid_dist, calc_grid
global dump_depth_image, dump_csv
if not calc_plane and not done:
calc_plane = True
plane_first = None
plane_second = None
'''
Grab a frame, select the plane
If the plane is selected, wait for key, then update frame and select the points
If points are selected, get depth point from image
'''
# Grab a new depth frame
depth_frame = depth_stream.read_frame()
depth_frame_data = depth_frame.get_buffer_as_uint16()
# Grab a new color frame
color_frame = color_stream.read_frame()
color_frame_data = color_frame.get_buffer_as_uint8()
# Get the time of frame capture
t = time.time()
dt = datetime.datetime.fromtimestamp(t)
# Put the depth frame into a numpy array and reshape it
depth_img = np.frombuffer(depth_frame_data, dtype=np.uint16)
depth_img.shape = (1, 480, 640)
depth_img = np.concatenate((depth_img, depth_img, depth_img), axis=0)
depth_img = np.swapaxes(depth_img, 0, 2)
depth_img = np.swapaxes(depth_img, 0, 1)
depth_image = np.ndarray((depth_frame.height, depth_frame.width),
dtype=np.uint16,
buffer=depth_frame_data)
# Put the color frame into a numpy array, reshape it, and convert from bgr to rgb
color_image = np.frombuffer(color_frame_data, dtype=np.uint8)
color_image.shape = (480, 640, 3)
color_image = color_image[..., ::-1]
depth_image = np.fliplr(depth_image)
depth_img = np.fliplr(depth_img)
color_image = np.fliplr(color_image)
color_img = color_image.copy()
depth_img = depth_img.copy()
image_color = color_image.copy()
image_depth = depth_img.copy()
shape_img = np.zeros_like(color_img)
text_img = np.zeros_like(color_img)
mask_img = np.zeros_like(color_img)
drawing = True
try:
# click only
if len(refPt) == 1 and not selecting:
point = (refPt[0][0], refPt[0][1])
point_distance = float(depth_img[refPt[0][1]][refPt[0][0]][0] /
10000.0)
# First point
cv2.circle(shape_img, refPt[0], 3, (0, 0, 255), 1)
cv2.putText(text_img,
"Point X,Y: {},{}".format(point[0], point[1]),
(10, 15), cv2.FONT_HERSHEY_SIMPLEX, .5, textcolor)
cv2.putText(text_img,
"Point distance in Meters: {}".format(point_distance),
(10, 30), cv2.FONT_HERSHEY_SIMPLEX, .5, textcolor)
# click+drag
if selecting and not done:
cv2.rectangle(shape_img, refPt[0], (mousex, mousey), (0, 255, 0),
2)
if len(refPt) == 2 and done:
mask = np.zeros((mask_img.shape[0], mask_img.shape[1]))
cv2.rectangle(mask, refPt[0], refPt[1], 1, thickness=-1)
mask = mask.astype(np.bool)
mask_img[mask] = color_img[mask]
cv2.rectangle(shape_img, refPt[0], refPt[1], (0, 255, 0), 2)
left = min(refPt[0][0], refPt[1][0])
top = min(refPt[0][1], refPt[1][1])
right = max(refPt[0][0], refPt[1][0])
bottom = max(refPt[0][1], refPt[1][1])
#print "{}:{} {}:{}".format(top, left, bottom, right)
roi = depth_image[
top:bottom,
left:right] # because the image is 480x640 not 640x480
# polygon
if len(refPt) > 1 and not done:
cv2.polylines(shape_img, np.array([refPt]), False, (255, 0, 0), 3)
cv2.line(shape_img, refPt[-1], (mousex, mousey), (0, 255, 0), 3)
if len(refPt) > 2 and done:
cv2.polylines(shape_img, np.array([refPt]), True, (0, 255, 0), 3)
mask = np.zeros((mask_img.shape[0], mask_img.shape[1]))
points = np.array(refPt, dtype=np.int32)
cv2.fillConvexPoly(mask, points, 1)
mask = mask.astype(np.bool)
mask_img[mask] = color_img[mask]
left, top = tuple(points.min(axis=0))
right, bottom = tuple(points.max(axis=0))
cv2.rectangle(shape_img, (left, top), (right, bottom),
color=(255, 0, 0),
thickness=3)
#print "{}:{} {}:{}".format(top, left, bottom, right)
#roi_rect = depth_image[top:bottom,left:right] # because the image is 480x640 not 640x480
roi_mask = np.zeros_like(depth_image)
roi_mask[mask] = depth_image[mask]
roi = roi_mask[top:bottom, left:right]
if mgrid_dist and calc_grid:
distPt = []
count = (mgrid_count[0] + 1, mgrid_count[1] + 1)
mn = (0, 0)
mx = (roi.shape[1], roi.shape[0])
X = np.linspace(mn[0],
mx[0],
count[0],
dtype=np.int16,
endpoint=False)
Y = np.linspace(mn[1],
mx[1],
count[1],
dtype=np.int16,
endpoint=False)
xv, yv = np.meshgrid(X, Y)
# get rid of first row and column
xv = xv[1:, 1:]
yv = yv[1:, 1:]
# at end, add offset from top, left to points:
for j in range(mgrid_count[1]): #row
for i in range(mgrid_count[0]): #column
distPt.append(
tuple(map(sum, zip((left, top),
(xv[j, i], yv[j, i])))))
calc_grid = False
# roi stats
if len(refPt) > 1 and done:
valid = (roi > 0) & (roi < 65536.0)
roi = np.where(valid, roi, np.nan)
roi_mean = np.round(np.nanmean(roi) / 10000, 5)
roi_max = np.round(np.nanmax(roi) / 10000, 5)
roi_min = np.round(np.nanmin(roi) / 10000, 5)
roi_std = np.round(np.nanstd(roi) / 10000, 5)
#cv2.rectangle(color_img, (5,5), (250,65), (50, 50, 50, 0), -1)
cv2.putText(text_img, "Mean of ROI: {}".format(roi_mean), (10, 15),
cv2.FONT_HERSHEY_SIMPLEX, .5, textcolor)
cv2.putText(text_img, "Max of ROI: {}".format(roi_max), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, .5, textcolor)
cv2.putText(text_img, "Min of ROI: {}".format(roi_min), (10, 45),
cv2.FONT_HERSHEY_SIMPLEX, .5, textcolor)
cv2.putText(text_img,
"Standard Deviation of ROI: {}".format(roi_std),
(10, 60), cv2.FONT_HERSHEY_SIMPLEX, .5, textcolor)
if debug_print:
print("Mean of ROI: ", roi_mean)
print("Max of ROI: ", roi_max)
print("Min of ROI: ", roi_min)
print("Standard Deviation of ROI: ", roi_std)
if distPt is not None:
for pt in distPt:
cv2.circle(shape_img, pt, 3, (0, 0, 255, 255), 1)
if inspectPt is not None:
cv2.circle(shape_img, inspectPt, 3, (0, 255, 255), 1)
if plane_first is not None:
C = np.round(plane_first[1], 4)
text_plane = '1 z= {}x^2 + {}y^2 + {}xy {}x + {}y + {}'.format(
C[4], C[5], C[3], C[1], C[2], C[0])
cv2.putText(text_img, text_plane, (10, 75),
cv2.FONT_HERSHEY_SIMPLEX, .5, textcolor)
if plane_second is not None:
C = np.round(plane_second[1], 4)
text_plane = '2 z= {}x^2 + {}y^2 + {}xy {}x + {}y + {}'.format(
C[4], C[5], C[3], C[1], C[2], C[0])
cv2.putText(text_img, text_plane, (10, 90),
cv2.FONT_HERSHEY_SIMPLEX, .5, textcolor)
except Exception as e:
print(e)
drawing = False
min_depth = 4500.0
max_depth = float(max(np.nanmax(depth_img), 65535.0 / 4.0))
#alpha = float(min_depth/max_depth)
depth_img = cv2.convertScaleAbs(depth_img, alpha=(255.0 / (65535.0 / 2.0)))
cv2.addWeighted(mask_img, alpha, color_img, 1 - alpha, 0, color_img)
cv2.addWeighted(shape_img, 1, color_img, .75, 0, color_img)
#cv2.bitwise_and(shape_img>0, color_img, color_img)
#cv2.addWeighted(mask_img, alpha, depth_img, 1-alpha, 0, depth_img)
cv2.addWeighted(shape_img, 1, depth_img, .75, 0, depth_img)
depth_pt_image = depth_image.copy()
color_pt_img = color_img.copy()
#depth_pt_image = np.flipud(depth_image)
#depth_pt_image = np.fliplr(depth_pt_image)
#color_pt_img = np.flipud(color_img)
#color_pt_img = np.fliplr(color_pt_img)
cloud, colors = point_cloud(depth_pt_image, color_pt_img)
if openGL:
# Calculate a dynamic vertex size based on window dimensions and camera's position - To become the "size" input for the scatterplot's setData() function.
v_rate = 2.5 # Rate that vertex sizes will increase as zoom level increases (adjust this to any desired value).
v_scale = np.float32(v_rate) / w.opts[
'distance'] # Vertex size increases as the camera is "zoomed" towards center of view.
v_offset = (
w.geometry().width() / 1000
)**2 # Vertex size is offset based on actual width of the viewport.
size = v_scale + v_offset
x = cloud[:, :, 0].flatten()
y = cloud[:, :, 1].flatten()
z = cloud[:, :, 2].flatten()
N = max(x.shape)
pos = np.empty((N, 3))
pos[:, 0] = x
pos[:, 1] = y
pos[:, 2] = z
try:
if len(refPt) > 1 and done:
#roi_color = color_img[top:bottom,left:right] # because the image is 480x640 not 640x480
roi_cloud = np.zeros_like(cloud)
roi_colors = np.zeros_like(colors)
cloud_mask = mask.flatten()
color_mask = cloud_mask
#roi_cloud[cloud_mask] = cloud[cloud_mas4k]
roi_colors[color_mask] = colors[color_mask]
roi_x = np.zeros_like(x) * np.nan
roi_y = np.zeros_like(y) * np.nan
roi_z = np.zeros_like(z) * np.nan
roi_x[cloud_mask] = x[cloud_mask]
roi_y[cloud_mask] = y[cloud_mask]
roi_z[cloud_mask] = z[cloud_mask]
N = max(x.shape)
roi_points = np.empty((N, 3))
roi_points[:, 0] = roi_x
roi_points[:, 1] = roi_y
roi_points[:, 2] = roi_z
if openGL:
v2_rate = 2.5
v2_scale = np.float32(v2_rate) / w2.opts[
'distance'] # Vertex size increases as the camera is "zoomed" towards center of view.
v2_offset = (
w2.geometry().width() / 1000
)**2 # Vertex size is offset based on actual width of the viewport.
size2 = v2_scale + v2_offset
roi_data = np.c_[roi_x, roi_y, roi_z]
finite = np.isfinite(roi_data).all(axis=1)
roi_data = roi_data[finite]
roi_colors = roi_colors[finite]
if openGL:
sp3.setData(pos=roi_data,
color=roi_colors,
size=size2,
pxMode=True)
plane = None
# FIXME: outliers mess everything up
#roi_data = reject_outliers(roi_data, 0.2)
count = 50
X = np.linspace(np.min(roi_data[:, 0]), np.max(roi_data[:, 0]),
count)
Y = np.linspace(np.min(roi_data[:, 1]), np.max(roi_data[:, 1]),
count)
Xx = X.reshape(count, 1)
Yy = Y.reshape(1, count)
calc = 2
if calc == 1: #linear plane
A = np.c_[roi_data[:, 0], roi_data[:, 1],
np.ones(roi_data.shape[0])]
C, _, _, _ = np.linalg.lstsq(A, roi_data[:, 2]) # coefficients
# evaluate it on grid
Z = C[0] * Xx + C[1] * Yy + C[2]
if debug_print:
print("z = {}x + {}y + {}".format(np.round(C[0], 4),
np.round(C[1], 4),
np.round(C[2], 4)))
z = C[0] * X + C[1] * Y + C[2]
plane = (np.c_[X, Y, z], C)
elif calc == 2: #quadratic plane
A = np.c_[np.ones(roi_data.shape[0]), roi_data[:, :2],
np.prod(roi_data[:, :2], axis=1), roi_data[:, :2]**2]
C, _, _, _ = np.linalg.lstsq(A, roi_data[:, 2]) # coefficients
# evaluate it on grid
Z = C[4] * Xx**2. + C[5] * Yy**2. + C[3] * Xx * Yy + C[
1] * Xx + C[2] * Yy + C[0]
if debug_print:
print(('z= {}x^2 + {}y^2 + {}xy {}x + {}y + {}'.format(
C[4], C[5], C[3], C[1], C[2], C[0])))
z = C[4] * X**2. + C[5] * Y**2. + C[3] * X * Y + C[1] * X + C[
2] * Y + C[0]
plane = (np.c_[X, Y, z], C)
elif calc == 3: #lineplot
# FIXME: This is not efficient, also want to update these line plots in real time to visualize deflection
#for i in range(count):
'''
Loop through n segments with m resolution
take std of points from line to determine colors
'''
#y_resolution = 20
#Y = np.linspace(np.min(roi_data[:,1]), np.max(roi_data[:,1]), y_resolution)
#for i in range(y_resolution):
#x = roi_data[:,0]
#pts = np.vstack([x,yi,z]).transpose()
#sp5.setData(pos = pts, color=pg.glColor((i, n*1.3)), width=(i+1)/10.)
if openGL:
if calc < 3:
if calc_plane:
sp4.setData(x=X, y=Y, z=Z)
else:
sp5.setData(x=X, y=Y, z=Z)
if calc_plane:
plane_first = plane
else:
plane_second = plane
calc_plane = False
if inspectPt is not None:
distance = "NaN"
# FIXME: find nearest cluster of points to selection
pt = inspectPt
p = cloud[pt[1]][pt[0]]
distance = np.round(distance_to_plane(p, plane_first), 4)
cv2.putText(text_img,
"Point Dist to Plane: {}".format(distance),
(10, 105), cv2.FONT_HERSHEY_SIMPLEX, .5, textcolor)
except Exception as e:
print(e)
cv2.addWeighted(text_img, 1, color_img, 1, 0, color_img)
img = np.concatenate((color_img, depth_img), 1)
# Display the reshaped depth frame using OpenCV
cv2.imshow("Depth Image", img)
if openGL:
sp2.setData(pos=pos, color=colors, size=size, pxMode=True)
# Once you select the plane, save the raw images and composite
# dump_to_csv(filename, time, args)
# dump_image_to_file(image, name, time)
if dump_depth_image == 1:
dump_image_to_file(image_color, "image_color")
dump_image_to_file(image_depth, "image_depth")
dump_image_to_file(text_img, "image_text")
dump_image_to_file(shape_img, "image_shape")
dump_image_to_file(mask_img, "image_mask")
dump_image_to_file(img, "main")
if dump_csv:
grid_data = list()
grid_data.append(dt.strftime("%D"))
grid_data.append(dt.strftime('%H:%M:%S.%f')[:-3])
pt_top_left = cloud[top][left]
pt_top_left_on_plane = point_on_plane(pt_top_left, plane_first)
pts = np.asarray(distPt)
print(pts)
print(pts[0])
print(pts.shape)
pts.reshape(r, c) #reshape to rows, columns
for row in range(r):
for col in range(c):
pt = pts[row][col]
print(pt)
# get point in real-world coordinates
pt_to_world = cloud[pt[1]][pt[0]]
# offset to plane
# We should get the point distance from the plane
pt_on_plane = point_on_plane(pt_to_world, plane_first)
pt_out = np.subtract(pt_on_plane, pt_top_left_on_plane)
#pt_out = pt
grid_data.append(pt_out)
dump_to_csv("grid", grid_data, mode="a+")
dump_depth_image = 0
if dump_csv:
raw_data = list()
raw_data.append(dt.strftime("%D"))
raw_data.append(dt.strftime('%H:%M:%S.%f')[:-3])
# raw_data.append(dt.strftime("%T"))
for pt in distPt:
p = cloud[pt[1]][pt[0]]
distance = np.round(distance_to_plane(p, plane_first), 4)
if distance == np.nan:
distance = " "
raw_data.append(distance)
dump_to_csv("raw", raw_data)
key = cv2.waitKey(1) & 0xFF
if (key == 27 or key == ord('q') or key == ord('x') or key == ord("c")):
depth_stream.stop()
color_stream.stop()
openni2.unload()
cv2.destroyAllWindows()
sys.exit(0)
def cleanup(self) -> None:
"""Unload library."""
openni2.unload()
def __del__(self):
nite2.unload()
openni2.unload()
del self.midiout
def shutdown(self):
#Stop and Release
#self.ir_stream.stop()
self.depth_stream.stop()
unload()
return
def main(args):
'''HYPER PARAMETER'''
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
'''MODEL LOADING 加载模型'''
MODEL = importlib.import_module(args.model) # 导入模型所在的模块
classifier = MODEL.get_model(args.num_joint * 3,
normal_channel=args.normal).cuda()
experiment_dir = Path('./log/regression/pointnet2_reg_msg')
checkpoint = torch.load(
str(experiment_dir) + '/checkpoints/best_model.pth')
classifier.load_state_dict(checkpoint['model_state_dict'])
classifier.eval()
try:
''' 加载摄像机 '''
device = getOrbbec()
# 创建深度流
depth_stream = device.create_depth_stream()
depth_stream.set_mirroring_enabled(args.mirroring)
depth_stream.set_video_mode(
c_api.OniVideoMode(
resolutionX=args.width,
resolutionY=args.height,
fps=args.fps,
pixelFormat=c_api.OniPixelFormat.ONI_PIXEL_FORMAT_DEPTH_1_MM))
# 获取uvc
cap = cv2.VideoCapture(0)
# 设置 镜像 帧同步
device.set_image_registration_mode(True)
device.set_depth_color_sync_enabled(True)
depth_stream.start()
while True:
# 读取帧
frame_depth = depth_stream.read_frame()
frame_depth_data = frame_depth.get_buffer_as_uint16()
# 读取帧的深度信息 depth_array 也是可以用在后端处理的 numpy格式的
depth_array = np.ndarray((frame_depth.height, frame_depth.width),
dtype=np.uint16,
buffer=frame_depth_data)
# 变换格式用于 opencv 显示
depth_uint8 = 1 - 250 / (depth_array)
depth_uint8[depth_uint8 > 1] = 1
depth_uint8[depth_uint8 < 0] = 0
cv2.imshow('depth', depth_uint8)
# 读取 彩色图
_, color_array = cap.read()
cv2.imshow('color', color_array)
# 对彩色图 color_array 做处理
# 对深度图 depth_array 做处理
# 键盘监听
if cv2.waitKey(1) == ord('q'):
# 关闭窗口 和 相机
depth_stream.stop()
cap.release()
cv2.destroyAllWindows()
break
# 检测设备是否关闭（没什么用）
try:
openni2.unload()
print("Device unloaded \n")
except Exception as ex:
print("Device not unloaded: ", ex, "\n")
except:
# 读取 depth
depth_array = matplotlib.image.imread('test_depth_1.tif').astype(
np.float32)
# depth to UVD
cloud_point = depth2uvd(depth_array)
# 将点云归一化
cloud_point_normal, scale, centroid = pc_normalize(cloud_point)
cloud_point_normal = np.reshape(cloud_point_normal, (1, -1, 3))
cloud_point_normal = cloud_point_normal.transpose(0, 2, 1)
# 对归一化的点云做预测
cloud_point_normal = torch.from_numpy(cloud_point_normal).cuda()
pred, _ = classifier(cloud_point_normal)
# 对预测结果做还原
pred_reduction = pred.cpu().data.numpy()
pred_reduction = pred_reduction * np.tile(
scale, (args.num_joint * 3, 1)).transpose(1, 0)
pred_reduction = pred_reduction + np.tile(centroid,
(1, args.num_joint))
pred_reduction = np.reshape(pred_reduction, (-1, 3))
displayPoint2(cloud_point, pred_reduction, 'bear')
def oni_converter(file_path):
''' Convert oni file to color and depth avi files.
avi files will be saved in working directory
as color.avi and depth.avi
Parameters
----------
file_path : str
full path to oni file
'''
count = 0
result = None
PATH_TO_OPENNI2_SO_FILE = './OpenNI-Linux-x64-2.2/Redist'
t1_start = process_time()
openni2.initialize(PATH_TO_OPENNI2_SO_FILE)
dev = openni2.Device.open_file(file_path.encode('utf-8'))
c_stream, d_stream = dev.create_color_stream(), dev.create_depth_stream()
openni2.PlaybackSupport(dev).set_speed(ctypes.c_float(0.0))
d_stream.start()
c_stream.start()
c_images, d_images = [], []
n_c_frames = openni2.PlaybackSupport(
dev).get_number_of_frames(c_stream) - 1
n_d_frames = openni2.PlaybackSupport(dev).get_number_of_frames(d_stream)
if n_c_frames != n_d_frames:
print('Разное количество кадров в color и depth потоках!\n'
f'color - {n_c_frames}, depth - {n_d_frames}')
sys.exit()
for i in tqdm(range(n_c_frames)):
# process depth stream
d_frame = np.fromstring(d_stream.read_frame().get_buffer_as_uint16(),
dtype=np.uint16).reshape(480, 640)
# Correct the range. Depth images are 12bits
d_img = np.uint8(d_frame.astype(float) * 255 / 2**12 - 1)
d_img = cv2.cvtColor(d_img, cv2.COLOR_GRAY2RGB)
d_img = 255 - d_img
d_images.append(d_img)
if i == 0:
continue
# process color stream
c_frame = c_stream.read_frame()
c_frame_data = c_frame.get_buffer_as_uint8()
c_img_bgr = np.frombuffer(c_frame_data, dtype=np.uint8)
c_img_bgr.shape = (480, 640, 3)
c_img_rgb = cv2.cvtColor(c_img_bgr, cv2.COLOR_BGR2RGB)
c_images.append(c_img_rgb)
count += 1
# yield count
openni2.unload()
c_out = cv2.VideoWriter(
'color.avi', cv2.VideoWriter_fourcc(*'DIVX'), 30, (640, 480)) # надо добавить fps как переменную
d_out = cv2.VideoWriter(
'depth.avi', cv2.VideoWriter_fourcc(*'DIVX'), 30, (640, 480)) # надо добавить fps как переменную
for i in tqdm(range(len(c_images))):
c_out.write(c_images[i])
d_out.write(d_images[i])
count += 1
# yield count
c_out.release()
d_out.release()
t1_stop = process_time()
print(f"Process duration, seconds:, {round(t1_stop-t1_start, 3)}")
return "ONI file has been processed successfully."
def stop(self):
self._stop_color()
self._stop_depth()
openni2.unload()
def unload():
openni2.unload()
def main():
########### below this part will move to the main_feeding .py ###############################
s = 0
done = False
then = time.time() #Time before the operations start
cam_astras = astras()
while not done:
key = cv2.waitKey(1) & 255
## Read keystrokes
if key == 27: # terminate
print("\tESC key detected!")
done = True
elif key == ord("c"):
break
###rgb#####
#if mouth detection this (x,y) coordinate will transfer to depth
try:
rgb, mouth1, mouth2 = cam_astras.get_rgb()
x1 = (mouth1[0] + mouth2[0]) // 2
y1 = (mouth1[1] + mouth2[1]) // 2
print(x1, y1)
#else mouth not detect use this coordinate to the depth
except ValueError:
rgb = cam_astras.get_rgb()
x1 = 163
y1 = 209
# DEPTH
#print("rgb shape",rgb.shape)
dmap, d4d = cam_astras.get_depth()
z1 = dmap[y1, x1]
#z1 = dmap[x1 ,y1] #replace this x.y through depth
#print('dmap shape',dmap.shape)
canvas = np.hstack((d4d, rgb))
#coordinate pixel to mm
ccor = cam_astras.depth_coordinate(x1, y1, z1)
## Distance map
# cv2.putText(rgb,'Center pixel is {} mm away'.format(dmap[x1, y1]),(30,80),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255),2)#136.182
#cv2.putText(rgb,'Center pixel is {} mm away'.format(z1),(30,80),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255),2)#136.182
## Display the stream
cv2.namedWindow('rgb', 0)
cv2.resizeWindow('rgb', 1024, 640)
cv2.moveWindow('rgb', 640, 320)
cv2.imshow('rgb', canvas) #rgb)
now = time.time() #Time after it finished
print("It took: ", now - then, " seconds")
cv2.destroyAllWindows()
cam_astras.rgb_stream.stop()
cam_astras.depth_stream.stop()
openni2.unload()
cap.release()
print("END")
def close_capture_device(self):
nite2.unload()
openni2.unload()
def closeEvent(self, a0: QtGui.QCloseEvent):
if self.is_streaming:
self.close_streaming()
openni2.unload()
