def snap_and_save(args, frames=None):
if args.photo_mode == args.motion_mode and frames:
snap = np.vstack(frames[1:][::-1])
yoff = 930
else:
yoff = 450
if args.photo_mode == 'rgb':
rgb = freenect.sync_get_video()[0]
snap = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB)
else:
gray = freenect.sync_get_video(0, freenect.VIDEO_IR_8BIT)[0]
snap = cv2.equalizeHist(gray)
fname = "snapshots/{0}.jpg".format(str(int(time.time() * 10)))
dt_str = datetime.strftime(
datetime.now() + timedelta(seconds=-5*3600),
'%Y-%m-%d %H:%M:%S')
cv2.putText(snap, dt_str, (30, yoff), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 0, 255), 2)
cv2.imwrite(fname, snap, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
return fname
def captureimage():
global frames
depthframes = np.zeros((frames, rownum, colnum))
rgbframes = np.zeros((frames, rownum, colnum, 3))
for i in range(frames):
depthframes[i] = freenect.sync_get_depth()[0]
rgbframes[i] = freenect.sync_get_video()[0]
arargb = freenect.sync_get_video()[0]
time.sleep(0.05)
arargb = fc.robustavg(rgbframes)
aradepth = fc.robustavg(depthframes)
serial = time.time()
cv.SaveImage('img/depth%d.png' % serial, fc.depth_cv(aradepth.astype(int)))
cv.SaveImage('img/video%d.png' % serial, fc.video_cv(arargb.astype(np.uint8)))
#f = open('poly/poly%d.ply' % serial,'w')
meterdepth = fc.meter_depth(aradepth)
#newrgb2 = fc.matchrgb2(meterdepth, arargb)
newrgb = fc.matchrgb(meterdepth, arargb)
#meterdepth = ndi.gaussian_filter(fc.meter_depth(aradepth), [sigma, sigma])
meterdepth[meterdepth > 1.5] = -1.
meterdepth[meterdepth < 0.5] = -1.
scipy.io.savemat('data/aligned%d.mat' % serial, {'depth':meterdepth, 'rgb':newrgb})
def get_video(self):
if len(freenect.sync_get_video()) > 0:
rgb = freenect.sync_get_video()[0]
rgb = rgb[:, :, ::-1] # RGB -> BGR
return rgb.astype(numpy.int8)
#return frame_convert.video_cv(freenect.sync_get_video()[0])
return None
def main():
# The range where we allow before we consider
# movement left or right.
# Note, look at a normal distribution, we're
# talking about allowing a range for how much
# movement we allowing before we we start
# to say whether there was movement to the
# left or to the right.
alpha = 0
# Get previous image, resize it, convert it to grayscale.
prevgray,_ = freenect.sync_get_video()
prevgray = cv2.cvtColor(prevgray, cv2.COLOR_BGR2GRAY)
prevgray = cv2.resize(prevgray, size)
while True:
# Get new image, resize it, convert it to grayscale.
gray,_ = freenect.sync_get_video()
gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY)
gray = cv2.resize(gray, size)
# Calculate the optical flow using this handy built in method.
flow = cv2.calcOpticalFlowFarneback(prevgray, gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
# Set the newly gathered image to the previous one.
prevgray = gray
# Set the steps for do.
step = 16
# Draw the dots and lines on the screen.
h, w = gray.shape[:2]
y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2, -1).astype(int)
fx, fy = flow[y,x].T
# Get the average amount of displacement within the screen
# with positive being left and negative being right.
fx_avg = np.average(fx)
# Keep it straight!
if ((fx_avg > alpha)):
print('Moved right')
#driveLeft()
elif ((fx_avg < -alpha)):
print('Moved left')
#driveRight()
else:
print('Looking straight')
def show_video():
video, timestamp = freenect.sync_get_video()
#save_video(timestamp, video)
video = frame_convert.video_cv(video)
cv.Circle(video, (closest[1], closest[0]), 8, (0, 0, 255))
cv.Circle(video, (closest[1], closest[0]), 4, (0, 0, 255))
cv.ShowImage('Video', video)
def get_kinect_video():
if not kinect == None:
return get_kinect_video_cv()
depth, timestamp = freenect.sync_get_video()
if (depth == None):
return None
return depth[...,1]
def get_ir(self): if self.ir == 'KINECT': frame, timestamp = freenect.sync_get_video(freenect.VIDEO_IR_10BIT) return frame else: ret, img = self.ir.read() return img
def get_buffers():
'''get_buffers(): returns a KinectData object
KinectData members:
- real_kinect (boolean) (true if data comes fro ma real kinect)
- rgb array
- depth array
(buffers=numpy array)
the input is taken from a file if the kinect is missing or the library not present.
no memorization is done
'''
found_kinect = False
if freenect: # module has been imported
try:
# Try to obtain Kinect images.
(depth, _), (rgb, _) = freenect.sync_get_depth(), freenect.sync_get_video()
found_kinect = True
except TypeError:
pass
if found_kinect:
return KinectData(real_kinect=True, rgb=rgb, depth=depth)
else:
# Use local data files.
return _DEFAULT_DATA
def get_data(self):
while True:
(depth, _) = freenect.sync_get_depth()
(rgb , _) = freenect.sync_get_video()
depth8 = self._pretty_depth(depth)
yield depth, depth8, rgb
def frame(self):
""" Grab a frame and set it as self._frame.
Modified by Graham Jones to use libFreenect for kinect depth sensor
"""
if self._enteredFrame and self._frame is None:
if (self._uselibFreenect):
if (self.channel == depth.CV_CAP_OPENNI_BGR_IMAGE):
imgRGB, timestap = freenect.sync_get_video()
imgBGR = imgRGB # Create new image by copying original.
filters.bgr2rgb(imgRGB,imgBGR)
self._frame = imgBGR
elif (self.channel == depth.CV_CAP_OPENNI_DEPTH_MAP):
depthMap, timestamp = freenect.sync_get_depth()
#depthMap = depthMap.astype(numpy.uint16)
depthMap = depthMap.astype(numpy.uint8)
self._frame = depthMap
else:
print "Error - Unrecognised channel %d." % self.channel
self._frame = None
else:
retVal, self._frame = self._capture.retrieve(channel = self.channel)
self._nFrames = self._nFrames + 1
#print retVal, type(self._frame),
# self._frame.size, frameCount, self._nFrames
self._frame = cv2.cvtColor(self._frame, cv2.COLOR_BGR2GRAY)
#self._frame = self._frame.astype(numpy.uint8)
else:
pass
#print self._enteredFrame, self._frame
return self._frame
def update():
global projpts, rgb, depth
global hue,sat,val
global imgHsv,imgHue,imgSat,imgVal
range2=25.0
depth,_ = freenect.sync_get_depth()
rgb,_ = freenect.sync_get_video()
#convert numpy to opencv image
img = cv.fromarray(rgb)
#MAIN IMAGE PROCESSING WORK IN OPENCV HERE
cv.CvtColor(img, imgHsv, cv.CV_BGR2HSV)
cv.Split(imgHsv, imgHue, imgSat, imgVal, None)
cv.InRangeS(imgHue,(hue-range2, 0, 0),(hue+range2, 0, 0),imgHue)
cv.InRangeS(imgSat,(sat, 0, 0),(255, 0, 0),imgSat)
cv.InRangeS(imgVal,(val, 0, 0),(255, 0, 0),imgVal)
cv.And(imgHue,imgSat,imgBin)
cv.And(imgBin,imgVal,imgBin)
cv.Erode(imgBin,imgBin,None)
cv.Dilate(imgBin,imgBin,None)
cv.CvtColor(imgBin, imgOut, cv.CV_GRAY2BGR)
cv.ShowImage("Binary",imgOut)
#FINISH IMAGE PROCESSING
#return to numpy array
rgb = np.asarray(imgOut)
q = depth.astype(np.uint16)
X,Y = np.meshgrid(range(IMGWIDTH),range(IMGHEIGHT))
d = 1
projpts = calib.depth2xyzuv(q[::d,::d],X[::d,::d],Y[::d,::d])
def run ( self ):
#set up opencv windows
cv.NamedWindow("Camera", 1)
cv.NamedWindow("Binary", 1)
cv.NamedWindow("Settings", 1)
#set up sliders
cv.CreateTrackbar("Hue", "Settings", hue, 180, on_hueTrackbar)
cv.CreateTrackbar("Sat", "Settings", sat, 255, on_satTrackbar)
cv.CreateTrackbar("Val", "Settings", val, 255, on_valTrackbar)
#run a blocking while loop to capture depth and rgb to opencv window
while 1:
#pull in camera data
(depth,_),(rgb,_)=freenect.sync_get_depth(),freenect.sync_get_video()
depth=depth.astype(np.uint8)
h1, w1 = depth.shape[:2]
h2, w2 = rgb.shape[:2]
maxHeight= max(h1,h2)
vis = np.zeros((maxHeight, w1+w2), np.uint8)
vis2 = np.zeros((h2,w2), np.uint8)
cv.CvtColor(cv.fromarray(rgb), cv.fromarray(vis2), cv.CV_BGR2GRAY)
#display in a single window
vis[:maxHeight, :w1] = depth
vis[:maxHeight, w1:w1+w2] = vis2
cv.ShowImage("Camera",cv.fromarray(vis))
cv.WaitKey(100)
def controlLoop(self):
while True:
if self.loop_should_stop:
break
img, _ = freenect.sync_get_video()
center = self.droneCenterFromImage(img)
center = (
int(center[0] - img.shape[1] / 2),
int(center[1] - img.shape[0] / 2),
1
)
del img
logger.debug("Position: %s", center)
self.position = center
if self.isAtTarget(self.charger.getCoordinates(), 10): # If at charger, slowly charge
self.battery_level += 0.005
if self.battery_level > 1.0:
self.battery_level = 1.0
def getFrame(self):
self.when = time.time()
depth,_ = freenect.sync_get_depth()
rgb,_ = freenect.sync_get_video()
# Retain a copy otherwise we crash later
self.depth = numpy.copy(depth)
self.rgb = numpy.copy(rgb)
def get_image(self):
"""
**SUMMARY**
This method returns the Kinect camera image.
**RETURNS**
The Kinect's color camera image.
**EXAMPLE**
>>> k = Kinect()
>>> while True:
... k.get_image().show()
"""
if not FREENECT_ENABLED:
logger.warning("You don't seem to have the freenect library "
"installed. This will make it hard to use "
"a Kinect.")
return
video = freenect.sync_get_video(self.device_number)[0]
self.capture_time = time.time()
#video = video[:, :, ::-1] # RGB -> BGR
return Factory.Image(video.transpose([1, 0, 2]), camera=self)
def disp_thresh(lower, upper, show_masked_rgb=True):
depth, timestamp = freenect.sync_get_depth()
min_depth = depth.min()
video,_ = freenect.sync_get_video()
if show_masked_rgb:
video = video.astype(np.uint8)
depthmask = (255*np.logical_and(depth>lower,depth<upper)).reshape(480,640,1)
depthmask = depthmask.astype(np.uint8)
masked_video = video & depthmask
#print reduce(lambda count, curr: curr>0 and count+1 or count,masked_video.flatten(),0)
cv.ShowImage('RGB',frame_convert.video_cv(masked_video.reshape(480,640,3)))
depth = 255 * np.logical_and(depth > lower, depth < upper)
depth = depth.astype(np.uint8)
image = cv.CreateImageHeader((depth.shape[1], depth.shape[0]),
cv.IPL_DEPTH_8U,
1)
cv.SetData(image, depth.tostring(),
depth.dtype.itemsize * depth.shape[1])
canny = doCanny(image,150.0,200.0,7)
templates = template_match(image,template)
smoothed = smooth(canny)
cv.ShowImage('Depth', image)
return depth,canny,min_depth
def get_frame(self):
image, success = freenect.sync_get_video()
# We are using Motion JPEG, but OpenCV defaults to capture raw images,
# so we must encode it into JPEG in order to correctly display the
# video stream.
ret, jpeg = cv2.imencode('.jpg', image[:, :, ::-1])
return jpeg.tobytes()
def show_detector():
image = frame_convert.video_cv(freenect.sync_get_video()[0]);
# cascade classifiers
face_cascade = cv2.CascadeClassifier('opencv_data/haarcascades/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('opencv_data/haarcascades/haarcascade_eye.xml')
# convert image to grayscale to use it with classifers
gray = cv2.cvtColor(cv2array(image), cv2.COLOR_BGR2GRAY);
# save previous image and use copy
img = image;
# detect and highlight faces
faces = face_cascade.detectMultiScale(gray, 1.3, 5);
for (x,y,w,h) in faces:
cv.Rectangle(img,(x,y),(x+w,y+h),(0,0,255),2)
# detect and highlight eyes
eyes = eye_cascade.detectMultiScale(gray)
for (ex,ey,ew,eh) in eyes:
cv.Rectangle(img,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
# show detector window
cv.ShowImage('Detector', img)
def showlive():
global count, frames
cv.NamedWindow('Depth')
cv.NamedWindow('Video')
cv.MoveWindow('Depth', 100, 100)
cv.MoveWindow('Video', 745, 100)
print('Press ESC in window to stop')
print('Press Space to convert current to PLY')
print('Press k to stop live capture')
while 1:
imgdepth = fc.depth_cv(freenect.sync_get_depth()[0])
imgvideo = fc.video_cv(freenect.sync_get_video()[0])
cv.ShowImage('Depth', imgdepth)
cv.ShowImage('Video', imgvideo)
inp = cv.WaitKey(100)
if inp != -1:
inp = chr(inp % 1048576)
if inp == ' ': # space for capture and convert
print 'capturing images'
captureimage()
print 'done capturing'
elif inp.isdigit():
frames = ord(inp) - ord('0')
print 'setting the number of frames to capture to %d' % frames
elif inp == 'k':
break
count = count + 1
cv.DestroyWindow('Depth')
cv.DestroyWindow('Video')
def get_bgr(self): if self.bgr == 'KINECT': frame, timestamp = freenect.sync_get_video() bgr = frame[:, :, ::-1] # RGB -> BGR return bgr else: ret, img = self.bgr.read() return img
def getVideo():
video, timestamp = freenect.sync_get_video()
np.clip(video, 0, 2**10 - 1, video)
# video >>= 2
video = video.astype(np.uint8)
return video
def detect():
matches = []
img = freenect.sync_get_video()[0]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cups = classifier_rect.detectMultiScale(gray, 1.3, 3)
for (x,y,w,h) in cups:
matches.append((x,y,w,h))
return (matches,img)
def get_real():
array = freenect.sync_get_video()[0]
#~ print "real "+str(np.shape(array))
array = cv2.cvtColor(array,cv2.COLOR_RGB2BGR)
#array = cv2.cvtColor(array,cv2.COLOR_RGB2GRAY)
return array
def capture_pair(index, ir_img):
rgb_img = freenect.sync_get_video(format=freenect.VIDEO_RGB)[0]
detected, corners = cv.FindChessboardCorners(prepare_cv(rgb_img), (8,6))
if not detected:
return index
imsave("ir%03d.png"%index, ir_img)
imsave("rgb%03d.png"%index, rgb_img)
return index + 1
def initialize():
matrix = np.load('matrix.npz')['mtx']
dist = np.load('dist.npz')['dist']
while(1):
ret = fiducial.find(freenect.sync_get_video()[0],matrix,dist)
if ret[0]:
break
print ret[1]
return (matrix, dist, ret[1])
def analyzeKinect(modelName):
#FeatureMatrix = np.load(modelName+".npy")
HAAR_CASCADE_PATH_FRONTAL = "haarcascade_frontalface_default.xml"
HAAR_CASCADE_PATH_PROFILE = "haarcascade_frontalface_default.xml"
cascadeFrontal = cv2.cv.Load(HAAR_CASCADE_PATH_FRONTAL);
cascadeProfile = cv2.cv.Load(HAAR_CASCADE_PATH_PROFILE);
storage = cv2.cv.CreateMemStorage()
count = 0
start = time.time()
prevTime = start
dirName1 = "Real_Scenario"
if os.path.exists(dirName1):
shutil.rmtree(dirName1)
os.makedirs(dirName1)
while 1:
rgb = freenect.sync_get_video()[0]
depth = freenect.sync_get_depth()[0]
bgr = cv2.cvtColor(rgb, cv2.cv.CV_RGB2BGR)
# elapsedTime = "%08.3f" % (time.time() - start)
elapsedTime = "%08.3f" % (time.time())
# process depth:
depth >> 5
meter = 1.0 / (depth * (-0.0030711016) + 3.3309495161)
faces = detect_faces(rgb, cascadeFrontal, cascadeProfile, storage, rgb.shape[1], 30)
countFaces = 0
bgr_plot = bgr.copy()
print len(faces)
if len(faces)>-1:
strFile =dirName1 + os.sep + "image{0:04d}".format(count)
cv2.imwrite(strFile+'.jpg',bgr)
cv2.cv.ShowImage("depth", visual_frame_convert.pretty_depth_cv(np.uint16(depth) )); cv2.moveWindow('depth', 720, 0)
cv2.imshow("bgr",bgr); cv2.moveWindow('rgb', 0, 0)
count += 1
curTime = time.time()
#print (curTime - prevTime)
prevTime = curTime
if cv2.cv.WaitKey(10) == 27:
break
def get_frame(): # Get the depth depth_frame, depth_timestamp = freenect.sync_get_depth() # Get the color image rgb_frame, rgb_timestamp = freenect.sync_get_video() return depth_frame, rgb_frame, depth_timestamp
def MatchAllCapture(save, maxdist=200):
from os.path import isfile, join
import freenect
from os import listdir
import cv2
import numpy as np
import itertools
import sys
import time
#Clear all cv windows
#cv2.destroyAllWindows()
#Prepare a list of different training images
pathlarge = "TrainingImages/AxonCups/LargeCup/"
pathmedium = "TrainingImages/AxonCups/MediumCup/"
pathsmall = "TrainingImages/AxonCups/SmallCup/"
pathtest = "TestImages"
largecups = [ f for f in listdir(pathlarge) if isfile(join(pathlarge,f)) and f[0]<>"."]
mediumcups = [ f for f in listdir(pathmedium) if isfile(join(pathmedium,f)) and f[0]<>"."]
smallcups = [ f for f in listdir(pathsmall) if isfile(join(pathsmall,f)) and f[0]<>"."]
testimages = [ f for f in listdir(pathtest) if isfile(join(pathtest,f)) and f[0]<>"."]
#img = cv2.imread(str(pathtest+"/"+testimages[ImageNo]))
img, timestamp = freenect.sync_get_video()
depth, timestamp = freenect.sync_get_depth(format=freenect.DEPTH_REGISTERED)
detector = cv2.FeatureDetector_create("FAST")
descriptor = cv2.DescriptorExtractor_create("SIFT")
skp = detector.detect(img)
skp, sd = descriptor.compute(img, skp)
KeyPointsTotalList = []
DistsTotalList = []
for i in largecups+mediumcups+smallcups:
if i in largecups:
temp = cv2.imread(str(pathlarge+"/"+i))
elif i in mediumcups:
temp = cv2.imread(str(pathmedium+"/"+i))
elif i in smallcups:
temp = cv2.imread(str(pathsmall+"/"+i))
#print i
KeyPointsOut = findKeyPointsDist(img,temp,skp,sd,maxdist)
KeyPointsTotalList += KeyPointsOut[0]
DistsTotalList += KeyPointsOut[1]
indices = range(len(DistsTotalList))
indices.sort(key=lambda i: DistsTotalList[i])
DistsTotalList = [DistsTotalList[i] for i in indices]
KeyPointsTotalList = [KeyPointsTotalList[i] for i in indices]
img1 = img
if save == 1:
saveImageMappedPoints(img, KeyPointsTotalList, 1)
return KeyPointsTotalList, DistsTotalList, img, depth
def show_faces():
image = video_to_bgr(freenect.sync_get_video()[0])
min_size = (20, 20)
image_scale = 2
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
gray = cv.CreateImage((image.width, image.height), 8, 1)
small_image = cv.CreateImage((cv.Round(image.width / image_scale), cv.Round(image.height / image_scale)), 8, 1)
cv.CvtColor(image, gray, cv.CV_BGR2GRAY)
cv.Resize(gray, small_image, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_image, small_image)
faces = cv.HaarDetectObjects(
small_image,
face_cascade,
cv.CreateMemStorage(0),
haar_scale,
min_neighbors,
haar_flags,
min_size
)
if faces:
for ((x, y, w, h), n) in faces:
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
cv.SetImageROI(image, (pt1[0], pt1[1], pt2[0] - pt1[0], int((pt2[1] - pt1[1]) * 0.7)))
eyes = cv.HaarDetectObjects(
image,
eye_cascade,
cv.CreateMemStorage(0),
haar_scale,
min_neighbors,
haar_flags,
(15, 15)
)
if eyes:
for eye in eyes:
cv.Rectangle(
image,
(eye[0][0], eye[0][1]),
(eye[0][0] + eye[0][2], eye[0][1] + eye[0][3]),
cv.RGB(255, 0, 0),
1,
8,
0
)
cv.ResetImageROI(image)
return image
def show_video():
video = freenect.sync_get_video()[0]
bgr = frame_convert.video_cv(video)
video = video / 255.00
bgra = bgra_from_depth(video, depth)
rgb = alpha_blend(bgra, background)
rgb = cv.fromarray(rgb)
cv.ShowImage('Video', rgb)
def get_video():
return frame_convert.video_cv(freenect.sync_get_video()[0])
) # K-means clustering
if __name__ == '__main__':
import sys
import getopt
import time
# create main window
cv2.namedWindow("camera", 1)
frame_count = 0
while True:
ch = 0xFF & cv2.waitKey(10)
if True:
# convert Bayer GB to RGB for display
rgb_frame = cv2.cvtColor(freenect.sync_get_video()[0],
cv2.COLOR_RGB2BGR)
# convert Bayer BG to Grayscale for corner detections
grey_frame = cv2.cvtColor(rgb_frame, cv2.COLOR_BGR2GRAY)
frames_str = "Frames for Calibration:" + str(frame_count)
cv2.putText(rgb_frame, frames_str, (50, 50), font, 1,
(255, 255, 255), 2, cv2.LINE_AA)
if ch == 32: # ord(' ') == 32
# find location of corners in image, this is really slow if no corners are seen.
found, corners = cv2.findChessboardCorners(
grey_frame, pattern_size, None)
if found:
# find sub pixel estimate for corner location
# Refines the corner locations, cv2.cornerSubPix(image, corners, winSize, zeroZone, criteria)
cv2.cornerSubPix(grey_frame, corners, (5, 5), (-1, -1),
criteria)
output_hsv = "H:%d, S:%d, V:%d" % (h, s, v)
tmp = img.copy()
cv2.putText(tmp,output_rgb, (10,20), font, 0.5, (255,255,255))
cv2.putText(tmp,output_hsv, (10,40), font, 0.5, (255,255,255))
cv2.imshow('window',tmp)
if event == cv2.EVENT_LBUTTONDOWN:
print "bgr: (%d,%d,%d) \n hsv: (%d,%d,%d)" % (b,g,r,h,s,v)
if(freenect.sync_get_depth() == None):
kinectConnected = False
else:
kinectConnected = True
if(kinectConnected):
rgb_frame = freenect.sync_get_video()[0]
img = cv2.cvtColor(rgb_frame, cv2.COLOR_RGB2BGR)
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
kernel = np.ones((5, 5), np.uint8)
cv2.namedWindow("window",cv2.WINDOW_AUTOSIZE)
cv2.imshow('window', img)
yellow_lower_range = np.array([23,200,200])
yellow_upper_range = np.array([27,255,255])
orange_lower_range = np.array([6,100,100])
orange_upper_range = np.array([14,255,255])
pink_lower_range = np.array([167, 100, 100])
pink_upper_range = np.array([170, 200, 255])
black_lower_range = np.array([0, 0, 0]) # a lot of the image is black so TODO find the least area and that will be the block
black_upper_range = np.array([180, 255, 50])
purple_lower_range = np.array([155, 90, 100])
def realsense_run():
# ROS node setup
frame_id = 'Realsense node'
if not test:
rospy.init_node(f'Realsense_main', anonymous=True)
diag_obj = diag_class(frame_id=frame_id,
user_id=args.user_id,
user_name=args.user_name,
queue=1)
rate = rospy.Rate(10)
if args.classify:
try:
frames = cam.pipeline.wait_for_frames()
if args.depth:
color_image, depth_colormap, depth_image = cam.depth_frames(
frames)
else:
color_image = np.array(
frame_convert2.video_cv(freenect.sync_get_video()[0]))
#color_image = cam.colour_frames(frames)
im_classifier = classifier(args.comp_device, args.weights,
args.img_size, color_image,
args.conf_thres, args.iou_thres)
if not test:
obj_obj = obj_class(frame_id=frame_id,
names=im_classifier.names,
queue=1)
except Exception as e:
print("**Classifier Load Error**")
traceback.print_exc(file=sys.stdout)
if not test:
diag_obj.publish(2, f"load classifier error: {e}")
raise
diag_timer = time.time()
while (not rospy.is_shutdown()) or test:
try:
# Get frameset of color and depth
frames = cam.pipeline.wait_for_frames()
if args.depth:
color_image, depth_colormap, depth_image = cam.depth_frames(
frames)
else:
#color_image = cam.colour_frames(frames)
color_image = np.array(
frame_convert2.video_cv(freenect.sync_get_video()[0]))
if args.classify:
try:
if args.depth:
color_image, det = im_classifier.detect(
color_image, depth_image, depth_histogram=False)
else:
color_image, det = im_classifier.detect(
color_image, None)
if not test:
obj_obj.publish(det)
except Exception as e:
print("**Classifier Detection Error**")
traceback.print_exc(file=sys.stdout)
if not test:
diag_obj.publish(2, f"load classifier error: {e}")
# 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)
#image = scale(bg_removed)
if (time.time() - diag_timer) > 1:
print(time.time())
if not test:
diag_obj.publish(0, f"Running")
diag_timer = time.time()
except TypeError as e:
time.sleep(1)
if not test:
diag_obj.publish(2, f"TypeError")
except Exception as e:
print("**Get Image Error**")
if not test:
diag_obj.publish(2, f"Realsense image error: {e}")
traceback.print_exc(file=sys.stdout)
break
#im_screw_states, tally = im_screw_detect.detect_screws(image, args.disp)
#im_screw_states = im_screw_states.tolist()
if args.disp:
if args.depth:
disp_im = np.hstack((color_image, depth_colormap))
else:
disp_im = color_image
cv2.namedWindow('Realsense viewer', cv2.WINDOW_AUTOSIZE)
cv2.imshow('Realsense viewer', disp_im)
key = cv2.waitKey(1)
# Press esc or 'q' to close the image window
if key & 0xFF == ord('q') or key == 27:
cv2.destroyAllWindows()
break
if not test:
rate.sleep()
else:
#time.sleep(1)
pass
def show_video():
cv.ShowImage('Video', frame_convert.video_cv(freenect.sync_get_video()[0]))
def get_video():
frame = frame_convert2.video_cv(freenect.sync_get_video()[0])
return frame
def get_video():
video, _ = freenect.sync_get_video()
return video
def get_video():
array, _ = freenect.sync_get_video()
array = cv2.cvtColor(array, cv2.COLOR_RGB2BGR)
ret, jpeg = cv2.imencode('.jpeg', array)
return jpeg.tostring()
def get_img(mode):
# This was intended to inhibit the stream warnings to the console, but it did not work:
#text_trap = io.StringIO()
#sys.stderr = text_trap
if (mode == IMG_RGB):
# gets one frame from the RGB camera
frame = freenect.sync_get_video()[0]
#frame = clahe.apply(frame)
#background subsraction
#Learning Rate Parameter / -1 auto, 0 not updated at all, 1 new from last frame
fgMask = backSub.apply(frame, learningRate=-1)
#threshold to get rid of any other color then black and white
#anything lighter then 127 will be set to 255 (white) anything lower to 0 (black)
# we dont need this if we turn shadows off
#thresold expects a single channel image // with shadows enabled its a greyscale image
#ret, fgMask = cv2.threshold(fgMask,127,255,cv2.THRESH_BINARY)
#erosion
#fgMask = cv2.erode(fgMask, kernel, iterations = 1)
#dilation
#fgMask = cv2.dilate(fgMask, kernel, iterations = 1)
fgMask = cv2.morphologyEx(
fgMask, cv2.MORPH_CLOSE,
kernel_big) # closes gaps smaller than 9x9 pixels
#change color space from grayscale to BGR so we can draw a colored box later around blobs
#col = cv2.cvtColor(fgMask, cv2.COLOR_GRAY2BGR)
elif (mode == IMG_DEPTH):
frame = freenect.sync_get_depth()[0] # gets the Kinect depth image
frame = 255 * np.logical_and(frame >= DEPTH - THRESHOLD,
frame <= DEPTH + THRESHOLD)
# we make sure its a 8-bit single-channel image // do we need this
#frame = frame.astype(np.uint8)
#background subsration // do we need this? // we do this already with the threshold
fgMask = backSub.apply(frame, learningRate=-1)
#do we need this // there are not grey colors in the depth image after the threshold
#ret, fgMask = cv2.threshold(fgMask,127,255,cv2.THRESH_BINARY)
#erosion
#fgMask = cv2.erode(fgMask, kernel, iterations = 1) # morphological erode with 3x3
#dilation
#fgMask = cv2.dilate(fgMask, kernel, iterations=1)
# do we need this
fgMask = cv2.morphologyEx(
fgMask, cv2.MORPH_CLOSE,
kernel_big) # closes gaps smaller than 9x9 pixels
#change color space from grayscale to BGR so we can draw a colored box later around blobs
#col = cv2.cvtColor(fgMask, cv2.COLOR_GRAY2BGR)
# Problem: this function gives us sometimes only one blob instead of two
ret, labels, stats, centroids = cv2.connectedComponentsWithStats(fgMask)
# Reset output to stdout:
#sys.stderr = sys.__stderr__
return ret, frame, fgMask, labels, stats, centroids
for v in range(rgb.size[1]):
for u in range(rgb.size[0]):
color = rgb.getpixel((u,v))
Z=(depth[v][u])*0.1
if Z==0:continue
X=(u - (w / 2.0)) * (Z + minDistance) * scaleFactor
Y=(v - (h / 2.0)) * (Z + minDistance) * scaleFactor
points.append("%f %f %f %d %d %d 0\n"%(X,Y,Z,color[0],color[1],color[2]))
with open(ply_file,"w") as ply:
ply.write("ply\n" + \
"format ascii 1.0\n" + \
"element vertex {}\n".format(len(points)) + \
"property float x\n" + \
"property float y\n" + \
"property float z\n" + \
"property uchar red\n" + \
"property uchar green\n" + \
"property uchar blue\n" + \
"property uchar alpha\n" + \
"end_header\n" + \
"{}".format("".join(points)))
rgb = Image.fromarray(freenect.sync_get_video()[0])
KinectDepth,_=freenect.sync_get_depth(0, freenect.DEPTH_MM)
depth=np.array(KinectDepth)
generate_pointcloud(rgb, depth, "PointCloud.ply")
def main():
print("Running...")
count = 0
# grab one frame at first to compare for background substraction
frame, timestamp = freenect.sync_get_video()
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
frame_resized = imutils.resize(frame, width=min(400, frame.shape[1]))
frame_resized_grayscale = cv2.cvtColor(frame_resized, cv2.COLOR_BGR2GRAY)
# initialize centroid
center = [[frame_resized.shape[1] / 2, frame_resized.shape[0] / 2]]
center_fix = []
# defining min cuoff area
min_area = (480 / 400) * frame_resized.shape[1]
key = ''
while key != 113: # for 'q' key
# start timer
timer = cv2.getTickCount()
starttime = time.time()
previous_frame = frame_resized_grayscale
# retrieve new RGB frame image
frame, timestamp = freenect.sync_get_video()
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
frame_resized = imutils.resize(frame, width=min(400, frame.shape[1]))
frame_resized_grayscale = cv2.cvtColor(frame_resized,
cv2.COLOR_BGR2GRAY)
temp = background_subtraction(previous_frame, frame_resized_grayscale,
min_area)
# retrieve depth map
depth, timestamp = freenect.sync_get_depth()
depth = imutils.resize(depth, width=min(400, depth.shape[1]))
# orig = image.copy()
if temp == 1:
frame_processed, center_fix = detect_people(frame_resized, center)
if (len(center_fix) > 0):
#xnorm = sercomm(center_fix[0][0]) # send x_norm to nucleo
#print("X_NORM: " + str(xnorm))
distance = depth[(int)(center_fix[0][1]),
(int)(center_fix[0][0])]
cv2.putText(frame_processed,
"distance: {:.2f}".format(distance),
(10, frame_processed.shape[0]),
cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 3)
#print("Distance: " + str(depth.shape) + str(frame_processed.shape))
i = 0
for b in center_fix:
cv2.putText(
frame_processed,
"Point " + str(i) + ": " + str(b[0]) + " " + str(b[1]),
(10, frame_processed.shape[0] - (i + 1) * 35), font, 0.65,
(0, 0, 255), 3)
#print(b)
#print("Point "+str(i)+": "+str(b[0])+" "+str(b[1]))
i = i + 1
cv2.imshow("Detected Human", frame_processed)
#cv2.imshow("Depth", depth)
# cv2.imshow("Original", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("b"):
break
endtime = time.time()
print("Time to process a frame: " + str(starttime - endtime))
else:
count = count + 1
print("Number of frame skipped in the video= " + str(count))
# cv2.putText(depth, "distance: {:.2f}".format(distance), (10, depth.shape[0] - 120), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 3)
# compute the fps
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
print("FPS: " + str(fps))
key = cv2.waitKey(5)
cv2.destroyAllWindows()
print("\nFINISH")
def show_video():
array, _ = freenect.sync_get_video()
array = cv.cvtColor(array, cv.COLOR_RGB2BGR)
cv.imshow('Video', array)
def __init__(self):
self.array, _ = freenect.sync_get_video()
def get_video(pts):
array, _ = freenect.sync_get_video()
array = cv2.cvtColor(array, cv2.COLOR_RGB2BGR)
return four_point_transform(array, pts)
def process(quit, callback, **kwargs):
size = (-1, -1)
while quit.value == 0:
use_depth = kwargs.get('use_depth', True)
use_video = kwargs.get('use_video', False)
video_format = kwargs.get('video_format', 'ir8')
index = kwargs.get('index', 0)
depth = None
video = None
depth_str = None
video_str = None
video_channels = 0
if video_format == 'rgb':
video_format = freenect.VIDEO_RGB
video_channels = 3
elif video_format == 'ir8':
video_format = freenect.VIDEO_IR_8BIT
video_channels = 1
else:
assert ('unsupported')
if use_depth:
# fetch depth from freenect
depth = freenect.sync_get_depth(index=index)
if depth is None:
print 'Kinect: kinect not available, restarting in 5 seconds.'
sleep(5)
continue
video = None
if use_video:
video = freenect.sync_get_video(index=index)
# extract data & timestamp
data, timestamp = depth
# update image size from the first image
if size == (-1, -1):
size = data.shape[1], data.shape[0]
print 'Kinect: size is', size
data = convert_depth_to_8bit(data)
# convert to opencv image
depth = cv.CreateImageHeader(size, cv.IPL_DEPTH_8U, 1)
cv.SetData(depth, data.tostring(),
data.dtype.itemsize * data.shape[1])
cv.Flip(depth, depth, 0)
depth_str = str(depth.tostring())
if use_video:
video = freenect.sync_get_video(index=index, format=video_format)
if video is None:
print 'Kinect: kinect not available, restarting in 5 seconds.'
sleep(5)
continue
data, timestamp = video
if size == (-1, -1):
size = data.shape[1], data.shape[0]
print 'Kinect: size is', size
# convert to opencv image
video = cv.CreateImageHeader(size, cv.IPL_DEPTH_8U, video_channels)
cv.SetData(video, data.tostring(),
data.dtype.itemsize * data.shape[1] * video_channels)
cv.Flip(video, video, 0)
video_str = video.tostring()
arg = {
'depth': depth,
'depth_str': depth_str,
'video': video,
'video_str': video_str,
'size': size
}
if callback(arg, kwargs) is False:
break
for q in kwargs.values():
if q.__class__.__name__ == 'Queue':
while q.qsize():
q.get()
def get_video(): array,_ = freenect.sync_get_video() array = cv2.cvtColor(array,cv2.COLOR_RGB2BGR) return array
def get_video():
return np.array(frame_convert2.video_cv(freenect.sync_get_video()[0]))
def get_video():
return freenect.sync_get_video()[0]