def depth_calibration():
(init_depth, _) = get_depth()
timer = time.time()
marge_noise = 200
for hd in range(0, 480):
for wd in range(0, 640):
if (init_depth[hd][wd] != 2047):
min_mat[hd][wd] = init_depth[hd][wd]
max_mat[hd][wd] = init_depth[hd][wd]
for i in range(0, 5):
print(i)
(depthc, _) = get_depth()
for hd in range(0, 480):
for wd in range(0, 640):
if (depthc[hd][wd] != 2047):
if depthc[hd][wd] < min_mat[hd][wd]:
min_mat[hd][wd] = depthc[hd][wd]
if depthc[hd][wd] > max_mat[hd][wd]:
max_mat[hd][wd] = depthc[hd][wd]
#Noise Calculation : Max - Min matrix
for hd in range(0, 480):
for wd in range(0, 640):
if (abs(max_mat[hd][wd] - min_mat[hd][wd]) < marge_noise):
noise[hd][wd] = max_mat[hd][wd] - min_mat[hd][wd]
else:
noise[hd][wd] = 0
timer = time.time() - timer
print('Calibration Time :', timer)
return (1)
def hand_tracker():
ControlVector = 2*[3*[0]]
(depth,_) = get_depth()
centroidList = list() #Initiate centroid list
#RGB Color tuples
pygame.init() #Initiates pygame
xSize,ySize = 640,480 #Sets size of window
screen = pygame.display.set_mode((xSize,ySize),pygame.RESIZABLE) #creates main surface
screenFlipped = pygame.display.set_mode((xSize,ySize),pygame.RESIZABLE) #creates surface that will be flipped (mirror display)
screen.fill(BLACK) #Make the window black
done = False #Iterator boolean --> Tells programw when to terminate
while not done:
screen.fill(BLACK) #Make the window black
(depth,_) = get_depth() #Get the depth from the kinect
depth = depth.astype(np.float32) #Convert the depth to a 32 bit float
_,depthThresh = cv2.threshold(depth, 750, 255, cv2.THRESH_BINARY_INV) #Threshold the depth for a binary image. Thresholded at 600 arbitary units
_,back = cv2.threshold(depth, 1000, 255, cv2.THRESH_BINARY_INV) #Threshold the background in order to have an outlined background and segmented foreground
blobData = BlobAnalysis(depthThresh) #Creates blobData object using BlobAnalysis class
blobDataBack = BlobAnalysis(back) #Creates blobDataBack object using BlobAnalysis class
for cont in blobDataBack.contours: #Iterates through contours in the background
pygame.draw.lines(screen,YELLOW,True,cont,3) #Colors the binary boundaries of the background yellow
for i in range(blobData.counter): #Iterate from 0 to the number of blobs minus 1
pygame.draw.circle(screen,BLUE,blobData.centroid[i],10) #Draws a blue circle at each centroid
centroidList.append(blobData.centroid[i]) #Adds the centroid tuple to the centroidList --> used for drawing
pygame.draw.lines(screen,RED,True,blobData.cHull[i],3) #Draws the convex hull for each blob
pygame.draw.lines(screen,GREEN,True,blobData.contours[i],3) #Draws the contour of each blob
"""try:
print(depth[blobData.centroid[i][1]][blobData.centroid[i][0]])
except:
pass"""
try:
ControlVector[0][0] = blobData.centroid[0][1]
ControlVector[0][1] = blobData.centroid[0][0]
ControlVector[0][2] = depth[ControlVector[0][0]][ControlVector[0][1]]
ControlVector[1][0] = blobData.centroid[1][1]
ControlVector[1][1] = blobData.centroid[1][0]
ControlVector[1][2] = depth[ControlVector[1][0]][ControlVector[1][1]]
print(ControlVector)
except:
pass
"""
#Drawing Loop
#This draws on the screen lines from the centroids
#Possible exploration into gesture recognition :D
for cent in centroidList:
pygame.draw.circle(screen,BLUE,cent,10)
"""
pygame.display.set_caption('Kinect Tracking') #Makes the caption of the pygame screen 'Kinect Tracking'
del depth #Deletes depth --> opencv memory issue
screenFlipped = pygame.transform.flip(screen,1,0) #Flips the screen so that it is a mirror display
screen.blit(screenFlipped,(0,0)) #Updates the main screen --> screen
pygame.display.flip() #Updates everything on the window
for e in pygame.event.get(): #Itertates through current events
if e.type is pygame.QUIT: #If the close button is pressed, the while loop ends
done = True
def main_engine():
while True:
contour_list = []
(depth, _) = get_depth()
(rgb, _) = get_video()
orig = np.array(rgb[::1, ::1, ::-1])
fgmask = fgbg.apply(orig)
ret, thresh = cv2.threshold(fgmask, 127, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
kernel = np.ones((5, 5), np.uint8)
erosion = cv2.erode(thresh, kernel, iterations=1)
im2, contours, hierarchy = cv2.findContours(erosion, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
index = 0
for cnt in contours:
for node in cnt:
for elem in node:
y = elem[0].astype(int)
x = elem[1].astype(int)
if (y > min(xlist)) and (y < max(xlist)) and (
x > min(ylist)) and (x < max(ylist)):
if (depth[x][y] + 8 < min_mat[x][y] - noise[x][y]
) and (depth[x][y] < 2047) and (
depth[x][y] != 0) and (depth[x][y] != 255 and
(min_mat[x][y] < 2047)):
contour_list.append([x, y])
index += 1
if (len(contour_list)):
print(str(contour_list))
def doloop():
global depth, rgb, initdepth
min = 0
(initdepth, _) = get_depth()
while True:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
#cv2.rectangle(rgb, (230, 100), (440, 300), (255, 0, 0), 2)
test = np.array(rgb[::2, ::2, ::-1])
cv2.imshow('FIRST', test)
# Build a two panel color image
d3 = np.dstack((depth, depth, depth)).astype(np.uint8)
da = np.hstack((d3, rgb))
# Simple Downsample
cv2.imshow('both', np.array(da[::2, ::2, ::-1]))
cv2.waitKey(5)
def doloop():
global depth, rgb
path = "./kpictures/"
num = glob.glob(str(path) + "*.avi")
i = len(num)
video = cv2.VideoWriter(path + 'video' + str(i) + '.avi',
cv2.cv.CV_FOURCC('D', 'I', 'V', 'X'), 20,
(1280, 480))
if not video.isOpened():
print 'error with video opening'
sys.exit(1)
print 'press "q" to exit'
while True:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
# Build a two panel color image
d3 = np.dstack((depth, depth, depth)).astype(np.uint8, copy=False)
bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
da = np.hstack((d3, bgr)).astype(np.uint8, copy=False)
# src = cv2.cv.fromarray(da)
cv2.imshow('both', da)
video.write(da)
k = cv2.waitKey(5)
if (k > -1) and (k < 256):
if chr(k) == 'q':
# video.release()
cv2.destroyAllWindows()
sys.exit(0)
def getDepth(self):
#self.depth = np.zeros((ROWS,COLS),dtype=np.uint8)
self.temp_depth = np.rot90(get_depth()[0])
for row in range(len(self.depth)):
for col in range(len(self.depth[0])):
#print("row:{}, col:{}, indexing from {}:{}, {}:{}".format(row, col, int(row * ROW_DIV), int((row + 1) * ROW_DIV), int(col * COL_DIV), int((col + 1) * COL_DIV)))
self.depth[row][col] = self.temp_depth[int(row * ROW_DIV):int((row + 1) * ROW_DIV), int(col * COL_DIV):int((col + 1) * COL_DIV)].mean() / 8
self.depth[row][col] = np.bitwise_xor(self.depth[row][col], 255)
self.depth[(self.depth >= 0) & (self.depth < 32)] = 0
self.depth[(self.depth >= 32) & (self.depth < 56)] = 32
self.depth[(self.depth >= 56) & (self.depth < 72)] = 56
self.depth[(self.depth >= 72) & (self.depth < 88)] = 72
self.depth[(self.depth >= 88) & (self.depth < 96)] = 88
self.depth[(self.depth >= 96) & (self.depth < 104)] = 96
self.depth[(self.depth >= 104) & (self.depth < 112)] = 104
self.depth[(self.depth >= 112) & (self.depth < 120)] = 112
self.depth[(self.depth >= 120) & (self.depth < 128)] = 120
self.depth[(self.depth >= 128) & (self.depth < 136)] = 128
self.depth[(self.depth >= 136) & (self.depth < 144)] = 136
self.depth[(self.depth >= 144) & (self.depth < 152)] = 144
self.depth[(self.depth >= 152) & (self.depth < 160)] = 152
self.depth[(self.depth >= 160) & (self.depth < 168)] = 160
self.depth[(self.depth >= 168) & (self.depth < 184)] = 168
self.depth[(self.depth >= 184) & (self.depth < 200)] = 184
self.depth[(self.depth >= 200) & (self.depth < 224)] = 200
self.depth[(self.depth >= 224) & (self.depth < 256)] = 224
def center_detect():
result = []
(rgb, _) = get_video()
(depth, _) = get_depth()
orig = np.array(rgb[::1, ::1, ::-1])
fgmask = fgbg.apply(orig)
ret, thresh = cv2.threshold(fgmask, 127, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
kernel = np.ones((5, 5), np.uint8)
erosion = cv2.erode(thresh, kernel, iterations=1)
im2, contours, hierarchy = cv2.findContours(erosion, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
index = 0
for cnt in contours:
if cv2.arcLength(cnt, True) < 250:
contours.pop(index)
else:
index += 1
M = cv2.moments(cnt)
if (M['m00'] != 0):
y = int(M['m10'] / M['m00'])
x = int(M['m01'] / M['m00'])
if (y > min(xlist)) and (y < max(xlist)) and (
x > min(ylist)) and (x < max(ylist)):
if (depth[x][y] + 8 < min_mat[x][y] - noise[x][y]) and (
depth[x][y] < 2047) and (depth[x][y] != 0) and (
depth[x][y] != 255 and (min_mat[x][y] < 2047)):
result.append([x, y])
return result
def doloop():
global depth, rgb
path = "./kpictures/"
num = glob.glob(str(path) + "*.jpg")
i = len(num)
count = 0
while True:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
# Build a two panel color image
d3 = np.dstack((depth, depth, depth)).astype(np.uint8, copy=False)
bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
da = np.hstack((d3, bgr)).astype(np.uint8, copy=False)
# src = cv2.cv.fromarray(da)
cv2.imshow('both', da)
k = cv2.waitKey(5)
if (k > -1) and (k < 256):
if chr(k) == 'q':
cv2.destroyAllWindows()
sys.exit(0)
if (count % total) == 0:
cv2.imwrite(path + "both" + str(i) + ".jpg", da)
i += 1
count += 1
def doloop():
global depth, rgb
path = "./kpictures/"
num = glob.glob(str(path) + "*.jpg")
i = len(num)
count = 0
while True:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
# Build a two panel color image
d3 = np.dstack((depth, depth, depth)).astype(np.uint8, copy=False)
bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
da = np.hstack((d3, bgr)).astype(np.uint8, copy=False)
# src = cv2.cv.fromarray(da)
cv2.imshow('both', da)
k = cv2.waitKey(5)
if (k > -1) and (k < 256):
if chr(k) == 'q':
cv2.destroyAllWindows()
sys.exit(0)
if (count % total) == 0:
cv2.imwrite(path + "both" + str(i) + ".jpg", da)
i += 1
count += 1
def doloop():
global depth, rgb
face_cascade = cv2.CascadeClassifier(
'haarcascade/haarcascade_frontalface_default.xml')
midx, midy = 0, 0
while True:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
# Build a two panel color image
mn = np.min(depth)
mx = np.max(depth)
output = np.uint8((depth - mn) * 255 / (mx - mn))
gray = cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY)
gray = cv2.equalizeHist(gray)
face = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in face:
midx, midy = x + w / 2, y + h / 2
ret2, th2 = cv2.threshold(output, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# d3 = np.dstack((depth,depth,depth)).astype(np.uint8)
# da = np.hstack((d3,rgb))
# Simple Downsample
# cv2.imshow('both',np.array(da[::2,::2,::-1]))
cv2.imshow('frame', th2)
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
def update(self):
rate = rospy.Rate(10) # 10hz
while not rospy.is_shutdown():
# Get a fresh frame
depth,_ = get_depth()
frame,_ = get_video()
self.BD.detect(frame)
self.DA.analyse(depth)
self.I2M.find(self.BD.get_xpos())
centroid = self.DA.get_centroid()
obstacle = self.DA.get_obstacle()
object_located = self.BD.get_objectLocated()
mea = self.I2M.get_pos()
rospy.loginfo("centroid: %d, obstacle: %d", centroid, obstacle)
rospy.loginfo("object_located: %d", object_located)
rospy.loginfo("im2mea: \n %s", mea)
self.pub_depth.publish(centroid, obstacle)
self.pub_blob.publish(object_located)
self.pub_mea.publish(mea)
rate.sleep()
def doloop():
global depth, rgb
path = "./kpictures/"
num = glob.glob(str(path) + "*.avi")
i = len(num)
video = cv2.VideoWriter(path+'video'+str(i)+'.avi',cv2.cv.CV_FOURCC('D','I','V','X'),20,(1280,480))
if not video.isOpened():
print 'error with video opening'
sys.exit(1)
print 'press "q" to exit'
while True:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
# Build a two panel color image
d3 = np.dstack((depth, depth, depth)).astype(np.uint8, copy=False)
bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
da = np.hstack((d3, bgr)).astype(np.uint8, copy=False)
# src = cv2.cv.fromarray(da)
cv2.imshow('both', da)
video.write(da)
k = cv2.waitKey(5)
if (k > -1) and (k < 256):
if chr(k)=='q':
# video.release()
cv2.destroyAllWindows()
sys.exit(0)
def update_surface(baseplane,bounds,prev=None,FLOOR=-500,verbose=False):
"""Read updated topography and calibrate for use"""
#(depth,_)= get_depth()
d = []
for i in range(10):
(depth,_)= get_depth()
d.append(depth)
#time.sleep()
#time average the readings to reduce noise
#currently taking mean, maybe switch to median
depth = np.median(d[::],axis=0)#/10.
topo,pix = calibrate(depth,baseplane,bounds)
if verbose:
print 'SURFACE STATS'
print 'MEAN: %f \t MAX: %f\t MIN: %f\t MEDIAN: %f \n'%(np.mean(topo), np.max(topo),np.min(topo), np.median(topo) )
#if there are enough pixels above a threshold, ignore only those and show masked linear combo. of previous and new topos
#this is useful when hands are in the sandbox
if len(np.where(topo<FLOOR)[0]) + len(np.where(topo<FLOOR)[1]) > 10:# or np.mean(topo) > 1e3:
if prev == None:
return topo #- np.nanmedian(topo)
else:
a = np.zeros(prev.shape)
b = np.ones(prev.shape)
a[np.where(topo<FLOOR)] = 1
b[np.where(topo<FLOOR)] = 0
return prev*a + topo*b
return topo #- np.nanmedian(topo)
def calibrate():
(depth,_) = get_depth()
background_depth = avg_depth(depth)
print 'calibated depth of background: %d' % background_depth
f = open(BACK_DEPTH_FNAME, 'w')
pickle.dump(background_depth,f)
f.close()
def qualtest(integer):
warm_up, _ = get_depth()
time.sleep(5)
cf0, _ = get_depth()
all_data = cf0[np.newaxis, ...]
ts0 = time.time()
ts = ts0
count = 0
while ts - ts0 < 60:
cf, _ = get_depth()
ts = time.time()
if ts > count * 0.4 + ts0:
all_data = np.vstack([all_data, cf[np.newaxis, ...]])
count += 1
#all_data[all_data == 0] = np.nan
np.save('bower_test_tank42_depth' + str(integer), all_data)
def doloop():
global depth, rgb
while True:
# Get a fresh frame
(depth,_), (rgb,_) = get_depth(), get_video(0, freenect.VIDEO_IR_8BIT)
#(depth,_), (rgb,_) = get_depth(), get_video()
# Build a two panel color image
#d3 = np.dstack((depth,depth,depth)).astype(np.uint8)
#da = np.hstack((d3,rgb))
# Simple Downsample
#cv.ShowImage('both', cv.fromarray(np.array(da[::2,::2,::-1])))
#rgbarray = cv.fromarray(np.array(rgb))
rgbarray = np.array(rgb)
#gray = cv2.cvtColor(rgbarray, cv2.COLOR_BGR2GRAY)
#blurred = cv.CloneMat(rgbarray)
#sobeled = cv.CreateMat(rgbarray.rows, rgbarray.cols, cv.CV_32F)
#cv.Sobel(rgbarray, sobeled, 1, 1)
#sobeled = cv2.Sobel(blurred, cv.CV_32F, 1, 1)
_, threshed = cv2.threshold(rgbarray, 250, 255, cv2.THRESH_BINARY)
blurred = cv2.GaussianBlur(threshed, (255, 255), 0)
#cv.Sobel(rgbarray2, sobeled, 0, 0, 1)
cv.ShowImage('both', cv.fromarray(threshed))
cv.WaitKey(5)
def calibrate():
(depth, _) = get_depth()
background_depth = avg_depth(depth)
print 'calibated depth of background: %d' % background_depth
f = open(BACK_DEPTH_FNAME, 'w')
pickle.dump(background_depth, f)
f.close()
def doloop():
global depth, rgb
while True:
# Get a fresh frame
(depth,_) = get_depth()
print depth
# Simple Downsample
cv.WaitKey(5)
def main():
# Get a fresh frame
(depth,_), (rgb,_) = get_depth(), get_video()
# Build a two panel color image
d3 = np.dstack((depth,depth,depth)).astype(np.uint8)
da = np.hstack((d3,rgb))
# Simple Downsample
cv2.imshow('both',np.array(da[::2,::2,::-1]))
def getBinaryImage(l1, l2): (d,_) = get_depth(format = DEPTH_MM) m1 = d < l2 m2 = d > l1 m = np.logical_and(m1, m2) m = m.astype(np.uint8) m = m*255 return (m,d)
def get_min_pos_kinect():
(depth, _) = get_depth()
minPos = np.argmin(
depth) #This is the raw index of the minimum value above
xPos = np.mod(minPos,
sensorWidth) #This is the x component of the raw index
yPos = minPos // sensorWidth #This is the y component of the raw index
return ((sensorWidth - xPos - 10) * (screenWidth / sensorWidth),
yPos * (screenHeight / sensorHeight))
def do_loop():
while True:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
# Show it
#show_and_move(rgb, depth)
show_and_move(rgb, depth)
show_depth(depth)
cv.WaitKey(5)
def GetDepth(self):
# get data from the sensor
(depth, _), (rgb, _) = get_depth(), get_video()
# convert data
depth = depth.astype(np.uint8)
rgb = cv.cvtColor(rgb, cv.COLOR_RGB2BGR)
return depth, rgb
def do_loop():
while True:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
# Show it
#show_and_move(rgb, depth)
show_and_move(rgb, depth)
show_depth(depth)
cv.WaitKey(5)
def get_frame():
frame = get_depth()[0] # get current frame from connect
frame = np.clip(frame, frame_clip_min, frame_clip_max)
frame = frame - frame_clip_min
frame = cv2.resize(frame,
dsize=(frame_width,
frame_height)) # resize the image to 28x28
frame = frame / (frame_clip_max - frame_clip_min
) # scale each depth between 0-1
return frame
def display():
global arr, depth, send_lock
while (True):
if send_lock.acquire():
(depth,_),(arr,_)=get_depth(),get_video()
d3 = np.dstack((depth,depth,depth)).astype(np.uint8)
da = np.hstack((d3,arr))
cv.ShowImage('both',cv.fromarray(np.array(da[::2,::2,::-1])))
send_lock.release()
cv.WaitKey(5)
def doloop():
capture=cv.CaptureFromCAM(0)
fourcc = cv.CV_FOURCC('X','V','I','D')
cv.SetCaptureProperty(capture, cv.CV_CAP_PROP_FPS, 30)
cv.SetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_WIDTH, 640)
cv.SetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_HEIGHT, 480)
global depth,ir, rgb
count = 0
while True:
rgb_frame=cv.QueryFrame(capture)
rgb = iplimage_to_numpy_color(rgb_frame)
(depth,_), (ir,_) = get_depth(), get_video(format=2)
np.resize(depth, (300,400))
np.resize(ir, (300,400))
np.resize(rgb, (300,400,3))
# Build a two panel color image
d3 = np.dstack((depth,depth,depth)).astype(np.uint8)
ir3 = np.dstack((ir, ir, ir)).astype(np.uint8)
depth_ir = np.hstack((d3,ir3))
# Form Frame/Image
image = cv.fromarray(np.array(depth_ir[:,:,::-1]))
rgb_image = numpy_to_iplimage_color(rgb)
d_cvMat = cv.fromarray(np.array(d3[:,:,::-1]))
depth_image = cvMat_to_iplimage_color(d_cvMat)
opacity = 0.4
cv.AddWeighted(depth_image, opacity, rgb_image, 1 - opacity, 0, rgb_image)
# Playback Frame
cv.ShowImage('Trio',image)
cv.ShowImage('RGB',rgb_image)
cv.WaitKey(5)
# Keyboard interrupt for Exit
c=cv.WaitKey(2)
if c==27: #Break if user enters 'Esc'.
break
# How does Downsample Works
# ::2, means you skip by 2 -> [1,2,3,4] -> [2,4]
# Downsample col -> cv.fromarray(np.array(rgb[:, ::2, ::-1]))
# Downsample row -> cv.fromarray(np.array(rgb[::2, :, ::-1]))
# Color Chanel: All Color -> cv.fromarray(np.array(rgb[::2, ::2, ::-1]))
# Color Chanel: Blue -> cv.fromarray(np.array(rgb[::2, ::2]))
# Color Chanel: Gray -> cv.fromarray(np.array(rgb[::2, ::2, 0]))
"""
def doloop():
global depth, rgb
while True:
# Get a fresh frame
(depth,_), (rgb,_) = get_depth(), get_video()
# Build a two panel color image
d3 = np.dstack((depth,depth,depth)).astype(np.uint8)
da = np.hstack((d3,rgb))
# Simple Downsample
cv.ShowImage('both',cv.fromarray(np.array(da[::2,::2,::-1])))
cv.WaitKey(5)
def doloop():
global depth, rgb
for i in range(1,10):
(depth,_), (rgb,_) = get_depth(), get_video()
bg=cv.CloneMat(cv.fromarray(depth.astype(numpy.uint8)))
scratch = cv.CreateImage((640,480),8,1)
scratch2 = cv.CreateImage((640,480),8,1)
cv.SaveImage('bg.png',bg)xz
while True:
# Get a fresh frame
(depth,_), (rgb,_) = get_depth(), get_video()
depth=cv.fromarray(depth.astype(numpy.uint8))
cv.AbsDiff(bg,depth,scratch)
cv.Sub(scratch,2,10,scratch2)
# cv.ConvertScale(scratch,scratch2,50)
cv.Add(depth,scratch2,scratch2)
# Simple Downsample
cv.ShowImage('both',scratch2)
cv.WaitKey(10)
def get_depth_object():
result = []
(depth, _) = get_depth()
cv2.rectangle(depth, (min(xlist), min(ylist)), (max(xlist), max(ylist)),
(0, 255, 0), 2)
for wd in range(min(xlist), max(xlist)):
for hd in range(min(ylist), max(ylist)):
if (depth[hd][wd] + 8 < min_mat[hd][wd] - noise[hd][wd]) and (
depth[hd][wd] < 2047) and (depth[hd][wd] != 0) and (
depth[hd][wd] != 255 and (min_mat[hd][wd] < 2047)
): #8 is the finger width ( approximatively)
result.append([hd, wd])
return result
def getCursorPosition():
(depth,_) = get_depth()
minVal = np.min(depth) #This is the minimum value from the depth image
minPos = np.argmin(depth) #This is the raw index of the minimum value above
xPos = np.mod(minPos, sensorWidth) #This is the x component of the raw index
yPos = minPos//sensorWidth #This is the y component of the raw index
xList.append(xPos)
del xList[0]
xPos = int(np.mean(xList))
yList.append(yPos)
del yList[0]
yPos = int(np.mean(yList))
return ((sensorWidth - xPos) * (screenWidth / sensorWidth), yPos * (screenHeight / sensorHeight));
def doloop():
global depth, rgb
while True:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
# Build a two panel color image
d3 = np.dstack((depth, depth, depth)).astype(np.uint8)
da = np.hstack((d3, rgb))
# Simple Downsample
cv.ShowImage('both', np.array(da[::2, ::2, ::-1]))
cv.WaitKey(5)
def hand_tracker(serial_object):
(depth,_) = get_depth()
cHullAreaCache = constList(5,12000) #Blank cache list for convex hull area
areaRatioCache = constList(5,1) #Blank cache list for the area ratio of contour area to convex hull area
centroidList = list() #Initiate centroid list
#RGB Color tuples
BLACK = (0,0,0)
RED = (255,0,0)
GREEN = (0,255,0)
PURPLE = (255,0,255)
BLUE = (0,0,255)
WHITE = (255,255,255)
YELLOW = (255,255,0)
done = False
previous_object_no_error = True
min_delta_pos = 1 # as a percentage of total possibilities
prev_vert_pos = -20
while not done:
(depth,_) = get_depth()
depth = depth.astype(np.float32)
_,depthThresh = cv2.threshold(depth, 600, 255, cv2.THRESH_BINARY_INV) #Threshold the depth for a binary image. Thresholded at 600 arbitary units
blobData = BlobAnalysis(depthThresh) #Creates blobData object using BlobAnalysis class
if blobData.counter == 1:
centroid = blobData.centroid[0]
previous_object_no_error = True
vert_pos = get_pos_for_serial(centroid)
# Check for significant changes
if np.abs(prev_vert_pos - vert_pos) > min_delta_pos:
prev_vert_pos = vert_pos
print str(vert_pos)
serial_object.write(str(vert_pos)+'\n')
elif blobData.counter == 0 and previous_object_no_error:
print 'No tracking objects found'
previous_object_no_error = False
elif blobData.counter > 1 and previous_object_no_error:
print 'Too many tracking objects: {} objects'.format(blobData.counter)
previous_object_no_error = False
def doloop():
while True:
# Get a fresh frame
(depth,_), (rgb,_) = get_depth(), get_video()
depth = depth[::2, ::2]
r,g,b = process(depth)
# Build a two panel color image
d3 = np.dstack((r,g,depth/20)).astype(np.uint8)
da = np.hstack((d3,rgb[::2, ::2]))
# Simple Downsample
cv.ShowImage('both',np.array(da[:,:,::-1]))
cv.WaitKey(5)
def stack_depth(stack=1,verbose=True):
depth = None
for i in range(stack):
(temp,_) = get_depth()
if depth is None:
depth = temp
if verbose:
print "Init invalids: %d" % depth[depth >= INVALID_DEPTH].shape[0]
else:
mask1 = (depth >= INVALID_DEPTH)
mask2 = ((mask1*temp > 0) * (mask1*temp < INVALID_DEPTH))
depth = depth.copy() + mask2*temp - mask2*INVALID_DEPTH
if verbose:
print "Final invalids: %d" % depth[depth >= INVALID_DEPTH].shape[0]
return depth
def stack_depth(stack=1, verbose=True):
depth = None
for i in range(stack):
(temp, _) = get_depth()
if depth is None:
depth = temp
if verbose:
print "Init invalids: %d" % depth[
depth >= INVALID_DEPTH].shape[0]
else:
mask1 = (depth >= INVALID_DEPTH)
mask2 = ((mask1 * temp > 0) * (mask1 * temp < INVALID_DEPTH))
depth = depth.copy() + mask2 * temp - mask2 * INVALID_DEPTH
if verbose:
print "Final invalids: %d" % depth[depth >= INVALID_DEPTH].shape[0]
return depth
def doloop():
global depth, rgb
while True:
# Get a fresh frame
(depth,_), (rgb,_) = get_depth(), get_video()
# Build a two panel color image
d3 = np.dstack((depth,depth,depth)).astype(np.uint8)
da = np.hstack((d3,rgb))
# Simple Downsample
cv2.imshow('both',np.array(da[::2,::2,::-1]))
print len(rgb), len(rgb[0]), len(rgb[0][0])
raw_input(':')
def readkinect():
(depth,_), (rgb,_) = get_depth(), get_video()
if dodepth:
da=np.dstack((depth,depth,depth)).astype(np.uint8) # this is correct depth
frame=np.array(da[::1,::1,::-1]);
else:
frame=rgb[::1,::1,::-1]
# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#frame=np.array() ### !!!! or rgb
#fgmask = fgbg.apply(frame)
#gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('frame',frame)
k = cv2.waitKey(3) & 0xff
return frame
def get_min_pos_kinect():
(depth,_) = get_depth()
minVal = np.min(depth) #This is the minimum value from the depth image
minPos = np.argmin(depth) #This is the raw index of the minimum value above
xPos = np.mod(minPos, xSize) #This is the x component of the raw index
yPos = minPos//xSize #This is the y component of the raw index
xList.append(xPos)
del xList[0]
xPos = int(np.mean(xList))
yList.append(yPos)
del yList[0]
yPos = int(np.mean(yList))
return (xSize - xPos-10, yPos, minVal)
def get_min_pos_kinect():
(depth,_) = get_depth()
minVal = np.min(depth) #This is the minimum value from the depth image
minPos = np.argmin(depth) #This is the raw index of the minimum value above
xPos = np.mod(minPos, xSize) #This is the x component of the raw index
yPos = minPos//xSize #This is the y component of the raw index
xList.append(xPos)
del xList[0]
xPos = int(np.mean(xList))
yList.append(yPos)
del yList[0]
yPos = int(np.mean(yList))
return (xSize - xPos-10, yPos, minVal)
def generateSingleFrame(self, interval, start, end, stride=8):
depth = get_depth()[0]
depth = self.adjustDepth(depth, stride)
self.depthCache = depth
layers = []
for i in range(start, end, interval):
layers.append(
self.generateSingleLayer(i,
depth,
c3=self.generateColour(
(float(i) /
(float(end) - float(start))))))
for i in range(1, len(layers)):
layers[0].paste(layers[i], (0, 0), layers[i])
layers = layers[0]
self.imageCache = layers
return layers
def get_bg_depth():
global corners
corners = get_corners()
mtx = np.matrix(corners)
mean = mtx.mean(0)
middle_coords = (int(mean.item((0, 0))), int(mean.item((0, 1))))
corners_arr = np.array(corners)
global lower_bound, upper_bound
lower_bound = np.amin(corners_arr, axis=0)
upper_bound = np.amax(corners_arr, axis=0)
global depth
(depth,_) = get_depth()
#d3 = np.dstack((depth, depth, depth)).astype(np.uint8)
d3 = np.array(depth)
d3 = np.array([y[lower_bound[0]:upper_bound[0]]
for y in d3[lower_bound[1]:upper_bound[1]]])
mean_vertical = d3.min(axis=0)
return mean_vertical.min(axis=0)
def find_position(self):
print "Kinect is trying to find the image"
(kinect_depth,_), (rgb,_) = get_depth(), get_video()
self.img = video_cv(rgb)
depth_img = pretty_depth_cv(kinect_depth)
position = self._get_pos(self.img)
depth = self._get_depth(depth_img, debug=False)
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 0, 1, 1)
fps = 1/(time.time() - self.lasttime)
s1 = "FPS:%.2f" % fps
self.lasttime = time.time()
cv.PutText(self.img,s1, (0,30),font, cv.CV_RGB(255, 0, 0))
dt = "Depth: %d" % depth
if position:
pt = "Pos: X=%d Y=%d" % (position[0], position[1])
else:
pt = "Pos: N/A"
cv.PutText(self.img, dt, (0,60),font, cv.CV_RGB(255, 0, 0))
cv.PutText(self.img, pt, (0,90),font, cv.CV_RGB(255, 0, 0))
offset = 120
for t in self.text:
cv.PutText(self.img, t, (0,offset),font, cv.CV_RGB(255, 0, 0))
offset += 30
cv.Circle(self.img, (self.sp[0], self.sp[1]) , 10, cv.CV_RGB(0, 255, 0), 1)
cv.ShowImage('RGB', self.img)
#cv.SaveImage('RGB-%d.png' % (time.time()*100), self.img)
#cv.ShowImage('DEPTH', depth_img)
cv.WriteFrame(self.writer, self.img)
cv.WaitKey(5)
#cv.ShowImage('depth_mask', depth_mask)
try:
return (position[0], position[1], depth)
except:
return (None, None, None)
def doloop(c, conn, insert_snapshot):
global depth, rgb, counter
counter = 0
while True:
# Get a fresh frame
(depth, _) = get_depth()
fn = './images/{}.png'.format(uuid4())
cv2.imwrite(fn, frame_convert2.video_cv(freenect.sync_get_video()[0]))
# Build a two panel color image
# d3 = np.dstack((depth,depth,depth)).astype(np.uint8)
# da = np.hstack((d3,rgb))
# print(d3, rgb)
# time.sleep(3)
counter += 1
insert_snapshot(c, fn, depth)
(ok, ) = c.execute('select count(1) from snapshot;')
print(ok)
conn.commit()
time.sleep(1)
def doloop():
global depth,ir, rgb
while True:
"""
ctypedef enum freenect_video_format:
FREENECT_VIDEO_RGB
FREENECT_VIDEO_BAYER
FREENECT_VIDEO_IR_8BIT
FREENECT_VIDEO_IR_10BIT
FREENECT_VIDEO_IR_10BIT_PACKED
FREENECT_VIDEO_YUV_RGB
FREENECT_VIDEO_YUV_RAW
"""
(depth,_), (ir,_) = get_depth(), get_video(format=2)
# How does Downsample Works
# ::2, means you skip by 2 -> [1,2,3,4] -> [2,4]
# Downsample col -> cv.fromarray(np.array(rgb[:, ::2, ::-1]))
# Downsample row -> cv.fromarray(np.array(rgb[::2, :, ::-1]))
# Color Chanel: All Color -> cv.fromarray(np.array(rgb[::2, ::2, ::-1]))
# Color Chanel: Blue -> cv.fromarray(np.array(rgb[::2, ::2]))
# Color Chanel: Gray -> cv.fromarray(np.array(rgb[::2, ::2, 0]))
# Build a two panel color image
d3 = np.dstack((depth,depth,depth)).astype(np.uint8)
i3 = np.dstack((ir, ir, ir)).astype(np.uint8)
da = np.hstack((d3,i3))
# Form Frame/Image
image = cv.fromarray(np.array(da[:,:,::-1]))
# Playback Frame
cv.ShowImage('Dual',image)
cv.WaitKey(5)
# Keyboard interrupt for Exit
c=cv.WaitKey(2)
if c==27: #Break if user enters 'Esc'.
break
def getData():
global depth, rgb
i = 0
# for the first ten frames
while i < 10:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
data.append([depth, rgb])
# data = data + str(depth) + " \n $$$ \n" + str(rgb) + "\n !!! \n"
# Build a two panel color image
d3 = np.dstack((depth, depth, depth)).astype(np.uint8)
da = np.hstack((d3, rgb))
# Simple Downsample
cv.ShowImage("Depth and RGB", cv.fromarray(np.array(da[::2, ::2, ::-1])))
cv.WaitKey(5)
i = i + 1
def getData():
global depth, rgb
i = 0
#for the first ten frames
while i < 10:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
data.append([depth, rgb])
#data = data + str(depth) + " \n $$$ \n" + str(rgb) + "\n !!! \n"
# Build a two panel color image
d3 = np.dstack((depth, depth, depth)).astype(np.uint8)
da = np.hstack((d3, rgb))
# Simple Downsample
cv.ShowImage('Depth and RGB',
cv.fromarray(np.array(da[::2, ::2, ::-1])))
cv.WaitKey(5)
i = i + 1
def doloop():
global depth, rgb
while True:
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
# Build a two panel color image
d3 = np.dstack((depth, depth, depth)).astype(np.uint8)
da = np.hstack((d3, rgb))
# detect apriltag
data = da[::2, ::2, ::-1]
#image = cv2.imread(data)
gray = cv2.cvtColor(data, cv2.COLOR_RGB2GRAY)
detect_apriltag(gray, data)
# Simple Downsample
cv2.imshow('both', np.array(data))
if cv2.waitKey(1000) == 27:
break
def main():
print ('[*] Start')
start = datetime.now()
folder_name=get_args()
os.mkdir(folder_name)
os.chdir(folder_name)
i=1
buff=[]
try:
while True:
print '[*] Recording Index %s'%(str(i))
(depth,_), (rgb,_) = get_depth(), get_video()
buff.append([depth.copy(),rgb.copy()])
i+=1
time.sleep(0.0001)
if i>=1000000:
break
except KeyboardInterrupt:
sync_stop() # stop the sync_get_video...etc
print '\n[*] End Buff with following information :'
duration = str((datetime.now()-start).total_seconds() ).split('.')[0]
fps = i/((datetime.now()-start).total_seconds())
print '[*] Duration is { %s }'%(duration)
print '[*] FPS is { %s }'%(str(fps).split('.')[0])
print '\n[*] Start Saving IMG from Buff'
try:
for j in range(i):
#list.pop(index)
depth,rgb = buff.pop(0)
a=""
if len(str(j))<frame_length_limit_order:
a='0'*(frame_length_limit_order-len(str(j)))
depth=depth_to_gray(depth)
# io.imsave to a series of .png
io.imsave('depth'+a+str(j)+'.png',depth)
io.imsave('rgb'+a+str(j)+'.png',rgb)
print '[*] Saving Index %s'%(str(j))
except:
print '\n[*] End Saving IMG '
def doloop(self):
global depth, rgb
while True:
(depth, _), (rgb, _) = get_depth(), get_video()
d3 = np.dstack((depth, depth, depth)).astype(np.uint8)
redAvg = 0
greenAvg = 0
blueAvg = 0
distanceAvg = 0
for i in range(len(d3)):
redAvg += d3[0][i][0]
greenAvg += d3[0][i][1]
blueAvg += d3[0][i][2]
redAvg = redAvg / len(d3)
greenAvg = greenAvg / len(d3)
blueAvg = blueAvg / len(d3)
distanceAvg = (redAvg + blueAvg + greenAvg) / 3
cv.ShowImage("both", cv.fromarray(np.array(d3[::2, ::2, ::-1])))
self.sendCommandASCII("128") # sets mode to passive
self.sendCommandASCII("131") # sets mode to safe
if distanceAvg >= 255 or distanceAvg <= 10:
self.sendCommandASCII("140 3 1 64 16 141 3") # beep
self.callMovementCommand(0, -100)
elif distanceAvg > distanceThreshold:
self.sendCommandASCII("140 3 1 64 16 141 3") # beep
self.callMovementCommand(100, 100)
else:
self.callMovementCommand(0, 0) # stop
self.callMovementCommand(0, 100) # Right turn
cv.WaitKey(10)
def main():
print ('[*] Start')
start = datetime.now()
folder_name=get_args()
os.mkdir(folder_name)
os.chdir(folder_name)
i=1
try:
while True:
(depth,_), (rgb,_) = get_depth(), get_video()
a=""
if len(str(i))<frame_length_limit_order:
a='0'*(frame_length_limit_order-len(str(i)))
elif len(str(i))>frame_length_limit_order:
break
depth=depth_to_gray(depth)
io.imsave('depth'+a+str(i)+'.png',depth)
io.imsave('rgb'+a+str(i)+'.png',rgb)
i+=1
except KeyboardInterrupt:
duration=str((datetime.now()-start).total_seconds() ).split('.')[0]
print '\n[*] Recoding Duration is { %s }'%(duration)
def update(self):
rate = rospy.Rate(10) # 10hz
while not rospy.is_shutdown():
# Get a fresh frame
depth,_ = get_depth()
frame,_ = get_video()
self.BD.detect(frame)
self.DA.analyse(depth)
centroid = self.DA.get_centroid()
obstacle = self.DA.get_obstacle()
x_pos = self.BD.get_xpos()
object_located = self.BD.get_objectLocated()
rospy.loginfo("centroid: %d obstacle: %s", centroid, obstacle)
rospy.loginfo("x_pos: %d object_located: %s", x_pos, object_located)
self.pub_depth.publish(centroid, obstacle)
self.pub_blob.publish(x_pos, object_located)
rate.sleep()
from opencv.cv import * from opencv import adaptors from opencv.highgui import * from time import time from freenect import sync_get_depth as get_depth, sync_get_video as get_video, init import numpy as np from rx_config import * from timing_stats import * #initialize the camera ctx = init() # Grab an initial frame to get the video size global depth, rgb (depth,_), (rgb,_) = get_depth(), get_video() rgbFrameSize = cvGetSize(rgb) depthSize = cvGetSize(depth) dwnFrameSize = cvSize(rgbFrameSize.width / 2, rgbFrameSize.height / 2) dwnDepthSize = cvSize(depthSize.width / 2, depthSize.height / 2) print 'rgbSize = %d %d' % (rgbFrameSize.width, rgbFrameSize.height) print 'depthSize = %d %d' % (depthSize.width, depthSize.height) # Allocate processing chain image buffers the same size as # the video frame rgbFrame = cvCreateImage( rgbFrameSize, cv.IPL_DEPTH_8U, 3 ) depthFrame = cvCreateImage( depthSize, cv.IPL_DEPTH_16U, 3 ) dwnDepthFrame = cvCreateImage( dwnDepthSize, cv.IPL_DEPTH_16U, 1 )#tbd 3 or 1?
def get_min_pos_kinect():
(depth,_) = get_depth()
minPos = np.argmin(depth) #This is the raw index of the minimum value above
xPos = np.mod(minPos, sensorWidth) #This is the x component of the raw index
yPos = minPos//sensorWidth #This is the y component of the raw index
return ((sensorWidth - xPos-10) * (screenWidth / sensorWidth),yPos * (screenHeight / sensorHeight))
def hand_tracker():
(depth,_) = get_depth()
cHullAreaCache = constList(5,12000) #Blank cache list for convex hull area
areaRatioCache = constList(5,1) #Blank cache list for the area ratio of contour area to convex hull area
centroidList = list() #Initiate centroid list
#RGB Color tuples
BLACK = (0,0,0)
RED = (255,0,0)
GREEN = (0,255,0)
PURPLE = (255,0,255)
BLUE = (0,0,255)
WHITE = (255,255,255)
YELLOW = (255,255,0)
pygame.init() #Initiates pygame
xSize,ySize = 640,480 #Sets size of window
screen = pygame.display.set_mode((xSize,ySize),pygame.RESIZABLE) #creates main surface
screenFlipped = pygame.display.set_mode((xSize,ySize),pygame.RESIZABLE) #creates surface that will be flipped (mirror display)
screen.fill(BLACK) #Make the window black
done = False #Iterator boolean --> Tells programw when to terminate
dummy = False #Very important bool for mouse manipulation
while not done:
screen.fill(BLACK) #Make the window black
(depth,_) = get_depth() #Get the depth from the kinect
depth = depth.astype(np.float32) #Convert the depth to a 32 bit float
_,depthThresh = cv2.threshold(depth, 600, 255, cv2.THRESH_BINARY_INV) #Threshold the depth for a binary image. Thresholded at 600 arbitary units
_,back = cv2.threshold(depth, 900, 255, cv2.THRESH_BINARY_INV) #Threshold the background in order to have an outlined background and segmented foreground
blobData = BlobAnalysis(depthThresh) #Creates blobData object using BlobAnalysis class
blobDataBack = BlobAnalysis(back) #Creates blobDataBack object using BlobAnalysis class
for cont in blobDataBack.contours: #Iterates through contours in the background
pygame.draw.lines(screen,YELLOW,True,cont,3) #Colors the binary boundaries of the background yellow
for i in range(blobData.counter): #Iterate from 0 to the number of blobs minus 1
pygame.draw.circle(screen,BLUE,blobData.centroid[i],10) #Draws a blue circle at each centroid
centroidList.append(blobData.centroid[i]) #Adds the centroid tuple to the centroidList --> used for drawing
pygame.draw.lines(screen,RED,True,blobData.cHull[i],3) #Draws the convex hull for each blob
pygame.draw.lines(screen,GREEN,True,blobData.contours[i],3) #Draws the contour of each blob
for tips in blobData.cHull[i]: #Iterates through the verticies of the convex hull for each blob
pygame.draw.circle(screen,PURPLE,tips,5) #Draws the vertices purple
"""
#Drawing Loop
#This draws on the screen lines from the centroids
#Possible exploration into gesture recognition :D
for cent in centroidList:
pygame.draw.circle(screen,BLUE,cent,10)
"""
pygame.display.set_caption('Kinect Tracking') #Makes the caption of the pygame screen 'Kinect Tracking'
del depth #Deletes depth --> opencv memory issue
screenFlipped = pygame.transform.flip(screen,1,0) #Flips the screen so that it is a mirror display
screen.blit(screenFlipped,(0,0)) #Updates the main screen --> screen
pygame.display.flip() #Updates everything on the window
#Mouse Try statement
try:
centroidX = blobData.centroid[0][0]
centroidY = blobData.centroid[0][1]
if dummy:
mousePtr = display.Display().screen().root.query_pointer()._data #Gets current mouse attributes
dX = centroidX - strX #Finds the change in X
dY = strY - centroidY #Finds the change in Y
if abs(dX) > 1: #If there was a change in X greater than 1...
mouseX = mousePtr["root_x"] - 2*dX #New X coordinate of mouse
if abs(dY) > 1: #If there was a change in Y greater than 1...
mouseY = mousePtr["root_y"] - 2*dY #New Y coordinate of mouse
move_mouse(mouseX,mouseY) #Moves mouse to new location
strX = centroidX #Makes the new starting X of mouse to current X of newest centroid
strY = centroidY #Makes the new starting Y of mouse to current Y of newest centroid
cArea = cacheAppendMean(cHullAreaCache,blobData.cHullArea[0]) #Normalizes (gets rid of noise) in the convex hull area
areaRatio = cacheAppendMean(areaRatioCache, blobData.contourArea[0]/cArea) #Normalizes the ratio between the contour area and convex hull area
'''
if cArea < 10000 and areaRatio > 0.82: #Defines what a click down is. Area must be small and the hand must look like a binary circle (nearly)
#click_down(1)
else:
#click_up(1)
'''
else:
strX = centroidX #Initializes the starting X
strY = centroidY #Initializes the starting Y
dummy = True #Lets the function continue to the first part of the if statement
except: #There may be no centroids and therefore blobData.centroid[0] will be out of range
dummy = False #Waits for a new starting point
for e in pygame.event.get(): #Itertates through current events
if e.type is pygame.QUIT: #If the close button is pressed, the while loop ends
done = True