def display_depth(dev, data, timestamp):
global keep_running
global conn
global addr
buf = buffered_readLine(conn)
#if not buf:
# conn, addr = sock.accept()
# return
#print buf
auto = int(float(buf))
shifted = int(buf) >> 7
# print("auto: " + str(auto))
# print(" buf: " +str(buf))
# print(" shifted: " + str(shifted))
depthimg = frame_convert2.pretty_depth_cv(data)
eroded = cv2.erode(depthimg, (7, 7), iterations=3) #filter the image
dilated = cv2.dilate(eroded, (7, 7), iterations=3) #filter the image more
#cv2.rectangle(dilated, (280, 200), (360, 280), (0), 3) #draw rectangle in the center for analysis
roi = dilated[200:280, 280:360] #actual pixels to be analyzed for the mean
mask = cv2.inRange(dilated, closeVal, farVal)
topHalf = mask[0:240, 0:640]
#cv2.imshow('Depth', topHalf)
contours = cv2.findContours(topHalf.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
centerX = 320
if (shifted == 1):
if len(contours) > 0:
humanCnt = max(contours,
key=cv2.contourArea) #pick the biggest shape
x, y, w, h = cv2.boundingRect(humanCnt)
centerX = (x + x + w) / 2
#print(centerX)
cv2.rectangle(topHalf, (x, y), (x + w, y + h), 125, 2)
meanValue = roi.mean()
depthft = meanValue * meanValue * .000892 - .292 * meanValue + 26.5
#print(depthft)
if (depthft < 6): #stop
# arduino.write(0x00)
arduino.write('\x00')
elif (centerX < 300): #left
# arduino.write(0x4D)
arduino.write('\x4D')
elif (centerX > 340): #right
# arduino.write(0x1D)
arduino.write('\x1D')
else: #forward
# arduino.write(0x2D)
arduino.write('\x2D')
else: #stop
# arduino.write(0x00)
arduino.write('\x00')
else:
arduino.write(chr(int(buf)))
cv2.imshow('Mask', topHalf)
if cv2.waitKey(10) == 27:
keep_running = False
print dilated.shape
print dilated.dtype
arduino.write('\x00')
sock.close()
def display_depth(dev, data, timestamp):
global keep_running
myImg = frame_convert2.pretty_depth_cv(data)
myImg = cv2.erode(myImg, None, iterations=5)
myImg = cv2.dilate(myImg, None, iterations=5)
## cv2.imshow('Depth', myImg)
depthColorLower = (100)
depthColorHigher = (150)
mask = cv2.inRange(myImg, depthColorLower, depthColorHigher)
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
center = None
## bbox = (287, 23, 86, 320)
## if len(cnts) > 0:
## c = max(cnts, key=cv2.contourArea)
## if ok:
## p1 = (int(bbox[0]), int(bbox[1]))
## p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
## cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1)
c = max(cnts, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(c)
if (w > h):
print("width is bigger")
else:
print("height is bigger")
cv2.rectangle(myImg, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Tracking", mask)
cv2.imshow('depth', myImg)
if cv2.waitKey(10) == 27:
keep_running = False
def set_image_up(data): # Gives us a depth, binary and contoured image
img = frame_convert2.pretty_depth_cv(data)
img = cv2.medianBlur(img, 9)
thresh, binary = cv2.threshold(img, 50, 255, cv2.THRESH_BINARY_INV)
contour, hierarchy = cv2.findContours(binary, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
return img, binary, contour
def collect_depth(dev, data, timestamp):
global keep_running
global depth_index
np.save('{0}/{1}/{2}'.format(ROOT_PATH, DEPTH_PATH, depth_index), data)
cv2.imshow('Depth', frame_convert2.pretty_depth_cv(data))
if cv2.waitKey(10) == 27:
keep_running = False
depth_index += 1
def get_depth():
#saving as 11 bit for csv file
#frame = freenect.sync_get_depth()[0]
#good for displaying and saving depth image
frame = frame_convert2.pretty_depth_cv(freenect.sync_get_depth()[0])
#print('\nframeDepth =\n')
#print(frame)
return frame
def show_depth(depth,window):
"""
passes a raw depth from the kinect and displays it
ont a image called window
"""
#depth = depth.astype(np.uint8)
depth = frame_convert2.pretty_depth_cv(depth)
cv2.imshow(window, depth)
def show_depth(depth, window):
"""
passes a raw depth from the kinect and displays it
ont a image called window
"""
#depth = depth.astype(np.uint8)
depth = frame_convert2.pretty_depth_cv(depth)
cv2.imshow(window, depth)
def display_depth(dev, data, timestamp):
global keep_running
cv2.imshow('Depth', frame_convert2.pretty_depth_cv(data))
c = cv2.waitKey(10)
if 'q' == chr(c & 255):
keep_running = False
if 's' == chr(c & 255):
cv2.imwrite("saved1.jpg", data)
def main():
all_images_path = "../data/u_turn"
images_path, cfg_path, weights_path, data_path, num_images = all_images_path + "/rgb/", "./neural_net/yolo-obj.cfg", "./neural_net/rover.weights", "./neural_net/obj.data", 10
image_sim = BoundingBoxSim(images_path=images_path,
weights_path=weights_path,
data_path=data_path,
num_images=num_images,
cfg_path=cfg_path)
image_sim.simulate()
# Analyzing variables
global world_coords
world_coords = []
xvel = []
yvel = []
zvel = []
while image_sim.step():
# Get current step and state
curr_state = image_sim.curr_state
step_num = image_sim.curr_step
print "Step number ", step_num
print "Current state is ", curr_state
# Compute image rects and coords
img = rgb_rect(curr_state)
depth_path = all_images_path + '/depth/' + str(step_num) + '.npy'
depth, coords = calc_coords(depth_path, curr_state)
# Analyze obtained coordinates
analyze_coords(coords)
print "World Coordinates (cm)", coords
world_coords.append([100 * coord for coord in coords])
if len(world_coords) > 1:
T = 1 / 30
xvel.append((world_coords[-1][0] - world_coords[-2][0]) / T)
yvel.append((world_coords[-1][1] - world_coords[-2][1]) / T)
zvel.append((world_coords[-1][2] - world_coords[-2][2]) / T)
print "Velocities (cm / s)", xvel[-1], yvel[-1], zvel[-1]
# Display images
cv2.imshow('image', img)
cv2.imshow('Depth', frame_convert2.pretty_depth_cv(depth))
cv2.waitKey(10)
cv2.destroyAllWindows()
# Plotting functions
world_coords = np.array(world_coords)
plot_states(x=world_coords[:, 0],
y=world_coords[:, 1],
z=world_coords[:, 2],
xvel=xvel,
yvel=yvel,
zvel=zvel)
def display_depth(dev, data, timestamp):
global keep_running
t_start = datetime.datetime.now()
data2 = frame_convert2.pretty_depth_cv(cv2.resize(data, (0, 0), fx=0.25, fy=0.25))
#data2 = frame_convert2.pretty_depth_cv(data)
#data2 = cv2.applyColorMap(data.astype(np.uint8), cv2.COLORMAP_JET)
t_end = datetime.datetime.now()
t_delta = t_end - t_start
print(1000000/t_delta.microseconds)
cv2.imshow('Depth', cv2.resize(data2, (0, 0), fx=4, fy=4))
if cv2.waitKey(10) == 27:
keep_running = False
def display_depth(delay):
i = 0
while 1:
try:
data = np.load('{0}/{1}/{2}.npy'.format(ROOT_PATH, DEPTH_PATH, i))
cv2.imshow('Depth', frame_convert2.pretty_depth_cv(data))
if cv2.waitKey(delay) == 27:
break
except Exception:
break
if DEBUG:
print i
i += 1
def get_depth():
return frame_convert2.pretty_depth_cv(freenect.sync_get_depth()[0])
return cv2.fastNlMeansDenoising(f, None, 10, 7, 21)
#OUTDOOR = [20-70,100-255,50-255]
#INDOOR = [30-50,100-255,100-255]
##########################
##########INITIALIZE CAMERA##########
'''
cv_a = cv2.VideoCapture(0)
ret1,cv_b=cv_a.read()
print 'Starting Camera...'
while ret1!=True:
cv_a=0
cv_a=cv2.VideoCapture(0)
ret1,cv_b=cv_a.read()
print 'DONE'
'''
cv_depth = frame_convert2.pretty_depth_cv(freenect.sync_get_depth()[0])
cv_b = frame_convert2.video_cv(freenect.sync_get_video()[0])
#####################################
count = 0
cv_esc = 0
cv_eqn1 = 0
cv_area_m = 0
cv_timeout = 0
cv_cnt2 = 0
cv_cnt1 = 0
cv_rot = 0
cv_prev_radius = 0
cv_prev_cx = 0
cv_prev_cy = 0
cv_test_area = 0
cv_cx = 0
def get_depth():
""" Get's the current depth data from Kinect """
return frame_convert2.pretty_depth_cv(freenect.sync_get_depth()[0])
def get_ir():
array, _ = freenect.sync_get_video(0, freenect.VIDEO_IR_10BIT)
return imutils.rotate(
cv2.cvtColor(frame_convert2.pretty_depth_cv(array),
cv2.COLOR_GRAY2RGB), -90)
def findCountours():
cnts = cv2.findContours(frame_convert2.pretty_depth_cv(data), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
def get_depth():
depth = frame_convert2.pretty_depth_cv(freenect.sync_get_depth()[0])
return np.where(np.array(depth) == 255, 0, 255).astype(np.dtype('uint8'))
def get_depth(img):
return frame_convert2.pretty_depth_cv(img)
def get_depth():
return frame_convert2.pretty_depth_cv(freenect.sync_get_depth()[0])
def display_depth(dev, data, timestamp):
global keep_running
cv2.imshow('Depth', frame_convert2.pretty_depth_cv(data))
if cv2.waitKey(10) == 27:
keep_running = False
def get_depth():
return frame_convert2.pretty_depth_cv(freenect.sync_get_depth(format=freenect.DEPTH_REGISTERED)[0])
def display_depth(dev, data, timestamp):
global keep_running
cv2.imshow('Depth', frame_convert2.pretty_depth_cv(data))
if cv2.waitKey(10) == 27:
keep_running = False
def getDepth(dsRate):
return frame_convert2.pretty_depth_cv(
freenect.sync_get_depth()[0])[::dsRate, ::dsRate]
def main():
print("Running...")
flag_track2 = 0
count = 0
counttrack = 0
prev_y_pixel = 0
prev_x_pixel = 0
tetaperpixel = 0.994837 / 400.0
tracker = KCF.kcftracker(True, False, True,
False) # hog, fixed_window, multiscale, lab
counttrack2 = 0
prev_distance2 = 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)
print(frame_resized_grayscale.shape)
# 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]
min_area = (0.01) * frame_resized.shape[1]
print(frame_resized.shape) # (300,400,3)
boxcolor = (0, 255, 0)
timeout = 0
#variable for counting time elapsed
key = ''
temp = 1
# save video
countsave = 0
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]))
print(depth.shape)
depth2 = np.copy(depth)
# orig = image.copy()
if temp == 1:
if (flag_track2 == 0):
frame_processed, center_fix, pick2 = detect_people(
frame_resized, center, frame_resized, boxcolor)
if (len(center_fix) > 0):
i = 0
for b in center_fix:
#print(b)
#print("Point "+str(i)+": "+str(b[0])+" "+str(b[1]))
x_pixel = b[1]
y_pixel = b[0]
print("x1:" + str(x_pixel) + "y1:" + str(y_pixel))
rawDisparity = depth[(int)(x_pixel), (int)(y_pixel)]
print("raw:" + str(rawDisparity))
distance = 1 / (-0.00307 * rawDisparity + 3.33)
if (distance < 0):
distance = 0.5
print("Distance : " + str(distance))
cv2.putText(
frame_resized, "distance: {:.2f}".format(distance),
(10, (frame_resized.shape[0] - (i + 1) * 25) - 50),
font, 0.65, (0, 0, 255), 3)
cv2.putText(
frame_resized, "Point " + str(i) + ": " +
str(b[0]) + " " + str(b[1]),
(10, frame_resized.shape[0] - (i + 1) * 25), font,
0.65, (0, 0, 255), 3)
i = i + 1
y_pix, x_pix = center_fix[0]
endtime = time.time()
#nucleo.write(("8,"+str(x_person)+","+str(y_person)).encode()) # send x_person and y_person
if ((abs(prev_x_pixel - x_pix)) < 50
and (abs(prev_y_pixel - y_pix)) < 50):
timeout = timeout + (endtime - starttime)
if (timeout > 5):
flag_track2 = 1
boxcolor = (255, 0, 0)
else:
timeout = 0
boxcolor = (0, 255, 0)
prev_y_pixel, prev_x_pixel = y_pix, x_pix
# DEBUGGING #
#print("Teta: " + str(teta) + "Distance: " + str(distance))
print("Timeout: " + str(timeout))
#print ("Distance : " + str(distance))
elif (len(center_fix) <= 0):
timeout = 0
boxcolor = (0, 255, 0)
elif (flag_track2 == 1):
if (counttrack2 == 0):
iA, iB, iC, iD = pick2[0]
# Draw new bounding box from body to only head figures
tracker.init([iA, iB, iC - iA, iD - iB], frame_resized)
counttrack2 = counttrack2 + 1
elif (counttrack2 == 1):
print(pick2[0])
print("iA:" + str(iA) + "iB:" + str(iB) + "iC:" + str(iC) +
"iD:" + str(iD))
boundingbox = tracker.update(
frame_resized) #frame had better be contiguous
boundingbox = list(map(int, boundingbox))
cv2.rectangle(frame_resized,
(boundingbox[0], boundingbox[1]),
(boundingbox[0] + boundingbox[2],
boundingbox[1] + boundingbox[3]),
(255, 0, 0), 3)
#GENERAL ASSUMPTION SINGLE PERSON TRACKING
# start tracking...
x_track = ((boundingbox[2]) / 2.0) + boundingbox[0]
y_track = ((boundingbox[3]) / 2.0) + boundingbox[1]
print("x:" + str(x_track) + "y:" + str(y_track))
x_center = (frame_resized.shape[1] + 1) / 2
y_center = (frame_resized.shape[0] + 1) / 2
print(x_center, y_center)
# compute teta asumsi distance lurus
rawDisparity2 = depth2[(int)(y_track), (int)(x_track)]
print("raw2:" + str(rawDisparity2))
distance2 = 1 / (-0.00307 * rawDisparity2 + 3.33)
if (distance2 < 0):
distance2 = prev_distance2
prev_distance2 = distance2
#realx = (x_track-x_center)+(distance/30.0)
#teta = math.atan(realx/distance) # if distance is tangensial
#teta = math.asin((0.026458333*(x_track-x_center)/distance)) # if distance is euclidean
teta = (x_track - x_center) * tetaperpixel
print("Teta: " + str(teta))
print("Distance2 : " + str(distance2))
cv2.putText(frame_resized,
"distance: {:.2f}".format(distance2),
(10,
(frame_resized.shape[0] - (i + 1) * 25) - 50),
font, 0.65, (0, 0, 255), 3)
cv2.putText(
frame_resized, "Point " + str(0) + ": " +
str(x_track) + " " + str(y_track),
(10, frame_resized.shape[0] - (i + 1) * 25), font,
0.65, (0, 0, 255), 3)
# send the teta and distance
#nucleo.flush()
#if(teta<0.0):
#flag= nucleo.write(("7,"+format(teta,'1.2f')+","+format(distance2,'1.3f')).encode())
#elif(teta>0.0):
#flag= nucleo.write(("7,"+format(teta,'1.3f')+","+format(distance2,'1.3f')).encode())
#print("WRITEIN1" + str(flag))
print("Peak: " + str(tracker.getpeakvalue()))
if (tracker.getpeakvalue() < 0.6):
counttrack2 = 0
flag_track2 = 0
#nucleo.flush()
#nucleo.write("8,,,,,,,,,,,,".encode())
print("WRITEOUT")
#frame_resized = cv2.flip(frame_resized, 0)
cv2.imshow("Detected Human", frame_resized)
cv2.imshow("Depth", frame_convert2.pretty_depth_cv(depth))
#cv2.imshow("Depth2", frame_convert2.pretty_depth_cv(depth2))
# cv2.imshow("Original", frame)
else:
count = count + 1
print("Number of frame skipped in the video= " + str(count))
# compute the fps
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
print("FPS: " + str(fps))
#outframe = open("/home/ubuntu/Progress\ TA/Integrasi/rgb640/%d.jpg" % countsave, 'wb+')
cv2.imwrite('%d.jpg' % countsave, frame_resized) # Save image...
countsave = countsave + 1
key = cv2.waitKey(5)
cv2.destroyAllWindows()
freenect.sync_stop()
nucleo.close()
print("\nFINISH")
def get_depth():
frame = frame_convert2.pretty_depth_cv(freenect.sync_get_depth()[0])
cv2.imwrite('depthcapture.jpg', frame)
print('frameDepth = [%d]\n' % (frame))
return frame
def kinect_get_ir():
# get the IR image and make it correct format
array, t = freenect.sync_get_video(0,freenect.VIDEO_IR_10BIT)
array = frame_convert2.pretty_depth_cv(array)
return array
def get_depth():
return frame_convert2.pretty_depth_cv(freenect.sync_get_depth()[0])
def get_depth():
#good for displaying and saving depth image
frame = frame_convert2.pretty_depth_cv(freenect.sync_get_depth()[0])
return frame