def getKinectData(self):
delay = 1 / self.fps
#Wait to ports are setup before streaming
time.sleep(10)
freenect.sync_stop()
while self.running:
if self.depth == True:
#get a frame from depth sensor
depth = self.get_depth()
#write depth image to file
cv.imwrite('/var/www/html/stream/depthImage.jpg', depth)
if self.image == True:
#get a frame from depth sensor
image = self.get_video()
#write depth image to file
cv.imwrite('/var/www/html/stream/image.jpg', image)
#frame delay
time.sleep(delay)
print "INFO: Shutting down Kinect Data Capture."
freenect.sync_stop()
def testkinect1():
# test video modes
[ctx,dev] = initkinect()
print(freenect.get_video_format(dev))
freenect.set_video_mode(dev,1,1)
print(freenect.get_video_format(dev))
killkinect(ctx,dev)
old_time = time.time()
while time.time()-old_time < 10:
cv.ShowImage('Video', get_video())
cv2.waitKey(1)
cv2.destroyAllWindows()
cv2.waitKey(1000)
freenect.sync_stop()
[ctx,dev] = initkinect()
freenect.set_video_mode(dev,1,2)
print(freenect.get_video_format(dev))
killkinect(ctx,dev)
old_time = time.time()
while time.time()-old_time < 10:
cv.ShowImage('Video', get_video())
cv2.waitKey(1)
cv2.destroyAllWindows()
cv2.waitKey(1000)
freenect.sync_stop()
[ctx,dev] = initkinect()
freenect.set_video_mode(dev,1,3)
print(freenect.get_video_format(dev))
killkinect(ctx,dev)
old_time = time.time()
while time.time()-old_time < 10:
cv.ShowImage('Video', get_video())
cv2.waitKey(1)
cv2.destroyAllWindows()
freenect.sync_stop()
[ctx,dev] = initkinect()
freenect.set_video_mode(dev,1,5)
print(freenect.get_video_format(dev))
killkinect(ctx,dev)
old_time = time.time()
while time.time()-old_time < 10:
cv.ShowImage('Video', get_video())
cv2.waitKey(1)
cv2.destroyAllWindows()
freenect.sync_stop()
[ctx,dev] = initkinect()
freenect.set_video_mode(dev,1,6)
print(freenect.get_video_format(dev))
killkinect(ctx,dev)
old_time = time.time()
while time.time()-old_time < 10:
cv.ShowImage('Video', get_video())
cv2.waitKey(1)
cv2.destroyAllWindows()
freenect.sync_stop()
def capture_emotions(person_name, emotions, duration):
"""Capture each emotion in turn"""
captured_emotions = {}
print """I am going to prompt you to pretend to feel the following emotions for %s seconds each: %s""" % (duration, ', '.join(emotions))
for emotion in emotions:
capture_emotion(captured_emotions, person_name, emotion, duration)
freenect.sync_stop()
save_emotions(captured_emotions, person_name)
def capture_emotions(person_name, emotions, duration):
"""Capture each emotion in turn"""
captured_emotions = {}
print """I am going to prompt you to pretend to feel the following emotions for %s seconds each: %s""" % (
duration, ', '.join(emotions))
for emotion in emotions:
capture_emotion(captured_emotions, person_name, emotion, duration)
freenect.sync_stop()
save_emotions(captured_emotions, person_name)
def disp_thresh(lower, upper,depth):
l1=map(lower,0,1800,0,255)
depth = (255-l1) * np.logical_and(depth > lower, depth < upper)
depth = depth.astype(np.uint8)
if cv2.waitKey(1) == 27:
freenect.sync_stop()
cv2.destroyAllWindows()
exit (0)
return depth
def kill():
[ctx, dev] = initkinect()
cv2.destroyAllWindows()
freenect.sync_stop()
freenect.stop_video(dev)
freenect.stop_depth(dev)
freenect.close_device(dev)
freenect.shutdown(ctx)
quit()
def stop(self):
"""
Safely stop the Kinect Thread, freeing the Kinect properly.
Set a variable to stop Thread execution by terminating the while loop in
run().
"""
freenect.sync_stop()
self.exit = True
def keyboard_press_event(self, key, x, y):
if key == chr(27):
#freenect.sync_set_led(1)
freenect.sync_stop()
elif key == ' ':
print "Toggled the RGB/IR image."
self.show_ir = not self.show_ir
super(PointCloudDemo, self).keyboard_press_event(key, x, y)
def keyPressed(key, x: int, y: int):
global color
if key == b'C' or key == b'c':
color = not color
# break;
if key == b'Q' or key == b'q' or key == b'\x1b': # ESC
glutDestroyWindow(window)
# device->stopDepth();
# device->stopVideo();
freenect.sync_stop()
exit(0)
def keyboard_press_event(self, key, x, y):
if key == chr(27):
#freenect.sync_set_led(1)
freenect.sync_stop()
#np.savez("data", vertices=self.vertices_measure.get())
#np.save("F", self.ffusion.F_gpu.get())
#np.save("W", self.ffusion.W_gpu.get())
if key == 'm':
self.draw_flags['measure'] ^= True
if key == 'r':
self.draw_flags['raycast'] ^= True
if key == 't':
self.ffusion.active_tracking ^= True
super(DenseDemo, self).keyboard_press_event(key, x, y)
def keyboard_press_event(self, key, x, y):
if key == chr(27):
#freenect.sync_set_led(1)
freenect.sync_stop()
#np.savez("data", vertices=self.vertices_measure.get())
#np.save("F", self.ffusion.F_gpu.get())
#np.save("W", self.ffusion.W_gpu.get())
if key == 'm':
self.draw_flags['measure'] ^= True
if key == 'r':
self.draw_flags['raycast'] ^= True
if key == 't':
self.ffusion.active_tracking ^= True
super(DenseDemo, self).keyboard_press_event(key, x, y)
def temp_test():
fn_ctx = fn.init()
fn_dev = fn.open_device(fn_ctx, fn.num_devices(fn_ctx) - 1)
fn.set_tilt_degs(fn_dev, 0)
fn.close_device(fn_dev)
while True:
dep = get_depth()
dep *= (dep * 1.3).astype(np.uint8)
print("{}\t,\t{}".format(np.min(dep), np.max(dep)))
cv2.imshow('depth', dep)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
fn.sync_stop()
break
def capture_one(emotion, duration):
"""Capture the emotion to tsv files in person_name/emotion"""
while True:
print "\aPlease start (pretending that you are) feeling %s" % emotion
print "I am going to start capturing data in %s seconds" % \
SECONDS_TO_WAIT_BEFORE_CAPTURING
time.sleep(SECONDS_TO_WAIT_BEFORE_CAPTURING)
frames_and_eeg_data = capture_data(duration)
print "\aDone."
print "Did you manage to hold the feeling the entire time? [y/n]"
if raw_input().lower().strip() in ["y", "yes"]:
break
else:
print "Trying again..."
#TODO: Expose this less :-)
freenect.sync_stop()
return frames_and_eeg_data
def capture_one(emotion, duration):
"""Capture the emotion to tsv files in person_name/emotion"""
while True:
print "\aPlease start (pretending that you are) feeling %s" % emotion
print "I am going to start capturing data in %s seconds" % \
SECONDS_TO_WAIT_BEFORE_CAPTURING
time.sleep(SECONDS_TO_WAIT_BEFORE_CAPTURING)
frames_and_eeg_data = capture_data(duration)
print "\aDone."
print "Did you manage to hold the feeling the entire time? [y/n]"
if raw_input().lower().strip() in ["y", "yes"]:
break
else:
print "Trying again..."
#TODO: Expose this less :-)
freenect.sync_stop()
return frames_and_eeg_data
def masking (rgb):
hsv = cv2.cvtColor(rgb, cv2.COLOR_BGR2HSV)
rgb_lthresh = np.array([24, 100, 50])
rgb_uthresh = np.array([50, 255, 255])
rgb_mask = cv2.inRange(hsv, rgb_lthresh, rgb_uthresh)
rgb_f = cv2.bitwise_and(rgb,rgb,mask=rgb_mask)
rgb_md = cv2.medianBlur(rgb_f, 15)
rgb_gray = cv2.cvtColor(rgb_md, cv2.COLOR_BGR2GRAY)
rgb_mask = cv2.adaptiveThreshold(rgb_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 15, 5)
rgb_edges = cv2.Canny(rgb_mask, 100, 255)
rgb_dil = cv2.dilate(rgb_edges, kernel=(3, 3), iterations=1)
cv2.imshow('RGB',rgb_dil)
if cv2.waitKey(1) == 27:
freenect.sync_stop()
cv2.destroyAllWindows()
exit (0)
return rgb_dil,rgb_mask
def density_plot():
fn_ctx = fn.init()
fn_dev = fn.open_device(fn_ctx, fn.num_devices(fn_ctx) - 1)
fn.set_tilt_degs(fn_dev, 0)
fn.close_device(fn_dev)
show_image = cv2.imshow
waitkey = cv2.waitKey
ravel = np.ravel
countbin = np.bincount
length = 256
nums = np.arange(0, length, 1)
zero = np.zeros_like(nums)
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
line, = ax.plot(nums, zero)
ax.set_ylim(0, 10000)
ax.set_xlim(0, 256)
set_y_data = line.set_ydata
def update(data):
set_y_data(data)
return line,
def get_dep():
dep = get_depth()
dep = cv2.medianBlur(dep, 3, dep)
dep = ravel(dep)
# dep = medfilt(dep, 21).astype(np.uint8)
return dep
def data_gen():
while True:
yield countbin(get_dep(), minlength=length)
ani = animation.FuncAnimation(fig, update, data_gen)
plt.show()
cv2.destroyAllWindows()
fn.sync_stop()
def set_kinect_angle(angle, device_index=0):
# Clamp angle to [-30, 30]
angle = min(angle, max(angle, -30), 30)
print "Setting Kinect angle to", angle
# We have to stop the synchronous runloop to interact with the device.
freenect.sync_stop()
# Open the device
ctx = freenect.init()
dev = freenect.open_device(ctx, device_index)
# Set angle
freenect.set_tilt_degs(dev, angle)
# Shutdown context, allowing synchronous runloop to start
freenect.shutdown(ctx)
return angle
def streamKinectImage(self):
try:
self.image = True
self.port = self.port + 1
port = self.port - 1
print "INFO: Adding kinect RGB image to stream on port ", str(
port), "."
startKinectImageStream = self.serverCommand + " -o 'output_http.so -w ./www/html -p " + str(
port
) + "' -i 'input_file.so -f /var/www/html/stream -n image.jpg -d 0'"
os.system(startKinectImageStream)
except self.running == False:
print "INFO: Shutting down the kinect RGB image stream"
self.image = False
freenect.sync_stop()
sys.exit()
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 '
#!/usr/bin/env python
import freenect
import cv
import numpy as np
cv.NamedWindow("Depth")
cv.NamedWindow("Video")
ind = 0
while 1:
print(ind)
try:
depth, timestamp = freenect.sync_get_depth(ind)
rgb, timestamp = freenect.sync_get_video(ind)
except TypeError:
ind = 0
continue
ind += 1
cv.ShowImage("Depth", depth.astype(np.uint8))
cv.ShowImage("Video", rgb[:, :, ::-1].astype(np.uint8))
cv.WaitKey(10)
freenect.sync_stop() # NOTE: May remove if you have good USB bandwidth
def get_video():
array,_ = freenect.sync_get_video()
array = cv2.cvtColor(array,cv2.COLOR_RGB2BGR)
freenect.sync_stop()
return array
def get_depth():
array,_ = freenect.sync_get_depth()
array = array.astype(np.uint8)
freenect.sync_stop()
return array
panel.display() oldDepth = depth for event in pygame.event.get() : if event.type == pygame.QUIT : finish = True if event.type == pygame.KEYDOWN : if event.key == pygame.K_ESCAPE : finnish = True if event.key == pygame.K_q : finnish = True if event.key == pygame.K_r : backgroundDepth[:] = 0 if event.key == pygame.K_l : flooding = not flooding if event.key == pygame.K_f : pygame.display.toggle_fullscreen() if event.key == pygame.K_v : panel.toggle_video_capture() fps.tic() del fps print "Stopping kinect" freenect.sync_stop() print "Stopping pygame"
[slice(None) if i != axis else 0 for i in range(dif.ndim)])
index = [
mesh[i][index].ravel() if i != axis else location.ravel()
for i in range(bins.ndim)
]
counts = bins[tuple(index)].reshape(location.shape)
index[axis] = indices[tuple(index)]
modals = srt[tuple(index)].reshape(location.shape)
return (modals, counts)
date = datetime.date.today().strftime("%B-%d-%Y")
os.mkdir(date)
os.chdir(date)
logging.basicConfig(filename='EventLog.txt', level=logging.DEBUG)
a = KinectTracker()
#z = a.calculate_masked_background()
#y,z = a.identify_valid_masked_background()
# Functionally, a.average_background() is just getting a single snapshot. Write another function that calls it periodically to handle it as a full time thing
camera = PiCamera()
camera.resolution = (1296, 972)
camera.framerate = 30
# allow the camera to warmup
time.sleep(1)
camera.start_recording(date + "_vid.h264", bitrate=7500000)
print "start picam recording"
a.long_term_recording()
camera.stop_recording()
print "stopped recording"
freenect.sync_stop()
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 main_vision(load):
# inits
fn_ctx = fn.init()
fn_dev = fn.open_device(fn_ctx, fn.num_devices(fn_ctx) - 1)
fn.set_tilt_degs(fn_dev, 0)
fn.close_device(fn_dev)
key_point = KeyPoints(150)
predictor = prediction(ModelPath)
preds = []
# optimization
t0 = 0.0
t1 = 0.0
fps = 0.0
total_fps = 0.0
frames = 0
kp_speed = key_point._get_kp_speedup()
draw_speed = key_point._get_draw_speedup()
proc_speed = key_point._get_process_speedup()
cvtColor = cv2.cvtColor
BGR2RGB = cv2.COLOR_BGR2RGB
get_kp = key_point.get_key_points
draw_kp = key_point.draw_key_points
process_image = key_point.__process_image
show_image = cv2.imshow
wait_for_key = cv2.waitKey
copy_thing = copy.copy
num_features = key_point.get_num_features()
arr_shape = np.shape
shape_check = (num_features, 32)
ravel = np.ravel
append_pred = preds.append
get_time = time.time
current_class = 0
if load:
brain = predictor.load_brain()
pred_speed = predictor.get_pred_speed()
predict = predictor.predict
else:
add_speed = predictor.get_add_speed()
add_data = predictor.add_data
get_length = predictor.get_data_length
if load:
net = Neural_Net(predictor.brain.getPoint(), np.vstack(predictor.brain.getData()), 4800 * 2, num_features)
nl_predict = net.predict
nl_speed = net.get_neural_speed()
# mainLoop
while True:
t0 = get_time()
# Get a fresh frame
depth = get_depth()
frame = get_video()
show_image('Raw Image', cvtColor(frame, BGR2RGB))
# Process Depth Image
# depth = remove_background(depth, 25)
depth = remove_background_percent(depth, .5, 50)
depth = convert_to_bw(depth)
mask = make_mask(depth)
# Process Image
frame = cvtColor(frame, BGR2RGB)
video = copy_thing(frame)
frame = process_image(frame, proc_speed)
# Make Masked Frame
masked_frame = copy_thing(frame)
masked_frame[mask] = 0
# Process Key Points
kp, des = get_kp(masked_frame, kp_speed)
video = draw_kp(video, kp, True, speedup=draw_speed)
# Predict current
if (load) and (des is not None) and (arr_shape(des) == shape_check):
pred = predict(ravel(des), pred_speed)
append_pred(pred)
print(pred)
print(nl_predict([ravel(des)], nl_speed))
# Add object description to data set
if (not load) and (des is not None) and (arr_shape(des) == shape_check):
add_data(add_speed, np.ravel(des), current_class)
print('Current Class and Length:\t%i\t%i' % (get_length(), current_class))
t1 = get_time()
fps = (1 / (t1 - t0))
total_fps += fps
frames += 1
print('%.2f FPS' % fps)
show_image('masked image', masked_frame)
show_image('depth', depth)
show_image('key points', video)
# show_image('all', frame, masked_frame, depth, video)
if wait_for_key(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
if load:
break
print('Current Class: %i\nn : Next Class\nr : Continue Current Class\nq : Quit' % (current_class))
inp = raw_input()
if inp == 'n':
current_class += 1
elif inp == 'q':
break
# print(np.mean(preds))
cv2.destroyAllWindows()
print('Average FPS: %.2f' % (total_fps / frames))
fn.sync_stop()
if not load:
predictor.create_brain()
main_vision(True)
#!/usr/bin/env python
import freenect
import cv
import numpy as np
cv.NamedWindow('Depth')
cv.NamedWindow('Video')
ind = 0
while 1:
print(ind)
try:
depth, timestamp = freenect.sync_get_depth(ind)
rgb, timestamp = freenect.sync_get_video(ind)
except TypeError:
ind = 0
continue
ind += 1
cv.ShowImage('Depth', depth.astype(np.uint8))
cv.ShowImage('Video', rgb[:, :, ::-1].astype(np.uint8))
cv.WaitKey(10)
freenect.sync_stop() # NOTE: May remove if you have good USB bandwidth
def StopKinect():
freenect.sync_stop()
#cv.NamedWindow('Video')
ind = 0
print('%s\nPress ESC to stop' % __doc__)
def get_depth(ind):
#return frame_convert.pretty_depth_cv(freenect.sync_get_depth(ind)[0])
array = freenect.sync_get_depth(ind)[0]
ret_array = array.astype(np.uint8)
return ret_array
def get_video(ind):
return frame_convert.video_cv(freenect.sync_get_video(ind)[0])
while 1:
print(ind)
try:
depth = get_depth(ind)
# video = get_video(ind)
except TypeError:
ind = 0
continue
ind += 1
cv.ShowImage('Depth', depth)
# cv.ShowImage('Video', video)
if cv.WaitKey(10) == 27:
break
freenect.sync_stop() # NOTE: Uncomment if your machine can't handle it
def close(event, x, y, flags, param):
if event == cv.CV_EVENT_LBUTTONDOWN:
freenect.sync_stop()
sys.exit()
def main():
print("Running...");
flag_track = 0
count = 0
counttrack = 0
prev_y_pixel = 0
prev_x_pixel = 0
tetaperpixel = 0.994837/400.0
# 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]
min_area=(0.01)*frame_resized.shape[1]
print(frame_resized.shape)
boxcolor=(0,255,0)
timeout = 0; #variable for counting time elapsed
key = ''
temp = 1
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_grayscale,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]
rawDisparity = depth[(int)(x_pixel),(int)(y_pixel)]
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()
if (flag_track == 0):
#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_track = 1;
boxcolor = (255,0,0)
else:
timeout = 0
boxcolor = (0,255,0)
elif (flag_track == 1):
#GENERAL ASSUMPTION SINGLE PERSON TRACKING
# start tracking...
#nucleo.write("9,,,,,,,,,,,,".encode())
x_track,y_track = x_pix,y_pix
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
rawDisparity = depth[(int)(x_track),(int)(y_track)]
distance = 1/(-0.00307 * rawDisparity + 3.33)
if (distance<0):
distance = 0.5
#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 = (y_track-x_center)*tetaperpixel
print("teta="+str(teta)+"x:"+str(x_track)+"y:"+str(y_track))
# send the teta and distance
nucleo.flush()
if(teta<0.0):
flag= nucleo.write(("7,"+format(teta,'1.2f')+","+format(distance,'1.3f')).encode())
elif(teta>0.0):
flag= nucleo.write(("7,"+format(teta,'1.3f')+","+format(distance,'1.3f')).encode())
print("WRITEIN1" + str(flag))
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):
# count how many frame skipped without people detected while tracking
if (flag_track==1):
counttrack = counttrack + 1
# continue tracking after a maximum of 50 frame without people taking previous location as target
if (counttrack<=50):
# still print the last condition to the output image
cv2.putText(frame_resized, "distance: {:.2f}".format(distance), (10, (frame_resized.shape[0]-25)-50), font, 0.65, (0, 255, 255), 3)
cv2.putText(frame_resized, "Point "+str(i)+": "+str(b[0])+" "+str(b[1]), (10, frame_resized.shape[0]-25), font, 0.65, (0, 255, 255), 3)
# still send the nucleo tracking state
nucleo.flush()
if(teta<0.0):
flag= nucleo.write(("7,"+format(teta,'1.2f')+","+format(distance,'1.3f')).encode())
elif(teta>0.0):
flag= nucleo.write(("7,"+format(teta,'1.3f')+","+format(distance,'1.3f')).encode())
print("WRITEIN" + str(flag))
elif (counttrack>50):
counttrack = 0
flag_track = 0
# tell nucleo to leave tracking state
nucleo.flush()
nucleo.write("8,,,,,,,,,,,,".encode())
print("WRITEOUT")
elif (flag_track==0):
timeout = 0
boxcolor = (0,255,0)
#frame_resized = cv2.flip(frame_resized, 0)
cv2.imshow("Detected Human", frame_resized)
#cv2.imshow("Depth", depth)
# 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))
key = cv2.waitKey(5)
cv2.destroyAllWindows()
freenect.sync_stop()
nucleo.close()
print("\nFINISH")
def close(event, x, y, flags, param):
if event == cv.CV_EVENT_LBUTTONDOWN:
freenect.sync_stop()
sys.exit()
def kinect_shutdown():
freenect.sync_stop()
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]
print(frame_resized.shape[1])
boxcolor = (0, 255, 0)
timeout = 0
#variable for counting time elapsed
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_grayscale, center, frame_resized, boxcolor)
if (len(center_fix) > 0):
xnorm = encodex(
center_fix[0][0]) # retrieve coded position from image
prev_xnorm = xnorm
#nucleo.write(struct.pack('>B',xnorm))
endtime = time.time()
if (prev_xnorm == xnorm):
timeout = timeout + (endtime - starttime)
if (timeout > 10):
boxcolor = (255, 0, 0)
else:
timeout = 0
boxcolor = (0, 255, 0)
print("X_NORM: " + str(xnorm))
print("Timeout: " + str(timeout))
rawDisparity = depth[(int)(center_fix[0][1]),
(int)(center_fix[0][0])]
distance = 100 / (-0.00307 * rawDisparity + 3.33)
cv2.putText(frame_resized, "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))
else:
timeout = 0
boxcolor = (0, 255, 0)
i = 0
for b in center_fix:
cv2.putText(
frame_resized,
"Point " + str(i) + ": " + str(b[0]) + " " + str(b[1]),
(10, frame_resized.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
#frame_resized = cv2.flip(frame_resized, 0)
cv2.imshow("Detected Human", frame_resized)
#cv2.imshow("Depth", depth)
# 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))
key = cv2.waitKey(5)
cv2.destroyAllWindows()
freenect.sync_stop()
nucleo.close()
print("\nFINISH")
from misc.demo import frame_convert
cv.NamedWindow('Depth')
cv.NamedWindow('Video')
ind = 0
print('%s\nPress ESC to stop' % __doc__)
def get_depth(ind):
return frame_convert.pretty_depth_cv(freenect.sync_get_depth(ind)[0])
def get_video(ind):
return frame_convert.video_cv(freenect.sync_get_video(ind)[0])
while 1:
print(ind)
try:
depth = get_depth(ind)
video = get_video(ind)
except TypeError:
ind = 0
continue
ind += 1
cv.ShowImage('Depth', depth)
cv.ShowImage('Video', video)
if cv.WaitKey(10) == 27:
break
freenect.sync_stop() # NOTE: Uncomment if your machine can't handle it
def handler(signum, frame): video_writer.release() freenect.sync_stop() exit()
def close(self): freenect.sync_stop() self.sim.stop()