def displayRedOrBlue(dev, data, timestamp):
global keep_running
global red
global iter
if(red):
lower = np.array(boundaries[0], dtype = "uint8")
upper = np.array(boundaries[1], dtype = "uint8")
iter+=1
elif(not red):
lower = np.array(boundaries[2], dtype = "uint8")
upper = np.array(boundaries[3], dtype = "uint8")
iter+=1
if(iter%10==0):
red = not red
mask = cv2.inRange(frame_convert2.video_cv(data), lower, upper)
output = cv2.bitwise_and(frame_convert2.video_cv(data), frame_convert2.video_cv(data), mask = mask)
gray = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
# perform edge detection, then perform a dilation + erosion to
# close gaps in between object edges
edged = cv2.Canny(gray, 50, 100)
edged = cv2.dilate(edged, None, iterations=1)
edged = cv2.erode(edged, None, iterations=1)
# find contours in the edge map
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cv2.imshow('Red and Blue', output)
if cv2.waitKey(10) == 27:
keep_running = False
def display_rgb(dev, data, timestamp):
global keep_running
print frame_convert2.video_cv(data)
c = cv2.waitKey(10)
if 'q' == chr(c & 255):
keep_running = False
if 's' == chr(c & 255):
cv2.imwrite("saved1.jpg", frame_convert2.video_cv(data))
def display_blue(dev, data, timestamp):
global keep_running
lower = np.array(boundaries[2], dtype = "uint8")
upper = np.array(boundaries[3], dtype = "uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(frame_convert2.video_cv(data), lower, upper)
output = cv2.bitwise_and(frame_convert2.video_cv(data), frame_convert2.video_cv(data), mask = mask)
cv2.imshow('Blue', output)
if cv2.waitKey(10) == 27:
keep_running = False
def draw_canny(data):
gray = frame_convert2.video_cv(data)
gray = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 35, 125)
return edged
def collect_rgb(dev, data, timestamp):
global keep_running
global rgb_index
np.save('{0}/{1}/{2}'.format(ROOT_PATH, RGB_PATH, rgb_index), data)
rgb_index += 1
cv2.imshow('RGB', frame_convert2.video_cv(data))
if cv2.waitKey(10) == 27:
keep_running = False
def get_video():
global cam
data = frame_convert2.video_cv(freenect.sync_get_video()[0])
#data,_ = freenect.sync_get_video()
#data = cv2.cvtColor(data, cv2.COLOR_RGB2BGR)
img = cv2.imencode('.JPEG', data)[1].tostring()
enData = base64.b64encode(img).decode('UTF-8')
return enData
def display_rgb(dev, data, timestamp):
global keep_running
global faceCascade
global light
global tilt
global search
global faces
global ser
global last_time
camera_x_center = 320
image = frame_convert2.video_cv(data)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect faces in the image
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags = cv2.CASCADE_SCALE_IMAGE
)
print "found {0} face(s)".format(len(faces))
if len(faces) > 0:
search = False
light = 1
face_x_center = faces[0][0] + faces[0][2]/2
face_y_center = faces[0][1] + faces[0][3]/2
diff = face_x_center - camera_x_center
# print diff
if diff > 32:
print "face on the right side, turn right!"
elif diff < -32:
print "face on the left side, turn left!"
else :
print "face straight ahead"
else:
print "no face"
diff = 640
search = True
light = 3
print diff
ser.write(str(diff-641))
c = cv2.waitKey(10)
if 'q' == chr(c & 255):
keep_running = False
if 's' == chr(c & 255):
cv2.imwrite('detected.jpg', image)
image = cv2.imread('detected.jpg')
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imwrite('detected.jpg', image)
def display_rgb(dev, data, timestamp):
# pass
global keep_running
im = frame_convert2.video_cv(data)
# im = im_or.copy()
# pdb.set_trace()
if len(im) is not 0:
cls,det = CNN.detect(net,im)
# print(det[0,0:4])
print det.shape
def display_camera_half(self, dev, data, timestamp):
faces = self.facial_recog(data)
for (x, y, w, h) in faces:
cv2.rectangle(data, (x, y), (x + w, y + h), (0, 255, 0), 2)
data = cv2.resize(data, (1280, 720))
cv2.imshow('RGB', frame_convert2.video_cv(data))
if cv2.waitKey(1) == 27:
self.keep_running = [False]
def display_rgb(dev, data, timestamp):
global keep_running
global tilt
global light
cv2.imshow('RGB', frame_convert2.video_cv(data))
c = cv2.waitKey(10)
if 'q' == chr(c & 255):
keep_running = False
if 's' == chr(c & 255):
cv2.imwrite("saved.jpg", frame_convert2.video_cv(data))
if 'u' == chr(c & 255):
tilt = 25
if 'd' == chr(c & 255):
tilt = 5
if '1' == chr(c & 255):
light = 1
if '2' == chr(c & 255):
light = 2
if '3' == chr(c & 255):
light = 3
def display_camera_full(self, dev, data, timestamp):
data = cv2.resize(data, (1900, 1050))
faces = self.facial_recog(data)
for (x, y, w, h) in faces:
cv2.rectangle(data, (x, y), (x + w, y + h), (0, 255, 0), 2)
rgb = data[:, :, ::-1]
self.recognizer.recognize(rgb)
cv2.imshow('RGB', frame_convert2.video_cv(data))
if cv2.waitKey(1) == 27:
self.keep_running = [False]
def display_rgb(delay):
i = 0
while 1:
try:
data = np.load('{0}/{1}/{2}.npy'.format(ROOT_PATH, RGB_PATH, i))
cv2.imshow('RGB', frame_convert2.video_cv(data))
if cv2.waitKey(delay) == 27:
break
except Exception:
break
if DEBUG:
print i
i += 1
def display_camera_fourth(self, dev, data, timestamp):
faces = self.facial_recog(data)
for (x, y, w, h) in faces:
cv2.rectangle(data, (x, y), (x + w, y + h), (0, 255, 0), 2)
print('faces')
#print(faces)
rgb = data[:, :, ::-1]
locations = [[y, x + w, y + h, x] for (x, y, w, h) in faces]
print(locations)
if len(locations) > 0:
print(self.recognizer.recognize_face_loc(rgb, locations))
cv2.imshow('RGB', frame_convert2.video_cv(data))
if cv2.waitKey(1) == 27:
self.keep_running = [False]
def display_rgb(dev, data, timestamp):
t_start = datetime.datetime.now()
global keep_running
#result = draw_canny(data)
data = frame_convert2.video_cv(data)
result = data.copy()
t_end = datetime.datetime.now()
t_delta = t_end - t_start
cv2.putText(result, str(1000000/t_delta.microseconds), (0, 25), cv2.FONT_HERSHEY_PLAIN, 2, (127, 0, 0), 2)
cv2.imshow('RGB', result)
if cv2.waitKey(10) == 27:
keep_running = False
def kinect_run():
# ROS node setup
pub = rospy.Publisher('Image_Screws', kinect_msg, queue_size=1)
rospy.init_node('Kinect_Main', anonymous=True)
rate = rospy.Rate(10)
msg = kinect_msg()
status = 3 # Current status = setting up
try:
im_screw_detect = ImScrewDetector()
except Exception:
print("**Screw Detect Error**")
status = 0
traceback.print_exc(file=sys.stdout)
while not rospy.is_shutdown():
try:
image = np.array(
frame_convert2.video_cv(freenect.sync_get_video()[0]))
image = scale(image)
status = 1 # Status ready
except TypeError as e:
time.sleep(1)
except Exception as e:
print("**Get Image Error**")
status = 0 # error status
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()
# For each of 4 points, probability is each of 5 classes
# ['screw_full', 'screw_empty', 'bolt_full', 'bolt_empty', 'null']
msg.im_screw_probs_1 = im_screw_states[0]
msg.im_screw_probs_2 = im_screw_states[1]
msg.im_screw_probs_3 = im_screw_states[2]
msg.im_screw_probs_4 = im_screw_states[3]
msg.tally = tally # Bolts and Screws Tally
msg.im_stat = status # Vision status no.
msg.safe_move = False # future use variable for robot
pub.publish(msg)
rate.sleep()
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 rgb(dev, data, timestamp):
global keep_running
frame = frame_convert2.video_cv(data)
_target = frame.copy()
# find the horizontal centre pixel
_center1 = np.uint16(np.round(frame.shape[1] / 2))
# find the vertical centre pixel
_center2 = np.uint16(np.round(frame.shape[0] / 2))
cv2.circle(_target, (_center1, _center2), 2, (0, 0, 255), 3)
# apply a red dot to the centre of the frame so the user can target the object to find HSV components from.
cv2.imshow('RGB', _target)
# show targeting frame in a window
# Convert to HSV colourspace
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# find the Hue, Saturation, Value components of the frame
CH_H, CH_S, CH_V = cv2.split(frame)
if cv2.waitKey(10) == 27:
# find Hue value of middle pixel
pxl_H = CH_H[int(frame.shape[0] / 2), int(frame.shape[1] / 2)]
# find Saturation value of middle pixel
pxl_S = CH_S[int(frame.shape[0] / 2), int(frame.shape[1] / 2)]
# find intensity value of middle pixel
pxl_V = CH_V[int(frame.shape[0] / 2), int(frame.shape[1] / 2)]
cv2.imwrite("measure_hsv.jpg", _target)
print("Hue", pxl_H)
print("Saturation", pxl_S)
print("Intensity", pxl_V)
keep_running = False
def get_video():
""" Get's the current video data from Kinect """
return frame_convert2.video_cv(freenect.sync_get_video()[0])
def get_rgb():
return imutils.rotate(
frame_convert2.video_cv(freenect.sync_get_video()[0]), -90)
else:
rate = rospy.Rate(1)
if debug:
Counter = 0
while not rospy.is_shutdown():
rate.sleep()
if debug:
if Counter % 100 == 0:
print(Counter)
#if Counter > 30:
# break
Counter += 1
depth, timestamp = freenect.sync_get_depth()
#depth,safe = isBlocked(depth)
video = frame_convert2.video_cv(freenect.sync_get_video()[0])
pub.publish(True)
maskArray = k.findObjects(video)
for mask in maskArray:
keypointsArray, bluredIm = k.findBlobs(mask)
#this is added up here becuase as part ofthis bluredIm goes from a 1 dimensional
# ndarray to 3 channels which we need later
# TODO Figure out how to do this without that.
bluredIm = cv2.drawKeypoints(
bluredIm, keypointsArray, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cannyEdges = k.ourCannyBlob(mask)
for point in keypointsArray:
#try:
def get_video():
return frame_convert2.video_cv(
freenect.sync_get_video_with_res(
resolution=freenect.RESOLUTION_HIGH)[0])
def display_camera_reg(self, dev, data, timestamp):
cv2.imshow('RGB', frame_convert2.video_cv(data))
if cv2.waitKey(1) == 27:
self.keep_running = [False]
def show_video():
cv2.imshow('Video', frame_convert2.video_cv(freenect.sync_get_video()[0]))
return None
diff = None
lower = 0
upper = 100
max_upper = 1900
cv_prev_cx = 0
cv_prev_cy = 0
cv_prev_radius = 0
depth_area = 0
depth_x = 0
depth_y = 0
#freenect.set_depth_mode(freenect.RESOLUTION_MEDIUM,freenect.DEPTH_REGISTERED)
while 1:
while upper < max_upper:
rgb = frame_convert2.video_cv(freenect.sync_get_video()[0])
rgb = rgb.astype(np.uint8)
rgb_dil,rgb_mask = masking(rgb)
depth, timestamp = freenect.sync_get_depth()
#print('%d < depth < %d' % (lower, upper))
depth = disp_thresh(lower,upper,depth)
mblur = cv2.medianBlur(depth, 15)
apt_thresh = cv2.adaptiveThreshold(mblur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 15, 5)
canny = cv2.Canny(apt_thresh,100,255)
dilate = cv2.dilate(canny, kernel=(3, 3), iterations=1)
comb = cv2.bitwise_and(apt_thresh,rgb_mask)
_, cnt, hierarchy = cv2.findContours(dilate, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
_,rgb_cnt,rgb_heir = cv2.findContours(rgb_dil,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
if rgb_cnt:
epsilon = 0.1 * cv2.arcLength(rgb_cnt[0], True)
approx = cv2.approxPolyDP(rgb_cnt[0], epsilon, True)
def display_rgb(dev, data, timestamp):
global keep_running
cv2.imshow('RGB', frame_convert2.video_cv(data))
if cv2.waitKey(10) == 27:
keep_running = False
def show_video():
cv2.imshow('Video', frame_convert2.video_cv(freenect.sync_get_video()[0]))
def show_video():
cv2.imshow('Video', frame_convert2.video_cv(frame))
def kinect_get_video():
# get the rgb image and make it correct format
return frame_convert2.video_cv(freenect.sync_get_video()[0])
def get_circle_pos(data):
""" Get's the x,y location of circles in the current frame """
frame = frame_convert2.video_cv(data)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# setup lower threshold masks for filtering (H,S,V)
lower_red = np.array([165, 220, 50])
# setup upper threshold masks for filtering (H,S,V)
upper_red = np.array([185, 255, 200])
# use higher than 180 hue to wrap around to 0
# cast to type that the opencv can process consistently
lower = np.array(lower_red, dtype="uint8")
upper = np.array(upper_red, dtype="uint8")
_mask = cv2.inRange(frame, lower, upper) # find boolean mask for filtering
# bitwise-and original frame using the mask above to find the filtered frame.
frame = cv2.bitwise_and(frame, frame, mask=_mask)
# convert filtered frame back to BGR colourspace (opencv likes BGR rather than RGB)
frame = cv2.cvtColor(frame, cv2.COLOR_HSV2BGR)
# CIRCLE DETECTION
# convert to greyscale colourspace
cimg = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# (optional: smooth image to reduce detection errors, must be an odd number)
cimg = cv2.GaussianBlur(cimg, (9, 9), 0)
circles = cv2.HoughCircles(cimg,
cv2.HOUGH_GRADIENT,
1,
200,
param1=40,
param2=15,
minRadius=1,
maxRadius=50)
# this function will need some tuning and trial & error, see the opencv documentation
# if(circles != None): # if there are circle detected in the frame
point = Point(0, 0)
if circles is not None:
circles = np.uint16(np.around(circles))
# loop through all the circles detected
# for i in range(len(circles[0])):
i = 0
point.x = circles[0][i][0]
point.y = circles[0][i][1]
# draw the outer circle
cv2.circle(frame, (circles[0][i][0], circles[0][i][1]),
circles[0][i][2], (0, 255, 0), 2)
# draw the centre of the circle
cv2.circle(frame, (circles[0][i][0], circles[0][i][1]), 2, (0, 0, 255),
3)
# see documentation for draw functions
else:
point.x = -1
point.y = -1
# show frame in a separate window
cv2.imshow('RGB', frame)
return point
def doloop():
"""
Primary process loop.
"""
global depth, rgb, current_target
current_target = None
positions = []
create_windows()
nothing = lambda x: None
# cv2.createTrackbar('distance min', 'color', 0, 255, nothing)
# cv2.createTrackbar('distance max', 'color', 0, 255, nothing)
cv2.createTrackbar('min', 'color', MIN_A, 500, nothing)
cv2.createTrackbar('max', 'color', MAX_A, 1000, nothing)
cv2.createTrackbar('trim', 'color', 0, 200, nothing)
cv2.createTrackbar('user', 'grid', 0, 640, nothing)
cv2.createTrackbar('difficulty', 'grid', 0, 4, nothing)
#cv2.createTrackbar('Top Motor', 'grid', MOTOR1, 1023, nothing)
#cv2.createTrackbar('Bottom Motor', 'grid', MOTOR2, 1023, nothing)
cv2.createTrackbar('rotation', 'grid', 90, 180, nothing)
times = []
while True:
#time.sleep(1)
start = time.time()
# Trackbar updates
distance_min = cv2.getTrackbarPos('distance min', 'color')
distance_max = cv2.getTrackbarPos('distance max', 'color')
min_a = cv2.getTrackbarPos('min', 'color')
max_a = cv2.getTrackbarPos('max', 'color')
trim = cv2.getTrackbarPos('trim', 'color')
difficulty = cv2.getTrackbarPos('difficulty', 'grid')
userpos = cv2.getTrackbarPos('user', 'grid')
if distance_min == -1: distance_min = DISTANCE_MIN
if distance_max == -1: distance_max = DISTANCE_MAX
if min_a == -1: min_a = MIN_A
if max_a == -1: max_a = MAX_A
if trim == -1: trim = EDGE
# Get a fresh frame
(depth, _), (rgb, _) = get_depth(), get_video()
depth = prepare_depth(depth)
# Trim frame
if trim:
depth = depth[:, trim:depth.shape[1] - trim]
rgb = rgb[:, trim:rgb.shape[1] - trim]
# Threshold
ret, thresh1 = cv2.threshold(depth, distance_min, 255,
cv2.THRESH_TOZERO)
ret, thresh = cv2.threshold(thresh1, distance_max, 255,
cv2.THRESH_TOZERO_INV)
if 'd' in windows: cv2.imshow('depth', depth)
if 't' in windows: cv2.imshow('threshold', thresh)
# Get target point
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
target = get_contour2(contours, min_a, max_a)
if not target is None:
M = cv2.moments(target)
if M['m00'] == 0: print "0"
else:
target_point = (int(M['m10'] / M['m00']),
int(M['m01'] / M['m00']))
x_val = (640.0 / float(target_point[0]) * 255)
_, radius = cv2.minEnclosingCircle(target)
#cv2.putText(rgb, str(radius), target_point, cv2.FONT_HERSHEY_SIMPLEX, \
#1, (0,255,0), 2, cv2.CV_AA)
cv2.circle(rgb, target_point, 4, (255, 0, 0), -1)
cv2.drawContours(rgb, [target], -1, (0, 255, 0), 3)
if userpos == -1: userpos = target_point[0]
#Clip userpos
userpos = max(min(2 * userpos - WIDTH / 2, WIDTH), 0)
if difficulty:
current_target = pick_target(userpos, difficulty)
current_speed = pick_speed(userpos, difficulty)
# Keep a list of the detected positions
positions.append(current_target)
if len(positions) == 6:
# select and send target
send_target(select_target(positions, difficulty))
positions = []
if 'g' in windows:
cv2.imshow('grid', drawTable(userpos, current_target))
if 'c' in windows: cv2.imshow('color', video_cv(rgb))
char = cv2.waitKey(10)
if char == 27:
cv2.destroyAllWindows()
break
elif char == 115:
cv2.imwrite('capture.png', video_cv(rgb))
cv2.namedWindow('saved')
cv2.imshow('saved', video_cv(rgb))
elif char == ord('r'):
if serial_out:
serial_out.close()
serial_out.open()
end = time.time()
times.append(end - start)
def kinect_get_image(self):
# im: A numpy array, shape:(480, 640, 3) dtype:np.uint8
# timestamp: int representing the time
img, timestamp = freenect.sync_get_video()
return frame_convert2.video_cv(img)
def display_rgb(dev, data, timestamp):
global keep_running
cv2.imshow('RGB', frame_convert2.video_cv(data))
if cv2.waitKey(10) == 27:
keep_running = False
def show_depth(warp=False):
global threshold
global current_depth
global last_image
global client
global last_note
depth, timestamp = freenect.sync_get_depth()
rgb = frame_convert2.video_cv(freenect.sync_get_video()[0])
depth = 255 * np.logical_and(depth >= current_depth - threshold,
depth <= current_depth + threshold)
depth = depth.astype(np.uint8)
depth_rgb = cv2.cvtColor(depth, cv2.COLOR_GRAY2RGB)
# kernel = np.ones((9,9), np.uint8)
# depth_rgb = cv2.dilate(depth_rgb, kernel, iterations=1)
# depth_rgb = cv2.erode(depth_rgb, kernel, iterations=1)
hSobel = cv2.Sobel(depth_rgb, cv2.CV_32F, 0, 1, -1)
vSobel = cv2.Sobel(depth_rgb, cv2.CV_32F, 1, 0, -1)
depth_rgb = cv2.add(hSobel, vSobel)
depth_rgb = cv2.convertScaleAbs(depth_rgb)
depth_rgb = cv2.GaussianBlur(depth_rgb, (3, 3), 0)
ret, depth_rgb = cv2.threshold(depth_rgb, 80, 255, cv2.THRESH_BINARY)
# depth_rgb_mask = cv2.cvtColor(depth, cv2.COLOR_GRAY2RGB)
# depth_rgb = cv2.bitwise_and(rgb,rgb,mask = depth)
h, w = depth.shape[:2]
# img = cv2.GaussianBlur(depth_rgb,(3,3),0)
# laplacian = cv2.Laplacian(img,cv2.CV_8U)
if (not last_image is None):
x_offset = int(w * (scale - 1.0) / 2.0)
y_offset = int(h * (scale - 1.0) / 2.0)
scaled = cv2.resize(last_image, None, fx=scale,
fy=scale)[y_offset:h + y_offset,
x_offset:w + x_offset]
color_img = np.zeros((h, w, 3), dtype=np.uint8)
color_img[:, :] = [
random.randint(1, 255),
random.randint(1, 255),
random.randint(1, 255)
]
color_scale = cv2.bitwise_and(color_img, scaled)
image = cv2.bitwise_or(color_scale, depth_rgb)
# image = depth_rgb
# overlay = cv2.bitwise_xor(rgb, image)
# cv2.line(image, (0, y_threshold), (w, y_threshold), (0, 255, 0), 3)
# overlay_with_keypoints = cv2.drawKeypoints(overlay, keypoints, np.array([]), (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
if False:
params = cv2.SimpleBlobDetector_Params()
params.minThreshold = 0
params.maxThreshold = 255
params.minArea = 200
params.maxArea = w * h
params.filterByArea = True
params.filterByCircularity = False
params.filterByConvexity = False
params.filterByColor = False
params.filterByInertia = False
detector = cv2.SimpleBlobDetector_create(params)
keypoints = detector.detect(depth)
print("Detected " + str(len(keypoints)))
for keypoint in keypoints:
cX = int(keypoint.pt[0])
cY = int(keypoint.pt[1])
location = str(cX) + "," + str(cY)
cv2.circle(image, (cX, cY), 15, (255, 0, 255), -1)
cv2.putText(image, location, (cX - 25, cY - 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)
# if(last_note >= 0):
# # Already playing a note
# if(cY > y_threshold):
# # Center is below line, stop playing
# client.publish("note-off", last_note)
# last_note = -1
# else:
# # Center is still above the line, leave current note
# client.publish("note-off", last_note)
# note = int(map_range((0, w), (88, 0), cX))
# # Only change if we've moved by 2 semitones
# if(abs(note - last_note) > 2):
# last_note = note
# client.publish("note-on", note)
# pass
# else:
# # No active note
# if(cY < y_threshold):
# # Center is above the line, start a new note
# note = int(map_range((0, w), (88, 0), cX))
# last_note = note
# client.publish("note-on", note)
# else:
# # Center is below the line, don't start anything
# pass
if (warp):
src = np.array([[0, 0], [w - 1, 0], [w - 1, h - 1], [0, h - 1]],
dtype="float32")
dst = np.array(
[[150, 50], [w - 1, 0], [w - 1, h - 1], [150, h - 51]],
dtype="float32")
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(overlay, M, (w, h))
cv2.imshow('Depth', warped)
else:
# cv2.imshow('Depth', overlay)
cv2.imshow('Depth', image)
last_image = image
else:
last_image = depth_rgb
def get_video():
f = frame_convert2.video_cv(freenect.sync_get_video()[0])
return cv2.fastNlMeansDenoisingColored(f, None, 10, 10, 3, 3)
def get_video():
return frame_convert2.video_cv(freenect.sync_get_video()[0])
def get_video():
return frame_convert2.video_cv(freenect.sync_get_video()[0])
def get_video():
frame = frame_convert2.video_cv(freenect.sync_get_video()[0])
#print('\nframeVideo =\n')
#print(frame)
return frame