def getWatershedMask(self):
'''
Uses the watershed algorithm to refine the foreground mask.
Currently, this doesn't work well on real video...maybe grabcut would be better.
'''
cvMarkerImg = cv.CreateImage(self._fgMask.size, cv.IPL_DEPTH_32S, 1)
cv.SetZero(cvMarkerImg)
#fill each contour with a different gray level to label connected components
seq = self._contours
c = 50
while not (seq == None) and len(seq) != 0:
if cv.ContourArea(seq) > self._minArea:
c += 10
moments = cv.Moments(seq)
m00 = cv.GetSpatialMoment(moments, 0, 0)
m01 = cv.GetSpatialMoment(moments, 0, 1)
m10 = cv.GetSpatialMoment(moments, 1, 0)
centroid = (int(m10 / m00), int(m01 / m00))
cv.Circle(cvMarkerImg, centroid, 3, cv.RGB(c, c, c),
cv.CV_FILLED)
seq = seq.h_next()
if (c > 0):
img = self._annotateImg.asOpenCV()
cv.Watershed(img, cvMarkerImg)
tmp = cv.CreateImage(cv.GetSize(cvMarkerImg), cv.IPL_DEPTH_8U, 1)
cv.CvtScale(cvMarkerImg, tmp)
return pv.Image(tmp)
def do1Image(self, image, prevpoints):
#http://www.aishack.in/2010/07/tracking-colored-objects-in-opencv/
#http://nashruddin.com/OpenCV_Region_of_Interest_(ROI)
#http://opencv-users.1802565.n2.nabble.com/Python-cv-Moments-Need-Help-td6044177.html
#http://stackoverflow.com/questions/5132874/change-elements-in-a-cvseq-in-python
img = self.getThreshold(image)
points = []
for i in range(4):
cv.SetImageROI(img, (int(
self.RectanglePoints[i][0]), int(self.RectanglePoints[i][1]),
int(self.RectanglePoints[i][2]),
int(self.RectanglePoints[i][3])))
storage = cv.CreateMemStorage(0)
contours = cv.FindContours(img, storage)
moments = cv.Moments(contours)
moment10 = cv.GetSpatialMoment(moments, 1, 0)
moment01 = cv.GetSpatialMoment(moments, 0, 1)
area = cv.GetCentralMoment(moments, 0, 0)
cv.ResetImageROI(img)
if (area != 0):
x = self.RectanglePoints[i][0] + (moment10 / area)
y = self.RectanglePoints[i][1] + (moment01 / area)
else:
if (prevpoints[i][0] == 0):
x = self.RectanglePoints[i][0]
y = self.RectanglePoints[i][1]
else:
x = prevpoints[i][0]
y = prevpoints[i][1]
points.append([x, y])
return points
def get_center(moments):
m00 = cv.GetSpatialMoment(moments, 0, 0)
m10 = cv.GetSpatialMoment(moments, 1, 0)
m01 = cv.GetSpatialMoment(moments, 0, 1)
x = float(m10) / m00
y = float(m01) / m00
return (x, y)
def __init__(
self, BW
): #Constructor. BW es una imagen binaria en forma de una matriz numpy
self.BW = BW
cs = cv.FindContours(cv.fromarray(self.BW.astype(np.uint8)),
cv.CreateMemStorage(),
mode=cv.CV_RETR_EXTERNAL) #Finds the contours
counter = 0
"""
Estas son listas dinamicas usadas para almacenar variables
"""
centroid = list()
cHull = list()
contours = list()
cHullArea = list()
contourArea = list()
while cs: #Iterar a traves de CvSeq, cs.
if abs(
cv.ContourArea(cs)
) > 2000: #Filtra contornos de menos de 2000 pixeles en el area
contourArea.append(
cv.ContourArea(cs)
) #Se agrega contourArea con el area de contorno mas reciente
m = cv.Moments(
cs) #Encuentra todos los momentos del contorno filtrado
try:
m10 = int(cv.GetSpatialMoment(m, 1,
0)) #Momento espacial m10
m00 = int(cv.GetSpatialMoment(m, 0,
0)) #Momento espacial m00
m01 = int(cv.GetSpatialMoment(m, 0,
1)) #Momento espacial m01
centroid.append(
(int(m10 / m00), int(m01 / m00))
) #Aniade la lista de centroides con las coordenadas mas nuevas del centro de gravedad del contorno
convexHull = cv.ConvexHull2(
cs, cv.CreateMemStorage(), return_points=True
) #Encuentra el casco convexo de cs en el tipo CvSeq
cHullArea.append(
cv.ContourArea(convexHull)
) #Agrega el area del casco convexo a la lista cHullArea
cHull.append(
list(convexHull)
) #Agrega la lista del casco convexo a la lista de cHull
contours.append(
list(cs)
) #Agrega la forma de lista del contorno a la lista de contornos
counter += 1 #Agrega al contador para ver cuantos blobs hay
except:
pass
cs = cs.h_next() #Pasa al siguiente contorno en cs CvSeq
"""
A continuacion, las variables se convierten en campos para hacer referencias posteriores
"""
self.centroid = centroid
self.counter = counter
self.cHull = cHull
self.contours = contours
self.cHullArea = cHullArea
self.contourArea = contourArea
def extract_circles(contours, rgb):
global current_pose_editor, side
circles = []
for i in contours:
moments = cv.Moments(cv.fromarray(i), binary=1)
area = cv.GetCentralMoment(moments, 0, 0)
if area > OBJECT_AREA:
x = int(cv.GetSpatialMoment(moments, 1, 0) / area)
y = int(cv.GetSpatialMoment(moments, 0, 1) / area)
radius = int(math.sqrt(area / math.pi))
circles.append((x, y, int(radius * 1.5)))
if (y > 100):
adjust_carrot(x, y, area)
if (side == 'left' and x < 420):
adjust_carrot(x, y, area) #use just visual servo
elif (side == 'right' and x > 420):
adjust_carrot(x, y, area) #use just visual servo
'''
point = check_lidar((x,y),radius) #use if you want to use lidar to confirm waypoint
if (point[0]):
print 'going to:',point[1]
waypoint.send_goal_and_wait(current_pose_editor.relative(np.array([point[1][0], point[1][1] + .5, 0])).as_MoveToGoal(speed = .5))
circles.append((x,y,int(radius*1.5)))
'''
return circles
def getStandardizedRects(self):
'''
@return: the boxes centered on the target center of mass +- n_sigma*std
@note: You must call detect() before getStandardizedRects() to see updated results.
'''
#create a list of the top-level contours found in the contours (cv.Seq) structure
rects = []
if len(self._contours) < 1: return (rects)
seq = self._contours
while not (seq == None):
(x, y, w, h) = cv.BoundingRect(seq)
if (cv.ContourArea(seq) >
self._minArea): # and self._filter(rect)
r = pv.Rect(x, y, w, h)
moments = cv.Moments(seq)
m_0_0 = cv.GetSpatialMoment(moments, 0, 0)
m_0_1 = cv.GetSpatialMoment(moments, 0, 1)
m_1_0 = cv.GetSpatialMoment(moments, 1, 0)
mu_2_0 = cv.GetCentralMoment(moments, 2, 0)
mu_0_2 = cv.GetCentralMoment(moments, 0, 2)
cx = m_1_0 / m_0_0
cy = m_0_1 / m_0_0
w = 2.0 * self._rect_sigma * np.sqrt(mu_2_0 / m_0_0)
h = 2.0 * self._rect_sigma * np.sqrt(mu_0_2 / m_0_0)
r = pv.CenteredRect(cx, cy, w, h)
rects.append(r)
seq = seq.h_next()
if self._filter != None:
rects = self._filter(rects)
return rects
def angle(self, img):
# extract position of red blue yellow markers
# find distance between pairs
# return angle from inverse cosine
imgHSV = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.CvtColor(img, imgHSV, cv.CV_BGR2HSV)
cv.NamedWindow("red", cv.CV_WINDOW_AUTOSIZE)
cv.MoveWindow("red", 800, 0)
cv.NamedWindow("blue", cv.CV_WINDOW_AUTOSIZE)
cv.MoveWindow("blue", 800, 100)
cv.NamedWindow("yellow", cv.CV_WINDOW_AUTOSIZE)
cv.MoveWindow("yellow", 800, 200)
dot_coords = []
# use the corresponding thresholds for each color of marker #
for h_low, h_high, col in [self.red_hues, self.yellow_hues, self.blue_hues]:
imgThresh = cv.CreateImage(cv.GetSize(img), 8, 1)
cv.InRangeS(imgHSV, cv.Scalar(h_low, 70, 70), cv.Scalar(h_high, 255, 255), imgThresh)
moments = cv.Moments(cv.GetMat(imgThresh))
x_mov = cv.GetSpatialMoment(moments, 1, 0)
y_mov = cv.GetSpatialMoment(moments, 0, 1)
area = cv.GetCentralMoment(moments, 0, 0)
small_thresh = cv.CreateImage((self.fit_camera_width, self.fit_camera_height), 8, 1)
cv.Resize(imgThresh, small_thresh)
if col == "r":
cv.ShowImage("red", small_thresh)
elif col == "b":
cv.ShowImage("blue", small_thresh)
elif col == "y":
cv.ShowImage("yellow", small_thresh)
if area > 0:
posX = float(x_mov)/float(area)
posY = float(y_mov)/float(area)
else:
posX = 0
posY = 0
dot_coords.append([posX, posY])
r = dot_coords[0]
y = dot_coords[1]
b = dot_coords[2]
# get side lengths
y_r = self.dist(r[0], r[1], y[0], y[1])
r_b = self.dist(b[0], b[1], r[0], r[1])
y_b = self.dist(b[0], b[1], y[0], y[1])
# apply law of cosines
angle_in_rads = math.pow(y_r, 2) + math.pow(r_b, 2) - math.pow(y_b, 2)
denom = 2.0 * y_r * r_b
if denom > 0:
angle_in_rads /= 2.0 * y_r * r_b
else:
angle_in_rads = 0
rads = math.acos(angle_in_rads)
# convert to degrees
degs = rads * float(180.0 / math.pi)
if degs < 0 or degs > 360:
degs = 0
return degs
def detect_and_draw(img):
t1 = time.time()
# allocate temporary images
gray = cv.CreateImage((img.width, img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(
img.width / image_scale), cv.Round(img.height / image_scale)), 8, 1)
# blur the source image to reduce color noise
cv.Smooth(img, img, cv.CV_BLUR, 3)
hsv_img = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.CvtColor(img, hsv_img, cv.CV_BGR2HSV)
thresholded_img = cv.CreateImage(cv.GetSize(hsv_img), 8, 1)
#cv.InRangeS(hsv_img, (120, 80, 80), (140, 255, 255), thresholded_img)
# White
sensitivity = 15
cv.InRangeS(hsv_img, (0, 0, 255 - sensitivity), (255, sensitivity, 255),
thresholded_img)
# Red
#cv.InRangeS(hsv_img, (0, 150, 0), (5, 255, 255), thresholded_img)
# Blue
#cv.InRangeS(hsv_img, (100, 50, 50), (140, 255, 255), thresholded_img)
# Green
#cv.InRangeS(hsv_img, (40, 50, 50), (80, 255, 255), thresholded_img)
mat = cv.GetMat(thresholded_img)
moments = cv.Moments(mat, 0)
area = cv.GetCentralMoment(moments, 0, 0)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if (area > 5000):
#determine the x and y coordinates of the center of the object
#we are tracking by dividing the 1, 0 and 0, 1 moments by the area
x = cv.GetSpatialMoment(moments, 1, 0) / area
y = cv.GetSpatialMoment(moments, 0, 1) / area
x = int(round(x))
y = int(round(y))
#create an overlay to mark the center of the tracked object
overlay = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.Circle(overlay, (x, y), 2, (0, 0, 0), 20)
cv.Add(img, overlay, img)
#add the thresholded image back to the img so we can see what was
#left after it was applied
#cv.Merge(thresholded_img, None, None, None, img)
t2 = time.time()
message = "Color tracked!"
print "detection time = %gs x=%d,y=%d" % (round(t2 - t1, 3), x, y)
cv.ShowImage("Color detection", img)
def yellow(img):
global yx
global yy
global bx
global by
#blur the source image to reduce color noise
cv.Smooth(img, img, cv.CV_BLUR, 3)
#convert the image to hsv(Hue, Saturation, Value) so its
#easier to determine the color to track(hue)
hsv_img = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.CvtColor(img, hsv_img, cv.CV_BGR2HSV)
#limit all pixels that don't match our criteria, in this case we are
#looking for purple but if you want you can adjust the first value in
#both turples which is the hue range(120,140). OpenCV uses 0-180 as
#a hue range for the HSV color model
thresholded_img = cv.CreateImage(cv.GetSize(hsv_img), 8, 1)
cv.InRangeS(hsv_img, (20, 100, 100), (30, 255, 255),
thresholded_img) #yellow
#determine the objects moments and check that the area is large
#enough to be our object
mat = cv.GetMat(thresholded_img)
moments = cv.Moments(mat, 0)
area = cv.GetCentralMoment(moments, 0, 0)
#there can be noise in the video so ignore objects with small areas
if (area > 100000):
#determine the x and y coordinates of the center of the object
#we are tracking by dividing the 1, 0 and 0, 1 moments by the area
x = cv.GetSpatialMoment(moments, 1, 0) / area
y = cv.GetSpatialMoment(moments, 0, 1) / area
x = int(x)
y = int(y)
yx = x
yy = y
cv.Circle(img, (x, y), 5, (0, 0, 0), -1)
cv.InRangeS(hsv_img, (100, 80, 80), (120, 255, 255),
thresholded_img) #pink
#determine the objects moments and check that the area is large
#enough to be our object
mat = cv.GetMat(thresholded_img)
moments = cv.Moments(mat, 0)
area = cv.GetCentralMoment(moments, 0, 0)
#there can be noise in the video so ignore objects with small areas
if (area > 100):
x = cv.GetSpatialMoment(moments, 1, 0) / area
y = cv.GetSpatialMoment(moments, 0, 1) / area
x = int(x)
y = int(y)
bx = x
by = y
cv.Circle(img, (x, y), 5, (0, 0, 255), -1)
def __init__(
self, BW
): #Constructor. BW is a binary image in the form of a numpy array
self.BW = BW
cs = cv.FindContours(cv.fromarray(self.BW.astype(np.uint8)),
cv.CreateMemStorage(),
mode=cv.CV_RETR_EXTERNAL) #Finds the contours
counter = 0
"""
These are dynamic lists used to store variables
"""
centroid = list()
cHull = list()
contours = list()
cHullArea = list()
contourArea = list()
while cs: #Iterate through the CvSeq, cs.
if abs(
cv.ContourArea(cs)
) > 2000: #Filters out contours smaller than 2000 pixels in area
contourArea.append(cv.ContourArea(
cs)) #Appends contourArea with newest contour area
m = cv.Moments(
cs) #Finds all of the moments of the filtered contour
try:
m10 = int(cv.GetSpatialMoment(m, 1,
0)) #Spatial moment m10
m00 = int(cv.GetSpatialMoment(m, 0,
0)) #Spatial moment m00
m01 = int(cv.GetSpatialMoment(m, 0,
1)) #Spatial moment m01
centroid.append(
(int(m10 / m00), int(m01 / m00))
) #Appends centroid list with newest coordinates of centroid of contour
convexHull = cv.ConvexHull2(
cs, cv.CreateMemStorage(), return_points=True
) #Finds the convex hull of cs in type CvSeq
cHullArea.append(
cv.ContourArea(convexHull)
) #Adds the area of the convex hull to cHullArea list
cHull.append(
list(convexHull)
) #Adds the list form of the convex hull to cHull list
contours.append(
list(cs)
) #Adds the list form of the contour to contours list
counter += 1 #Adds to the counter to see how many blobs are there
except:
pass
cs = cs.h_next() #Goes to next contour in cs CvSeq
"""
Below the variables are made into fields for referencing later
"""
self.centroid = centroid
self.counter = counter
self.cHull = cHull
self.contours = contours
self.cHullArea = cHullArea
self.contourArea = contourArea
def center_of_mass(contour):
moment = cv.Moments(contour)
mass = cv.GetSpatialMoment(moment, 0, 0)
mx = cv.GetSpatialMoment(moment, 1, 0)
my = cv.GetSpatialMoment(moment, 0, 1)
X = mx / mass
Y = my / mass
return X, Y
def computeMoment(self, contour):
moments = cv.Moments(contour, 1)
area = cv.GetSpatialMoment(moments, 0, 0)
if area == 0: return
x = cv.GetSpatialMoment(moments, 1, 0)
y = cv.GetSpatialMoment(moments, 0, 1)
return x, y, area
def get_contour_center(moments):
spatial_moment10 = cv.GetSpatialMoment(moments, 1, 0)
spatial_moment01 = cv.GetSpatialMoment(moments, 0, 1)
area = abs(cv.GetCentralMoment(moments, 0, 0))
# Ensuring that threre is no division by zero.
# PLEASE DO NOT TOUCH THIS, DO NOT TRY TO AVOID 0 DIVISION BY ADDING
# A VALUE TO AREA BELOW, BECAUSE IT WOULD FAIL IN SOME CASES
area = area or 0.01
return (spatial_moment10 / area, spatial_moment01 / area)
def contourCenter(thisContour, smoothness=4):
positions_x, positions_y = [0] * smoothness, [0] * smoothness
if cv.ContourArea(thisContour) > 2.0:
moments = cv.Moments(thisContour, 1)
positions_x.append(cv.GetSpatialMoment(moments, 1, 0) / cv.GetSpatialMoment(moments, 0, 0))
positions_y.append(cv.GetSpatialMoment(moments, 0, 1) / cv.GetSpatialMoment(moments, 0, 0))
positions_x, positions_y = positions_x[-smoothness:], positions_y[-smoothness:]
pos_x = (sum(positions_x) / len(positions_x))
pos_y = (sum(positions_y) / len(positions_y))
return (int(pos_x * smoothness), int(pos_y * smoothness))
def main():
color_tracker_window = "output"
thresh_window = "thresh"
capture = cv.CaptureFromCAM(-1)
cv.NamedWindow(color_tracker_window, 1)
cv.NamedWindow(thresh_window, 1)
imgScrible = None
global posX
global posY
fido.init_servos()
while True:
frame = cv.QueryFrame(capture)
cv.Smooth(frame, frame, cv.CV_BLUR, 3)
if (imgScrible is None):
imgScrible = cv.CreateImage(cv.GetSize(frame), 8, 3)
imgThresh = GetThresholdedImage(frame)
mat = cv.GetMat(imgThresh)
#Calculating the moments
moments = cv.Moments(mat, 0)
area = cv.GetCentralMoment(moments, 0, 0)
moment10 = cv.GetSpatialMoment(moments, 1, 0)
moment01 = cv.GetSpatialMoment(moments, 0, 1)
#lastX and lastY stores the previous positions
lastX = posX
lastY = posY
#Finding a big enough blob
if (area > 100000):
#Calculating the coordinate postition of the centroid
posX = int(moment10 / area)
posY = int(moment01 / area)
print 'x: ' + str(posX) + ' y: ' + str(posY) + ' area: ' + str(
area)
#drawing lines to track the movement of the blob
if (lastX > 0 and lastY > 0 and posX > 0 and posY > 0):
cv.Line(imgScrible, (posX, posY), (lastX, lastY),
cv.Scalar(0, 255, 255), 5)
#Adds the three layers and stores it in the frame
#frame -> it has the camera stream
#imgScrible -> it has the line tracking the movement of the blob
cv.Add(frame, imgScrible, frame)
cv.ShowImage(thresh_window, imgThresh)
cv.ShowImage(color_tracker_window, frame)
c = cv.WaitKey(10)
if (c != -1):
break
def run(self):
while True:
img = cv.QueryFrame( self.capture )
#blur the source image to reduce color noise
cv.Smooth(img, img, cv.CV_BLUR, 3);
#convert the image to hsv(Hue, Saturation, Value) so its
#easier to determine the color to track(hue)
hsv_img = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.CvtColor(img, hsv_img, cv.CV_BGR2HSV)
#limit all pixels that don't match our criteria, in this case we are
#looking for purple but if you want you can adjust the first value in
#both turples which is the hue range(120,140). OpenCV uses 0-180 as
#a hue range for the HSV color model
thresholded_img = cv.CreateImage(cv.GetSize(hsv_img), 8, 1)
cv.InRangeS(hsv_img, (115, 75, 75), (135, 255, 255), thresholded_img)
#determine the objects moments and check that the area is large
#enough to be our object
thresholded_img2 = cv.GetMat(thresholded_img)
moments = cv.Moments(thresholded_img2,0)
area = cv.GetCentralMoment(moments, 0, 0)
#there can be noise in the video so ignore objects with small areas
if(area > 100000):
#determine the x and y coordinates of the center of the object
#we are tracking by dividing the 1, 0 and 0, 1 moments by the area
x = cv.GetSpatialMoment(moments, 1, 0)/area
y = cv.GetSpatialMoment(moments, 0, 1)/area
# print 'x: ' + str(x) + ' y: ' + str(y) + ' area: ' + str(area)
x = int(x)
y = int(y)
#create an overlay to mark the center of the tracked object
overlay = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.Circle(overlay, (x, y), 2, (255, 255, 255), 20)
cv.Add(img, overlay, img)
#add the thresholded image back to the img so we can see what was
#left after it was applied
cv.Merge(thresholded_img, None, None, None, img)
#display the image
cv.ShowImage(color_tracker_window, img)
if cv.WaitKey(10) == 27:
break
def _get_pos_spatial(self, th_img):
print "Getting spacial position (?)"
moments = cv.Moments(cv.GetMat(th_img))
mom10 = cv.GetSpatialMoment(moments, 1, 0)
mom01 = cv.GetSpatialMoment(moments, 0, 1)
area = cv.GetCentralMoment(moments, 0, 0)
if area > 10:
pos = [int(mom10/area), int(mom01/area)]
else:
pos = None
return pos
def find_orientation(mask, center_point):
cv.Circle(mask, center_point, 19, cv.RGB(0, 0, 0), -1)
moments = cv.Moments(mask, 1)
M00 = cv.GetSpatialMoment(moments, 0, 0)
M10 = cv.GetSpatialMoment(moments, 1, 0)
M01 = cv.GetSpatialMoment(moments, 0, 1)
if M00 == 0:
M00 = 0.01
center_of_mass = (round(M10 / M00), round(M01 / M00))
return (int(calculate_bearing(center_of_mass, center_point)) - 180) % 360
def main():
s = scratch.Scratch()
capture = cv.CaptureFromCAM(0)
cv.NamedWindow("Track", 1)
while True:
#capture frame
frame_o = cv.QueryFrame(capture)
frame = cv.CreateImage((frame_o.width * 3 / 8, frame_o.height * 3 / 8),
frame_o.depth, frame_o.nChannels)
cv.Resize(frame_o, frame)
cv.Smooth(frame, frame, cv.CV_GAUSSIAN, 3, 3)
#Convert to HSV
imgHSV = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.CvtColor(frame, imgHSV, cv.CV_BGR2HSV)
#Thresh
imgThreshed = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.InRangeS(imgHSV, cv.Scalar(0, 124, 221), cv.Scalar(10, 255, 256),
imgThreshed)
cv.Smooth(imgThreshed, imgThreshed, cv.CV_GAUSSIAN, 3, 3)
mat = cv.GetMat(imgThreshed)
moments = cv.Moments(mat)
moment10 = cv.GetSpatialMoment(moments, 1, 0)
moment01 = cv.GetSpatialMoment(moments, 0, 1)
area = cv.GetCentralMoment(moments, 0, 0)
if area > 1000:
posX = int(moment10 / area)
posY = int(moment01 / area)
if posX >= 0 and posY >= 0:
print("X: " + str(posX) + ", Y: " + str(posY))
cv.Rectangle(frame, (posX - 10, posY - 10),
(posX + 10, posY + 10), cv.RGB(0, 255, 0))
s.sensorupdate({'X': posX})
s.sensorupdate({'Y': posY})
cv.ShowImage("Track", frame)
k = cv.WaitKey(70)
if k % 0x100 == 27:
break
def extract_circles(contours, rgb):
circles = []
for i in contours:
moments = cv.Moments(cv.fromarray(i), binary=1)
area = cv.GetCentralMoment(moments, 0, 0)
if area > OBJECT_AREA:
x = int(cv.GetSpatialMoment(moments, 1, 0) / area)
y = int(cv.GetSpatialMoment(moments, 0, 1) / area)
radius = int(math.sqrt(area / math.pi))
point = check_lidar((x, y), radius)
if (point[0]):
circles.append((x, y, int(radius * 1.5)))
append_marker(point[1], rgb)
global new_buoy
new_buoy = True
return circles
def __init__(
self, BW
): #Constructor. BW is a binary image in the form of a numpy array
self.BW = BW
cs = cv.FindContours(cv.fromarray(self.BW.astype(np.uint8)),
cv.CreateMemStorage(),
mode=cv.CV_RETR_EXTERNAL) #Finds the contours
counter = 0
centroid = list()
cHull = list()
contours = list()
cHullArea = list()
contourArea = list()
while cs: #Iterate through the CvSeq, cs.
if abs(cv.ContourArea(cs)) > 2000:
contourArea.append(cv.ContourArea(cs))
m = cv.Moments(cs)
try:
m10 = int(cv.GetSpatialMoment(m, 1,
0)) #Spatial moment m10
m00 = int(cv.GetSpatialMoment(m, 0,
0)) #Spatial moment m00
m01 = int(cv.GetSpatialMoment(m, 0,
1)) #Spatial moment m01
centroid.append((int(m10 / m00), int(m01 / m00)))
convexHull = cv.ConvexHull2(cs,
cv.CreateMemStorage(),
return_points=True)
cHullArea.append(cv.ContourArea(convexHull))
cHull.append(list(convexHull))
contours.append(list(cs))
counter += 1
except:
pass
cs = cs.h_next()
self.centroid = centroid
self.counter = counter
self.cHull = cHull
self.contours = contours
self.cHullArea = cHullArea
self.contourArea = contourArea
def main():
pr_window = "imagen"
capture = cv.CaptureFromCAM(-1)
cv.NamedWindow(pr_window, 1)
# seteo tamanio de la ventana |-| comentar cuando no se necesite mostrar ventana
cv.SetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_WIDTH, config.ancho)
cv.SetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_HEIGHT, config.alto)
delay = 0
while True:
if (not (delay == 20)):
delay += 1
img = cv.QueryFrame(capture)
#cv.ReleaseCapture( img )
else:
delay = 0
frame = cv.QueryFrame(capture)
maskN = cv.CreateImage(cv.GetSize(frame), 8, 1)
hsvN = cv.CloneImage(frame)
cv.Smooth(frame, frame, cv.CV_BLUR, 3)
cv.CvtColor(frame, hsvN, cv.CV_BGR2HSV)
cv.InRangeS(hsvN, config.min_range, config.max_range, maskN)
moment = cv.Moments(cv.GetMat(maskN), 0)
a = cv.GetCentralMoment(moment, 0, 0)
if a > config.min_area:
X = int(cv.GetSpatialMoment(moment, 1, 0) / a)
print "X: " + str(X)
print "min: " + str(config.min_x)
print "max: " + str(config.max_x)
#Y = int(cv.GetSpatialMoment (moment, 0, 1) / a)
if X > config.max_x:
print "derecha"
elif X < config.min_x:
print "izquierda"
else:
print "centrado"
else:
print "objeto no detectado o muy pequeno"
cv.ShowImage(pr_window, maskN)
# descomentar para debug
# X = int(cv.GetSpatialMoment (moment, 1, 0) / a)
# print 'x: ' + str (X) + ' area: ' + str (a)
# Con esto corto y salgo
# if cv.WaitKey (100) != -1:
# break
return
def getPupil(frame):
pupilImg = cv.CreateImage(cv.GetSize(frame), 8, 1)
cv.InRangeS(frame, (30, 30, 30), (80, 80, 80), pupilImg)
contours = cv.FindContours(pupilImg, cv.CreateMemStorage(0), mode=cv.CV_RETR_EXTERNAL)
del pupilImg
pupilImg = cv.CloneImage(frame)
while contours:
moments = cv.Moments(contours)
area = cv.GetCentralMoment(moments, 0, 0)
if (area > 50):
pupilArea = area
x = cv.GetSpatialMoment(moments, 1, 0) / area
y = cv.GetSpatialMoment(moments, 0, 1) / area
pupil = contours
global centroid
centroid = (int(x), int(y))
cv.DrawContours(pupilImg, pupil, (0, 0, 0), (0, 0, 0), 2, cv.CV_FILLED)
break
contours = contours.h_next()
return (pupilImg)
def has_intersection(amap, apoly, maxwidth, maxheight):
polymap = cv.CreateImage((maxwidth,
maxheight),
cv.IPL_DEPTH_8U,1)
cv.FillPoly(polymap, [apoly], im.color.blue)
intersection = cv.CreateImage((maxwidth,
maxheight),
cv.IPL_DEPTH_8U,1)
cv.And(polymap, amap, intersection)
m=cv.Moments(cv.GetMat(intersection), True)
return bool(cv.GetSpatialMoment(m, 0, 0))
def extract_circles(contours, rgb):
circles = []
global shot
max_ = OBJECT_AREA
for i in contours:
moments = cv.Moments(cv.fromarray(i), binary=1)
area = cv.GetCentralMoment(moments, 0, 0)
if area > max_:
max_ = area
best = [moments, area]
try:
x = int(cv.GetSpatialMoment(best[0], 1, 0) / best[1])
y = int(cv.GetSpatialMoment(best[0], 0, 1) / best[1])
radius = int(math.sqrt(best[1] / math.pi))
if (shot == False):
circles.append((x, y, int(radius)))
adjust_carrot(x, y)
except UnboundLocalError:
print "not found"
return circles
def track_blobs(self, frame):
spare = cv.CloneImage(frame)
size = cv.GetSize(frame)
hsv = cv.CreateImage(size, cv.IPL_DEPTH_8U, 3)
out = cv.CreateImage(size, cv.IPL_DEPTH_8U, 3)
thresh = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
print self.min_hue, self.value_dict['min_hue']
cv.Smooth(spare, spare, cv.CV_BLUR, 22, 22)
cv.CvtColor(spare, hsv, cv.CV_BGR2HSV)
cv.InRangeS(hsv, cv.Scalar(self.min_hue, self.min_sat, self.min_val),
cv.Scalar(self.max_hue, self.max_sat, self.max_val),
thresh)
cv.Merge(thresh, thresh, thresh, None, out)
contours = cv.FindContours(thresh, self.storage, cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_SIMPLE)
try:
M = cv.Moments(contours)
except:
return out
m0 = cv.GetCentralMoment(M, 0, 0)
if m0 > 1.0:
self.cx = cv.GetSpatialMoment(M, 1, 0) / m0
self.cy = cv.GetSpatialMoment(M, 0, 1) / m0
cv.Circle(frame, (int(self.cx), int(self.cy)), 2, (255, 0, 0), 20)
if self.show_frame is not True:
return out
else:
return frame
pass
def main ():
capture = cv.CaptureFromCAM (-1)
cv.SetCaptureProperty (capture, cv.CV_CAP_PROP_FRAME_WIDTH, 160)
cv.SetCaptureProperty (capture, cv.CV_CAP_PROP_FRAME_HEIGHT, 120)
fps = cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FPS)
while True:
frame = cv.QueryFrame (capture)
maskN = cv.CreateImage (cv.GetSize (frame), 8, 1)
hsvN = cv.CloneImage (frame)
cv.Smooth (frame, frame, cv.CV_BLUR, 3)
cv.CvtColor (frame, hsvN, cv.CV_BGR2HSV)
cv.InRangeS (hsvN, config.min_range, config.max_range, maskN)
moment = cv.Moments (cv.GetMat (maskN), 0)
a = cv.GetCentralMoment (moment, 0, 0)
if a > config.min_area:
X = int(cv.GetSpatialMoment (moment, 1, 0) / a)
print "area: " + str (a)
print "X: " + str (X)
#Y = int(cv.GetSpatialMoment (moment, 0, 1) / a)
if X > config.max_x:
print "derecha"
elif X < config.min_x:
print "izquierda"
else:
print "centrado"
else:
print "objeto no detectado o muy pequeno"
# Con esto corto y salgo
if cv.WaitKey (int(1000/fps)) != -1:
break
return;
def action(self):
if (not (self.delay == 5)):
self.delay += 1
else:
self.delay = 0
frame = cv.QueryFrame(self.capture)
maskN = cv.CreateImage(cv.GetSize(frame), 8, 1)
hsvN = cv.CloneImage(frame)
cv.Smooth(frame, frame, cv.CV_BLUR, 3)
cv.CvtColor(frame, hsvN, cv.CV_BGR2HSV)
cv.InRangeS(hsvN, config.min_range, config.max_range, maskN)
moment = cv.Moments(cv.GetMat(maskN), 0)
a = cv.GetCentralMoment(moment, 0, 0)
if a == 0:
a = 1
self.data.write('Camara::area', a)
self.data.write('Camara::lata_x',
int(cv.GetSpatialMoment(moment, 1, 0) / a))
cv.WaitKey(10)
def main():
# Initialize capturing live feed from the camera
capture = 0
capture = cv.CaptureFromCAM(0)
# Couldn't get a device? Throw an error and quit
if (not capture):
print "Could not initialize capturing...\n"
return -1
# The two windows we'll be using
cv.NamedWindow("video")
cv.NamedWindow("thresh")
# This image holds the "scribble" data...
imgScribble = 0
# a flag which indicates a valid mouse click
clicked = 0
clicked1 = 0
# to held previous co-ordinate values.
prevXred = 0
prevYred = 0
prevXyellow = 0
prevYyellow = 0
prevXblue = 0
prevYblue = 0
#initialising fake motioning
import ctypes
c = ctypes.CDLL("libhelper.so.1")
libc = ctypes.CDLL("libc.so.6")
c.helper_init()
#to held current co-ordinate values
Xred = -1
Yred = -1
Xyellow = -1
Yyellow = -1
Xblue = -1
Yblue = -1
# An infinite loop
while (True):
#----------------------------------------------------------------------------------------
# Will hold a frame captured from the camera
frame = 0
frame = cv.QueryFrame(capture)
# If we couldn't grab a frame... quit
if (not frame):
break
# If this is the first frame, we need to initialize it
if (imgScribble == 0):
imgScribble = cv.CreateImage(cv.GetSize(frame), 8, 3)
# representative co-ordinates of RED & YELLOW finger-tips, initialized.
dx = 0
dy = 0
dz = 0
#----------------------------------------------------------------------------------------
# Holds the RED thresholded image (red = white, rest = black)
imgRedThresh = GetRedThresholded(frame)
moments = 0
# Calculate the moments to estimate the position of RED finger-tip
moments = cv.Moments(imgRedThresh)
# The actual moment values
moment01 = cv.GetSpatialMoment(moments, 0, 1)
moment10 = cv.GetSpatialMoment(moments, 1, 0)
area = cv.GetSpatialMoment(moments, 0, 0)
if (area == 0):
continue
prevXred = Xred
prevYred = Yred
if area:
Xred = moment10 / area
Yred = moment01 / area
# Print it out for debugging purposes
#print "position "+ str(Xred)+' '+ str(Yred)+'\n'
#----------------------------------------------------------------------------------------
# Holds the BLUE thresholded image (red = white, rest = black)
imgBlueThresh = GetBlueThresholded(frame)
moments = 0
# Calculate the moments to estimate the position of RED finger-tip
moments = cv.Moments(imgBlueThresh)
# The actual moment values
moment01 = cv.GetSpatialMoment(moments, 0, 1)
moment10 = cv.GetSpatialMoment(moments, 1, 0)
area = cv.GetSpatialMoment(moments, 0, 0)
if (area == 0):
continue
prevXblue = Xblue
prevYblue = Yblue
if area:
Xblue = moment10 / area
Yblue = moment01 / area
# Print it out for debugging purposes
#print "position "+ str(Xred)+' '+ str(Yred)+'\n'
#----------------------------------------------------------------------------------------
# Holds the YELLOW thresholded image (yellow = white, rest = black)
imgYellowThresh = GetYellowThresholded(frame)
moments = 0
# Calculate the moments to estimate the position of YELLOW finger-tip
moments = cv.Moments(imgYellowThresh)
# The actual moment values
moment01 = cv.GetSpatialMoment(moments, 0, 1)
moment10 = cv.GetSpatialMoment(moments, 1, 0)
area = cv.GetSpatialMoment(moments, 0, 0)
if (area == 0): continue
prevXyellow = Xyellow
prevYyellow = Yyellow
Xyellow = moment10 / area
Yyellow = moment01 / area
#dx = (Xyellow - prevXyellow)
#dy = (Yyellow - prevYyellow)
#x=1390-Xyellow*2.125
#y=Yyellow*1.575
Xt = 1390 - (abs(Xred + Xyellow) * 1.125)
Yt = (abs(Yred + Yyellow) * 0.787)
libc.usleep(150)
c.helper_mov_absxy(int(Xt), int(Yt))
#c.helper_mov_relxy(int(dx),int(dy))
# Print it out for debugging purposes
#print "position "+ str(Xyellow)+' '+ str(Yyellow)
#----------------------------------------------------------------------------------------
# find distance between RED & YELLOW finger-tips
# for faster calculation, individually Xdiff, Ydiff
# considered, instead of sqrt((x2-x1)^2 + (y2-y2)^2)
Xdiff = abs(Xred - Xyellow)
Ydiff = abs(Yred - Yyellow)
Xdiff1 = abs(Xred - Xblue)
Ydiff1 = abs(Yred - Yblue)
# determine the 'clicked' state, using approximation to circle method.
d = 50
#if((21*Xdiff + 50*Ydiff <= 50*d) and (50*Xdiff + 21*Ydiff <= 50*d)):
if (Xdiff * Xdiff + Ydiff * Ydiff < d * d):
#if(Xdiff < 25 and Ydiff < 25):
if (not clicked):
clicked = 1
print "left clicked"
else:
if (clicked):
clicked = 0
c.helper_release(1)
if clicked:
c.helper_press(1)
d = 45
#if((21*Xdiff + 50*Ydiff <= 50*d) and (50*Xdiff + 21*Ydiff <= 50*d)):
if (Xdiff1 * Xdiff1 + Ydiff1 * Ydiff1 < d * d):
#if(Xdiff < 25 and Ydiff < 25):
if (not clicked1):
clicked1 = 1
print "right clicked"
else:
if (clicked1):
clicked1 = 0
if clicked1:
c.helper_press(3)
c.helper_release(3)
#----------------------------------------------------------------------------------------
# # We want to draw a line only if its a valid position
# if(lastX>0 and lastY>0 and posX>0 and posY>0):
#
# # Draw a yellow line from the previous point to the current point
# cv.Line(imgScribble, cv.Point(posX, posY), cv.Point(lastX, lastY), cv.Scalar(0,255,255), 5)
#
#
# # Add the scribbling image and the frame... and we get a combination of the two
# cv.Add(frame, imgScribble, frame)
#cv.ShowImage("thresh", imgYellowThresh)
#cv.ShowImage("video", frame)
#----------------------------------------------------------------------------------------
# Wait for a keypress
cin = cv.WaitKey(10)
if not (cin == -1):
# If pressed, break out of the loop
break
# Release the thresholded image... we need no memory leaks.. please
#----------------------------------------------------------------------------------------
# We're done using the camera. Other applications can now use it
return 0
def camerainfo():
# initialize camera feed
capture = cv.CaptureFromCAM(MY_CAMERA)
if not capture:
print "Could not initialize camera feed!"
exit(1)
# create display windows
cv.NamedWindow('camera', cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow('threshed', cv.CV_WINDOW_AUTOSIZE)
cv.MoveWindow('threshed', 400, 0)
# holds the tracked position of the ball
image_scribble = None
# the position of the ball
pos_x = 0
pos_y = 0
last_x = 0
last_y = 0
# my creation to find good ball position
listofxpos = []
listofypos = []
ballcoordinates = [0, 0]
count_of_measurements = 0
# read from the camera
print "Tracking ball... press any key to quit"
while 1:
image = cv.QueryFrame(capture)
if not image:
return 'no image found'
# if this is the first frame, we need to initialize it
if not image_scribble:
image_scribble = cv.CreateImage(cv.GetSize(image), image.depth, 3)
# get the thresholded image
image_threshed = thresholded_image(image)
# finds the contours in our binary image
contours = cv.FindContours(cv.CloneImage(image_threshed), cv.CreateMemStorage())
# if there is a ball in the frame
if len(contours) != 0:
# calculate the moments to estimate the position of the ball
moments = cv.Moments(contours, 1)
moment10 = cv.GetSpatialMoment(moments, 1, 0)
moment01 = cv.GetSpatialMoment(moments, 0, 1)
area = cv.GetCentralMoment(moments, 0, 0)
# if we got a good enough blob
if area>1:
last_x = pos_x
last_y = pos_y
pos_x = moment10/area
pos_y = moment01/area
print("pos=(%s,%s)"%(pos_x,pos_y))
# draw the tracking line
if last_x>0 and last_y>0 and pos_x>0 and pos_y>0:
pt1 = (int(last_x), int(last_y))
pt2 = (int(pos_x), int(pos_y))
cv.Line(image_scribble, pt1, pt2, (0, 255, 255), 5)
# add the scribble to the original frame
cv.Add(image, image_scribble, image)
cv.ShowImage('threshed', image_threshed)
cv.ShowImage('camera', image)
# my creation to find good ball position
if last_x != pos_x or last_y != pos_y:
if count_of_measurements < 4:
listofxpos.append(pos_x)
listofypos.append(pos_y)
count_of_measurements += 1
elif count_of_measurements >= 4 and count_of_measurements < 7:# add coordinate selection/filter
listofxpos.append(pos_x)
listofypos.append(pos_y)
count_of_measurements += 1
elif count_of_measurements >= 7:
ballcoordinates[0] = sum(listofxpos)/count_of_measurements
ballcoordinates[1] = sum(listofypos)/count_of_measurements
# if ballcoordinates[0] > 200 or ballcoordinates[0] <150:
# amount = (175 - ballcoordinates[0])/5
# if amount > 20:
# turn(20)
# else:
# turn(amount)
# else:
# go(20)
count_of_measurements = 0
listofxpos = []
listofypos = []
last_x = pos_x
last_y = pos_y
print count_of_measurements
# break from the loop if there is a key press
c = cv.WaitKey(10)
if not c == -1:
break
