def is_next_to(cont1, cont2):
win, c1, r1 = cv.MinEnclosingCircle(cont1)
if not win:
return False
win, c2, r2 = cv.MinEnclosingCircle(cont2)
if not win:
return False
return dist(c1, c2) < r1 * 1.2 or dist(c1, c2) < r2 * 1.2
def draw_common(points):
success, center, radius = cv.MinEnclosingCircle(points)
if success:
cv.Circle(img, roundxy(center), cv.Round(radius),
cv.CV_RGB(255, 255, 0), 1, cv.CV_AA, 0)
box = cv.MinAreaRect2(points)
box_vtx = [roundxy(p) for p in cv.BoxPoints(box)]
cv.PolyLine(img, [box_vtx], 1, cv.CV_RGB(0, 255, 255), 1, cv.CV_AA)
def find_limbs(self, contours):
""" return the 3 biggest contours """
blobs = []
while contours:
(i, center, radius) = cv.MinEnclosingCircle(contours)
blobs.append(
(int(round(radius)), tuple([int(round(c)) for c in center])))
contours = contours.h_next()
blobs.sort()
blobs.reverse()
return blobs[:3]
def find_limbs(self, contours):
""" return the 3 biggest contours """
blobs = []
while contours:
(i, center, radius) = cv.MinEnclosingCircle(contours)
blobs.append((radius, center))
contours = contours.h_next()
blobs.sort()
blobs.reverse()
return blobs[:3]
def segment(self, cv_source_image):
self.get_tracker().flush()
hue = self.pre_process_source(cv_source_image)
size = (hue.width, hue.height)
cv_out = cv.CreateImage(size, 8, 3)
cv.Zero(cv_out)
bounds = self.find_color_bounds(hue)
for bound in bounds:
(tmp1d, avg_hue) = self.extract_hue_channel(hue, bound)
contours = cv.FindContours(tmp1d, cv.CreateMemStorage(),
cv.CV_RETR_CCOMP)
while contours:
cSize = abs(cv.ContourArea(contours))
if cSize < self.min_contour_size:
contours = contours.h_next()
continue
(_, center, radius) = cv.MinEnclosingCircle(contours)
center = (int(center[0]), int(center[1]))
id = self.get_tracker().add(center, radius, avg_hue, size)
cv.DrawContours(cv_out, contours, cv.CV_RGB(255, 255, avg_hue),
cv.CV_RGB(255, 255, avg_hue), 1, -1, 1)
contours = contours.h_next()
#font = cv.InitFont(cv.CV_FONT_HERSHEY_PLAIN, 0.8, 1, 0, 1, 4);
#cv.Circle( cv_out, (x,y), int(radius), cv.CV_RGB(255,255,int(avg_hue)), 1 );
#cv.PutText( cv_out, "%d:%d"%(id, avg_hue), (x,y), font, cv.CV_RGB(255,255,150));
return cv_out
contours2 = False
hull = False
blank = cv.CreateImage(cv.GetSize(image), 8, 3)
while contours:
contours2 = contours
contours = contours.h_next()
if contours2:
hull = cv.ConvexHull2(contours2, cv.CreateMemStorage(),
cv.CV_CLOCKWISE, 0)
defects = cv.ConvexityDefects(contours2, hull, cv.CreateMemStorage())
cv.DrawContours(image, contours2, (255, 0, 0), (0, 255, 0), 0, 5)
noOfDefects = 0
comx, comy = 0, 0
Z, (p, q), radius = cv.MinEnclosingCircle(contours2)
if Z:
cv.Circle(image, (int(p), int(q)), int(5), [0, 255, 255], -1)
for defect in defects:
start, end, far, d = defect
xpos, ypos = far
cv.Line(image, start, end, [0, 255, 0], 2)
if d > 20 and not pointInFace(xpos, ypos, x, y, w, h):
cv.Circle(image, far, 5, [0, 0, 255], -1)
if noOfDefects:
cv.Circle(image, end, 10, [0, 0, 0], -1)
#cv.Line(image,end,(int(p),int(q)),[0,0,0],3)
x, y = end
def listen(self):
bgimg = cv.CreateImage((self.background.width, self.background.height),
8, 3)
img = cv.CreateImage((self.background.width, self.background.height),
8, 3)
cv.Copy(self.background, bgimg)
smallimg = cv.CreateImage((self.background.width / self.zoom,
self.background.height / self.zoom), 8, 3)
cv.GetRectSubPix(
bgimg, smallimg,
(self.background.width / (2 * self.zoom) + self.offset[0],
self.background.height / (2 * self.zoom) + self.offset[1]))
cv.Resize(smallimg, img)
cv.Smooth(img, img, cv.CV_GAUSSIAN)
if (self.cp != False):
cv.Circle(img, self.zoomPt(int(self.cp.x), int(self.cp.y)), 3,
cv.RGB(0, 255, 0), -1)
mask = thresholding.threshold(img,
thresholding.CUSTOM,
False,
crop_rect=None,
cam_info=None,
listener=None,
hue_interval=(self.hue_low, self.hue_up))
cv.Not(mask, mask)
new_img = cv.CloneImage(img)
cv.SetZero(new_img)
cv.Copy(img, new_img, mask)
new_img = thresholding.sat_threshold(new_img, 50)
cv.Line(img, (self.ch_x - 25, self.ch_y), (self.ch_x + 25, self.ch_y),
cv.RGB(255, 255, 0))
cv.Line(img, (self.ch_x, self.ch_y - 25), (self.ch_x, self.ch_y + 25),
cv.RGB(255, 255, 0))
image_gray = cv.CreateImage(cv.GetSize(new_img), 8, 1)
cv.CvtColor(new_img, image_gray, cv.CV_RGB2GRAY)
cv.MorphologyEx(image_gray, image_gray, None, None, cv.CV_MOP_OPEN, 1)
storage = cv.CreateMemStorage(0)
seq = cv.FindContours(image_gray, storage)
points = []
contour = seq
centers = []
ccs = []
while contour:
bound_rect = cv.BoundingRect(list(contour))
area = cv.ContourArea(contour)
cc = contour
contour = contour.h_next()
if area < 50 or area > 2500:
continue
ccs.append(cc)
win, center, radius = cv.MinEnclosingCircle(cc)
cv.DrawContours(new_img, cc, (0, 255, 0), (0, 255, 0), 0, 1)
pt1 = (bound_rect[0], bound_rect[1])
pt2 = (bound_rect[0] + bound_rect[2],
bound_rect[1] + bound_rect[3])
points.append(pt1)
points.append(pt2)
cv.Circle(new_img, center, radius, (0, 0, 255))
centers.append(center)
#cv.Rectangle(new_img, pt1, pt2, cv.CV_RGB(255,0,0), 1)
font = cv.InitFont(cv.CV_FONT_HERSHEY_PLAIN, 1, 1)
cv.PutText(new_img, "%.2f" % area, pt1, font, (255, 255, 255))
for cont1, cont2 in itertools.combinations(ccs, 2):
if is_next_to(cont1, cont2):
win, c1, r1 = cv.MinEnclosingCircle(cont1)
win, c2, r2 = cv.MinEnclosingCircle(cont2)
cv.Line(new_img, c1, c2, (255, 255, 0))
#DRAW
cv.ShowImage(self.name, new_img)
#Do some funny business
imgcs = {}
satt = thresholding.sat_threshold(img, 50)
for color in HueRanges.__dict__:
if color == color.upper():
img_c = thresholding.filter_color(satt, color)
cv.ShowImage(color, img_c)
cv.WaitKey(25)