def generate_meshing_info(imgInfo,method = LUCID_RECT_BBOX,showVisuals=False,zoom=0,virtCenter=None):
"""|
:param imgInfo: Information about the input image. Two types allowed yet : Image path or Numpy array
:type imgInfo: String or Numpy array
:param method: Method option for the generation of meshing information
:type method: LUCID_CENTER_PROC (for use on centering), LUCID_RECT_BBOX (for generate rectangle for meshing, return Xmin, Ymin, Xmax, Ymax), LUCID_POLY_BBOX (for generate polygon for meshing, return an array of points)
:param showVisuals: Display for debug
:type showVisuals: Boolean
:param zoom: Zoom level
:type zoom: uint
:param virtCenter: A virtual center if reqal center use is not wanted
:type virtCenter: Tuple (uint,uint)
:returns: (label,x,y) result from loop_detection function
"""
image=imgInfo2cvImage(imgInfo)
image2=cv.CloneImage(image)
image3=cv.CloneImage(image)
cv.Zero(image3)
#image treatment
dilate(image,image,2)
cv.AdaptiveThreshold(image,image2,255,cv.CV_ADAPTIVE_THRESH_MEAN_C,cv.CV_THRESH_BINARY_INV,21+50*zoom,2)
storage = cv.CreateMemStorage()
contours = cv.FindContours(cv.CloneImage(image2),storage,cv.CV_RETR_EXTERNAL,cv.CV_CHAIN_APPROX_SIMPLE,(0,0))
contours=cv.ApproxPoly(contours,storage,cv.CV_POLY_APPROX_DP,3,True);
cv.DrawContours(image3,contours,cv.RGB(255,255,255),cv.RGB (255,255,255),2)
#Virtual center treatment
virtCenter = virtualCenterTreatment(image3,virtCenter)
#Rays treatment
store,storeInfo = findInternalPoints(image3,image,4,virtCenter)
#Adapt rays
ellipsecoord = fitLoop(store,storeInfo,image,virtCenter)
imageClone = cv.CloneImage(image)
#Choose type of box for meshing
if method==LUCID_CENTER_PROC:
if showVisuals:
return getBoundingBox(method,store,storeInfo,(image.width//2,image.height//2)),imageClone,image3,store,virtCenter
else:
return getBoundingBox(method,store,storeInfo,(image.width//2,image.height//2)),image3
else:
if showVisuals:
return getBoundingBox(method,store,storeInfo,(image.width//2,image.height//2))
else:
return getBoundingBox(method,store,storeInfo,(image.width//2,image.height//2))
def face_detection(imgInfo,showVisuals=False,zoom=0):
"""|
:param imgInfo: Information about the input image. Two types allowed yet : Image path or Numpy array
:type imgInfo: String or Numpy array
:param showVisuals: Display for debug
:type showVisuals: Boolean
:param zoom: Zoom level
:type zoom: uint
:returns: (label,x,y) result from loop_detection function
"""
image=imgInfo2cvImage(imgInfo)
image2=cv.CloneImage(image)
if zoom == 0:
cv.AdaptiveThreshold(image2,image2,255,cv.CV_ADAPTIVE_THRESH_MEAN_C,cv.CV_THRESH_BINARY_INV,45,6)
else:
cv.AdaptiveThreshold(image2,image2,255,cv.CV_ADAPTIVE_THRESH_MEAN_C,cv.CV_THRESH_BINARY_INV,121,6)
image3=cv.CloneImage(image2)
cv.Zero(image3)
storage = cv.CreateMemStorage()
contours = cv.FindContours(cv.CloneImage
(image2),storage,cv.CV_RETR_EXTERNAL,cv.CV_CHAIN_APPROX_SIMPLE,(0,0))
cv.DrawContours(image3,contours,cv.RGB(255,255,255),cv.RGB (255,255,255),2,cv.CV_FILLED)
cv.Smooth(image3,image3,cv.CV_MEDIAN,11)
image4 = cv.CloneImage(image3)
if zoom == 0:
erode(image4,image4,9)
dilate(image4,image4,9)
else:
erode(image4,image4,25)
dilate(image4,image4,25)
count = white_detect(image4)
if showVisuals:
displayCv(('Resultat',image),('ErosionDilation',image4),('AdaptiveThresh',image2),('FinalTreatmentVisualisation',image3))
cv.WaitKey(0)
return count
def loop_detection(imgInfo,showVisuals=False,zoom=0):
"""|
:param imgInfo: Information about the input image. Two types allowed yet : Image path or Numpy array
:type imgInfo: String or Numpy array
:param showVisuals: Display for debug
:type showVisuals: Boolean
:param zoom: Zoom level
:type zoom: uint
:returns: (label,x,y) result from loop_detection function
"""
image=imgInfo2cvImage(imgInfo)
image2=cv.CloneImage(image)
#if zoom == 0:
cv.AdaptiveThreshold(image2,image2,255,cv.CV_ADAPTIVE_THRESH_MEAN_C,cv.CV_THRESH_BINARY_INV,45,6)
#else:
#cv.Threshold(image2,image2,30,255,cv.CV_THRESH_BINARY)
image3=cv.CloneImage(image2)
cv.Zero(image3)
storage = cv.CreateMemStorage()
contours = cv.FindContours(cv.CloneImage
(image2),storage,cv.CV_RETR_EXTERNAL,cv.CV_CHAIN_APPROX_SIMPLE,(0,0))
cv.DrawContours(image3,contours,cv.RGB(255,255,255),cv.RGB (255,255,255),2,cv.CV_FILLED)
cv.Smooth(image3,image3,cv.CV_MEDIAN,11)
image4 = cv.CloneImage(image3)
#check if the loop is in the image
count = white_detect(image4[10:image4.height-10,10:image4.width-10])
if count < 50:
return ("No loop detected",-1,-1)
#dilation and erosion if there is no zoom
if zoom == 0:
erode(image4,image4,11)
dilate(image4,image4,11)
count = white_detect(image4[10:image4.height-10,10:image4.width-10])
if count < 50:
(xcenter,ycenter) = find_approx_loop(image3)
if showVisuals==1:
cv.Line(image,(int(xcenter),int(0)),(int(xcenter),int(image.height)),cv.RGB(120.0,120.0,120.0))
cv.Line(image,(int(0),int(ycenter)),(int(image.width),int(ycenter)),cv.RGB(120.0,120.0,120.0))
displayCv(('Resultat',image),('ErosionDilation',image4),('AdaptiveThresh',image2),('FinalTreatmentVisualisation',image3))
cv.WaitKey(0)
return ('ApproxCoord',xcenter-15,ycenter)
if zoom == 0:
(xcenter,ycenter) = only_robot(image3)
if xcenter > 0:
if showVisuals:
cv.Line(image,(int(xcenter),int(0)),(int(xcenter),int(image.height)),cv.RGB(120.0,120.0,120.0))
cv.Line(image,(int(0),int(ycenter)),(int(image.width),int(ycenter)),cv.RGB(120.0,120.0,120.0))
displayCv(('Resultat',image),('ErosionDilation',image4),('AdaptiveThresh',image2),('FinalTreatmentVisualisation',image3))
return ("ArmRobot",xcenter,ycenter)
clean_the_robot(image3,image4)
adjust_detection(image3)
if zoom == 0:
(xcenter,ycenter) = calculate_center(image3,image3)
else:
(xcenter,ycenter) = calculate_center(image3,image3)
if showVisuals:
cv.Line(image,(int(xcenter),int(0)),(int(xcenter),int(image.height)),cv.RGB(120.0,120.0,120.0))
cv.Line(image,(int(0),int(ycenter)),(int(image.width),int(ycenter)),cv.RGB(120.0,120.0,120.0))
displayCv(('Resultat',image),('ErosionDilation',image4),('AdaptiveThresh',image2),('FinalTreatmentVisualisation',image3))
cv.WaitKey(0)
return ("Coord",xcenter,ycenter)
