def simplest_color_balance(image, s1=0, s2=0, mask=None):
#tick = time.time()
UMAX = 255
copy = image.copy()
layers = cv2.split(copy)
for i, layer in enumerate(layers):
hist = cv2.calcHist([layer], [0], mask, [UMAX + 1], [0, UMAX])
cumsum = hist.cumsum()
size = cumsum[-1]
lb = size * s1 / 100.0
ub = size * (1 - s2 / 100.0)
# reinit borders
left = bisect.bisect_left(cumsum, lb)
right = bisect.bisect_right(cumsum, ub)
if (right - left) <= 0:
left = UMAX / 2 - 1
right = UMAX / 2
#print left, right
# replacing values
if left != 0:
left_mask = cv2.inRange(layer, np.array(0), np.array(left))
cv.Set(cv.fromarray(layer), left, cv.fromarray(left_mask))
if right != UMAX:
right_mask = cv2.inRange(layer, np.array(right), np.array(UMAX))
cv.Set(cv.fromarray(layer), right, cv.fromarray(right_mask))
layers[i] = layer
layers = map(lambda x: cv2.normalize(x, x, 0, UMAX, cv2.NORM_MINMAX),
layers)
copy = cv2.merge(layers)
#cv2.imshow('filter', copy)
#tock = time.time()
#print tock - tick
return copy
def rangeGUI():
""" creates and displays a GUI for the range finder data
Ranges window: shows range finder values as red lines
coming from the center of the range finder
HoughLines window: shows the result of using a Hough
transformation on image containing the range values as points.
"""
global D
init_GUI() # initialize images and windows
D.dangerList = [] # Set up the danger point list
# main loop
while rospy.is_shutdown() == False:
# loop through every angle
for angle in range(REV):
magnitude = MAG_SCALE * D.ranges[angle]
x = int(CENTER + magnitude * cos(pi / 180 * (angle + ANGLE_OFFSET))
) # find x and y coordinates based on the angle
y = int(CENTER - magnitude *
sin(pi / 180 *
(angle + ANGLE_OFFSET))) # and the length of the line
# put the danger points into the list
if x > CENTER - 30 and x < CENTER + 30 and y < CENTER - 30 and y > CENTER - 90:
D.dangerList.append((x, y))
if 0 < magnitude < MAX_MAG: # if data is within "good data" range
# add line to the ranges image
cv.Line(D.image, (CENTER, CENTER), (x, y), cv.RGB(255, 0, 0))
# add dot to image being used in Hough transformation
cv.Line(D.hough, (x, y), (x, y), cv.RGB(255, 0, 0))
# wait and check for quit request
key_code = cv.WaitKey(1) & 255
key_press = chr(key_code)
if key_code == 27 or key_press == 'q': # if ESC or 'q' was pressed
rospy.signal_shutdown("Quitting...")
# find walls and add to image using Hough transformation
findHoughLines()
# show image with range finder data and calculated walls
cv.ShowImage("Ranges", D.image)
# show image used in Hough transformation if option is selected
if SHOW_HOUGH:
cv.ShowImage("HoughLines", D.color_dst)
# clear the images for next loop
cv.Set(D.image, cv.RGB(0, 0, 0))
cv.Set(D.hough, cv.RGB(0, 0, 0))
# clear the danger list for next loop
D.dangerList = []
D.tank(0, 0) # stops the robot
print "Quitting..."
def _computeBGDiff(self):
self._flow.update( self._imageBuffer.getLast() )
n = len(self._imageBuffer)
prev_im = self._imageBuffer[0]
forward = None
for i in range(0,n/2):
if forward == None:
forward = self._imageBuffer[i].to_next
else:
forward = forward * self._imageBuffer[i].to_next
w,h = size = prev_im.size
mask = cv.CreateImage(size,cv.IPL_DEPTH_8U,1)
cv.Set(mask,0)
interior = cv.GetSubRect(mask, pv.Rect(2,2,w-4,h-4).asOpenCV())
cv.Set(interior,255)
mask = pv.Image(mask)
prev_im = forward(prev_im)
prev_mask = forward(mask)
next_im = self._imageBuffer[n-1]
back = None
for i in range(n-1,n/2,-1):
if back == None:
back = self._imageBuffer[i].to_prev
else:
back = back * self._imageBuffer[i].to_prev
next_im = back(next_im)
next_mask = back(mask)
curr_im = self._imageBuffer[n/2]
prevImg = prev_im.asMatrix2D()
curImg = curr_im.asMatrix2D()
nextImg = next_im.asMatrix2D()
prevMask = prev_mask.asMatrix2D()
nextMask = next_mask.asMatrix2D()
# Compute transformed images
delta1 = sp.absolute(curImg - prevImg) #frame diff 1
delta2 = sp.absolute(nextImg - curImg) #frame diff 2
delta1 = sp.minimum(delta1,prevMask)
delta2 = sp.minimum(delta2,nextMask)
#use element-wise minimum of the two difference images, which is what
# gets compared to threshold to yield foreground mask
return sp.minimum(delta1, delta2)
def add_alpha_channel(bgr, alpha_val):
w, h = cv.GetSize(bgr)
bgra = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 4)
alpha = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 1)
chan1 = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 1)
chan2 = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 1)
chan3 = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 1)
[cv.Set(c, 0) for c in [chan1, chan2, chan3, bgra, alpha]]
cv.Split(bgr, chan1, chan2, chan3, None)
cv.Set(alpha, (alpha_val))
cv.Merge(chan1, chan2, chan3, alpha, bgra)
return bgra
def __init__(self, img):
self.img = cv.fromarray(img)
self.width = self.img.cols
self.height = self.img.rows
self.cannyImg = cv.CreateMat(self.height, self.width, cv.CV_8UC1)
self.outputImg = cv.CreateMat(self.height, self.width, cv.CV_8UC3)
self.integralImg = cv.CreateMat(self.height + 1, self.width + 1,
cv.CV_32FC1)
self.calculatedImg = cv.CreateMat(self.height, self.width, cv.CV_8UC1)
cv.Canny(self.img, self.cannyImg, 40, 100)
cv.Integral(self.cannyImg, self.integralImg)
cv.Set(self.calculatedImg, 0)
cv.Set(self.outputImg, 0)
def measure(im, blur, noise=None, debug=False):
focus_points = blur[0]
#is_noisy = noise[2]
size = cv.GetSize(im)
npixels = size[0] * size[1]
#if focused_regions is None:
# focused_regions = image.new_from(im)
# cv.Set(focused_regions, 0)
# groups = form_groups(focus_points,
# estimated_size=min(max(int(len(npixels) / 1000), 2), 15))
# #groups = form_groups(points, threshold=max(cv.GetSize(im))/16)
# #print 'groups', len(groups)
# draw_groups(groups, focused_regions)
im2 = cv.CloneImage(im)
g = Grid(cv.GetSize(im2))
if debug:
image.show(g.draw(im2), "Image with Grid + ROI")
roi = image.new_from(im, nChannels=1)
cv.Set(roi, 0)
#g.draw_lines(roi, thickness=int(max(min((size[0] + size[1]) * 1/100.0, 255), 1)))
g.draw_regions(roi)
area = cv.Sum(roi)[0]
(_, face_rects), face_score = faces.measure(im)
face_block = image.new_from(im, nChannels=1)
cv.Set(face_block, 0)
for r in face_rects:
r.draw(face_block, color=cv.RGB(255,255,255), thickness=cv.CV_FILLED)
if debug:
face_roi = cv.CloneImage(im)
cv.Set(face_roi, 0, image.invert(roi))
cv.Set(face_roi, 0, image.invert(image.threshold(face_block, threshold=1)))
image.show(face_block, "Faces in Binary")
image.show(g.draw(face_roi), "Face + ROI")
return (im, (
measure_focused_roi(im, roi, area, focus_points, debug),
#measure_color_roi(im, roi, area, focus_points, debug),
measure_contrast(im, debug),
measure_saturation(im, debug),
faces.measure(im, debug)[1],
))
def get_canny_img(img):
size = sizeOf(img)
plate = cv.CreateImage(size, 8, 1)
cv.Set(plate, 255)
for k in (50, 100,150,200,250):
# k=100
edges = cv.CreateImage(size, 8, 1)
cv.Canny(img, edges, k-20, k)
# show_image(edges)
if k >= 100:
cv.Dilate(edges,edges)
else:
k+=50
# cv.Erode(edges,edges)
cv.Set(plate, 255 - k, edges)
return plate
def gen_image(self, size, color_value):
color = self.parse_hex_color(color_value)
if not color:
raise ValueError('Color %s is not valid.' % color_value)
img0 = cv.CreateImage(size, 8, 3)
cv.Set(img0, color)
return img0
def test_encode_decode_cv1(self):
import cv
fmts = [
cv.IPL_DEPTH_8U, cv.IPL_DEPTH_8S, cv.IPL_DEPTH_16U,
cv.IPL_DEPTH_16S, cv.IPL_DEPTH_32S, cv.IPL_DEPTH_32F,
cv.IPL_DEPTH_64F
]
cvb_en = CvBridge()
cvb_de = CvBridge()
for w in range(100, 800, 100):
for h in range(100, 800, 100):
for f in fmts:
for channels in (1, 2, 3, 4):
original = cv.CreateImage((w, h), f, channels)
cv.Set(original, (1, 2, 3, 4))
rosmsg = cvb_en.cv_to_imgmsg(original)
newimg = cvb_de.imgmsg_to_cv(rosmsg)
self.assert_(
cv.GetElemType(original) == cv.GetElemType(newimg))
self.assert_(
cv.GetSize(original) == cv.GetSize(newimg))
self.assert_(
len(original.tostring()) == len(newimg.tostring()))
def high_freq(img, pct): f = float_version(img) cv.DFT(f, f, cv.CV_DXT_FORWARD) mask = cv.CreateImage( cv.GetSize(img), 8, 1) cv.Set(mask, 0) #cv.Set(mask, 255) w, h = cv.GetSize(img) dw = int(w*pct*0.5) dh = int(h*pct*0.5) #cv.Rectangle(mask, (0,0), (int(w*pct), int(h*pct)), 255, -1) #cv.Rectangle(mask, (int(w*pct), int(h*pct)), (w,h), 255, -1) cv.Rectangle(mask, (w/2-dw,h/2-dh), (w/2+dw,h/2+dh), 255, -1) cv.Set(f,0,mask) return f cv.DFT(f, f, cv.CV_DXT_INVERSE_SCALE) return f
def spin(self):
time.sleep(1.0)
started = time.time()
counter = 0
cvim = cv.CreateImage((640, 480), cv.IPL_DEPTH_8U, 1)
ball_xv = 10
ball_yv = 10
ball_x = 100
ball_y = 100
cvb = CvBridge()
while not rospy.core.is_shutdown():
cv.Set(cvim, 0)
cv.Circle(cvim, (ball_x, ball_y), 10, 255, -1)
ball_x += ball_xv
ball_y += ball_yv
if ball_x in [10, 630]:
ball_xv = -ball_xv
if ball_y in [10, 470]:
ball_yv = -ball_yv
self.pub.publish(cvb.cv_to_imgmsg(cvim))
time.sleep(0.03)
def measure_focused_roi(im, roi, area, focus_points, debug=False):
g = Grid(cv.GetSize(im))
canvas = image.new_from(im)
cv.Set(canvas, 0)
focus_in_roi = image.And(focus_points, roi)
if debug:
image.show(focus_in_roi, "ROI + Focused Points")
densities = []
points = convert_to_points(focus_in_roi)
groups = form_groups(points, estimated_size=24, iter=5)
for group in groups:
ch = ConvexHull(map(lambda x: (x[0], x[1]), group))
ppp = ch.points_per_pixel()
a = int(ppp * 255)
ch.draw_filled_hull(canvas, rgb=(a,a,a))
if debug:
image.show(canvas, "Focused Regions in ROI")
quadrants = g.split_in_four(canvas)
sums = []
for i,quad in enumerate(quadrants):
sums.append(cv.Sum(quad)[0] / float(area/4))
arr = array(sums)
print arr.mean(), arr.std()
diff = max(sums) - min(sums)
return diff, arr.std()
def repaintCCs(image, doRepaint=None, returnMask=False, resizeMask=True, doFillBackground=True, bgPoint=(0, 0), newcol=255, connectivity=4):
if doRepaint is None:
doRepaint = lambda comp, col: False
resultMask = cv.CreateImage((image.width + 2, image.height + 2), image.depth, image.nChannels)
tempMask = cv.CreateImage((image.width + 2, image.height + 2), image.depth, image.nChannels)
visitMask = cv.CreateImage((image.width + 2, image.height + 2), image.depth, image.nChannels)
cv.Zero(resultMask)
cv.Zero(tempMask)
cv.Zero(visitMask)
if doFillBackground:
cv.FloodFill(image, bgPoint, 0, 0, 0, connectivity + cv.CV_FLOODFILL_MASK_ONLY + (255 << 8), visitMask)
for x in xrange(image.width):
for y in xrange(image.height):
if visitMask[y + 1, x + 1] == 255:
continue
comp = cv.FloodFill(image, (x, y), 0, 0, 0, connectivity + cv.CV_FLOODFILL_MASK_ONLY + (255 << 8), tempMask)
region = shiftRect(comp[2], 1, 1)
cv.SetImageROI(tempMask, region)
cv.SetImageROI(visitMask, region)
cv.Or(tempMask, visitMask, visitMask)
if doRepaint(comp, image[y, x]):
cv.SetImageROI(resultMask, region)
cv.Or(tempMask, resultMask, resultMask)
cv.ResetImageROI(resultMask)
cv.Zero(tempMask)
cv.ResetImageROI(tempMask)
cv.ResetImageROI(visitMask)
if returnMask:
if resizeMask: return cap.getSubImage(resultMask, (1, 1, image.width, image.height))
else: return resultMask
else:
cv.SetImageROI(resultMask, (1, 1, image.width, image.height))
cv.Set(image, newcol, resultMask)
return image
def setup(flipped, capture, thehandcolor):
"""Initializes camera and finds initial skin tone"""
#creates initial window and prepares text
color = (40, 0, 0)
font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1.0, 1.0)
textsize1 = (cv.GetSize(flipped)[0] / 2 - 150,
cv.GetSize(flipped)[1] / 2 - 140)
textsize2 = (cv.GetSize(flipped)[0] / 2 - 150,
cv.GetSize(flipped)[1] / 2 - 110)
point1 = (cv.GetSize(flipped)[0] / 2 - 25, cv.GetSize(flipped)[1] / 2 - 25)
point2 = (cv.GetSize(flipped)[0] / 2 + 25, cv.GetSize(flipped)[1] / 2 + 25)
#until Enter is pressed
while (cv.WaitKey(10) != 10):
#captures live video, and draws sub-box and text
frame = cv.QueryFrame(capture)
cv.Copy(frame, flipped)
cv.Flip(flipped, flipped, 1)
cv.Rectangle(flipped, point1, point2, color, 2)
cv.PutText(flipped, "Put your hand in the box ", textsize1, font,
color)
cv.PutText(flipped, "and press enter", textsize2, font, color)
cv.ShowImage("w2", flipped)
#Creates sub-image inside box, and returns average color in box
sub = cv.GetSubRect(flipped, (cv.GetSize(flipped)[0] / 2 - 25,
cv.GetSize(flipped)[1] / 2 - 25, 50, 50))
cv.Set(thehandcolor, cv.Avg(sub))
return cv.Avg(sub)
def getData():
for i in range (0 , classes):
for j in range (0, train_samples):
if j < 10 :
fichero = "OCR/"+str(i) + "/"+str(i)+"0"+str(j)+".pbm"
else:
fichero = "OCR/"+str(i) + "/"+str(i)+str(j)+".pbm"
src_image = cv.LoadImage(fichero,0)
prs_image = preprocessing(src_image, size, size)
row = cv.GetRow(trainClasses, i*train_samples + j)
cv.Set(row, cv.RealScalar(i))
row = cv.GetRow(trainData, i*train_samples + j)
img = cv.CreateImage( ( size, size ), cv.IPL_DEPTH_32F, 1)
cv.ConvertScale(prs_image,img,0.0039215, 0)
data = cv.GetSubRect(img, (0,0, size,size))
row1 = cv.Reshape( data, 0, 1 )
cv.Copy(row1, row)
def show_shapes(shapes):
""" Function to show all of the shapes which are passed to it
"""
cv.NamedWindow("Shape Model", cv.CV_WINDOW_AUTOSIZE)
# Get size for the window
max_x = int(max([pt.x for shape in shapes for pt in shape.pts]))
max_y = int(max([pt.y for shape in shapes for pt in shape.pts]))
min_x = int(min([pt.x for shape in shapes for pt in shape.pts]))
min_y = int(min([pt.y for shape in shapes for pt in shape.pts]))
i = cv.CreateImage((max_x - min_x + 20, max_y - min_y + 20),
cv.IPL_DEPTH_8U, 3)
cv.Set(i, (0, 0, 0))
for shape in shapes:
r = randint(0, 255)
g = randint(0, 255)
b = randint(0, 255)
#r = 0
#g = 0
#b = 0
for pt_num, pt in enumerate(shape.pts):
# Draw normals
#norm = shape.get_normal_to_point(pt_num)
#cv.Line(i,(pt.x-min_x,pt.y-min_y), \
# (norm[0]*10 + pt.x-min_x, norm[1]*10 + pt.y-min_y), (r, g, b))
cv.Circle(i, (int(pt.x - min_x), int(pt.y - min_y)), 2,
(r, g, b), -1)
cv.ShowImage("Shape Model", i)
def main():
root = "/Users/soswow/Documents/Face Detection/test/negative"
# root = "/Users/soswow/Documents/Face Detection/test/sets/negative"
# root = "/Users/soswow/Documents/Face Detection/test/edge_view/positive"
# root = "/Users/soswow/Documents/Face Detection/test/sobel/positive"
# root = "/Users/soswow/Documents/Face Detection/test/sets/positive"
# root = "/Users/soswow/Documents/Face Detection/test/falses"
for folder in os.listdir(root):
path = p.join(root, folder)
if p.isdir(path):
sum = cv.CreateMat(32, 32, cv.CV_32F)
cv.Zero(sum)
k = 0
for path, _ in directory_files(path):
try:
img = cv.LoadImage(path, iscolor=False)
except IOError:
continue
mat = cv.CreateMat(32, 32, cv.CV_32F)
cv.Zero(mat)
cv.Convert(cv.GetMat(img), mat)
cv.Add(mat, sum, sum)
k += 1
avg = cv.CreateMat(32, 32, cv.CV_32F)
cv.Zero(avg)
count = cv.CreateMat(32, 32, cv.CV_32F)
cv.Zero(count)
cv.Set(count, k)
cv.Div(sum, count, avg)
new_img = cv.CreateImage((32, 32), 8, 0)
cv.Zero(new_img)
cv.Convert(avg, new_img)
cv.SaveImage(p.join(root, "%s-avg.png" % folder), new_img)
def getForegroundPixels(self, bgcolor=None):
'''
@param bgcolor: The background color to use. Specify as an (R,G,B) tuple.
Specify None for a blank/black background.
@return: The full color foreground pixels on either a blank (black)
background, or on a background color specified by the user.
@note: You must call detect() before getForegroundPixels() to
get updated information.
'''
if self._fgMask == None: return None
#binary mask selecting foreground regions
mask = self._fgMask.asOpenCVBW()
#full color source image
image = self._annotateImg.copy().asOpenCV()
#dest image, full color, but initially all zeros (black/background)
# we will copy the foreground areas from image to here.
dest = cv.CloneImage(image)
if bgcolor == None:
cv.SetZero(dest)
else:
cv.Set(dest, cv.RGB(*bgcolor))
cv.Copy(image, dest,
mask) #copy only pixels from image where mask != 0
return pv.Image(dest)
def getAverageValues2(self, images):
""" get the average values over all the images
for every two images, divides the images by 2
then adds them together
"""
if len(images) == 0:
return None
if len(images) == 1:
return images[0]
width = images[0].width
height = images[0].height
# create image with only 2's
# this will be used for division
divisionImage = cv.CreateImage((width, height), cv.IPL_DEPTH_8U, 1)
cv.Set(divisionImage, 2)
image1 = cv.CreateImage((width, height), cv.IPL_DEPTH_8U, 1)
image2 = cv.CreateImage((width, height), cv.IPL_DEPTH_8U, 1)
avgImage = cv.CloneImage(images[0])
for image in images:
# divide images by 2
cv.Div(avgImage, divisionImage, image1)
cv.Div(image, divisionImage, image2)
# add them to get result
cv.Add(image1, image2, avgImage)
return avgImage
def removeBadBackground(seg):
threshUp = cv.CreateImage(cv.GetSize(seg), cv.IPL_DEPTH_8U, 1)
comparison = cv.CreateImage(cv.GetSize(seg), cv.IPL_DEPTH_8U, 1)
visitMask = cv.CreateImage(cv.GetSize(seg), cv.IPL_DEPTH_8U, 1)
ffMask = cv.CreateImage((seg.width + 2, seg.height + 2), cv.IPL_DEPTH_8U,
1)
cv.Threshold(seg, threshUp, 1, 255, cv.CV_THRESH_BINARY)
cv.Zero(visitMask)
cv.Zero(ffMask)
for x in xrange(seg.width):
for y in xrange(seg.height):
if seg[y, x] != 96 or visitMask[y, x] == 255: continue
comp = cv.FloodFill(threshUp, (x, y), 0, 0, 0,
4 + cv.CV_FLOODFILL_MASK_ONLY + (255 << 8),
ffMask)
rect = comp[2]
cv.SetImageROI(ffMask, cap.shiftRect(rect, 1, 1))
cv.OrS(ffMask, 1, ffMask)
cv.SetImageROI(seg, rect)
cv.SetImageROI(comparison, rect)
cv.Cmp(
seg, ffMask, comparison,
cv.CV_CMP_EQ) # 'comparison' does not need to be zeroed later
intersect = cv.CountNonZero(comparison)
cv.SetImageROI(visitMask, rect)
cv.Or(visitMask, ffMask, visitMask)
cv.ResetImageROI(visitMask)
if intersect == 0:
cv.Set(seg, 0, ffMask)
cv.Zero(ffMask)
cv.ResetImageROI(seg)
cv.ResetImageROI(ffMask)
return seg
def recognise(image, addr, extras):
result = ""
x = image.width - 1
channels = getChannels(image)
bestBounds = []
#cv.NamedWindow("pic", 1)
#cv.NamedWindow("cols", 0)
while len(result) < nSegs and x >= minW:
x = cap.getBound(image, cap.CAP_BOUND_RIGHT, start=x)
ratings = []
for w in xrange(minW, min(maxW + 1, x)):
bounds = findBounds(image, x, w)
subImage = cap.getSubImage(image, bounds)
flags = findColors(subImage)
for index, flag in enumerate(flags):
if not flag: continue
seg = getSegment(channels[index], image, bounds)
seg = cap.flattenImage(adjustSize(seg, segSize))
guesses = ann.run(seg)
charIndex = cap.argmax(guesses)
ratings.append(
(guesses[charIndex], charIndex, index, bounds, seg))
best = max(ratings, key=itemgetter(0))
result += charset[best[1]]
bestChannel = channels[best[2]]
cv.SetImageROI(bestChannel, best[3])
cv.Set(bestChannel, 96, bestChannel)
cv.ResetImageROI(bestChannel)
bestBounds.append(best[3])
bestW = best[3][2]
x -= bestW
#print ann.run(best[4])
cap.processExtras([cap.drawComponents(image, bestBounds)], addr, extras,
cap.CAP_STAGE_RECOGNISE)
return result[::-1]
def drawHistogram(self, image, chnum, hist_arr, plateaus):
positions = (0, (self.Ihist.height + 10), 2 * self.Ihist.height + 20)
colour_values = _blue, _green, _red
colour = colour_values[chnum]
Y = positions[chnum]
cv.Set(self.Ihist, _trans)
bin_w = cv.Round(float(self.Ihist.width) / self.hist_size)
# min_value, max_value, pmin, pmax = cv.GetMinMaxHistValue(hist)
X = image.width - self.Ihist.width
rect = (X, Y, self.Ihist.width, self.Ihist.height)
cv.SetImageROI(image, rect)
scaling = self.Ihist.height / max(hist_arr)
hist_arr *= scaling
for i, v in enumerate(hist_arr):
cv.Rectangle(self.Ihist, (i * bin_w, self.Ihist.height),
((i + 1) * bin_w, self.Ihist.height - round(v)),
colour, -1, 8, 0)
for i in plateaus[chnum]:
cv.Rectangle(
self.Ihist, (i * bin_w, self.Ihist.height),
((i + 1) * bin_w, self.Ihist.height - round(hist_arr[i])),
_white, -1, 8, 0)
cv.AddWeighted(image, 1 - self.hist_visibility, self.Ihist,
self.hist_visibility, 0.0, image)
cv.ResetImageROI(image)
def findCCs(image, erasecol=0, doContinue=None, doSkip=None, bRange=0, connectivity=8):
"""
Finds all connected components in the image.
doContinue is a function applied to the color of every new pixel in the image.
If it is true, this pixel is ignored. Default: <= 128
doSkip is a function applied to every new connected component found by the
function. If it is true, this component will not be included in the result.
Default: do not skip anything.
"""
if doContinue is None:
doContinue = lambda col: col <= 128
if doSkip is None:
doSkip = lambda comp: False
mask = cv.CreateImage((image.width + 2, image.height + 2), cv.IPL_DEPTH_8U, 1)
cv.Zero(mask)
components = []
for x in range(image.width):
for y in range(image.height):
if doContinue(image[y, x]):
continue
comp = cv.FloodFill(image, (x, y), 0, bRange, bRange, connectivity + cv.CV_FLOODFILL_MASK_ONLY + (255 << 8), mask) # here 3rd argument is ignored
region = shiftRect(comp[2], 1, 1)
if not doSkip(comp):
seg = cvext.getSubImage(mask, region)
components.append((comp[0], comp[1], comp[2], seg))
cv.SetImageROI(image, comp[2])
cv.SetImageROI(mask, region)
cv.Set(image, erasecol, mask)
cv.Zero(mask)
cv.ResetImageROI(image)
cv.ResetImageROI(mask)
return components
def blockfacemask(bgrimg):
global lastrects
rects = detect_faces(bgrimg)
rects = addheights(rects)
newimg = im.newgray(bgrimg)
cv.Set(newimg, 255)
if rects:
lastrects = rects
else:
rects = lastrects
if rects:
for rect in rects:
cv.SetImageROI(newimg, rect)
cv.Set(newimg, 0)
cv.ResetImageROI(newimg)
return newimg
def redraw_monocular(self, drawable):
width, height = cv.GetSize(drawable.scrib)
display = cv.CreateMat(max(480, height), width + 100, cv.CV_8UC3)
cv.Zero(display)
cv.Copy(drawable.scrib, cv.GetSubRect(display, (0, 0, width, height)))
cv.Set(cv.GetSubRect(display, (width, 0, 100, height)),
(255, 255, 255))
self.buttons(display)
if not self.c.calibrated:
if drawable.params:
for i, (label, lo, hi, progress) in enumerate(drawable.params):
(w, _), _ = cv.GetTextSize(label, self.font)
cv.PutText(display, label,
(width + (100 - w) / 2, self.y(i)), self.font,
(0, 0, 0))
color = (0, 255, 0)
if progress < 1.0:
color = (0, int(progress * 255.), 255)
cv.Line(display, (int(width + lo * 100), self.y(i) + 20),
(int(width + hi * 100), self.y(i) + 20), color, 4)
else:
cv.PutText(display, "lin.", (width, self.y(0)), self.font,
(0, 0, 0))
linerror = drawable.linear_error
if linerror < 0:
msg = "?"
else:
msg = "%.2f" % linerror
#print "linear", linerror
cv.PutText(display, msg, (width, self.y(1)), self.font, (0, 0, 0))
self.show(display)
def anaglyph(left_color, right_color, correction):
#create oversized image
#result = cv.CreateImage(cv.GetSize(right_color), cv.IPL_DEPTH_8U, 4)
w, h = cv.GetSize(left_color)
bgra = cv.CreateImage((w * 2, h), cv.IPL_DEPTH_8U, 4)
cv.Set(bgra, 0)
right_bgra = add_alpha_channel(right_color,
round(255 / 2.)) #cyan (remove red?)
left_bgra = add_alpha_channel(left_color,
round(255 / 2.)) #red (remove blue?, green?)
#remove blue & green from left => red
left_red = remove_channels(left_bgra, [0, 1])
#remove red from right_bgra => cyan
right_cyan = remove_channels(right_bgra, [2])
if correction < 0:
left_area = cv.GetSubRect(bgra, (-correction, 0, w, h))
right_area = cv.GetSubRect(bgra, (0, 0, w, h))
valid_area = cv.GetSubRect(bgra, (-correction, 0, w + correction, h))
else:
#copy left & right onto bgra
left_area = cv.GetSubRect(bgra, (0, 0, w, h))
right_area = cv.GetSubRect(bgra, (correction, 0, w, h))
valid_area = cv.GetSubRect(bgra, (correction, 0, w - correction, h))
cv.Add(left_red, left_area, left_area)
cv.Add(right_cyan, right_area, right_area)
#return right_cyan
#return left_red
#return left_bgra
#return bgra
return valid_area
def to_img(self,
height=200,
color=cv.RGB(0, 0, 0),
offset=(0, 0),
bg=(255, 255, 255)):
im = cv.CreateImage((self.bins * self.scale, height), 8, 3)
cv.Set(im, bg)
return self.draw(im, color, offset=(0, 0))
def cutLetters(image, thresh, log):
mask = createMask(image, thresh)
log.log(mask)
h = cv.CreateImage(cv.GetSize(image), image.depth, 1)
cv.CvtColor(image, image, cv.CV_BGR2HSV)
cv.Split(image, h, None, None, None)
log.log(h)
cv.Set(h, cv.ScalarAll(255), mask)
return h
def update_high_color(self, pos):
self.busy_updating = True
self.high_color = pos
cv.NamedWindow("End at color", cv.CV_WINDOW_AUTOSIZE)
cv.MoveWindow("End at color", 750, 0)
color_swatch = cv.CreateImage((200, 140), 8, 3)
cv.Set(color_swatch, HSV_to_RGB(self.high_color))
cv.ShowImage("End at color", color_swatch)
self.busy_updating = False
def test_2686307(self):
lena = cv.LoadImage(find_sample("lena.jpg"), 1)
dst = cv.CreateImage((512, 512), 8, 3)
cv.Set(dst, (128, 192, 255))
mask = cv.CreateImage((512, 512), 8, 1)
cv.Zero(mask)
cv.Rectangle(mask, (10, 10), (300, 100), 255, -1)
cv.Copy(lena, dst, mask)
self.snapL([lena, dst, mask])