def save(self, filename, cropFlag = False):
"""save cropped and rotated image"""
if not cropFlag:
cv.SaveImage(filename +'-vectors.png',self.render())
else:
tmp = rotate(self.cImage, self.center, self.cardinalOffset +\
self.north)
#mask horizon
mask = cv.CreateImage(self.res, 8, 1)
cv.Zero(mask)
cv.Circle(mask, self.center, self.radius, (255,255,255))
cv.FloodFill(mask,(1,1),(0,0,0))
cv.FloodFill(mask, self.center,
(255,255,255),lo_diff=cv.RealScalar(5))
masked = cv.CloneImage(tmp)
cv.Zero(masked)
cv.Copy(tmp, masked, mask)
cv.SaveImage(filename +'-cropped.png',crop(masked, self))
#CSV output
array = magnitudeToTheta(self.polarArray,self.radius)
f = open(filename + '.csv', 'w')
f.write('00\n')
f.write(','.join([str(v[0]) for v in array]))
f.write('\n')
f.write(','.join([str(v[1]) for v in array]))
f.write('\n')
f.flush()
f.close()
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 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 get_motion_mask(self, img, diff_threshold=30):
self.timestamp = time.clock(
) / self.CLOCKS_PER_SEC # get current time in seconds
size = cv.GetSize(img) # get current frame size
idx1 = self.last
if not self.mhi or cv.GetSize(self.mhi) != size:
for i in range(self.N):
self.buf[i] = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
cv.Zero(self.buf[i])
self.mhi = cv.CreateImage(size, cv.IPL_DEPTH_32F, 1)
cv.Zero(self.mhi) # clear MHI at the beginning
self.orient = cv.CreateImage(size, cv.IPL_DEPTH_32F, 1)
self.segmask = cv.CreateImage(size, cv.IPL_DEPTH_32F, 1)
self.mask = cv.CreateImage(size, 8, 1)
self.test = cv.CreateImage(size, 8, 3)
cv.CvtColor(img, self.buf[self.last],
cv.CV_BGR2GRAY) # convert frame to grayscale
#self.buf[self.last] = cv.CloneImage(img)
idx2 = (self.last + 1) % self.N # index of (last - (N-1))th frame
self.last = idx2
self.silh = self.buf[idx2]
cv.AbsDiff(self.buf[idx1], self.buf[idx2],
self.silh) # get difference between frames
cv.Threshold(self.silh, self.silh, diff_threshold, 1,
cv.CV_THRESH_BINARY) # and threshold it
cv.UpdateMotionHistory(self.silh, self.mhi, self.timestamp,
self.MHI_DURATION) # update MHI
cv.CvtScale(self.mhi, self.mask, 255. / self.MHI_DURATION,
(self.MHI_DURATION - self.timestamp) * 255. /
self.MHI_DURATION)
#cv.ShowImage("motion mask", self.mask)
max_rect = self.segment_motion()
return self.mask
def segment_motion(self):
min_area = 100
size = cv.GetSize(self.mask) # get current frame size
temp = cv.CloneImage(self.mask)
cv.CalcMotionGradient(self.mhi, temp, self.orient, self.MAX_TIME_DELTA,
self.MIN_TIME_DELTA, 3)
#cv.ShowImage("orient", self.orient);
if not self.storage:
self.storage = cv.CreateMemStorage(0)
seq = cv.SegmentMotion(self.mhi, self.segmask, self.storage,
self.timestamp, self.MAX_TIME_DELTA)
#cv.ShowImage("segmask", self.segmask)
max = 0
max_idx = -1
for i in range(len(seq)):
(area, value, comp_rect) = seq[i]
if area > max:
max = area
max_idx = i
if max_idx == -1:
cv.Zero(self.mask)
return
(area, value, comp_rect) = seq[max_idx]
if (area < 100):
cv.Zero(self.mask)
return
cv.Zero(self.mask)
cv.Rectangle(
self.mask, (comp_rect[0], comp_rect[1]),
(comp_rect[0] + comp_rect[2], comp_rect[1] + comp_rect[3]),
(255, 255, 255), cv.CV_FILLED)
def homografia(real_width, real_height, width, height):
global points, teclado
if len(points)!=4 :
raise Exception('falto algun punto')
p = cv.CreateMat(2,4,cv.CV_64FC1)
h = cv.CreateMat(2,4,cv.CV_64FC1)
p2h = cv.CreateMat(3,3,cv.CV_64FC1)
cv.Zero(p)
cv.Zero(h)
cv.Zero(p2h)
for i in range(4):
(x,y) = points[i]
p[0,i] = (float(real_width)/float(width)) * x
p[1,i] = (float(real_height)/float(height)) * y
h[0,0] = 0
h[1,0] = real_height
h[0,1] = real_width
h[1,1] = real_height
h[0,2] = real_width
h[1,2] = 0
h[0,3] = 0
h[1,3] = 0
cv.FindHomography(p,h,p2h)
return p2h
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 main(argv):
if len(argv) < 10:
print('Usage: %s input-file fx fy cx cy k1 k2 p1 p2 output-file' %
argv[0])
sys.exit(-1)
src = argv[1]
fx, fy, cx, cy, k1, k2, p1, p2, output = argv[2:]
intrinsics = cv.CreateMat(3, 3, cv.CV_64FC1)
cv.Zero(intrinsics)
intrinsics[0, 0] = float(fx)
intrinsics[1, 1] = float(fy)
intrinsics[2, 2] = 1.0
intrinsics[0, 2] = float(cx)
intrinsics[1, 2] = float(cy)
dist_coeffs = cv.CreateMat(1, 4, cv.CV_64FC1)
cv.Zero(dist_coeffs)
dist_coeffs[0, 0] = float(k1)
dist_coeffs[0, 1] = float(k2)
dist_coeffs[0, 2] = float(p1)
dist_coeffs[0, 3] = float(p2)
src = cv.LoadImage(src)
dst = cv.CreateImage(cv.GetSize(src), src.depth, src.nChannels)
mapx = cv.CreateImage(cv.GetSize(src), cv.IPL_DEPTH_32F, 1)
mapy = cv.CreateImage(cv.GetSize(src), cv.IPL_DEPTH_32F, 1)
cv.InitUndistortMap(intrinsics, dist_coeffs, mapx, mapy)
cv.Remap(src, dst, mapx, mapy,
cv.CV_INTER_LINEAR + cv.CV_WARP_FILL_OUTLIERS, cv.ScalarAll(0))
# cv.Undistort2(src, dst, intrinsics, dist_coeffs)
cv.SaveImage(output, dst)
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 update_mhi(img, dst, diff_threshold):
global last
global mhi
global mask
timestamp = time.clock() / CLOCKS_PER_SEC # get current time in seconds
size = cv.GetSize(img) # get current frame size
idx1 = last
if not mhi or cv.GetSize(mhi) != size:
for i in range(N):
buf[i] = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
cv.Zero(buf[i])
mhi = cv.CreateImage(size, cv.IPL_DEPTH_32F, 1)
cv.Zero(mhi) # clear MHI at the beginning
mask = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
cv.CvtColor(img, buf[last], cv.CV_BGR2GRAY) # convert frame to grayscale
idx2 = (last + 1) % N # index of (last - (N-1))th frame
last = idx2
silh = buf[idx2]
cv.AbsDiff(buf[idx1], buf[idx2], silh) # get difference between frames
cv.Threshold(silh, silh, diff_threshold, 1,
cv.CV_THRESH_BINARY) # and threshold it
cv.UpdateMotionHistory(silh, mhi, timestamp, MHI_DURATION) # update MHI
cv.CvtScale(mhi, mask, 255. / MHI_DURATION,
(MHI_DURATION - timestamp) * 255. / MHI_DURATION)
cv.Zero(dst)
cv.Merge(mask, None, None, None, dst)
def camera():
print "# Starting initialization..."
#camera capture
#cap = cv.CaptureFromCAM(0)
intrinsics = cv.CreateMat(3, 3, cv.CV_64FC1)
cv.Zero(intrinsics)
#camera data
intrinsics[0, 0] = 1100.850708957251072
intrinsics[1, 1] = 778.955239997982062
intrinsics[2, 2] = 1.0
intrinsics[0, 2] = 348.898495232253822
intrinsics[1, 2] = 320.213734835526282
dist_coeffs = cv.CreateMat(1, 4, cv.CV_64FC1)
cv.Zero(dist_coeffs)
dist_coeffs[0, 0] = -0.326795877008420
dist_coeffs[0, 1] = 0.139445565548056
dist_coeffs[0, 2] = 0.001245710462327
dist_coeffs[0, 3] = -0.001396618726445
#pFrame = cv.QueryFrame(cap)
print "# intrinsics loaded!"
#prepare memory
capture = cv.CaptureFromCAM(0)
src = cv.QueryFrame(capture)
size = GetSize(src)
dst0 = cv.CreateImage(size, src.depth, src.nChannels)
# bg = cv.LoadImage("00000005.jpg")
image_ROI = (0,70,640,340)
size = (640,340)
red = cv.CreateImage(size, 8, 1)
green = cv.CreateImage(size, 8, 1)
blue = cv.CreateImage(size, 8, 1)
hue = cv.CreateImage(size, 8, 1)
sat = cv.CreateImage(size, 8, 1)
val = cv.CreateImage(size, 8, 1)
ball = cv.CreateImage(size, 8, 1)
yellow = cv.CreateImage(size, 8, 1)
ballx = 0
bally = 0
ballmiss = 0
yellowmiss = 0
bluemiss = 0
dst2 = cv.CreateImage(size, 8, 3)
hsv = cv.CreateImage(size,8,3)
print "# base images created..."
#####------------------ajustment data---------------------###############
#shadow
high = 40
low = 300
#threshold
thresBallInit = 116
thresYellowInit = 94
thresBlueInit = 18
ballRangeInit = 8.0
def update_mhi(img, dst, diff_threshold):
global last
global mhi
global storage
global mask
global orient
global segmask
timestamp = time.clock() / CLOCKS_PER_SEC # get current time in seconds
size = cv.GetSize(img) # get current frame size
idx1 = last
if not mhi or cv.GetSize(mhi) != size:
for i in range(N):
buf[i] = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
cv.Zero(buf[i])
mhi = cv.CreateImage(size,cv. IPL_DEPTH_32F, 1)
cv.Zero(mhi) # clear MHI at the beginning
orient = cv.CreateImage(size,cv. IPL_DEPTH_32F, 1)
segmask = cv.CreateImage(size,cv. IPL_DEPTH_32F, 1)
mask = cv.CreateImage(size,cv. IPL_DEPTH_8U, 1)
cv.CvtColor(img, buf[last], cv.CV_BGR2GRAY) # convert frame to grayscale
idx2 = (last + 1) % N # index of (last - (N-1))th frame
last = idx2
silh = buf[idx2]
cv.AbsDiff(buf[idx1], buf[idx2], silh) # get difference between frames
cv.Threshold(silh, silh, diff_threshold, 1, cv.CV_THRESH_BINARY) # and threshold it
cv.UpdateMotionHistory(silh, mhi, timestamp, MHI_DURATION) # update MHI
cv.CvtScale(mhi, mask, 255./MHI_DURATION,
(MHI_DURATION - timestamp)*255./MHI_DURATION)
cv.Zero(dst)
cv.Merge(mask, None, None, None, dst)
cv.CalcMotionGradient(mhi, mask, orient, MAX_TIME_DELTA, MIN_TIME_DELTA, 3)
if not storage:
storage = cv.CreateMemStorage(0)
seq = cv.SegmentMotion(mhi, segmask, storage, timestamp, MAX_TIME_DELTA)
for (area, value, comp_rect) in seq:
if comp_rect[2] + comp_rect[3] > 100: # reject very small components
color = cv.CV_RGB(255, 0,0)
silh_roi = cv.GetSubRect(silh, comp_rect)
mhi_roi = cv.GetSubRect(mhi, comp_rect)
orient_roi = cv.GetSubRect(orient, comp_rect)
mask_roi = cv.GetSubRect(mask, comp_rect)
angle = 360 - cv.CalcGlobalOrientation(orient_roi, mask_roi, mhi_roi, timestamp, MHI_DURATION)
count = cv.Norm(silh_roi, None, cv.CV_L1, None) # calculate number of points within silhouette ROI
if count < (comp_rect[2] * comp_rect[3] * 0.05):
continue
magnitude = 30.
center = ((comp_rect[0] + comp_rect[2] / 2), (comp_rect[1] + comp_rect[3] / 2))
cv.Circle(dst, center, cv.Round(magnitude*1.2), color, 3, cv.CV_AA, 0)
cv.Line(dst,
center,
(cv.Round(center[0] + magnitude * cos(angle * cv.CV_PI / 180)),
cv.Round(center[1] - magnitude * sin(angle * cv.CV_PI / 180))),
color,
3,
cv.CV_AA,
0)
def compare_imgs(self, img1, img2):
back_proj_img, hist1 = self.back_project_hs(img1)
back_proj_img2, hist2 = self.back_project_hs(img2)
scratch = cv.CreateImage(cv.GetSize(back_proj_img2), 8, 1)
scratch2 = cv.CreateImage(cv.GetSize(back_proj_img2), 8, 1)
cv.Zero(scratch)
cv.Zero(scratch2)
#cv.Sub(back_proj_img, back_proj_img2, scratch2) #opposite noise, but excludes object
cv.Sub(back_proj_img2, back_proj_img, scratch2) #noise, but includes object if failed,
cv.Sub(scratch2, ha.avg_noise, scratch)
return scratch
def process_image(self, slider_pos):
"""
This function finds contours, draws them and their approximation by ellipses.
"""
stor = cv.CreateMemStorage()
# Create the destination images
image02 = cv.CloneImage(self.source_image)
cv.Zero(image02)
image04 = cv.CreateImage(cv.GetSize(self.source_image),
cv.IPL_DEPTH_8U, 3)
cv.Zero(image04)
# Threshold the source image. This needful for cv.FindContours().
cv.Threshold(self.source_image, image02, slider_pos, 255,
cv.CV_THRESH_BINARY)
# Find all contours.
cont = cv.FindContours(image02, stor, cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE, (0, 0))
for c in contour_iterator(cont):
# Number of points must be more than or equal to 6 for cv.FitEllipse2
if len(c) >= 6:
# Copy the contour into an array of (x,y)s
PointArray2D32f = cv.CreateMat(1, len(c), cv.CV_32FC2)
for (i, (x, y)) in enumerate(c):
PointArray2D32f[0, i] = (x, y)
# Draw the current contour in gray
gray = cv.CV_RGB(100, 100, 100)
cv.DrawContours(image04, c, gray, gray, 0, 1, 8, (0, 0))
# Fits ellipse to current contour.
(center, size, angle) = cv.FitEllipse2(PointArray2D32f)
# Convert ellipse data from float to integer representation.
center = (cv.Round(center[0]), cv.Round(center[1]))
size = (cv.Round(size[0] * 0.5), cv.Round(size[1] * 0.5))
angle = -angle
# Draw ellipse in random color
color = cv.CV_RGB(random.randrange(256), random.randrange(256),
random.randrange(256))
cv.Ellipse(image04, center, size, angle, 0, 360, color, 2,
cv.CV_AA, 0)
# Show image. HighGUI use.
cv.ShowImage("Result", image04)
def get_hand(img):
global h_lower, h_upper, s_lower, s_upper
storage = cv.CreateMemStorage(0)
contours, hull, max_contours, max_hull = (0, 0, 0, 0)
max_rect = (1, 1, 100, 100)
dst = cv.CreateImage((img.width, img.height), 8, 3)
hsv = cv.CreateImage((img.width, img.height), 8, 3)
frame = cv.CreateImage((img.width, img.height), 8, 1)
con = cv.CreateImage((img.width, img.height), 8, 1)
cv.Zero(con)
cv.Smooth(img, dst, cv.CV_GAUSSIAN, 5, 5)
for i in range(3): cv.Smooth(dst, dst, cv.CV_GAUSSIAN, 5, 5)
cv.CvtColor(dst, hsv, cv.CV_RGB2HSV)
cv.InRangeS(hsv, (h_lower, s_lower, 0), (h_upper, s_upper, 256), frame)
kernel = cv.CreateStructuringElementEx(3, 3, 0, 0, cv.CV_SHAPE_RECT)
#cv.MorphologyEx(frame, frame, None, kernel, cv.CV_MOP_CLOSE , 7)
# cv.MorphologyEx(frame, frame, None, kernel, cv.CV_MOP_OPEN , 3)
#contours = im.find_contours(frame)
#hull = im.find_convex_hull(contours)
print contours
#max_hull_area, max_contour_area = (0, 0)
#print "xxxxxxxx"
#contour = contours.h_next()
#print "........"
#while (contour != 0):
# hull = cv.ConvexHull2(contour, storage, cv.CV_CLOCKWISE, 1);
# maxv = cv.ContourArea(hull)
# contour = contour.h_next()
#cv.DrawContours(con, contours, red, blue, 1, 3, 8)
cv.ShowImage("result", con)
def dummy_window():
cv.NamedWindow('keyboard')
cv.MoveWindow('keyboard', 0, 0)
D.size = (100, 100)
D.dummy = cv.CreateImage(D.size, 8, 1)
D.created_images = True
cv.ShowImage('keyboard', cv.Zero(D.dummy))
def backgroundDiff(self, img, Imask):
#cv.Zero(self.Imaskt)
cv.Zero(Imask)
cv.CvtScale(img, self.Iscratch, 1, 0)
cv.InRange(self.Iscratch, self.IlowF, self.IhiF, Imask)
cv.SubRS(Imask, 255, Imask)
cv.MorphologyEx(Imask, Imask, None, None, cv.CV_MOP_OPEN, 1)
cv.MorphologyEx(Imask, Imask, None, None, cv.CV_MOP_CLOSE, 2)
#######This stuff was for when it was 3 channel model, now only 1
#######for backprojection
#cv.Split(self.Iscratch, self.Igray1, self.Igray2, self.Igray3, None)
#cv.InRange(self.Igray1, self.Ilow1, self.Ihi1, Imask)
# cv.InRange(self.Igray2, self.Ilow2, self.Ihi2, self.Imaskt)
# cv.Or(Imask, self.Imaskt, Imask)
# cv.InRange(self.Igray3, self.Ilow3, self.Ihi3, self.Imaskt)
# cv.Or(Imask, self.Imaskt, Imask)
# cv.SubRS(Imask, 255, Imask)
#cv.SaveImage('/home/mkillpack/Desktop/mask.png', Imask)
#cv.Erode(Imask, Imask)
return Imask
def get_dirmarker(img, angle, Dist, radius):
X, Y = entCenter(robot)
Len, _ = entSize(robot)
point = (X + (Dist + Len / 2.0) * cos(angle),
Y - (Dist + Len / 2.0) * sin(angle))
point = intPoint(point)
#For visualisation:
# cv.Circle( frame, point, radius, (0,200,200), 1 )
point2 = point[0] - nhood[0], point[1] - nhood[1]
out = cv.CloneImage(img)
cv.Zero(out)
cv.Circle(out, point2, radius, (255, 255, 255), -1)
cv.And(out, img2, out)
center1 = self.centralMoment(out)
count1 = cv.CountNonZero(out)
cv.Erode(out, out)
center2 = self.centralMoment(out)
count2 = cv.CountNonZero(out)
if count2 == 0 and count1 > 10:
return center1
else:
return center2
def plot_1dhisto(histogram,
scale=2,
gap=0,
histh=200,
histimg=None,
origin='bottom'):
"""
Given histogram data, make a visual represntation in the form of a
IplImage, helper function for calc_1dhisto().
@param scale Width of each bar (pix)
@param gap Gap between bars (pix)
@param histh Bar plot height (pix)
@param origin Origin of histogram bars, 'bottom' (for positive values) or 'center' (for any value)
@return Graphical cv.iplimage representation of the histogram
"""
nbin = len(histogram)
histh, scale, gap = int(histh), int(scale), int(gap)
if (not histimg):
histimg = cv.CreateImage((nbin * scale, histh), cv.IPL_DEPTH_8U, 1)
cv.Zero(histimg)
for i, bin_h in enumerate(histogram):
if (origin == 'bottom'):
cv.Rectangle(histimg, (i * scale, histh),
((i + 1) * scale - 1 - gap, histh - int(bin_h)), 256,
cv.CV_FILLED)
else:
cv.Rectangle(histimg, (i * scale, histh / 2),
((i + 1) * scale - 1 - gap, histh / 2 - int(bin_h)),
256, cv.CV_FILLED)
return histimg
def get_normalized_rgb_planes(r, g, b):
size = cv.GetSize(r)
# r,g,b = get_three_planes(img)
nr_plane = cv.CreateImage(size, 8, 1)
ng_plane = cv.CreateImage(size, 8, 1)
nb_plane = cv.CreateImage(size, 8, 1)
r32 = cv.CreateImage(size, cv.IPL_DEPTH_32F, 1)
g32 = cv.CreateImage(size, cv.IPL_DEPTH_32F, 1)
b32 = cv.CreateImage(size, cv.IPL_DEPTH_32F, 1)
sum = cv.CreateImage(size, cv.IPL_DEPTH_32F, 1)
cv.Zero(sum)
cv.Convert(r, r32)
cv.Convert(g, g32)
cv.Convert(b, b32)
cv.Add(r32, g32, sum)
cv.Add(b32, sum, sum)
tmp = cv.CreateImage(size, cv.IPL_DEPTH_32F, 1)
cv.Div(r32, sum, tmp)
cv.ConvertScale(tmp, nr_plane, scale=255)
cv.Div(g32, sum, tmp)
cv.ConvertScale(tmp, ng_plane, scale=255)
cv.Div(b32, sum, tmp)
cv.ConvertScale(tmp, nb_plane, scale=255)
# res = image_empty_clone(img)
# cv.Merge(nr_plane,ng_plane,nb_plane,None,res)
return nr_plane, ng_plane, nb_plane
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 publish_debug(img, results):
imgsize = cv.GetSize(img)
sizelist = [cv.GetSize(tmp[1]) for tmp in templates]
width = max(imgsize[0], sum([s[0] for s in sizelist]))
height = imgsize[1] + max([s[1] for s in sizelist])
output = cv.CreateImage((width, height), cv.IPL_DEPTH_8U, 1)
cv.Zero(output)
cur_x = 0
view_rect = (0, height-imgsize[1], imgsize[0], imgsize[1])
cv.SetImageROI(output, view_rect)
cv.Copy(img, output)
for template in templates:
size = cv.GetSize(template[1])
cv.SetImageROI(output, (cur_x, 0, size[0], size[1]))
cv.Copy(template[1], output)
cur_x += size[0]
#cv.PutText(output, tempname, (0,size[1]-16), font, cv.CV_RGB(255,255,255))
#cv.PutText(output, str(tempthre)+'<'+str(status[1]), (0,size[1]-8), font, cv.CV_RGB(255,255,255))
for _,status in [s for s in results if s[0] == template[3]]:
print status
cv.PutText(output, template[3], (0,size[1]-42), font, cv.CV_RGB(255,255,255))
cv.PutText(output, "%7.5f"%(status[0]), (0,size[1]-24), font, cv.CV_RGB(255,255,255))
cv.PutText(output, "%7.5f"%(status[2]), (0,size[1]-8), font, cv.CV_RGB(255,255,255))
if status[3] :
cv.Rectangle(output, (0, 0), size, cv.RGB(255,255,255), 9)
cv.SetImageROI(output, view_rect)
for _,status in [s for s in results if s[0] == template[3]]:
pt2 = (status[1][0]+size[0], status[1][1]+size[1])
if status[3] :
cv.Rectangle(output, status[1], pt2, cv.RGB(255,255,255), 5)
cv.ResetImageROI(output)
debug_pub.publish(bridge.cv_to_imgmsg(output, encoding="passthrough"))
def calc_stats(self):
cv.NamedWindow("noise", cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow("img1_back", cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow("img2_back", cv.CV_WINDOW_AUTOSIZE)
self.check_for_hist()
self.avg_noise = cv.CreateImage(cv.GetSize(self.background_noise[0]),
8, 1)
cv.Zero(self.avg_noise)
for i in xrange(len(self.background_noise) - 1):
cv.ShowImage("noise", self.avg_noise)
back_proj_img1, hist1 = self.back_project_hs(
self.background_noise[i])
back_proj_img2, hist2 = self.back_project_hs(
self.background_noise[i + 1])
self.accumulateBackground(back_proj_img1)
cv.ShowImage("img1_back", back_proj_img1)
cv.ShowImage("img2_back", back_proj_img2)
scratch = cv.CreateImage(cv.GetSize(back_proj_img2), 8, 1)
scratch2 = cv.CreateImage(cv.GetSize(back_proj_img2), 8, 1)
# do something clever with ands ors and diffs
cv.Zero(scratch)
cv.Zero(scratch2)
cv.Sub(back_proj_img2, back_proj_img1,
scratch2) #noise, but includes object if failed,
#cv.Sub(scratch2, self.avg_noise, scratch)
#cv.Or(self.avg_noise, scratch2, self.avg_noise)
cv.Or(self.avg_noise, scratch2, self.avg_noise)
cv.ShowImage("diff_back", scratch2)
cv.ShowImage("diff_noise_scratch", scratch)
cv.WaitKey(-1)
self.createModelsfromStats()
print self.Icount
cv.NamedWindow("Ilow", cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow("Ihi", cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow("IavgF", cv.CV_WINDOW_AUTOSIZE)
cv.ShowImage("Ihi", self.IhiF)
cv.ShowImage("Ilow", self.IlowF)
cv.ShowImage("IavgF", self.IavgF)
def edge_threshold(image, roi=None, debug=0):
thresholded = cv.CloneImage(image)
horizontal = cv.CreateImage(cv.GetSize(image), cv.IPL_DEPTH_16S, 1)
magnitude32f = cv.CreateImage(cv.GetSize(image), cv.IPL_DEPTH_32F, 1)
vertical = cv.CloneImage(horizontal)
v_edge = cv.CloneImage(image)
magnitude = cv.CloneImage(horizontal)
storage = cv.CreateMemStorage(0)
mag = cv.CloneImage(image)
cv.Sobel(image, horizontal, 0, 1, 1)
cv.Sobel(image, vertical, 1, 0, 1)
cv.Pow(horizontal, horizontal, 2)
cv.Pow(vertical, vertical, 2)
cv.Add(vertical, horizontal, magnitude)
cv.Convert(magnitude, magnitude32f)
cv.Pow(magnitude32f, magnitude32f, 0.5)
cv.Convert(magnitude32f, mag)
if roi:
cv.And(mag, roi, mag)
cv.Normalize(mag, mag, 0, 255, cv.CV_MINMAX, None)
cv.Threshold(mag, mag, 122, 255, cv.CV_THRESH_BINARY)
draw_image = cv.CloneImage(image)
and_image = cv.CloneImage(image)
results = []
threshold_start = 17
for window_size in range(threshold_start, threshold_start + 1, 1):
r = 20
for threshold in range(0, r):
cv.AdaptiveThreshold(image, thresholded, 255, \
cv.CV_ADAPTIVE_THRESH_MEAN_C, cv.CV_THRESH_BINARY_INV, window_size, threshold)
contour_image = cv.CloneImage(thresholded)
contours = cv.FindContours(contour_image, storage, cv.CV_RETR_LIST)
cv.Zero(draw_image)
cv.DrawContours(draw_image, contours, (255, 255, 255),
(255, 255, 255), 1, 1)
if roi:
cv.And(draw_image, roi, draw_image)
cv.And(draw_image, mag, and_image)
m1 = np.asarray(cv.GetMat(draw_image))
m2 = np.asarray(cv.GetMat(mag))
total = mag.width * mag.height #cv.Sum(draw_image)[0]
coverage = cv.Sum(and_image)[0] / (mag.width * mag.height)
if debug:
print threshold, coverage
cv.ShowImage("main", draw_image)
cv.ShowImage("main2", thresholded)
cv.WaitKey(0)
results.append((coverage, threshold, window_size))
results.sort(lambda x, y: cmp(y, x))
_, threshold, window_size = results[0]
cv.AdaptiveThreshold(image, thresholded, 255, cv.CV_ADAPTIVE_THRESH_MEAN_C, \
cv.CV_THRESH_BINARY, window_size, threshold)
return thresholded
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])
def get_mask(w, h):
img = cv.CreateImage((w, h), 8, 1)
cv.Zero(img)
t = int(w / 5)
k = int(w / 15)
p = w - k - 1
poly = ((k, t), (t + k, 0), (p - t, 0), (p, t), (p, h - t), (p - t, h),
(t + k, h), (k, h - t))
cv.FillPoly(img, (poly, ), 255)
return img
def rects_to_mask(regions, mask, inverted=False, value=255):
if inverted:
cv.Set(mask, cv.ScalarAll(value))
colour = cv.ScalarAll(0)
else:
cv.Zero(mask)
colour = cv.ScalarAll(value)
for rect in regions:
cv.Rectangle(mask, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]), colour, -1)
def initialize_interface():
"""Initializes all windows needed by our program."""
#Create window to monitor robot status
cv.NamedWindow('Monitor')
cv.MoveWindow('Monitor', 0, 0)
D.size = (100, 100)
D.dummy = cv.CreateImage(D.size, 8, 1)
D.image = cv.Zero(D.dummy)
cv.ShowImage('Monitor', D.image)
def add_features(self, cv_image, face):
""" Look for any new features around the current feature cloud """
""" Create the ROI mask"""
roi = cv.CreateImage(cv.GetSize(cv_image), 8, 1)
""" Begin with all black pixels """
cv.Zero(roi)
""" Get the coordinates and dimensions of the current track box """
try:
((x, y), (w, h), a) = face.track_box
except:
logger.info("Track box has shrunk to zero...")
return
""" Expand the track box to look for new features """
w = int(face.expand_roi * w)
h = int(face.expand_roi * h)
roi_box = ((x, y), (w, h), a)
""" Create a filled white ellipse within the track_box to define the ROI. """
cv.EllipseBox(roi, roi_box, cv.CV_RGB(255, 255, 255), cv.CV_FILLED)
""" Create the temporary scratchpad images """
eig = cv.CreateImage(cv.GetSize(self.grey), 32, 1)
temp = cv.CreateImage(cv.GetSize(self.grey), 32, 1)
if self.feature_type == 0:
""" Get the new features using Good Features to Track """
features = cv.GoodFeaturesToTrack(self.grey,
eig,
temp,
self.max_count,
self.quality,
self.good_feature_distance,
mask=roi,
blockSize=3,
useHarris=0,
k=0.04)
elif self.feature_type == 1:
""" Get the new features using SURF """
features = []
(surf_features, descriptors) = cv.ExtractSURF(
self.grey, roi, cv.CreateMemStorage(0),
(0, self.surf_hessian_quality, 3, 1))
for feature in surf_features:
features.append(feature[0])
""" Append new features to the current list if they are not too
far from the current cluster """
for new_feature in features:
try:
distance = self.distance_to_cluster(new_feature, face.features)
if distance > self.add_feature_distance:
face.features.append(new_feature)
except:
pass
""" Remove duplicate features """
face.features = list(set(face.features))
def cv_convolution(image, b):
#cv.ShowImage('image', cv.fromarray(image))
dft_m = cv.GetOptimalDFTSize(image.shape[0] + b.shape[0] - 1)
dft_n = cv.GetOptimalDFTSize(image.shape[1] + b.shape[1] - 1)
print dft_m, dft_n
#
c = cv.CreateMat(image.shape[0] + d - 1, image.shape[1] + d - 1, cv.CV_8U)
# getting gaussian dft
dft_b = cv.CreateMat(dft_m, dft_n, cv.CV_64F)
#
tmp = cv.GetSubRect(dft_b, (0, 0, b.shape[1], b.shape[0]))
cv.Copy(cv.fromarray(b), tmp)
tmp = cv.GetSubRect(dft_b,
(b.shape[1], 0, dft_b.cols - b.shape[1], b.shape[0]))
cv.Zero(tmp)
#
cv.DFT(dft_b, dft_b, cv.CV_DXT_FORWARD, b.shape[0])
# getting layers dft
dfts = []
new_channels = []
channels = cv2.split(image)
for i, channel in enumerate(channels):
#cv2.imshow('channel %d'%i, channel)
a = np.array(channel, dtype='float')
dft_a = cv.CreateMat(dft_m, dft_n, cv.CV_64F)
#
tmp = cv.GetSubRect(dft_a, (0, 0, a.shape[1], a.shape[0]))
cv.Copy(cv.fromarray(a), tmp)
tmp = cv.GetSubRect(
dft_a, (a.shape[1], 0, dft_a.cols - a.shape[1], a.shape[0]))
cv.Zero(tmp)
#
cv.DFT(dft_a, dft_a, cv.CV_DXT_FORWARD, a.shape[0])
cv.MulSpectrums(dft_a, dft_b, dft_a, 0)
cv.DFT(dft_a, dft_a, cv.CV_DXT_INV_SCALE, c.rows)
tmp = cv.GetSubRect(dft_a, (0, 0, c.cols, c.rows))
#cv.Copy(tmp, c)
channel = np.array(tmp, dtype='uint8')
cv.ShowImage('new channel %d' % i, cv.fromarray(channel))
new_channels.append(channel)
result = cv2.merge(new_channels)
return result
