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 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))
# 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 _mixImageAlphaMask(self, wipeSettings, level, image1, image2, image2mask, mixMat):
if(level < 0.99):
wipeMode, wipePostMix, wipeConfig = wipeSettings
if((wipeMode == WipeMode.Fade) or (wipeMode == WipeMode.Default)):
valueCalc = int(256 * (1.0 - level))
rgbColor = cv.CV_RGB(valueCalc, valueCalc, valueCalc)
whiteColor = cv.CV_RGB(255, 255, 255)
cv.Set(mixMat, whiteColor)
cv.Set(mixMat, rgbColor, image2mask)
cv.Mul(image1, mixMat, image1, 0.004)
valueCalc = int(256 * level)
rgbColor = cv.CV_RGB(valueCalc, valueCalc, valueCalc)
cv.Zero(mixMat)
cv.Set(mixMat, rgbColor, image2mask)
cv.Mul(image2, mixMat, image2, 0.004)
cv.Add(image1, image2, image1)
return image1
else:
if(wipePostMix == False):
image2, image2mask = self._wipeImage(wipeMode, wipeConfig, level, image2, image2mask, mixMat, False)
cv.Copy(image2, image1, image2mask)
return image1
else:
cv.Copy(image1, mixMat)
cv.Copy(image2, mixMat, image2mask)
return self._wipeMix(wipeMode, wipeConfig, level, image1, mixMat, image2)
cv.Copy(image2, image1, image2mask)
return image1
def rectif(self, image, cadreIn, cadreOut):
prev_image = cv.CloneImage(image)
cv.Zero(image)
mmat = cv.CreateMat(3, 3, cv.CV_32FC1)
print("mmat= %s" % repr(mmat))
cv.GetPerspectiveTransform(cadreIn, cadreOut, mmat)
cv.WarpPerspective(prev_image, image,
mmat) #, flags=cv.CV_WARP_INVERSE_MAP )
def draw_color_contours(self, color_contours, color=None):
'''Takes in list of (contour, color) tuples where contour is iterable for (x, y) tuples'''
gray_img = cv.CreateImage(cv.GetSize(self.image), 8, 1)
#cv.CvtColor( self.image, gray_img, cv.CV_BGR2GRAY )
cv.Zero(gray_img)
for (contour, color) in color_contours:
cv.PolyLine(gray_img, [contour], True, color)
cv.ShowImage("Contours", gray_img)
cv.WaitKey()
def get_working(self):
(width, height) = cv.GetSize(self.image)
dest = cv.CreateMat(height, width, cv.CV_8UC3)
mask8x1 = cv.CreateImage(cv.GetSize(self.image), 8, 1)
cv.Zero(mask8x1)
cv.FillConvexPoly(mask8x1, self.cur_contour, cv.ScalarAll(255))
# Could 8x3 mask copy but histogram mask will take care of it
cv.Copy(self.image, dest)
return (dest, mask8x1)
def gen_mask(frec, tpoly):
mask = cv.CreateMat(int(np.round(frec.height())),
int(np.round(frec.width())), cv.CV_8UC1)
cv.Zero(mask)
pts = tuple((int(np.round(x - frec.left)), int(np.round(y - frec.top)))
for (x, y) in tpoly.contour(0))
cv.FillPoly(mask, [pts], cv.Scalar(1), lineType=8, shift=0)
return np.asarray(mask)
def minarea_seq(img, count, storage):
points = [(randint(img.width / 4, img.width * 3 / 4),
randint(img.height / 4, img.height * 3 / 4))
for i in range(count)]
cv.Zero(img)
for p in points:
cv.Circle(img, roundxy(p), 2, cv.CV_RGB(255, 0, 0), cv.CV_FILLED,
cv.CV_AA, 0)
draw_common(points)
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])
m = cv.CreateMat(480, 640, cv.CV_8UC1)
print "ji", m
print m.rows, m.cols, m.type, m.step
def compute_ref(self):
'''Compute a reference histogram that matched regions should approximate'''
(image, polygon) = self.get_ref()
(width, height) = cv.GetSize(image)
# (rows, cols,...)
dest = cv.CreateMat(height, width, cv.CV_8UC3)
mask8x1 = cv.CreateImage(cv.GetSize(image), 8, 1)
cv.Zero(mask8x1)
cv.FillConvexPoly(mask8x1, polygon, cv.ScalarAll(255))
cv.Copy(image, dest)
self.ref_hist = hs_histogram(dest, mask8x1)
def minarea_array(img, count):
pointMat = cv.CreateMat(count, 1, cv.CV_32SC2)
for i in range(count):
pointMat[i, 0] = (randint(img.width / 4, img.width * 3 / 4),
randint(img.height / 4, img.height * 3 / 4))
cv.Zero(img)
for i in range(count):
cv.Circle(img, roundxy(pointMat[i, 0]), 2, cv.CV_RGB(255, 0, 0),
cv.CV_FILLED, cv.CV_AA, 0)
draw_common(pointMat)
def undistort(self, img):
# intrinsic parameters
fx = self.C[0, 0]
fy = self.C[1, 1]
cx = self.C[0, 2]
cy = self.C[1, 2]
# radial distortion coefficients
k1, k2 = self.dist[0:2]
# †angential distortion coefficients
p1, p2 = self.dist[2:4]
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)
src = cv.fromarray(img)
dst = img.copy()
dst = cv.fromarray(dst)
#dst = cv.CreateImage(cv.GetSize(src), src.type, src.channels)
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)
return np.array(dst)
def draw_contour(self, contour, color=None):
gray_img = cv.CreateImage(cv.GetSize(self.image), 8, 1)
#cv.CvtColor( self.image, gray_img, cv.CV_BGR2GRAY )
cv.Zero(gray_img)
# DrawContours(img, contour, external_color, hole_color, max_level [, thickness [, lineType [, offset]]]) -> None
# in my small example external didn't get value but internal did
# void cvPolyLine(CvArr* img,
# CvPoint** pts, int* npts,
# int contours, int is_closed, CvScalar color,
# int thickness=1, int lineType=8, int shift=0)
if color is None:
# White for default black background
#color = cv.CV_RGB(255, 255, 255)
color = cv.ScalarAll(255)
cv.PolyLine(gray_img, [contour], True, color)
cv.ShowImage("Contours", gray_img)
cv.WaitKey()
def Process(image, pos_var, pos_w, pos_phase, pos_psi):
global kernel_size
if kernel_size % 2 == 0:
kernel_size += 1
kernel = cv.CreateMat(kernel_size, kernel_size, cv.CV_32FC1)
# kernelimg = cv.CreateImage((kernel_size,kernel_size),cv.IPL_DEPTH_32F,1)
# big_kernelimg = cv.CreateImage((kernel_size*20,kernel_size*20),cv.IPL_DEPTH_32F,1)
src = cv.CreateImage((image.width, image.height), cv.IPL_DEPTH_8U, 1)
src_f = cv.CreateImage((image.width, image.height), cv.IPL_DEPTH_32F, 1)
# src = image #cv.CvtColor(image,src,cv.CV_BGR2GRAY) #no conversion is needed
if cv.GetElemType(image) == cv.CV_8UC3:
cv.CvtColor(image, src, cv.CV_BGR2GRAY)
else:
src = image
cv.ConvertScale(src, src_f, 1.0 / 255, 0)
dest = cv.CloneImage(src_f)
dest_mag = cv.CloneImage(src_f)
var = pos_var / 10.0
w = pos_w / 10.0
phase = pos_phase * cv.CV_PI / 180.0
psi = cv.CV_PI * pos_psi / 180.0
cv.Zero(kernel)
for x in range(-kernel_size / 2 + 1, kernel_size / 2 + 1):
for y in range(-kernel_size / 2 + 1, kernel_size / 2 + 1):
kernel_val = math.exp(-(
(x * x) +
(y * y)) / (2 * var)) * math.cos(w * x * math.cos(phase) +
w * y * math.sin(phase) + psi)
cv.Set2D(kernel, y + kernel_size / 2, x + kernel_size / 2,
cv.Scalar(kernel_val))
# cv.Set2D(kernelimg,y+kernel_size/2,x+kernel_size/2,cv.Scalar(kernel_val/2+0.5))
cv.Filter2D(src_f, dest, kernel, (-1, -1))
# cv.Resize(kernelimg,big_kernelimg)
cv.Pow(dest, dest_mag, 2)
# return (dest_mag, big_kernelimg, dest)
return (dest_mag, dest)
# cv.ShowImage("Mag",dest_mag)
# cv.ShowImage("Kernel",big_kernelimg)
# cv.ShowImage("Process window",dest)
def findImage(img):
#Set up storage for images
frame_size = cv.GetSize(img)
img2 = cv.CreateImage(frame_size,8,3)
tmp = cv.CreateImage(frame_size,8,cv.CV_8U)
h = cv.CreateImage(frame_size,8,1)
#copy original image to do work on
cv.Copy(img,img2)
#altering the image a bit for smoother processing
cv.Smooth(img2,img2,cv.CV_BLUR,3)
cv.CvtColor(img2,img2,cv.CV_BGR2HSV)
#make sure temp is empty
cv.Zero(tmp)
#detection based on HSV value
#30,100,90 lower limit on pic 41,255,255 on pic
#cv.InRangeS(img2,cv.Scalar(25,100,87),cv.Scalar(50,255,255),tmp)
#Range for green plate dot in my Living room
#cv.InRangeS(img2,cv.Scalar(55,80,60),cv.Scalar(65,95,90),tmp)
#classroom
#cv.InRangeS(img2,cv.Scalar(55,80,60),cv.Scalar(70,110,70),tmp)
#Kutztowns Gym
cv.InRangeS(img2,cv.Scalar(65,100,112),cv.Scalar(85,107,143),tmp)
elmt_shape=cv.CV_SHAPE_ELLIPSE
pos = 3
element = cv.CreateStructuringElementEx(pos*2+1, pos*2+1, pos, pos, elmt_shape)
cv.Dilate(tmp,tmp,element,6)
cv.Erode(tmp,tmp,element,2)
cv.Split(tmp,h,None,None,None)
storage = cv.CreateMemStorage()
scan = sc.FindContours(h,storage)
xyImage=drawCircles(scan,img)
if xyImage != None:
return (xyImage,tmp)
else:
return None
def crack(tocrack,withContourImage=False):
#Function that intent to release all characters on the image so that the ocr can detect them
#We just apply 4 filters but with multiples rounds
resized = resizeImage(tocrack, (tocrack.width*6, tocrack.height*6))
dilateImage(resized, 4)
erodeImage(resized, 4)
thresholdImage(resized, 200, cv.CV_THRESH_BINARY)
if withContourImage: #If we want the image made only with contours
contours = getContours(resized, 5)
contourimage = cv.CreateImage(cv.GetSize(resized), 8, 3)
cv.Zero(contourimage)
cv.DrawContours(contourimage, contours, cv.Scalar(255), cv.Scalar(255), 2, cv.CV_FILLED)
contourimage = resizeImage(contourimage, cv.GetSize(tocrack))
resized = resizeImage(resized, cv.GetSize(tocrack))
return resized, contourimage
resized = resizeImage(resized, cv.GetSize(tocrack))
return resized
def test_2542670(self):
xys = [(94, 121), (94, 122), (93, 123), (92, 123), (91, 124), (91, 125), (91, 126), (92, 127), (92, 128), (92, 129), (92, 130), (92, 131), (91, 132), (90, 131), (90, 130), (90, 131), (91, 132), (92, 133), (92, 134), (93, 135), (94, 136), (94, 137), (94, 138), (95, 139), (96, 140), (96, 141), (96, 142), (96, 143), (97, 144), (97, 145), (98, 146), (99, 146), (100, 146), (101, 146), (102, 146), (103, 146), (104, 146), (105, 146), (106, 146), (107, 146), (108, 146), (109, 146), (110, 146), (111, 146), (112, 146), (113, 146), (114, 146), (115, 146), (116, 146), (117, 146), (118, 146), (119, 146), (120, 146), (121, 146), (122, 146), (123, 146), (124, 146), (125, 146), (126, 146), (126, 145), (126, 144), (126, 143), (126, 142), (126, 141), (126, 140), (127, 139), (127, 138), (127, 137), (127, 136), (127, 135), (127, 134), (127, 133), (128, 132), (129, 132), (130, 131), (131, 130), (131, 129), (131, 128), (132, 127), (133, 126), (134, 125), (134, 124), (135, 123), (136, 122), (136, 121), (135, 121), (134, 121), (133, 121), (132, 121), (131, 121), (130, 121), (129, 121), (128, 121), (127, 121), (126, 121), (125, 121), (124, 121), (123, 121), (122, 121), (121, 121), (120, 121), (119, 121), (118, 121), (117, 121), (116, 121), (115, 121), (114, 121), (113, 121), (112, 121), (111, 121), (110, 121), (109, 121), (108, 121), (107, 121), (106, 121), (105, 121), (104, 121), (103, 121), (102, 121), (101, 121), (100, 121), (99, 121), (98, 121), (97, 121), (96, 121), (95, 121)]
#xys = xys[:12] + xys[16:]
pts = cv.CreateMat(len(xys), 1, cv.CV_32SC2)
for i,(x,y) in enumerate(xys):
pts[i,0] = (x, y)
storage = cv.CreateMemStorage()
hull = cv.ConvexHull2(pts, storage)
hullp = cv.ConvexHull2(pts, storage, return_points = 1)
defects = cv.ConvexityDefects(pts, hull, storage)
vis = cv.CreateImage((1000,1000), 8, 3)
x0 = min([x for (x,y) in xys]) - 10
x1 = max([x for (x,y) in xys]) + 10
y0 = min([y for (y,y) in xys]) - 10
y1 = max([y for (y,y) in xys]) + 10
def xform(pt):
x,y = pt
return (1000 * (x - x0) / (x1 - x0),
1000 * (y - y0) / (y1 - y0))
for d in defects[:2]:
cv.Zero(vis)
# First draw the defect as a red triangle
cv.FillConvexPoly(vis, [xform(p) for p in d[:3]], cv.RGB(255,0,0))
# Draw the convex hull as a thick green line
for a,b in zip(hullp, hullp[1:]):
cv.Line(vis, xform(a), xform(b), cv.RGB(0,128,0), 3)
# Draw the original contour as a white line
for a,b in zip(xys, xys[1:]):
cv.Line(vis, xform(a), xform(b), (255,255,255))
self.snap(vis)
c = cv.WaitKey(0) % 0x100
if c == 27:
print("Exiting ...")
sys.exit(0)
elif c == ord('c'):
if (is_color):
print("Grayscale mode is set")
cv.CvtColor(color_img, gray_img, cv.CV_BGR2GRAY)
is_color = 0
else:
print("Color mode is set")
cv.Copy(im, color_img, None)
cv.Zero(mask)
is_color = 1
elif c == ord('m'):
if (is_mask):
cv.DestroyWindow("mask")
is_mask = 0
else:
cv.NamedWindow("mask", 0)
cv.Zero(mask)
cv.ShowImage("mask", mask)
is_mask = 1
elif c == ord('r'):
print("Original image is restored")
def process_image(self, slider_pos):
global cimg, source_image1, ellipse_size, maxf, maxs, eoc, lastcx, lastcy, lastr
"""
This function finds contours, draws them and their approximation by ellipses.
"""
stor = cv.CreateMemStorage()
# Create the destination images
cimg = cv.CloneImage(self.source_image)
cv.Zero(cimg)
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))
maxf = 0
maxs = 0
size1 = 0
for c in contour_iterator(cont):
if len(c) > ellipse_size:
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))
if iter == 0:
strng = segF + '/' + 'contour1.png'
cv.SaveImage(strng, image04)
color = (255, 255, 255)
(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))
if iter == 1:
if size[0] > size[1]:
size2 = size[0]
else:
size2 = size[1]
if size2 > size1:
size1 = size2
size3 = size
# Fits ellipse to current contour.
if eoc == 0 and iter == 2:
rand_val = abs((lastr - ((size[0] + size[1]) / 2)))
if rand_val > 20 and float(max(size[0], size[1])) / float(
min(size[0], size[1])) < 1.5:
lastcx = center[0]
lastcy = center[1]
lastr = (size[0] + size[1]) / 2
if rand_val > 20 and float(max(size[0], size[1])) / float(
min(size[0], size[1])) < 1.4:
cv.Ellipse(cimg, center, size, angle, 0, 360, color, 2,
cv.CV_AA, 0)
cv.Ellipse(source_image1, center, size, angle, 0, 360,
color, 2, cv.CV_AA, 0)
elif eoc == 1 and iter == 2:
(int, cntr, rad) = cv.MinEnclosingCircle(PointArray2D32f)
cntr = (cv.Round(cntr[0]), cv.Round(cntr[1]))
rad = (cv.Round(rad))
if maxf == 0 and maxs == 0:
cv.Circle(cimg, cntr, rad, color, 1, cv.CV_AA, shift=0)
cv.Circle(source_image1,
cntr,
rad,
color,
2,
cv.CV_AA,
shift=0)
maxf = rad
elif (maxf > 0 and maxs == 0) and abs(rad - maxf) > 30:
cv.Circle(cimg, cntr, rad, color, 2, cv.CV_AA, shift=0)
cv.Circle(source_image1,
cntr,
rad,
color,
2,
cv.CV_AA,
shift=0)
maxs = len(c)
if iter == 1:
temp3 = 2 * abs(size3[1] - size3[0])
if (temp3 > 40):
eoc = 1
import cv2.cv as cv
from math import cos, sin, sqrt
import sys
if __name__ == "__main__":
A = [[1, 1], [0, 1]]
img = cv.CreateImage((500, 500), 8, 3)
kalman = cv.CreateKalman(2, 1, 0)
state = cv.CreateMat(2, 1, cv.CV_32FC1) # (phi, delta_phi)
process_noise = cv.CreateMat(2, 1, cv.CV_32FC1)
measurement = cv.CreateMat(1, 1, cv.CV_32FC1)
rng = cv.RNG(-1)
code = -1L
cv.Zero(measurement)
cv.NamedWindow("Kalman", 1)
while True:
cv.RandArr(rng, state, cv.CV_RAND_NORMAL, cv.RealScalar(0),
cv.RealScalar(0.1))
kalman.transition_matrix[0, 0] = 1
kalman.transition_matrix[0, 1] = 1
kalman.transition_matrix[1, 0] = 0
kalman.transition_matrix[1, 1] = 1
cv.SetIdentity(kalman.measurement_matrix, cv.RealScalar(1))
cv.SetIdentity(kalman.process_noise_cov, cv.RealScalar(1e-5))
cv.SetIdentity(kalman.measurement_noise_cov, cv.RealScalar(1e-1))
cv.SetIdentity(kalman.error_cov_post, cv.RealScalar(1))
img0 = cv.DecodeImage(imagefiledata, cv.CV_LOAD_IMAGE_COLOR)
print "Hot keys:"
print "\tESC - quit the program"
print "\tr - restore the original image"
print "\ti or ENTER - run inpainting algorithm"
print "\t\t(before running it, paint something on the image)"
cv.NamedWindow("image", 1)
cv.NamedWindow("inpainted image", 1)
img = cv.CloneImage(img0)
inpainted = cv.CloneImage(img0)
inpaint_mask = cv.CreateImage(cv.GetSize(img), 8, 1)
cv.Zero(inpaint_mask)
cv.Zero(inpainted)
cv.ShowImage("image", img)
cv.ShowImage("inpainted image", inpainted)
sk = Sketcher("image", [img, inpaint_mask])
while True:
c = cv.WaitKey(0) % 0x100
if c == 27 or c == ord('q'):
break
if c == ord('r'):
cv.Zero(inpaint_mask)
cv.Copy(img0, img)
cv.ShowImage("image", img)
#
# Works with OpenCV 2.3.1
cv.NamedWindow("imgOriginal", 1)
cv.NamedWindow("imgMarkers", 1)
cv.NamedWindow("imgWatershed", 1)
imgOriginal = cv.LoadImage("TouchingCircles.png")
imgMarkers = cv.CreateImage(cv.GetSize(imgOriginal), cv.IPL_DEPTH_8U, 1)
imgWatershed = cv.CreateImage(cv.GetSize(imgOriginal), cv.IPL_DEPTH_32S, 1)
cv.Zero(imgMarkers)
cv.Zero(imgWatershed)
# create markers for the background, upper circle, and lower circle
# markers are differentiated by colour
# background marker, identified by colour (80, 80, 80)
cv.Circle(imgMarkers, (60, 60), 3, (80, 80, 80), -1)
# upper circle marker, identified by colour (160, 160, 160)
cv.Circle(imgMarkers, (180, 180), 3, (160, 160, 160), -1)
# lower circle marker, identified by colour (240, 240, 240)
cv.Circle(imgMarkers, (320, 320), 3, (240, 240, 240), -1)
print "\tw - run watershed algorithm"
print "\t (before that, roughly outline several markers on the image)"
cv.NamedWindow("image", 1)
cv.NamedWindow("watershed transform", 1)
img = cv.CloneImage(img0)
img_gray = cv.CloneImage(img0)
wshed = cv.CloneImage(img0)
marker_mask = cv.CreateImage(cv.GetSize(img), 8, 1)
markers = cv.CreateImage(cv.GetSize(img), cv.IPL_DEPTH_32S, 1)
cv.CvtColor(img, marker_mask, cv.CV_BGR2GRAY)
cv.CvtColor(marker_mask, img_gray, cv.CV_GRAY2BGR)
cv.Zero(marker_mask)
cv.Zero(wshed)
cv.ShowImage("image", img)
cv.ShowImage("watershed transform", wshed)
sk = Sketcher("image", [img, marker_mask])
while True:
c = cv.WaitKey(0) % 0x100
if c == 27 or c == ord('q'):
break
if c == ord('r'):
cv.Zero(marker_mask)
cv.Copy(img0, img)
cv.ShowImage("image", img)
orig = cv.LoadImage("robin2.png")
#Convert in black and white
res = cv.CreateImage(cv.GetSize(orig), 8, 1)
cv.CvtColor(orig, res, cv.CV_BGR2GRAY)
#Operations on the image
openCloseImage(res)
dilateImage(res, 2)
erodeImage(res, 2)
smoothImage(res, 5)
thresholdImage(res, 150, cv.CV_THRESH_BINARY_INV)
#Get contours approximated
contourLow = getContours(res, 3)
#Draw them on an empty image
final = cv.CreateImage(cv.GetSize(res), 8, 1)
cv.Zero(final)
cv.DrawContours(final, contourLow, cv.Scalar(255), cv.Scalar(255), 2,
cv.CV_FILLED)
cv.ShowImage("orig", orig)
cv.ShowImage("image", res)
cv.SaveImage("modified.png", res)
cv.ShowImage("contour", final)
cv.SaveImage("contour.png", final)
cv.WaitKey(0)
if __name__ == "__main__":
motion = 0
capture = 0
if len(sys.argv)==1:
capture = cv.CreateCameraCapture(0)
elif len(sys.argv)==2 and sys.argv[1].isdigit():
capture = cv.CreateCameraCapture(int(sys.argv[1]))
elif len(sys.argv)==2:
capture = cv.CreateFileCapture(sys.argv[1])
if not capture:
print "Could not initialize capturing..."
sys.exit(-1)
cv.NamedWindow("Motion", 1)
while True:
image = cv.QueryFrame(capture)
if(image):
if(not motion):
motion = cv.CreateImage((image.width, image.height), 8, 3)
cv.Zero(motion)
#motion.origin = image.origin
update_mhi(image, motion, 30)
cv.ShowImage("Motion", motion)
if(cv.WaitKey(10) != -1):
break
else:
break
cv.DestroyWindow("Motion")
def _wipeImage(self, wipeMode, wipeConfig, level, image, imageMask, mixMat, whiteMode = False):
if((wipeMode == WipeMode.Push)):
wipeDirection = wipeConfig
if(whiteMode == True):
cv.Set(mixMat, (255,255,255))
else:
cv.SetZero(mixMat)
if(wipeDirection < 0.25):
wipePosX = int(self._internalResolutionX * level)
sourceLeft = self._internalResolutionX-wipePosX
sourceTop = 0
sourceWidth = wipePosX
sourceHeight = self._internalResolutionY
destLeft = 0
destTop = 0
elif(wipeDirection < 0.5):
wipePosX = self._internalResolutionX - int(self._internalResolutionX * level)
sourceLeft = 0
sourceTop = 0
sourceWidth = self._internalResolutionX-wipePosX
sourceHeight = self._internalResolutionY
destLeft = self._internalResolutionX-(self._internalResolutionX-wipePosX)
destTop = 0
elif(wipeDirection < 0.75):
wipePosY = int(self._internalResolutionY * level)
sourceLeft = 0
sourceTop = self._internalResolutionY-wipePosY
sourceWidth = self._internalResolutionX
sourceHeight = wipePosY
destLeft = 0
destTop = 0
else:
wipePosY = self._internalResolutionY - int(self._internalResolutionY * level)
sourceLeft = 0
sourceTop = 0
sourceWidth = self._internalResolutionX
sourceHeight = self._internalResolutionY-wipePosY
destLeft = 0
destTop = self._internalResolutionY-(self._internalResolutionY-wipePosY)
destWidth = sourceWidth
destHeight = sourceHeight
src_region = cv.GetSubRect(image, (sourceLeft, sourceTop, sourceWidth, sourceHeight))
dst_region = cv.GetSubRect(mixMat, (destLeft, destTop, destWidth, destHeight))
cv.Copy(src_region, dst_region)
cv.Zero(self._mixMixMask1)
dst_region = cv.GetSubRect(self._mixMixMask1, (destLeft, destTop, destWidth, destHeight))
cv.Set(dst_region, 255)
return mixMat, self._mixMixMask1
if(wipeMode == WipeMode.Noize):
scaleArg = wipeConfig
cv.Copy(image, mixMat)
noizeMask = getNoizeMask(level, self._internalResolutionX, self._internalResolutionY, 1.0 + (19.0 * scaleArg))
if(whiteMode == True):
cv.Set(image, (255,255,255))
else:
cv.Set(image, (0,0,0))
cv.Copy(mixMat, image, noizeMask)
return image, noizeMask
if(wipeMode == WipeMode.Zoom):
xMove, yMove = wipeConfig
xSize = int(self._internalResolutionX * level)
ySize = int(self._internalResolutionY * level)
xPos = int((self._internalResolutionX - xSize) * xMove)
yPos = int((self._internalResolutionY - ySize) * (1.0 - yMove))
if(whiteMode == True):
cv.Set(mixMat, (255,255,255))
else:
cv.SetZero(mixMat)
dst_region = cv.GetSubRect(mixMat, (xPos, yPos, xSize, ySize))
cv.Resize(image, dst_region,cv.CV_INTER_CUBIC)
cv.Copy(mixMat, image)
cv.SetZero(self._mixMixMask1)
if(imageMask == None):
dst_region = cv.GetSubRect(self._mixMixMask1, (xPos, yPos, xSize, ySize))
cv.Set(dst_region, 255)
else:
dst_region = cv.GetSubRect(self._mixMixMask1, (xPos, yPos, xSize, ySize))
cv.Resize(imageMask, dst_region,cv.CV_INTER_CUBIC)
return image, self._mixMixMask1
if(wipeMode == WipeMode.Flip):
flipRotation = wipeConfig
rotation = 1.0 - level
srcPoints = ((0.0, 0.0),(0.0,self._internalResolutionY),(self._internalResolutionX, 0.0))
destPoint1 = (0.0, 0.0)
destPoint2 = (0.0, self._internalResolutionY)
destPoint3 = (self._internalResolutionX, 0.0)
flipAngle = rotation / 4
destPoint1 = rotatePoint(flipRotation, destPoint1[0], destPoint1[1], self._halfResolutionX, self._halfResolutionY, flipAngle)
destPoint2 = rotatePoint(flipRotation, destPoint2[0], destPoint2[1], self._halfResolutionX, self._halfResolutionY, flipAngle)
destPoint3 = rotatePoint(flipRotation, destPoint3[0], destPoint3[1], self._halfResolutionX, self._halfResolutionY, flipAngle)
dstPoints = ((destPoint1[0], destPoint1[1]),(destPoint2[0], destPoint2[1]),(destPoint3[0],destPoint3[1]))
zoomMatrix = cv.CreateMat(2,3,cv.CV_32F)
# print "DEBUG pcn: trasform points source: " + str(srcPoints) + " dest: " + str(dstPoints)
cv.GetAffineTransform(srcPoints, dstPoints, zoomMatrix)
cv.WarpAffine(image, mixMat, zoomMatrix)
cv.Set(self._mixMixMask2, (255,255,255))
cv.WarpAffine(self._mixMixMask2, self._mixMixMask1, zoomMatrix)
return mixMat, self._mixMixMask1
calcMaskValue = 255 - (255* level)
cv.Set(self._mixMixMask1, (calcMaskValue, calcMaskValue, calcMaskValue))
return image, self._mixMixMask1
import cv2.cv as cv
im = cv.LoadImage("../img/lena.jpg", 3)
cv.SetImageROI(im, (50, 50, 150, 150))
cv.Zero(im)
#cv.Set(im, cv.RGB(100, 100, 100))
cv.ResetImageROI(im)
cv.ShowImage("Image", im)
cv.WaitKey(0)
def gen_sweep_plane(src_Hs, src_frames):
max_poly = None # the bound of the blended image
bldIm = None # the blended plane image
cost_info = None # An array structure to collect pixel colors of
# blended frames
nframes = len(src_frames)
o = np.array([[0], [0], [1]]) # origin = zero
# Get max polygon
for k in range(nframes):
cpoly = Rect(Point(0, 0),
Point(src_frames[k].shape[1],
src_frames[k].shape[0])).get_trans_regpoly(
src_Hs[k], 10)
if (k == 0):
max_poly = cpoly
else:
max_poly = max_poly | cpoly
#end if
#end for
(xmin, xmax, ymin, ymax) = max_poly.boundingBox(0)
max_rec = Rect(Point(xmin, ymin), Point(xmax, ymax))
#print "Max: " , max_rec, "W: ", max_rec.width(), " H: ", max_rec.height()
#alocate costs and count matrixes
cost_info = np.zeros(
[int(max_rec.height()),
int(max_rec.width()), nframes, 3],
dtype=np.int)
counts = np.zeros(
[int(max_rec.height()), int(max_rec.width())], dtype=np.int)
bldIm = cv.CreateMat(int(np.round(max_rec.height())),
int(np.round(max_rec.width())), cv.CV_32FC3)
cv.Zero(bldIm)
for k in range(nframes):
cur_H = src_Hs[k]
cur_o = np.dot(cur_H, o)
# translate the warped frame to origin from topleft corner
disp = np.array([[1, 0, (0 - cur_o[0, 0]) / cur_o[2, 0]],
[0, 1, (0 - cur_o[1, 0]) / cur_o[2, 0]], [0, 0, 1]])
o_H = np.dot(disp, cur_H)
tpoly = Rect(Point(0, 0),
Point(src_frames[k].shape[1],
src_frames[k].shape[0])).get_trans_poly(cur_H)
cpoly = Rect(Point(0, 0),
Point(src_frames[k].shape[1],
src_frames[k].shape[0])).get_trans_regpoly(
cur_H, 10)
(xmin, xmax, ymin, ymax) = cpoly.boundingBox(0)
frec = Rect(Point(xmin, ymin), Point(xmax, ymax))
mask = gen_mask(frec, tpoly)
#print "Rec: ", frec, "W: ", frec.width(), " H: ", frec.height()
if k == 0:
fwarp = cv2.warpPerspective(
src_frames[k], o_H, (int(frec.width()), int(frec.height())))
# get blended image
blend_views(bldIm, fwarp, mask, frec, max_rec)
# determine costs
collect_costs_info(cost_info, counts, fwarp, mask, frec, max_rec,
k)
else:
fwarp = cv2.warpPerspective(
src_frames[k], o_H, (int(frec.width()), int(frec.height())))
# get blended image
blend_views(bldIm, fwarp, mask, frec, max_rec)
# determine costs
collect_costs_info(cost_info, counts, fwarp, mask, frec, max_rec,
k)
#end if
#end for
bldIm = np.asarray(bldIm)
# Scale blended image to 8U
bldIm8U = scaleTo8U(bldIm, counts)
## Measure Cost
costs = None
costs = calculate_costs(cost_info, counts)
##return blended image and costs
return (bldIm8U, costs, max_rec)
moments = cv.Moments(cv.GetMat(imgYellowTresh), 1)
moment10 = cv.GetSpatialMoment(moments, 1, 0)
moment01 = cv.GetSpatialMoment(moments, 0, 1)
area = cv.GetCentralMoment(moments, 0, 0) #Get the center
lastx = posx
lasty = posy
if area == 0:
posx = 0
posy = 0
else:
posx = moment10 / area
posy = moment01 / area
if lastx > 0 and lasty > 0 and posx > 0 and posy > 0: #Mean we have received coordinates to print
#Draw the line
cv.Line(imgScribble, (int(posx), int(posy)), (int(lastx), int(lasty)),
cv.Scalar(0, 255, 255), 3, 1)
#Add the frame and the line image to see lines on the webcam frame
cv.Add(frame, imgScribble, frame)
cv.ShowImage("video", frame)
cv.ShowImage("thresh", imgYellowTresh)
c = cv.WaitKey(1)
if c == 27 or c == 1048603: #Break if user enters 'Esc'.
break
elif c == 1048690: # 'r' for reset
cv.Zero(imgScribble)
if k != cluster_count:
last = (k + 1) * sample_count / cluster_count
point_chunk = cv.GetRows(points, first, last)
cv.RandArr(rng, point_chunk, cv.CV_RAND_NORMAL,
cv.Scalar(center[0], center[1], 0, 0),
cv.Scalar(img.width * 0.1, img.height * 0.1, 0, 0))
# shuffle samples
cv.RandShuffle(points, rng)
cv.KMeans2(points, cluster_count, clusters,
(cv.CV_TERMCRIT_EPS + cv.CV_TERMCRIT_ITER, 10, 1.0))
cv.Zero(img)
for i in range(sample_count):
cluster_idx = int(clusters[i, 0])
pt = (cv.Round(points[i, 0][0]), cv.Round(points[i, 0][1]))
cv.Circle(img, pt, 2, color_tab[cluster_idx], cv.CV_FILLED,
cv.CV_AA, 0)
cv.ShowImage("clusters", img)
key = cv.WaitKey(0) % 0x100
if key in [27, ord('q'), ord('Q')]:
break
cv.DestroyWindow("clusters")