def update_brightcont(self):
# The algorithm is by Werner D. Streidt
# (http://visca.com/ffactory/archives/5-99/msg00021.html)
if self.contrast > 0:
delta = 127. * self.contrast / 100
a = 255. / (255. - delta * 2)
b = a * (self.brightness - delta)
else:
delta = -128. * self.contrast / 100
a = (256. - delta * 2) / 255.
b = a * self.brightness + delta
cv.ConvertScale(self.src_image, self.dst_image, a, b)
cv.ShowImage("image", self.dst_image)
cv.CalcArrHist([self.dst_image], self.hist)
(min_value, max_value, _, _) = cv.GetMinMaxHistValue(self.hist)
cv.Scale(self.hist.bins, self.hist.bins, float(self.hist_image.height) / max_value, 0)
cv.Set(self.hist_image, cv.ScalarAll(255))
bin_w = round(float(self.hist_image.width) / hist_size)
for i in range(hist_size):
cv.Rectangle(self.hist_image, (int(i * bin_w), self.hist_image.height),
(int((i + 1) * bin_w), self.hist_image.height - cv.Round(self.hist.bins[i])),
cv.ScalarAll(0), -1, 8, 0)
cv.ShowImage("histogram", self.hist_image)
def __init__(self, iplimage):
# Rough-n-ready but it works dammit
# alpha = cv.CreateMat(iplimage.height,iplimage.width, cv.CV_8UC1)
alpha = np.zeros((iplimage.shape[0], iplimage.shape[1]), np.uint8)
# Zieht ein schwarzes Rechteck ueber das Bild
cv2.rectangle(alpha, (0, 0), (iplimage.shape[1], iplimage.shape[0]),
cv.ScalarAll(255), -1)
rgba = np.zeros((iplimage.shape[0], iplimage.shape[1], 4), np.uint8)
#cv2.Set(rgba, (1, 2, 3, 4))
cv2.mixChannels(
[iplimage, alpha],
[rgba],
[
0,
0, # rgba[0] -> bgr[2]
1,
1, # rgba[1] -> bgr[1]
2,
2, # rgba[2] -> bgr[0]
3,
3 # rgba[3] -> alpha[0]
])
self.__imagedata = rgba.tostring()
super(IplQImage,
self).__init__(self.__imagedata, iplimage.shape[1],
iplimage.shape[0], QtGui.QImage.Format_RGB32)
def on_trackbar(edge_thresh):
cv.Threshold(gray, edge, float(edge_thresh), float(edge_thresh),
cv.CV_THRESH_BINARY)
#Distance transform
cv.DistTransform(edge, dist, cv.CV_DIST_L2, cv.CV_DIST_MASK_5)
cv.ConvertScale(dist, dist, 5000.0, 0)
cv.Pow(dist, dist, 0.5)
cv.ConvertScale(dist, dist32s, 1.0, 0.5)
cv.AndS(dist32s, cv.ScalarAll(255), dist32s, None)
cv.ConvertScale(dist32s, dist8u1, 1, 0)
cv.ConvertScale(dist32s, dist32s, -1, 0)
cv.AddS(dist32s, cv.ScalarAll(255), dist32s, None)
cv.ConvertScale(dist32s, dist8u2, 1, 0)
cv.Merge(dist8u1, dist8u2, dist8u2, None, dist8u)
cv.ShowImage(wndname, dist8u)
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 on_mouse(self, event, x, y, flags, param):
pt = (x, y)
if event == cv.CV_EVENT_LBUTTONUP or not (flags & cv.CV_EVENT_FLAG_LBUTTON):
self.prev_pt = None
elif event == cv.CV_EVENT_LBUTTONDOWN:
self.prev_pt = pt
elif event == cv.CV_EVENT_MOUSEMOVE and (flags & cv.CV_EVENT_FLAG_LBUTTON) :
if self.prev_pt:
for dst in self.dests:
cv.Line(dst, self.prev_pt, pt, cv.ScalarAll(255), 5, 8, 0)
self.prev_pt = pt
cv.ShowImage(self.windowname, img)
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 on_mouse(self, event, x, y, flags, param):
pt = (x, y)
if event == cv.CV_EVENT_LBUTTONUP or not (flags
& cv.CV_EVENT_FLAG_LBUTTON):
self.prev_pt = None
elif event == cv.CV_EVENT_LBUTTONDOWN:
self.prev_pt = pt
elif event == cv.CV_EVENT_MOUSEMOVE and (flags
& cv.CV_EVENT_FLAG_LBUTTON):
if self.prev_pt:
for im in [self.image] + self.chans:
cv.Line(im, self.prev_pt, pt, cv.ScalarAll(255), 5, 8, 0)
self.prev_pt = pt
cv.ShowImage("image", self.image)
cv.ShowImage("LSB", self.chans[0])
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 __init__(self, iplimage):
# Rough-n-ready but it works dammit
alpha = cv.CreateMat(iplimage.height, iplimage.width, cv.CV_8UC1)
cv.Rectangle(alpha, (0, 0), (iplimage.width, iplimage.height),
cv.ScalarAll(255), -1)
rgba = cv.CreateMat(iplimage.height, iplimage.width, cv.CV_8UC4)
cv.Set(rgba, (1, 2, 3, 4))
cv.MixChannels(
[iplimage, alpha],
[rgba],
[
(0, 0), # rgba[0] -> bgr[2]
(1, 1), # rgba[1] -> bgr[1]
(2, 2), # rgba[2] -> bgr[0]
(3, 3) # rgba[3] -> alpha[0]
])
self.__imagedata = rgba.tostring()
super(IplQImage, self).__init__(self.__imagedata, iplimage.width,
iplimage.height, QImage.Format_RGB32)
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)
if c == ord('w'):
storage = cv.CreateMemStorage(0)
#cv.SaveImage("wshed_mask.png", marker_mask)
#marker_mask = cv.LoadImage("wshed_mask.png", 0)
contours = cv.FindContours(marker_mask, storage, cv.CV_RETR_CCOMP,
cv.CV_CHAIN_APPROX_SIMPLE)
def contour_iterator(contour):
while contour:
yield contour
contour = contour.h_next()
cv.Zero(markers)
comp_count = 0
for c in contour_iterator(contours):
cv.DrawContours(markers, c, cv.ScalarAll(comp_count + 1),
cv.ScalarAll(comp_count + 1), -1, -1, 8)
comp_count += 1
cv.Watershed(img0, markers)
cv.Set(wshed, cv.ScalarAll(255))
# paint the watershed image
color_tab = [
(cv.RandInt(rng) % 180 + 50, cv.RandInt(rng) % 180 + 50,
cv.RandInt(rng) % 180 + 50) for i in range(comp_count)
]
for j in range(markers.height):
for i in range(markers.width):
idx = markers[j, i]
if c == ord('w'):
storage = cv.CreateMemStorage(0)
#cv.SaveImage("wshed_mask.png", marker_mask)
#marker_mask = cv.LoadImage("wshed_mask.png", 0)
contours = cv.FindContours(marker_mask, storage, cv.CV_RETR_CCOMP, cv.CV_CHAIN_APPROX_SIMPLE)
def contour_iterator(contour):
while contour:
yield contour
contour = contour.h_next()
cv.Zero(markers)
comp_count = 0
for c in contour_iterator(contours):
cv.DrawContours(markers,
c,
cv.ScalarAll(comp_count + 1),
cv.ScalarAll(comp_count + 1),
-1,
-1,
8)
comp_count += 1
cv.Watershed(img0, markers)
cv.Set(wshed, cv.ScalarAll(255))
# paint the watershed image
color_tab = [(cv.RandInt(rng) % 180 + 50, cv.RandInt(rng) % 180 + 50, cv.RandInt(rng) % 180 + 50) for i in range(comp_count)]
for j in range(markers.height):
for i in range(markers.width):
idx = markers[j, i]
cv.DFT( dft_A, dft_A, cv.CV_DXT_FORWARD, complexInput.height )
cv.NamedWindow("win", 0)
cv.NamedWindow("magnitude", 0)
cv.ShowImage("win", im)
# Split Fourier in real and imaginary parts
cv.Split( dft_A, image_Re, image_Im, None, None )
# Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2)
cv.Pow( image_Re, image_Re, 2.0)
cv.Pow( image_Im, image_Im, 2.0)
cv.Add( image_Re, image_Im, image_Re, None)
cv.Pow( image_Re, image_Re, 0.5 )
# Compute log(1 + Mag)
cv.AddS( image_Re, cv.ScalarAll(1.0), image_Re, None ) # 1 + Mag
cv.Log( image_Re, image_Re ) # log(1 + Mag)
# Rearrange the quadrants of Fourier image so that the origin is at
# the image center
cvShiftDFT( image_Re, image_Re )
min, max, pt1, pt2 = cv.MinMaxLoc(image_Re)
cv.Scale(image_Re, image_Re, 1.0/(max-min), 1.0*(-min)/(max-min))
cv.ShowImage("magnitude", image_Re)
cv.WaitKey(0)
cv.DestroyAllWindows()
font = cv.InitFont(random.randrange(0, 8),
random.randrange(0, 100) * 0.05 + 0.01,
random.randrange(0, 100) * 0.05 + 0.01,
random.randrange(0, 5) * 0.1,
random.randrange(0, 10), line_type)
cv.PutText(image, "Testing text rendering!", pt1, font,
random_color(random))
cv.ShowImage(window_name, image)
cv.WaitKey(delay)
# prepare a text, and get it's properties
font = cv.InitFont(cv.CV_FONT_HERSHEY_COMPLEX, 3, 3, 0.0, 5, line_type)
text_size, ymin = cv.GetTextSize("OpenCV forever!", font)
pt1 = ((width - text_size[0]) / 2, (height + text_size[1]) / 2)
image2 = cv.CloneImage(image)
# now, draw some OpenCV pub ;-)
for i in range(0, 512, 2):
cv.SubS(image2, cv.ScalarAll(i), image)
(r, g, b) = colorsys.hsv_to_rgb((i % 100) / 100., 1, 1)
cv.PutText(image, "OpenCV forever!", pt1, font,
cv.RGB(255 * r, 255 * g, 255 * b))
cv.ShowImage(window_name, image)
cv.WaitKey(delay)
# wait some key to end
cv.WaitKey(0)
cv.DestroyAllWindows()
