def redraw(self):
edge_img = cv.Mat()
# 边缘检测
cv.Canny(self.img_gray, edge_img, self.th1, self.th2)
3 ###
# 计算结果图
if self.show_canny:
show_img = cv.Mat()
cv.cvtColor(edge_img, show_img, cv.CV_GRAY2BGR)
else:
show_img = self.img.clone()
4 ###
# 线段检测
theta = self.theta / 180.0 * np.pi
lines = cv.HoughLinesP(edge_img, self.rho, theta, self.hough_th,
self.minlen, self.maxgap)
for line in lines:
cv.line(show_img, cv.asPoint(line[:2]), cv.asPoint(line[2:]),
cv.CV_RGB(255, 0, 0), 2)
5 ###
# 圆形检测
circles = cv.HoughCircles(self.img_smooth,
3,
self.dp,
self.mindist,
param1=self.param1,
param2=self.param2)
for circle in circles:
cv.circle(show_img, cv.Point(int(circle[0]), int(circle[1])),
int(circle[2]), cv.CV_RGB(0, 255, 0), 2)
cv.imshow("Hough Demo", show_img)
def make_grid_img(self):
img = self.img.clone()
for i in xrange(0, self.w, 30):
cv.line(img, cv.Point(i, 0), cv.Point(i, self.h), cv.CV_RGB(0, 0, 0), 1)
for i in xrange(0, self.h, 30):
cv.line(img, cv.Point(0, i), cv.Point(self.w, i), cv.CV_RGB(0, 0, 0), 1)
return img
def draw_lines(src, lines, color=(255, 0, 0), thickness=3):
if lines:
for p1, p2 in lines:
p1 = new_point(*p1)
p2 = new_point(*p2)
cv.line(src, p1, p2,
convert_color(color), thickness, 8)
def redraw(self):
# 同时显示两幅图像
w = self.img1.size().width
h = self.img1.size().height
show_img = cv.Mat(cv.Size(w * 2, h), cv.CV_8UC3)
for i in range(3):
show_img[:, :w, i] = self.img1[:]
show_img[:, w:, i] = self.img2[:]
# 绘制特征线条
if self.draw_circle:
self.draw_keypoints(show_img, self.keypoints1, 0)
self.draw_keypoints(show_img, self.keypoints2, w)
# 绘制直线连接距离小于阈值的两个特征点
for idx1 in np.where(self.mindist < self.max_distance)[0]:
idx2 = self.idx_mindist[idx1]
pos1 = self.keypoints1[int(idx1)].pt
pos2 = self.keypoints2[int(idx2)].pt
p1 = cv.Point(int(pos1.x), int(pos1.y))
p2 = cv.Point(int(pos2.x) + w, int(pos2.y))
cv.line(show_img, p1, p2, cv.CV_RGB(0, 255, 255), lineType=16)
cv.imshow("SURF Demo", show_img)
def redraw(self):
# 同时显示两幅图像
w = self.img1.size().width
h = self.img1.size().height
show_img = cv.Mat(cv.Size(w*2, h), cv.CV_8UC3)
for i in xrange(3):
show_img[:,:w,i] = self.img1[:]
show_img[:,w:,i] = self.img2[:]
# 绘制特征线条
if self.draw_circle:
self.draw_keypoints(show_img, self.keypoints1, 0)
self.draw_keypoints(show_img, self.keypoints2, w)
# 绘制直线连接距离小于阈值的两个特征点
for idx1 in np.where(self.mindist < self.max_distance)[0]:
idx2 = self.idx_mindist[idx1]
pos1 = self.keypoints1[int(idx1)].pt
pos2 = self.keypoints2[int(idx2)].pt
p1 = cv.Point(int(pos1.x), int(pos1.y))
p2 = cv.Point(int(pos2.x)+w, int(pos2.y))
cv.line(show_img, p1, p2, cv.CV_RGB(0,255,255), lineType=16)
cv.imshow("SURF Demo",show_img)
def redraw(self):
edge_img = cv.Mat()
# 边缘检测
cv.Canny(self.img_gray, edge_img, self.th1, self.th2)
3###
# 计算结果图
if self.show_canny:
show_img = cv.Mat()
cv.cvtColor(edge_img, show_img, cv.CV_GRAY2BGR)
else:
show_img = self.img.clone()
4###
# 线段检测
theta = self.theta / 180.0 * np.pi
lines = cv.HoughLinesP(edge_img,
self.rho, theta, self.hough_th, self.minlen, self.maxgap)
for line in lines:
cv.line(show_img,
cv.asPoint(line[:2]),
cv.asPoint(line[2:]),
cv.CV_RGB(255, 0, 0), 2)
5###
# 圆形检测
circles = cv.HoughCircles(self.img_smooth, 3,
self.dp, self.mindist, param1=self.param1, param2=self.param2)
for circle in circles:
cv.circle(show_img,
cv.Point(int(circle[0]), int(circle[1])), int(circle[2]),
cv.CV_RGB(0, 255, 0), 2)
cv.imshow("Hough Demo", show_img)
def make_grid_img(self):
img = self.img.clone()
for i in range(0, self.w, 30):
cv.line(img, cv.Point(i, 0), cv.Point(i, self.h),
cv.CV_RGB(0, 0, 0), 1)
for i in range(0, self.h, 30):
cv.line(img, cv.Point(0, i), cv.Point(self.w, i),
cv.CV_RGB(0, 0, 0), 1)
return img
def find_lines(src, t1, minlen=100):
# dst = canny(src, t1, t2)
dst = threshold(src, t1, invert=True)
lines = cv.HoughLinesP(dst, 1, cv.CV_PI / 180, 5, minlen, 20)
# print lines
dst = colorspace(dst)
for l in lines:
cv.line(dst, new_point(int(l[0]), int(l[1])),
new_point(int(l[2]), int(l[3])), convert_color((255, 0, 0)),
3, 8)
return dst, lines
def process(self, input_images, connected_outs):
if len(input_images) == 0:
return FAIL
src = input_images['Input']
dist_res = int( self.getParamContent('Distance resolution') )
angle_res = int( self.getParamContent('Angle resolution (degrees)') )
acc_thresh = int( self.getParamContent('Accumulator threshold') )
min_length = int( self.getParamContent('Minimum length') )
max_gap = int( self.getParamContent('Maximum gap') )
choice = self.getParamContent("Type of Hough transform")
if src.ndim > 2:
print "In '%s': The hough transform takes a binary image (or 8-bit) as input." %self.name
return FAIL
color_dst = numpy.empty( (src.shape[0], src.shape[1], 3),dtype='uint8' )
pycv.cvtColor( pycv.asMat(src), pycv.asMat(color_dst), pycv.CV_GRAY2BGR )
if choice == "Standard":
lines = pycv.HoughLines( pycv.asMat(src), dist_res, pycv.CV_PI/angle_res, acc_thresh )
margin = 0.04
n=8
pi = math.pi
h,w = src.shape[0:2]
for i in range(min(len(lines), int(self.getParamContent("draw # lines")))):
l = lines[i]
rho = l[0]
theta = l[1]
if theta > 3*pi/4: theta-=pi
if abs(rho)<w/n and abs(theta)<margin: pass
elif abs(rho)>w-w/n and abs(theta)<margin: pass
elif abs(rho)<h/n and abs(theta-pi/2)<margin: pass
elif abs(rho)>h-h/n and abs(theta-pi/2)<margin: pass
else:
continue
a = math.cos(theta)
b = math.sin(theta)
x0 = a*rho
y0 = b*rho
pt1 = pycv.Point( int(round(x0 + 2000*(-b))), int(round(y0 + 2000*(a))) )
pt2 = pycv.Point( int(round(x0 - 2000*(-b))), int(round(y0 - 2000*(a))) )
pycv.line( pycv.asMat(color_dst), pt1, pt2, pycv.CV_RGB(random.randint(0,255),
random.randint(0,255),
random.randint(0,255)), 2, 8 )
else:
lines = pycv.HoughLinesP( pycv.asMat(src), dist_res,
pycv.CV_PI/angle_res, acc_thresh, min_length, max_gap )
for l in lines:
pycv.line( pycv.asMat(color_dst), pycv.Point(int(l[0]), int(l[1])),
pycv.Point(int(l[2]), int(l[3])),
pycv.CV_RGB(*getRandColor()), 2, 8 )
self.lines = [(item[0],item[1]) for item in lines]
return {self.output_names[0] : color_dst, self.output_names[1]:self.lines}
w, h = img.size().width, img.size().height
def blend(img, img2):
"""
混合两幅图像, 其中img2有4个通道
"""
#使用alpha通道计算img2的混和值
b = img2[:,:,3:] / 255.0
a = 1 - b # img的混合值
#混合两幅图像
img[:,:,:3] *= a
img[:,:,:3] += b * img2[:,:,:3]
img2[:] = 0
for i in xrange(0, w, w/10):
cv.line(img2, cv.Point(i,0), cv.Point(i, h),
cv.Scalar(0, 0, 255, i*255/w), 5)
blend(img, img2)
img2[:] = 0
for i in xrange(0, h, h/10):
cv.line(img2, cv.Point(0,i), cv.Point(w, i),
cv.Scalar(0, 255, 0, i*255/h), 5)
blend(img, img2)
cv.namedWindow("Draw Demo")
cv.imshow("Draw Demo", img)
cv.waitKey(0)
def process(self, input_images, connected_outs):
if len(input_images) == 0:
return FAIL
src = input_images['Input']
dist_res = int(self.getParamContent('Distance resolution'))
angle_res = int(self.getParamContent('Angle resolution (degrees)'))
acc_thresh = int(self.getParamContent('Accumulator threshold'))
min_length = int(self.getParamContent('Minimum length'))
max_gap = int(self.getParamContent('Maximum gap'))
choice = self.getParamContent("Type of Hough transform")
if src.ndim > 2:
print "In '%s': The hough transform takes a binary image (or 8-bit) as input." % self.name
return FAIL
color_dst = numpy.empty((src.shape[0], src.shape[1], 3), dtype='uint8')
pycv.cvtColor(pycv.asMat(src), pycv.asMat(color_dst), pycv.CV_GRAY2BGR)
if choice == "Standard":
lines = pycv.HoughLines(pycv.asMat(src), dist_res,
pycv.CV_PI / angle_res, acc_thresh)
margin = 0.04
n = 8
pi = math.pi
h, w = src.shape[0:2]
for i in range(
min(len(lines),
int(self.getParamContent("draw # lines")))):
l = lines[i]
rho = l[0]
theta = l[1]
if theta > 3 * pi / 4: theta -= pi
if abs(rho) < w / n and abs(theta) < margin: pass
elif abs(rho) > w - w / n and abs(theta) < margin: pass
elif abs(rho) < h / n and abs(theta - pi / 2) < margin: pass
elif abs(rho) > h - h / n and abs(theta - pi / 2) < margin:
pass
else:
continue
a = math.cos(theta)
b = math.sin(theta)
x0 = a * rho
y0 = b * rho
pt1 = pycv.Point(int(round(x0 + 2000 * (-b))),
int(round(y0 + 2000 * (a))))
pt2 = pycv.Point(int(round(x0 - 2000 * (-b))),
int(round(y0 - 2000 * (a))))
pycv.line(
pycv.asMat(color_dst), pt1, pt2,
pycv.CV_RGB(random.randint(0, 255), random.randint(0, 255),
random.randint(0, 255)), 2, 8)
else:
lines = pycv.HoughLinesP(pycv.asMat(src), dist_res,
pycv.CV_PI / angle_res, acc_thresh,
min_length, max_gap)
for l in lines:
pycv.line(pycv.asMat(color_dst),
pycv.Point(int(l[0]), int(l[1])),
pycv.Point(int(l[2]), int(l[3])),
pycv.CV_RGB(*getRandColor()), 2, 8)
self.lines = [(item[0], item[1]) for item in lines]
return {
self.output_names[0]: color_dst,
self.output_names[1]: self.lines
}
# create a correspond Mat
correspond = cv.Mat(image.rows + object.rows, image.cols, cv.CV_8UC1,
cv.Scalar(0))
# copy the images to correspond -- numpy way
correspond[:object.rows, :object.cols] = object[:]
correspond[object.rows:, :image.cols] = image[:]
# find pairs
ptpairs = findPairs(surf, objectKeypoints, objectDescriptors,
imageKeypoints, imageDescriptors)
for pair in ptpairs:
cv.line(correspond, cv.asPoint(pair[0]),
cv.Point(int(pair[1].x), int(pair[1].y + object.rows)),
colors[8])
# locate planar object
if locatePlanarObject(ptpairs, src_corners, dst_corners):
for i in range(4):
r1 = dst_corners[i]
r2 = dst_corners[(i + 1) % 4]
cv.line(correspond, cv.Point(r1.x, r1.y + object.rows),
cv.Point(r2.x, r2.y + object.rows), colors[8])
# show the object correspondents
cv.imshow("Object Correspond", correspond)
# draw circles
for keypt in objectKeypoints:
print("Image Descriptors: %d\n" % len(imageKeypoints))
tt = float(cv.getTickCount()) - tt
print("Extraction time = %gms\n" % (tt/(cv.getTickFrequency()*1000.)))
# create a correspond Mat
correspond = cv.Mat(image.rows+object.rows, image.cols, cv.CV_8UC1, cv.Scalar(0))
# copy the images to correspond -- numpy way
correspond[:object.rows, :object.cols] = object[:]
correspond[object.rows:, :image.cols] = image[:]
# find pairs
ptpairs = findPairs(surf, objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors)
for pair in ptpairs:
cv.line( correspond, cv.asPoint(pair[0]), cv.Point(int(pair[1].x), int(pair[1].y+object.rows)), colors[8] )
# locate planar object
if locatePlanarObject( ptpairs, src_corners, dst_corners ):
for i in range(4):
r1 = dst_corners[i]
r2 = dst_corners[(i+1)%4]
cv.line( correspond, cv.Point(r1.x, r1.y+object.rows ), cv.Point(r2.x, r2.y+object.rows ), colors[8] )
# show the object correspondents
cv.imshow("Object Correspond", correspond)
# draw circles
for keypt in objectKeypoints:
cv.circle(object_color, cv.asPoint(keypt.pt), int(keypt.size*1.2/9.*2), colors[0], 1, 8, 0)
cv.imshow("Object", object_color)