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 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 affine(self):
self.img2 = cv.Mat()
M = cv.asMat(self.m, force_single_channel=True)
cv.warpAffine(self.img1,
self.img2,
M,
self.img1.size(),
borderValue=cv.CV_RGB(255, 255, 255))
def redraw(self):
M = cv.asMat(self.m, force_single_channel=True)
size = cv.Size(int(self.size[0, 0]), int(self.size[0, 1]))
img2 = cv.Mat()
if size.width > 0 and size.height > 0:
cv.warpAffine(self.img,
img2,
M,
size,
borderValue=cv.CV_RGB(255, 255, 255))
cv.imshow("Affine Demo", img2)
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
}
def draw_keypoints(self, img, keypoints, offset):
for kp in keypoints:
center = cv.Point(int(kp.pt.x) + offset, int(kp.pt.y))
cv.circle(img, center, int(kp.size * 0.25), cv.CV_RGB(255, 255, 0))
# 左键松开时，使用watershed进行图像分割
if event == cv.CV_EVENT_LBUTTONUP:
seed += 1
tmp_markers = markers.clone()
cv.watershed(img, tmp_markers)
color_map = tmp_markers[:].astype(np.int)
img3 = img2.clone()
img4 = cv.asMat(palette[color_map])
cv.addWeighted(img3, 1.0, img4, mask_opacity, 0, img3)
cv.imshow("Watershed Demo", img3)
# 区域的颜色列表
marks_color = [
cv.CV_RGB(0, 0, 0),
cv.CV_RGB(255, 0, 0),
cv.CV_RGB(0, 255, 0),
cv.CV_RGB(0, 0, 255),
cv.CV_RGB(255, 255, 0),
cv.CV_RGB(0, 255, 255),
cv.CV_RGB(255, 0, 255),
cv.CV_RGB(255, 255, 255)
]
# 将颜色列表转换为调色板数组，只取前三个通道的值
palette = np.array([c.ndarray[:-1] for c in marks_color], dtype=np.uint8)
seed = 1 # 从序号1开始设置区域颜色
mask_opacity = 0.5 # 绘制区域颜色的透明度