def houghlinePmethod(self, edges):
minLineLength = int(self.source_color.shape[0] / 5.0)
maxLineGap = int(self.source_color.shape[0] / 2.0)
linesP = cv2.HoughLinesP(edges, 1, np.pi / 180, 95, np.array([]),
minLineLength, maxLineGap)
print "linesP are: " + str(linesP)
for x1, y1, x2, y2 in linesP[0]:
#cv2.circle(self.source_color, (x1, y1), 6, (0, 255, 255), 1)
#cv2.circle(self.source_color, (x2, y2), 6, (255, 0, 255), 1)
cv2.line(self.source_color, (x1, y1), (x2, y2), (0, 0, 255))
#cv2.imshow("lines", self.source_color)
#cv2.waitKey(0)
# Initial Intersection class
inter = Intersections()
d = []
for i in range(0, linesP.shape[1]):
for j in range(i + 1, linesP.shape[1]):
line1 = linesP[0][i]
line2 = linesP[0][j]
# check the lines as a pair to compute their intersections
if inter.acceptLinesPPair(line1, line2, np.pi / 20.0):
intersection = inter.computeintersectP(line1, line2)
print intersection
if ((intersection[0] > 0
and intersection[0] < self.source_color.shape[1]) and
(intersection[1] > 0
and intersection[1] < self.source_color.shape[0])):
#if ((intersection[0] > 0) and (intersection[1] > 0)):
inter.append(intersection[0], intersection[1])
cv2.circle(self.source_color,
(intersection[0], intersection[1]), 10,
(0, 255, 0))
#cv2.line(self.source_color,(line1[0],line1[1]),(line1[2],line1[3]),(255, 255, 0))
#cv2.line(self.source_color,(line2[0],line2[1]),(line2[2],line2[3]),(255, 255, 0))
cv2.imwrite("images/result_images/Lines_intersection.jpg",
self.source_color)
#cv2.imshow("lines and intersections", self.source_color)
#cv2.waitKey(0)
# combination of the extracted intersection with harris corner detetction
gray = cv2.cvtColor(self.source, cv2.COLOR_BGR2GRAY)
'''harris corner detector and draw red circle around them'''
self.features = cv2.goodFeaturesToTrack(gray,
1000,
0.001,
5,
None,
None,
2,
useHarrisDetector=True,
k=0.00009)
if len(self.features.shape) == 3.0:
assert (self.features.shape[1:] == (1, 2))
self.features.shape = (self.features.shape[0], 2)
for x, y in self.features:
self.corners += [(x, y)]
inter.append_features(x, y)
cv2.circle(self.source_color, (x, y), 8, (255, 255, 255))
# subpixel accuracy
'''define the criteria to stop and refine the corners'''
criteria = (cv2.TERM_CRITERIA_MAX_ITER | cv2.TERM_CRITERIA_EPS, 100,
0.03)
#criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)
cv2.cornerSubPix(gray, self.features, (5, 5), (-1, -1), criteria)
for x, y in self.features:
self.refined_corners += [(x, y)]
inter.append_refined_features(x, y)
cv2.circle(self.source_color, (x, y), 4, (0, 255, 0))
'''Now draw them'''
res = np.hstack((self.corners, self.refined_corners))
res = np.int0(res)
self.source_color[res[:, 1], res[:, 0]] = [0, 0, 255]
self.source_color[res[:, 3], res[:, 2]] = [0, 255, 0]
#cv2.imwrite(str(self.IMAGE_NAME) + '_subpixel.png', self.im_new)
cv2.imwrite("images/result_images/Features and subpixel accuracy.jpg",
self.source_color)
#cv2.imshow("Features and subpixel accuracy", self.source_color)
#cv2.waitKey(0)
return inter
def houghlinemethod(self, edges):
lines = cv2.HoughLines(edges, 0.7, np.pi / 500, 130)
for rho, theta in lines[0]:
a = np.cos(theta)
b = np.sin(theta)
x0 = a * rho
y0 = b * rho
x1 = int(x0 + 1000 * (-b))
y1 = int(y0 + 1000 * (a))
x2 = int(x0 - 1000 * (-b))
y2 = int(y0 - 1000 * (a))
cv2.line(self.source_color, (x1, y1), (x2, y2), (0, 255, 255), 1)
# Initial Intersection class
inter = Intersections()
for i in range(0, lines.shape[1]):
for j in range(i + 1, lines.shape[1]):
line1 = lines[0][i]
line2 = lines[0][j]
# check the lines as a pair to compute their intersections
if inter.acceptLinePair(line1, line2, np.pi / 32):
intersection = inter.computeintersect(line1, line2)
#print intersection
if (intersection[0] > 0 and intersection[1] > 0):
inter.append(intersection[0], intersection[1])
cv2.circle(self.source_color,
(intersection[0], intersection[1]), 6,
(0, 255, 255), 2)
# combination of the extracted intersection with harris corner detetction
gray = cv2.cvtColor(self.source, cv2.COLOR_BGR2GRAY)
'''harris corner detector and draw red circle around them'''
self.features = cv2.goodFeaturesToTrack(gray,
1000,
0.001,
5,
None,
None,
2,
useHarrisDetector=True,
k=0.00009)
if len(self.features.shape) == 3.0:
assert (self.features.shape[1:] == (1, 2))
self.features.shape = (self.features.shape[0], 2)
for x, y in self.features:
self.corners += [(x, y)]
inter.append_features(x, y)
cv2.circle(self.source_color, (x, y), 8, (255, 255, 255))
cv2.imwrite("images/result_images/Lines_intersection.jpg",
self.source_color)
#cv2.imshow("lines and intersections", self.source_color)
#cv2.waitKey(0)
# subpixel accuracy
'''define the criteria to stop and refine the corners'''
criteria = (cv2.TERM_CRITERIA_MAX_ITER | cv2.TERM_CRITERIA_EPS, 100,
0.03)
#criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)
cv2.cornerSubPix(gray, self.features, (5, 5), (-1, -1), criteria)
for x, y in self.features:
self.refined_corners += [(x, y)]
inter.append_refined_features(x, y)
cv2.circle(self.source_color, (x, y), 4, (0, 255, 0))
'''Now draw them'''
res = np.hstack((self.corners, self.refined_corners))
res = np.int0(res)
self.source_color[res[:, 1], res[:, 0]] = [0, 0, 255]
self.source_color[res[:, 3], res[:, 2]] = [0, 255, 0]
#cv2.imwrite(str(self.IMAGE_NAME) + '_subpixel.png', self.im_new)
#cv2.imshow("corners", self.source_color)
#cv2.waitKey(0)
return inter