def HoughDetect(self, img):
# img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img = cv2.medianBlur(img, 5)
img = auto.AutoCanny(img)
# img = cv2.Canny(img,self.canny_lower,self.canny_upper)
img = cv2.resize(img, (300, 300))
img_line = np.zeros((300, 300))
img_contour, contours, hierarchy = cv2.findContours(
img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
img_contour = cv2.drawContours(img_contour, contours, -1,
(255, 255, 255), 1)
cv2.imshow("contour", img_contour)
lines = cv2.HoughLines(img, 1, np.pi / 1440, 180)
if lines is not None:
for line in lines:
rho, theta = line[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(img_line, (x1, y1), (x2, y2), (255, 255, 255), 2)
result = img_contour - img_line
# result = cv2.morphologyEx(result, cv2.MORPH_ERODE, self.kernel, iterations=2)
#result = cv2.morphologyEx(result, cv2.MORPH_OPEN, self.linek, iterations=1)
return result
def HoughDetect(self, img):
h, w = img.shape[:2]
img = cv2.medianBlur(img, 5)
img = auto.AutoCanny(img)
img_line = np.zeros((h, w))
img_contour, contours, hierarchy = cv2.findContours(
img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
img_contour = cv2.drawContours(img_contour, contours, -1,
(255, 255, 255), 3)
lines = cv2.HoughLines(img, 1, np.pi / 1440, 180)
if lines is not None:
for line in lines:
rho, theta = line[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(img_line, (x1, y1), (x2, y2), (255, 255, 255), 5)
result = img_contour - img_line
result = result[90:340, 175:425]
#result = cv2.morphologyEx(result, cv2.MORPH_ERODE, self.kernel, iterations=2)
#result = cv2.morphologyEx(result, cv2.MORPH_OPEN, self.linek, iterations=1)
return result
cam = cv2.VideoCapture(0)
while (cam.isOpened()):
I1 = []
I2 = []
I3 = []
ret, img = cam.read()
img = Dist.Undistort(img)
img = img[90:340, 175:425]
h, w = img.shape[:2]
img_h = auto.AutoCanny(img)
#img_h = Hough.HoughDetect(img)
#img_h = cv2.morphologyEx(img_h, cv2.MORPH_CLOSE, kernel, iterations=1)
cv2.imshow("canny", img_h)
npic_1 = np.zeros((h, w), dtype=np.float32)
npic_2 = np.zeros((h, w), dtype=np.float32)
npic_3 = np.zeros((h, w), dtype=np.float32)
points = newlib.GooBoundary(img_h)
key = cv2.waitKey(10)
len_x, len_y, npic_1 = newlib.MakeLength(img_h, points)
grad_x = (np.gradient(len_x))
grad_y = (np.gradient(len_y))
if (key == 27):
break
#Get webcam screen
ret, img = cam.read()
#Calibrating webcam
dst = Dist.Undistort(img)
h, w = dst.shape[:2]
#Grayscale image
gray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
#Canny image
dst = auto.AutoCanny(gray)
#Crop image
dst = dst[15:415, 100:500]
#Dilate to enhance image
dst = cv2.morphologyEx(dst, cv2.MORPH_DILATE, kernel, iterations=1)
#Construct blank image
pic1 = dst.copy()
pic1[:] = 0
pic2 = dst.copy()
pic2[:] = 0
#Get border points
points = dp(dst)