def apply_thresholing(self, image):
parameters = self._thresh_params
image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
# image_gray = clahe.apply(image_gray)
gradx_thresh = (parameters['sobel_x_thresh_min'], parameters['sobel_x_thresh_max'])
grady_thresh = (parameters['sobel_y_thresh_min'], parameters['sobel_y_thresh_max'])
magnitude_thresh = (parameters['mag_thresh_min'], parameters['mag_thresh_max'])
dir_thresh = (parameters['dir_thresh_min'], parameters['dir_thresh_max'])
ksize = parameters['sobel_kernel']
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(image_gray, orient='x', sobel_kernel=ksize, thresh=gradx_thresh)
grady = abs_sobel_thresh(image_gray, orient='y', sobel_kernel=ksize, thresh=grady_thresh)
mag_binary = mag_thresh(image_gray, sobel_kernel=ksize, thresh=magnitude_thresh)
dir_binary = dir_threshold(image_gray, sobel_kernel=ksize, thresh=dir_thresh)
gradient_threshold = np.zeros_like(dir_binary)
gradient_threshold[((gradx == 1) & (grady == 1)) | ((dir_binary == 1) & (mag_binary == 1))] = 1
color_threshold = create_binary_image(image)
binary = np.zeros_like(gradient_threshold)
binary[(color_threshold == 1) | (gradient_threshold == 1)] = 1
# binary[(gradient_threshold == 1)] = 1
return binary
def thresholding(img):
#print(img.shape)
#setting all sorts of thresholds
#cv2.imshow("orig", img)
x_thresh = utils.abs_sobel_thresh(img,
orient='x',
thresh_min=10,
thresh_max=230)
#cv2.imshow("x_thresh",x_thresh*255)
mag_thresh = utils.mag_thresh(img, sobel_kernel=3, mag_thresh=(30, 150))
#cv2.imshow("mag_thresh",mag_thresh*255)
dir_thresh = utils.dir_threshold(img, sobel_kernel=3, thresh=(0.7, 1.3))
#cv2.imshow("dir_thresh",dir_thresh*255)
hls_thresh = utils.hls_select(img, thresh=(180, 255))
#cv2.imshow("hls_thresh",hls_thresh*255)
lab_thresh = utils.lab_select(img, thresh=(155, 200))
#cv2.imshow("lab_thresh",lab_thresh*255)
luv_thresh = utils.luv_select(img, thresh=(225, 255))
#cv2.imshow("luv_thresh",luv_thresh)
#Thresholding combination
threshholded = np.zeros_like(x_thresh)
threshholded[((x_thresh == 1) & (mag_thresh == 1)) | ((dir_thresh == 1) &
(hls_thresh == 1)) |
(lab_thresh == 1) | (luv_thresh == 1)] = 1
#cv2.imshow("threshholded", threshholded*255)
#cv2.waitKey(0)
return threshholded
def thresholding(img):
# x_thresh = utils.abs_sobel_thresh(img, orient='x', thresh_min=55, thresh_max=100)
# mag_thresh = utils.mag_thresh(img, sobel_kernel=3, mag_thresh=(70, 255))
# dir_thresh = utils.dir_threshold(img, sobel_kernel=3, thresh=(0.7, 1.3))
# s_thresh = utils.hls_select(img,channel='s',thresh=(160, 255))
# s_thresh_2 = utils.hls_select(img,channel='s',thresh=(200, 240))
#
# white_mask = utils.select_white(img)
# yellow_mask = utils.select_yellow(img)
x_thresh = utils.abs_sobel_thresh(img,
orient='x',
thresh_min=10,
thresh_max=230)
mag_thresh = utils.mag_thresh(img, sobel_kernel=3, mag_thresh=(30, 150))
dir_thresh = utils.dir_threshold(img, sobel_kernel=3, thresh=(0.7, 1.3))
hls_thresh = utils.hls_select(img, thresh=(180, 255))
lab_thresh = utils.lab_select(img, thresh=(155, 200))
luv_thresh = utils.luv_select(img, thresh=(225, 255))
#Thresholding combination
threshholded = np.zeros_like(x_thresh)
threshholded[((x_thresh == 1) & (mag_thresh == 1)) | ((dir_thresh == 1) &
(hls_thresh == 1)) |
(lab_thresh == 1) | (luv_thresh == 1)] = 1
# threshholded = np.zeros_like(x_thresh)
# threshholded[((x_thresh == 1)) | ((mag_thresh == 1) & (dir_thresh == 1))| (white_mask>0)|(s_thresh == 1) ]=1
return threshholded
def process_video(self):
test_image = self.input_image
sobel_kernel = self.sobel_kernel.value
sobel_x_thresh_min = self.sobel_x_thresh_min.value
sobel_x_thresh_max = self.sobel_x_thresh_max.value
sobel_y_thresh_min = self.sobel_y_thresh_min.value
sobel_y_thresh_max = self.sobel_y_thresh_max.value
mag_kernel = self.mag_kernel.value
mag_thresh_min = self.mag_thresh_min.value
mag_thresh_max = self.mag_thresh_max.value
dir_kernel = self.dir_kernel.value
dir_thresh_min = self.dir_thresh_min.value / 10
dir_thresh_max = self.dir_thresh_max.value / 10
sat_thresh_min = self.sat_thresh_min.value
sat_thresh_max = self.sat_thresh_max.value
# gradient_threshold = threshold_image(test_image, ksize=sobel_kernel,
# gradx_thresh=(sobel_x_thresh_min, sobel_x_thresh_max),
# grady_thresh=(sobel_y_thresh_min, sobel_y_thresh_max),
# magnitude_thresh=(mag_thresh_min, mag_thresh_max),
# dir_thresh=(dir_thresh_min, dir_thresh_max))
ksize = sobel_kernel
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(test_image, orient='x', sobel_kernel=ksize, thresh=(sobel_x_thresh_min, sobel_x_thresh_max))
grady = abs_sobel_thresh(test_image, orient='y', sobel_kernel=ksize, thresh=(sobel_y_thresh_min, sobel_y_thresh_max))
mag_binary = mag_thresh(test_image, sobel_kernel=ksize, thresh=(mag_thresh_min, mag_thresh_max))
dir_binary = dir_threshold(test_image, sobel_kernel=ksize, thresh=(dir_thresh_min, dir_thresh_max))
gradient_threshold = np.zeros_like(dir_binary)
# gradient_threshold[(gradx == 1)] = 1
gradient_threshold[((gradx == 1) & (grady == 1)) | ((dir_binary == 1) & (mag_binary == 1))] = 1
image_hsl = cv2.cvtColor(test_image, cv2.COLOR_RGB2HLS)
s_channel = image_hsl[:, :, 2]
color_threshold = np.zeros_like(s_channel)
color_threshold[(s_channel >= sat_thresh_min) & (s_channel <= sat_thresh_max)] = 1
color_threshold = create_binary_image(test_image)
binary = np.zeros_like(gradient_threshold)
binary[(color_threshold == 1) | (gradient_threshold == 1)] = 1
# binary = np.zeros_like(gradient_threshold)
# binary[(color_threshold == 1) | (gradient_threshold == 1)] = 1
# binary[(gradient_threshold == 1)] = 1
self._write_image(binary)
self._binary = cv2.imread('out.jpg', 0)
print('Created image')
self._image.value = self._binary
self._image.repaint()
def thresholding(img):
x_thresh = utils.abs_sobel_thresh(img,
orient='x',
thresh_min=10,
thresh_max=230)
mag_thresh = utils.mag_thresh(img, sobel_kernel=3, mag_thresh=(30, 150))
dir_thresh = utils.dir_threshold(img, sobel_kernel=3, thresh=(0.7, 1.3))
hls_thresh = utils.hls_select(img, thresh=(180, 255))
lab_thresh = utils.lab_select(img, thresh=(155, 200))
luv_thresh = utils.luv_select(img, thresh=(225, 255))
#Thresholding combination
threshholded = np.zeros_like(x_thresh)
threshholded[((x_thresh == 1) & (mag_thresh == 1)) | ((dir_thresh == 1) &
(hls_thresh == 1)) |
(lab_thresh == 1) | (luv_thresh == 1)] = 1
return threshholded
undistorted.append(img)
trans_on_test=[]
for img in undistorted:
src = np.float32([[(203, 720), (585, 460), (695, 460), (1127, 720)]])
dst = np.float32([[(320, 720), (320, 0), (960, 0), (960, 720)]])
M = cv2.getPerspectiveTransform(src, dst)
trans = cv2.warpPerspective(img, M, img.shape[1::-1], flags=cv2.INTER_LINEAR)
trans_on_test.append(trans)
thresh = []
binary_wrapeds = []
histogram = []
for img in undistorted:
x_thresh = utils.abs_sobel_thresh(img, orient='x', thresh_min=55, thresh_max=100)
mag_thresh = utils.mag_thresh(img, sobel_kernel=3, mag_thresh=(70, 255))
dir_thresh = utils.dir_threshold(img, sobel_kernel=3, thresh=(0.7, 1.3))
s_thresh = utils.hls_select(img,channel='s',thresh=(160, 255))
s_thresh_2 = utils.hls_select(img,channel='s',thresh=(200, 240))
white_mask = utils.select_white(img)
yellow_mask = utils.select_yellow(img)
combined = np.zeros_like(mag_thresh)
# combined[(x_thresh==1) | ((mag_thresh == 1) & (dir_thresh == 1)) | (s_thresh==1)] = 1
# combined[((mag_thresh == 1) & (dir_thresh == 1))] = 1
combined[((x_thresh == 1) | (s_thresh == 1)) | ((mag_thresh == 1) & (dir_thresh == 1))| (white_mask>0)|(s_thresh_2 == 1) ]=1
src = np.float32([[(203, 720), (585, 460), (695, 460), (1127, 720)]])
dst = np.float32([[(320, 720), (320, 0), (960, 0), (960, 720)]])
M = cv2.getPerspectiveTransform(src, dst)
plt.figure(figsize=(20, 40))
for i in range(0, (len(undistorted_test_images))):
plt.subplot(len(undistorted_test_images), 2, 2 * i + 1)
plt.title('original image')
plt.imshow(test_imgs2[i])
plt.subplot(len(undistorted_test_images), 2, 2 * i + 2)
plt.title('after x_thresh_method')
plt.imshow(x_thresh_method[i], cmap='gray')
plt.savefig('x_thresh_method.png')
# In[12]:
#mag_thresholding methdods
mag_thresh_method = []
for img in test_imgs2:
mag_thresh = utils.mag_thresh(img, sobel_kernel=9, mag_thresh=(50, 100))
mag_thresh_method.append(mag_thresh)
# In[13]:
plt.figure(figsize=(20, 40))
for i in range(0, (len(undistorted_test_images))):
plt.subplot(len(undistorted_test_images), 2, 2 * i + 1)
plt.title('original image')
plt.imshow(test_imgs2[i])
plt.subplot(len(undistorted_test_images), 2, 2 * i + 2)
plt.title('after mag_thresh_method')
plt.imshow(mag_thresh_method[i], cmap='gray')
plt.savefig('mag_thresh_method.png')
# In[14]: