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 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=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
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)
binary_warped = cv2.warpPerspective(combined, M, img.shape[1::-1], flags=cv2.INTER_LINEAR)
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]:
#hls_thresholding methdods
hls_thresh_method = []
for img in test_imgs2:
hls_thresh = utils.hls_select(img, channel='s', thresh=(180, 255))
hls_thresh_method.append(hls_thresh)
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 hls_thresh_method')
plt.imshow(hls_thresh_method[i], cmap='gray')
plt.savefig('hls_thresh_method.png')
# In[15]:
#dir_thresholding methdods