def compare_transformations():
mtx, dist = calibrate.get_mtx_dist()
test_images_sl = glob.glob('./test_images/straight_lines*.jpg')
test_images = glob.glob('./test_images/test*.jpg')
for test_fname in test_images_sl + test_images:
img = cv2.imread(test_fname)
dst = cv2.undistort(img, mtx, dist, None, mtx)
# Choose a Sobel kernel size
ksize = 3 # Choose a larger odd number to smooth gradient measurements
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(dst,
orient='x',
sobel_kernel=ksize,
thresh=(30, 100))
grady = abs_sobel_thresh(dst,
orient='y',
sobel_kernel=ksize,
thresh=(30, 100))
mag_binary = mag_thresh(dst, sobel_kernel=ksize, mag_thresh=(50, 100))
dir_binary = dir_threshold(dst, sobel_kernel=ksize, thresh=(0.7, 1.2))
hls_binary = hls_select(dst, thresh=(120, 255))
hsv_binary = hsv_select(dst, thresh=(120, 255))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1) & (mag_binary == 1)) |
((hls_binary == 1) & (dir_binary == 1))] = 1
# Plot the result
f, ((ax1, ax2, ax3), (ax4, ax5, ax6),
(ax7, ax8, ax9)) = plt.subplots(3, 3, figsize=(36, 18))
f.tight_layout()
ax1.imshow(dst)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(gradx, cmap='gray')
ax2.set_title('Thresholded Grad. X', fontsize=50)
ax3.imshow(grady, cmap='gray')
ax3.set_title('Thresholded Grad. Y', fontsize=50)
ax4.imshow(mag_binary, cmap='gray')
ax4.set_title('Thresholded Grad. Mag.', fontsize=50)
ax5.imshow(dir_binary, cmap='gray')
ax5.set_title('Thresholded Grad. Dir.', fontsize=50)
ax6.imshow(combined, cmap='gray')
ax6.set_title('Combined', fontsize=50)
ax7.imshow(hls_binary, cmap='gray')
ax7.set_title('Thresholded HSL', fontsize=50)
ax8.imshow(hsv_binary, cmap='gray')
ax8.set_title('Thresholded HSV', fontsize=50)
ax9.imshow(hsv_binary, cmap='gray')
ax9.set_title('Thresholded Grad. Y', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.show()
def test():
mtx, dist = calibrate.get_mtx_dist()
test_images_sl = glob.glob('./test_images/straight_lines*.jpg')
test_images = glob.glob('./test_images/test*.jpg')
i = 0
for test_fname in test_images_sl + test_images:
i += 1
img = cv2.imread(test_fname)
wrp, udst = undist_warp.warp(img, mtx, dist)
lines, result = fit(wrp, udst, True, test_mode=True)
plt.imsave('./output_images/lines' + str(i) + '.png', lines)
cv2.imwrite('./output_images/result' + str(i) + '.jpg', result)
def write_color_transformed_test_images():
mtx, dist = calibrate.get_mtx_dist()
test_images_sl = glob.glob('./test_images/straight_lines*.jpg')
test_images = glob.glob('./test_images/test*.jpg')
i = 0
for test_fname in test_images_sl + test_images:
i += 1
img = cv2.imread(test_fname)
dst = cv2.undistort(img, mtx, dist, None, mtx)
cvt = combine(dst)
plt.imsave('./output_images/color_transformed' + str(i) + '.png',
cvt,
cmap=cm.gray)
def write_warped_test_images():
mtx, dist = calibrate.get_mtx_dist()
test_images_sl = glob.glob('./test_images/straight_lines*.jpg')
test_images = glob.glob('./test_images/test*.jpg')
i = 0
for test_fname in test_images_sl + test_images:
i += 1
img = cv2.imread(test_fname)
wrp, udst = warp(img, mtx, dist)
plt.imsave('./output_images/warped' + str(i) + '.png',
wrp,
cmap=cm.gray)
#cv2.imwrite('./output_images/warped' + str(i) + '.jpg', wrp)
#calibrate.write_undistorted_test_images()
#color_transform.write_color_transformed_test_images()
#write_warped_test_images()
def image_process(image):
mtx, dist = calibrate.get_mtx_dist()
wrp, udst = undist_warp.warp(image, mtx, dist)
out_img, result = fit(wrp, udst, verbose=False)
return result