def process_image(self, obs):
if not self.lane_detection:
if self.vae_used:
return obs
obs = skimage.color.rgb2gray(obs)
obs = skimage.transform.resize(obs, (img_rows, img_cols))
return obs
else:
obs = cv2.cvtColor(obs, cv2.COLOR_BGR2GRAY)
obs = cv2.resize(obs, (img_rows, img_cols))
edges = my_cv.detect_edges(obs,
low_threshold=50,
high_threshold=150)
rho = 0.8
theta = np.pi / 180
threshold = 25
min_line_len = 5
max_line_gap = 10
hough_lines = my_cv.hough_lines(edges, rho, theta, threshold,
min_line_len, max_line_gap)
left_lines, right_lines = my_cv.separate_lines(hough_lines)
filtered_right, filtered_left = [], []
if len(left_lines):
filtered_left = my_cv.reject_outliers(left_lines,
cutoff=(-30.0, -0.1),
lane='left')
if len(right_lines):
filtered_right = my_cv.reject_outliers(right_lines,
cutoff=(0.1, 30.0),
lane='right')
lines = []
if len(filtered_left) and len(filtered_right):
lines = np.expand_dims(np.vstack(
(np.array(filtered_left), np.array(filtered_right))),
axis=0).tolist()
elif len(filtered_left):
lines = np.expand_dims(np.expand_dims(np.array(filtered_left),
axis=0),
axis=0).tolist()
elif len(filtered_right):
lines = np.expand_dims(np.expand_dims(np.array(filtered_right),
axis=0),
axis=0).tolist()
ret_img = np.zeros((80, 80))
if len(lines):
try:
my_cv.draw_lines(ret_img, lines, thickness=1)
except:
pass
return ret_img
def process_image(self, obs, e, im_count):
# when lane_detection is false
if not agent.lane_detection:
# resize image and grayscale
obs = skimage.color.rgb2gray(obs)
obs = skimage.transform.resize(obs, (img_rows, img_cols))
# view resized output
#cv2.imshow("obs", obs)
#cv2.waitKey(0)
# save output as a jpg
#file_name = "/" + str(LANE_DETECT) + "_" + str(THROTTLE) + "_" + str(e+1) + "_" + str(im_count) + ".jpg"
##print(file_name)
#cv2.imwrite(IM_PATH+file_name, obs*255)
return obs
# with lane detection
else:
obs = cv2.cvtColor(obs, cv2.COLOR_BGR2GRAY)
obs = cv2.resize(obs, (img_rows, img_cols))
#obs = cv2.GaussianBlur(obs, (15, 15), 0)
#cv2.imshow("obs", obs)
#cv2.waitKey(0)
#file_name_a = "/" + str(LANE_DETECT) + "_" + str(THROTTLE) + "_" + str(e+1) + "_" + str(im_count) + "_a" + ".jpg"
#print(file_name)
#cv2.imwrite(IM_PATH+file_name_a, obs)
low_threshold = 50
high_threshold = 150
edges = my_cv.detect_edges(obs, low_threshold=50, high_threshold=150)
#cv2.imshow("edges", edges)
#cv2.waitKey(0)
rho = 0.8
theta = np.pi/180
threshold = 25
min_line_len = 5
max_line_gap = 10
# perform original CV processing
hough_lines = my_cv.hough_lines(edges, rho, theta, threshold, min_line_len, max_line_gap)
left_lines, right_lines = my_cv.separate_lines(hough_lines)
filtered_right, filtered_left = [],[]
if len(left_lines):
filtered_left = my_cv.reject_outliers(left_lines, cutoff=(-30.0, -0.1), lane='left')
if len(right_lines):
filtered_right = my_cv.reject_outliers(right_lines, cutoff=(0.1, 30.0), lane='right')
lines = []
if len(filtered_left) and len(filtered_right):
lines = np.expand_dims(np.vstack((np.array(filtered_left),np.array(filtered_right))),axis=0).tolist()
elif len(filtered_left):
lines = np.expand_dims(np.expand_dims(np.array(filtered_left),axis=0),axis=0).tolist()
elif len(filtered_right):
lines = np.expand_dims(np.expand_dims(np.array(filtered_right),axis=0),axis=0).tolist()
ret_img = np.zeros((80,80))
if len(lines):
try:
my_cv.draw_lines(ret_img, lines, thickness=1)
except:
pass
#cv2.imshow("ret_img", ret_img)
#cv2.waitKey(0)
#file_name_b = "/" + str(LANE_DETECT) + "_" + str(THROTTLE) + "_" + str(e+1) + "_" + str(im_count) + "_b" + ".jpg"
#print(file_name)
#cv2.imwrite(IM_PATH+file_name_b, ret_img)
return ret_img
