def test_search_lanes():
# img_path = '../test_output_folder/bin_img.jpg'
img_path = '../test_images/straight_lines1.jpg'
img = cv2.imread(img_path)
preprocessor = Preprocessor()
pimg = preprocessor.preprocess_image(img)
bimg, _ = color_n_edge_threshold(pimg)
lanedetector = LaneDetector()
# search_lanes(img,self.is_first_pass=False,l=l,r=r);
# plt.subplot(121);
# plt.title("Original Image")
# plt.imshow(img[:,:,::-1]);
# # preprocessed_img = preprocess_image(img);
# plt.subplot(122);
plt.title("Visulized Image")
plt.imshow(bimg, cmap='gray')
l, r, vis_img = lanedetector.search_lanes(bimg, is_first_pass=True)
import matplotlib.pyplot as plt
import numpy as np
from slidingwindowsearch import LaneDetector
if __name__ == '__main__':
test_output_folder = '../test_output_folder/'
test_folder = '../test_images/'
if not os.path.exists(test_folder):
raise Exception("Test Folder does not exists")
preprocessor = Preprocessor()
lanedetector = LaneDetector()
for file_name in os.listdir(test_folder):
img = cv2.imread(os.path.join(test_folder, file_name))
#img = img[...,::-1]; # Converting from BGR to RGB
pre_image = preprocessor.preprocess_image(img)
#cv2.imwrite(test_output_folder+'3.jpg',cv2.cvtColor(pre_image,cv2.COLOR_RGB2BGR));
thresh_binary_img, color_binary = color_n_edge_threshold(pre_image)
left_lane_fit, right_lane_fit, vis_img = lanedetector.search_lanes(
thresh_binary_img)
img_h = img.shape[0]
ploty = np.linspace(start=0, stop=img_h, num=img_h)
left_fitx = left_lane_fit[0] * ploty**2 + left_lane_fit[
1] * ploty + left_lane_fit[2]
right_fitx = right_lane_fit[0] * ploty**2 + right_lane_fit[
1] * ploty + right_lane_fit[2]
#thresh_binary_img = preprocessor.inv_perspective_transform(thresh_binary_img);
overlay = np.uint8(
np.dstack((thresh_binary_img, np.zeros_like(thresh_binary_img),