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),