1. Submission Files My project includes the following files: 2. Camera Calibration I start by preparing "object points", which will be the (x, y, z) coordinates of the chessboard corners in the world. Here I am assuming the chessboard is fixed on the (x, y) plane at z=0, such tha...
This project was done as part of the Udacity self driving car nanodegree program. Video Link The image processing pipeline used involves the following techniques Guassian blur to remove high frequency information. Canny edge detection. Region of interest mask. Hough transform line detection. Dataset ...
The output of LaneNet is a collection of pixels of each lane line, and a lane line needs to be regressed based on these pixels. The traditional method is to project the picture into a top view (a bird's-eye view), and then use a 2nd or 3rd order polynomial for fitting. In this m...
Here, I'm converting the white and yellow line images from the above into gray scale for edge detection. defconvert_gray_scale(image):returncv2.cvtColor(image, cv2.COLOR_RGB2GRAY) gray_images = list(map(convert_gray_scale, white_yellow_images)) Gaussian Smoothing (Gaussian Blur) When there ...
It seems that the best cue for lane detection is to use the lane markings painted on roads and it should be noticed that among passive and active sensors, the video sensors are the best candidate for finding lane markings.The project entitled Lane Detection System was developed and aimed at ...
| |-- denseline_postprocess_config.pb.txt | `-- lane.pb.txt //选择不同的模型做检测,默认使用的是darkSCNN模型 |-- interface | |-- base_camera_perception.h | |-- base_lane_detector.h | `-- base_lane_postprocessor.h |-- lane_detection_component.cc // 组件最外层类,主要初始化工...
base_path = "D:/Personal_Task/A3_ADASProject/Traffic_Line/dataset/train_set/" targetpath = "D:/Personal_Task/A3_ADASProject/Traffic_Line/dataset/" traintxt_path = "D:/Personal_Task/A3_ADASProject/Traffic_Line/dataset/test.txt" file = open(base_path + 'test_label.json', 'r') ...
“gradient vs. intercept” space. Points in the image will correspond to lines in hough space. An intersection of lines in hough space will thus correspond to a line in Cartesian space. Using this technique, we can find lines from the pixel outputs of the canny edge detection output. A ...
t: 78%|███████▊ | 172/221 [00:02<00:00, 61.41it/s, now=None]Traceback (most recent call last): File "E:/PythonProject/udacity_selfdriving/lane_extraction/lane_detection.py", line 229, in <module> white_clip.write_videofile(white_output, audio=False) #输出视频文件 ......
卷积神经网络CNN(8)—— Pix2Pix Application -- Aerialmap Lane Line Detection (Pix2Pix应用:航拍图车道线检测),程序员大本营,技术文章内容聚合第一站。