shangliy

import numpy as np
import cv2

cap = cv2.VideoCapture('vtest.avi')

while(cap.isOpened()):
    ret, frame = cap.read()

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

shangliy

"""
This is the test script;
"""
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

import sys
sys.path.append('.')

import unittest

shangliy

def extract_features_multiple(images_list, detector, extractor, workers=8):
    """This is a function to extract features from multiple images in multithreads way"""
    feature_hash = dict()
    with ThreadPoolExecutor(max_workers=workers) as executor:
        futures = [executor.submit(extract_feature_one, x, detector, extractor) for x in images_list]
    for task in concurrent.futures.as_completed(futures):
        r = task.result()
        if r[0]:
            feature_hash[r[1]] = r[2]
    return feature_hash

shangliy

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

shangliy

Advanced Lane Finding

This project is to use advanced technologies to solve lane detection problem.

Image/Frame Processing:

The steps of this project are the following:

1. Compute the camera calibration matrix and distortion coefficients given a set of chessboard images. The images for camera calibration are stored in the folder called camera_cal. Then I save the amera calibration matrix [mtx] and distortion coefficients [dist] to a pickle file wide_dist_pickle.p