Merwanski

import rosbag, sys, csv
import time
import string
import os #for file management make directory
import shutil #for file management, copy file

# verify correct input arguments: 1 or 2
if (len(sys.argv) > 2):
	print("invalid number of arguments:   " + str(len(sys.argv)))
	print("should be 2: 'bag2csv.py' and 'bagName'")

Merwanski

import cv2
import cv2.aruco as aruco

# Select type of aruco marker (size)
aruco_dict = aruco.Dictionary_get(aruco.DICT_5X5_1000)

# Create an image from the marker
# second param is the ID number
# last param is the image size
img = aruco.drawMarker(aruco_dict, 2, 700)

Merwanski

"""
Created on Mon Nov 13 16:12:42 2017
@author: merwan
"""

import numpy as np
import cv2
import time
import os
import argparse

Merwanski

from streamlit.web import cli as stcli
import streamlit as st
import rospy
from std_msgs.msg import String, Int32
import sys
import time

mynum=0
mydelta=0

Merwanski

# import necessary packages 
import numpy as np
import cv2

# path to image
path_to_image = "data/cv_image_000.jpg"
# read the image with cv2
image = cv2.imread(path_to_image)

print('Original Dimensions : ', image.shape)

Merwanski

from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np

# Load the model
model = load_model('keras_model.h5')

# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1.

Merwanski

# import necessary packages 
import tensorflow as tf
import tensorflow_datasets as tfds

# Construct a tf.data.Dataset
# Data loaded into ~/tensorflow_datasets/mnist
ds = tfds.load('mnist', split='train', shuffle_files=True)

# Build your input pipeline
ds=ds.shuffle(1024).batch(32).prefetch(tf.data.experimental.AUTOTUNE)

Merwanski

kinect_v2_ubuntu18

Kinect2 Setup Guide

https://scazlab.github.io/kinect2_setup_guide.html


Welcome to the Setup Guide for Kinect2.
System

Merwanski

import cv2
import os
import numpy as np
import glob

video_file_name = 'FILE_NAME.mp4'
path_to_images = 'PATH'

extension = "png" # png, jpg, ...

Merwanski

# import necessary packages
import numpy as np
import cv2

# path to image
path_to_image = "data/cv_image_000.jpg"
# read the image with cv2
image = cv2.imread(path_to_image)

print('Original Dimensions : ', image.shape)