rabssm

# Install packages needed
sudo apt install gpsd gpsd-clients pps-tools

###########################
## Add the following lines to /boot/config.txt to set the port used for the PPS h/w input (in this case GPIO pin 18)
# The next lines are for GPS PPS signals
dtoverlay=pps-gpio,gpiopin=18
###########################

rabssm

# Download correct driver from http://downloads.fars-robotics.net/wifi-drivers/8188fu-drivers/
# For example, for kernel version 4.14.79, download the following driver:
wget http://downloads.fars-robotics.net/wifi-drivers/8188fu-drivers/8188fu-4.14.79-1159.tar.gz

# Extract the driver
tar zxvf 8188fu-4.14.79-1159.tar.gz

# Install the driver
sudo ./install.sh

rabssm

# Interpolate video frames for a higher frame rate
ffmpeg -i source.mp4 -filter:v minterpolate -r 25 result.mp4
ffmpeg -i source.mp4 -vf minterpolate=50,tblend=all_mode=average,framestep=2 result.mp4

# Change the framerate (to 5 fps) of an input h264 raw video and convert to mkv
ffmpeg -r 5 -i input.h264 -vcodec copy output.mkv

# Crop a video to the bottom right quarter
ffmpeg -i in.mp4 -filter:v "crop=in_w/2:in_h/2:in_w/2:in_h/2" -c:a copy out.mp4

rabssm

# Fetch files on remotehost modified within the last 7 days
rsync -av user@remotehost:'`find /home/user/* -mtime -7 -print`' .

rabssm

#!/usr/bin/env python

# Program to calculate the sky brightness of a JPEG image of the night sky
# Assumptions:
# The EXIF data for the image contains the following valid tags:
#   ISOSpeedRatings, ApertureValue, ExposureTime

import numpy as np
import argparse
import PIL.Image

rabssm

import cv2

# Read in the image
image_file = 'original.jpg'
image = cv2.imread(image_file)
overlay = image.copy()
output = image.copy()
height, width = image.shape[:2]

# Stamp the image lower right corner and write it

rabssm

# Generate a spiral pattern, moving outwards from the centre position (0,0)
# The size parameter sets the size of the spiral "shell"
SIZE = 5

x, y = 0, 0
dx, dy = 0, -1

for i in range(SIZE**2):
    if (x == y) or (x < 0 and x == -y) or (x > 0 and x == 1-y):
        dx, dy = -dy, dx

rabssm

# Separate an image into RGB channels. Filenames 1, 2, 3 for red, green and blue respectively
convert image.jpg  -channel RGB  -separate  channels_%d.png

# Convert an image to grayscale
convert image.jpg -set colorspace Gray -separate -average image.png

# Threshold an image
convert -threshold 75 image.jpg image-thresh.jpg

# Combine images using max pixel values of each image

rabssm

import picamera
import picamera.array
import time, datetime

# Use the motiondetector analyze callback to annotate the video with a UTC time stamp
class MotionDetector(picamera.array.PiMotionAnalysis):
    def analyze(self, a):
        camera.annotate_text = datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]

with picamera.PiCamera() as camera:

rabssm

# Find a point that is mutually closest to two or more 3d lines in a least-squares sense.
# Based on: https://en.wikipedia.org/wiki/Line%E2%80%93line_intersection#In_three_dimensions_2
import numpy as np

# Create two or more 3d lines in the form of a matrix [[x1,y1,z1], [x2,y2,z2]]
lines = []
lines.append(np.asmatrix([[ 4, 0,0], [1,1,4]]))
lines.append(np.asmatrix([[-3,-2,0], [4,2,3]]))
lines.append(np.asmatrix([[ 0, 0,0], [2,2,6]]))
lines.append(np.asmatrix([[ 1, 2,0], [2,0,6]]))