Posts Tagged Raspberry Pi

LoRa and LoRaWAN for IoT: Getting Started with LoRa and LoRaWAN Protocols for Low Power, Wide Area Networking of IoT

Posted by Gary A. Stafford in C++ Development, IoT, Python, Raspberry Pi, Software Development, Technology Consulting on August 10, 2020

Gary Stafford · LoRa for IoT: Getting Started with LoRa and LoRaWAN Protocols for Low Power WAN of IoT

Introduction

According to the LoRa Alliance, Low-Power, Wide-Area Networks (LPWAN) are projected to support a major portion of the billions of devices forecasted for the Internet of Things (IoT). LoRaWAN is designed from the bottom up to optimize LPWANs for battery lifetime, capacity, range, and cost. LoRa and LoRaWAN permit long-range connectivity for the Internet of Things (IoT) devices in different types of industries. According to Wikipedia, LoRaWAN defines the communication protocol and system architecture for the network, while the LoRa physical layer enables the long-range communication link.

LoRa

Long Range (LoRa), the low-power wide-area network (LPWAN) protocol developed by Semtech, sits at layer 1, the physical layer, of the seven-layer OSI model (Open Systems Interconnection model) of computer networking. The physical layer defines the means of transmitting raw bits over a physical data link connecting network nodes. LoRa uses license-free sub-gigahertz radio frequency (RF) bands, including 433 MHz, 868 MHz (Europe), 915 MHz (Australia and North America), and 923 MHz (Asia). LoRa enables long-range transmissions with low power consumption.

LoRaWAN

LoRaWAN is a cloud-based medium access control (MAC) sublayer (layer 2) protocol but acts mainly as a network layer (layer 3) protocol for managing communication between LPWAN gateways and end-node devices as a routing protocol, maintained by the LoRa Alliance. The MAC sublayer and the logical link control (LLC) sublayer together make up layer 2, the data link layer, of the OSI model.

LoRaWAN is often cited as having greater than a 10-km-wide coverage area in rural locations. However, according to other sources, it is generally more limited. According to the Electronic Design article, 11 Myths About LoRaWAN, a typical LoRaWAN network range depends on numerous factors—indoor or outdoor gateways, the payload of the message, the antenna used, etc. On average, in an urban environment with an outdoor gateway, you can expect up to 2- to 3-km-wide coverage, while in the rural areas it can reach beyond 5 to 7 km. LoRa’s range depends on the “radio line-of-sight.” Radio waves in the 400 MHz to 900 MHz range may pass through some obstructions, depending on their composition, but will be absorbed or reflected otherwise. This means that the signal can potentially reach as far as the horizon, as long as there are no physical barriers to block it.

In the following hands-on post, we will explore the use of the LoRa and LoRaWAN protocols to transmit and receive sensor data, over a substantial distance, between an IoT device, containing a number of embedded sensors, and an IoT gateway.

Recommended Hardware

For this post, I have used the following hardware.

IoT Device with Embedded Sensors

I have used an Arduino single-board microcontroller as an IoT sensor, actually an array of sensors. The 3.3V AI-enabled Arduino Nano 33 BLE Sense board (Amazon: USD 36.00), released in August 2019, comes with the powerful nRF52840 processor from Nordic Semiconductors, a 32-bit ARM Cortex-M4 CPU running at 64 MHz, 1MB of CPU Flash Memory, 256KB of SRAM, and a NINA-B306 stand-alone Bluetooth 5 low energy (BLE) module.

The Sense also contains an impressive array of embedded sensors:

9-axis Inertial Sensor (LSM9DS1): 3D digital linear acceleration sensor, a 3D digital
angular rate sensor, and a 3D digital magnetic sensor
Humidity and Temperature Sensor (HTS221): Capacitive digital sensor for relative humidity and temperature
Barometric Sensor (LPS22HB): MEMS nano pressure sensor: 260–1260 hectopascal (hPa) absolute digital output barometer
Microphone (MP34DT05): MEMS audio sensor omnidirectional digital microphone
Gesture, Proximity, Light Color, and Light Intensity Sensor (APDS9960): Advanced Gesture detection, Proximity detection, Digital Ambient Light Sense (ALS), and Color Sense (RGBC).

The Arduino Sense is an excellent, low-cost single-board microcontroller for learning about the collection and transmission of IoT sensor data.

IoT Gateway

An IoT Gateway, according to TechTarget, is a physical device or software program that serves as the connection point between the Cloud and controllers, sensors, and intelligent devices. All data moving to the Cloud, or vice versa goes through the gateway, which can be either a dedicated hardware appliance or software program.

I have used an a third-generation Raspberry Pi 3 Model B+ single-board computer (SBC), to serve as an IoT Gateway. This Raspberry Pi model features a 1.4GHz Cortex-A53 (ARMv8) 64-bit quad-core processor System on a Chip (SoC), 1GB LPDDR2 SDRAM, dual-band wireless LAN, Bluetooth 4.2 BLE, and Gigabit Ethernet (Amazon: USD 42.99).

To follow along with the post, you could substitute the Raspberry Pi for any Linux-based machine to run the included sample Python script.

LoRa Transceiver Modules

To transmit the IoT sensor data between the IoT device, containing the embedded sensors, and the IoT gateway, I have used the REYAX RYLR896 LoRa transceiver module (Amazon: USD 19.50 x 2). The transceiver modules are commonly referred to as a universal asynchronous receiver-transmitter (UART). A UART is a computer hardware device for asynchronous serial communication in which the data format and transmission speeds are configurable.

According to the manufacturer, REYAX, the RYLR896 contains the Semtech SX1276 long-range, low power transceiver. The RYLR896 module provides ultra-long range spread spectrum communication and high interference immunity while minimizing current consumption. This transceiver operates at both the 868 and 915 MHz frequency ranges. We will be transmitting at 915 MHz for North America, in this post. Each RYLR896 module contains a small, PCB integrated, helical antenna.

Security

The RYLR896 is capable of the AES 128-bit data encryption. Using the Advanced Encryption Standard (AES), we will encrypt the data sent from the IoT device to the IoT gateway, using a 32 hex digit password (128 bits / 4 bits/hex digit = 32 hex digits). Using hexadecimal notation, the password is limited to digits 0–9 and characters A–F.

USB to TTL Serial Converter Adapter

Optionally, to configure, test, and debug the RYLR896 LoRa transceiver module directly from your laptop, you can use a USB to TTL serial converter adapter. I currently use the IZOKEE FT232RL FTDI USB to TTL Serial Converter Adapter Module for 3.3V and 5V (Amazon: USD 9.49 for 2). The 3.3V RYLR896 module easily connects to the USB to TTL Serial Converter Adapter using the TXD/TX, RXD/RX, VDD/VCC, and GND pins. We use serial communication to send and receive data through TX (transmit) and RX (receive) pins. The wiring is shown below: VDD to VCC, GND to GND, TXD to RX, and RXD to TX.

The FT232RL has support for baud rates up to 115,200 bps, which is the speed we will use to communicate with the RYLR896 module.

Arduino Sketch

For those not familiar with Arduino, a sketch is the name that Arduino uses for a program. It is the unit of code that is uploaded into non-volatile flash memory and runs on an Arduino board. The Arduino language is a set of C and C++ functions. All standard C and C++ constructs supported by the avr-g++ compiler should work in Arduino.

For this post, the sketch, lora_iot_demo.ino, contains all the code necessary to collect and securely transmit the environmental sensor data, including temperature, relative humidity, barometric pressure, RGB color, and ambient light intensity, using the LoRaWAN protocol. All code for this post, including the sketch, can be found on GitHub.

	/*
	Description: Transmits Arduino Nano 33 BLE Sense sensor telemetry over LoRaWAN,
	including temperature, humidity, barometric pressure, and color,
	using REYAX RYLR896 transceiver modules
	http://reyax.com/wp-content/uploads/2020/01/Lora-AT-Command-RYLR40x_RYLR89x_EN.pdf
	Author: Gary Stafford
	*/

	#include <Arduino_HTS221.h>
	#include <Arduino_LPS22HB.h>
	#include <Arduino_APDS9960.h>

	const int UPDATE_FREQUENCY = 5000; // update frequency in ms
	const float CALIBRATION_FACTOR = -4.0; // temperature calibration factor (Celsius)
	const int ADDRESS = 116;
	const int NETWORK_ID = 6;
	const String PASSWORD = "92A0ECEC9000DA0DCF0CAAB0ABA2E0EF";
	const String DELIMITER = "\|";

	void setup()
	{
	Serial.begin(9600);

	Serial1.begin(115200); // default baud rate of module is 115200
	delay(1000); // wait for LoRa module to be ready

	// needs all need to be same for receiver and transmitter
	Serial1.print((String)"AT+ADDRESS=" + ADDRESS + "\r\n");
	delay(200);
	Serial1.print((String)"AT+NETWORKID=" + NETWORK_ID + "\r\n");
	delay(200);
	Serial1.print("AT+CPIN=" + PASSWORD + "\r\n");
	delay(200);
	Serial1.print("AT+CPIN?\r\n"); // confirm password is set

	if (!HTS.begin())
	{ // initialize HTS221 sensor
	Serial.println("Failed to initialize humidity temperature sensor!");
	while (1);
	}

	if (!BARO.begin())
	{ // initialize LPS22HB sensor
	Serial.println("Failed to initialize pressure sensor!");
	while (1);
	}

	// avoid bad readings to start bug
	// https://forum.arduino.cc/index.php?topic=660360.0
	BARO.readPressure();
	delay(1000);

	if (!APDS.begin())
	{ // initialize APDS9960 sensor
	Serial.println("Failed to initialize color sensor!");
	while (1);
	}
	}

	void loop()
	{
	updateReadings();
	delay(UPDATE_FREQUENCY);
	}

	void updateReadings()
	{
	float temperature = getTemperature(CALIBRATION_FACTOR);
	float humidity = getHumidity();
	float pressure = getPressure();
	int colors[4];
	getColor(colors);

	String payload = buildPayload(temperature, humidity, pressure, colors);
	// Serial.println("Payload: " + payload); // display the payload for debugging

	Serial1.print(payload); // send the payload over LoRaWAN WiFi

	displayResults(temperature, humidity, pressure, colors); // display the results for debugging
	}

	float getTemperature(float calibration)
	{
	return HTS.readTemperature() + calibration;
	}

	float getHumidity()
	{
	return HTS.readHumidity();
	}

	float getPressure()
	{
	return BARO.readPressure();
	}

	void getColor(int c[])
	{
	// check if a color reading is available
	while (!APDS.colorAvailable())
	{
	delay(5);
	}

	int r, g, b, a;
	APDS.readColor(r, g, b, a);
	c[0] = r;
	c[1] = g;
	c[2] = b;
	c[3] = a;
	}

	void displayResults(float t, float h, float p, int c[])
	{
	Serial.print("Temperature: ");
	Serial.println(t);
	Serial.print("Humidity: ");
	Serial.println(h);
	Serial.print("Pressure: ");
	Serial.println(p);
	Serial.print("Color (r, g, b, a): ");
	Serial.print(c[0]);
	Serial.print(", ");
	Serial.print(c[1]);
	Serial.print(", ");
	Serial.print(c[2]);
	Serial.print(", ");
	Serial.println(c[3]);
	Serial.println("———-");
	}

	String buildPayload(float t, float h, float p, int c[])
	{
	String readings = "";
	readings += t;
	readings += DELIMITER;
	readings += h;
	readings += DELIMITER;
	readings += p;
	readings += DELIMITER;
	readings += c[0];
	readings += DELIMITER;
	readings += c[1];
	readings += DELIMITER;
	readings += c[2];
	readings += DELIMITER;
	readings += c[3];

	String payload = "";
	payload += "AT+SEND=";
	payload += ADDRESS;
	payload += ",";
	payload += readings.length();
	payload += ",";
	payload += readings;
	payload += "\r\n";

	return payload;
	}

view raw

lora_iot_demo.ino

hosted with ❤ by GitHub

AT Commands

Communications with the RYLR896’s long-range modem is done using AT commands. AT commands are instructions used to control a modem. AT is the abbreviation of ATtention. Every command line starts with “AT”. That is why modem commands are called AT commands, according to Developer’s Home. A complete list of AT commands can be downloaded as a PDF from the RYLR896 product page.

To efficiently transmit the environmental sensor data from the IoT sensor to the IoT gateway, the sketch concatenates the sensor values together in a single string. The string will be incorporated into AT command to send the data to the RYLR896 LoRa transceiver module. To make it easier to parse the sensor data on the IoT gateway, we will delimit the sensor values with a pipe (|), as opposed to a comma. The maximum length of the payload (sensor data) is 240 bytes.

Below, we see an example of an AT command used to send the sensor data from the IoT sensor and the corresponding unencrypted data received by the IoT gateway. Both strings contain the LoRa transmitter Address ID, payload length, and the payload. The data received by the IoT gateway also contains the Received signal strength indicator (RSSI), and Signal-to-noise ratio (SNR).

Configure, Test, and Debug

As discussed earlier, to configure, test, and debug the RYLR896 LoRa transceiver modules without the use of the IoT gateway, you can use a USB to TTL serial converter adapter. The sketch is loaded on the Arduino Sense (the IoT device) and actively transmits data through one of the RYLR896 modules (shown below right). The other RYLR896 module is connected to your laptop’s USB port, via the USB to TTL serial converter adapter (shown below left). Using a terminal and the screen command, or the Arduino desktop application’s Serial Terminal, we can receive the sensor data from the Arduino Sense.

Using a terminal on your laptop, we first need to locate the correct virtual console (aka virtual terminal). On Linux or Mac, the virtual consoles are represented by device special files, such as /dev/tty1, /dev/tty2, and so forth. To find the virtual console for the USB to TTL serial converter adapter plugged into the laptop, use the following command.

ls -alh /dev/tty.*

We should see a virtual console with a name similar to /dev/tty.usbserial-.

... /dev/tty.Bluetooth-Incoming-Port
... /dev/tty.GarysBoseQC35II-SPPDev
... /dev/tty.a483e767cbac-Bluetooth-
... /dev/tty.usbserial-A50285BI

To connect to the RYLR896 module via the USB to TTL serial converter adapter, using the virtual terminal, we use the screen command and connect at a baud rate of 115,200 bps.

screen /dev/tty.usbserial-A50285BI 115200

If everything is configured and working correctly, we should see data being transmitted from the Arduino Sense and received by the local machine, at five second intervals. Each line of unencrypted data transmitted will look similar to the following, +RCV=116,25,22.18|41.57|99.74|2343|1190|543|4011,-34,47. In the example below, the AES 128-bit data encryption is not enabled on the Arduino, yet. With encryption turned on the sensor data (the payload) would appear garbled.

Even easier than the screen command, we can also use the Arduino desktop application’s Serial Terminal, as shown in the following short screen recording. Select the correct Port (virtual console) from the Tools menu and open the Serial Terminal. Since the transmitted data should be secured using AES 128-bit data encryption, we need to send an AT command (AT+CPIN) containing the transceiver module’s common password, to correctly decrypt the data on the receiving device (e.g., AT+CPIN=92A0ECEC9000DA0DCF0CAAB0ABA2E0EF).

Receiving Data on IoT Gateway

The Raspberry Pi will act as an IoT gateway, receiving the environmental sensor data from the IoT device, the Arduino. The Raspberry Pi will run a Python script, rasppi_lora_receiver.py, which will receive and decrypt the data payload, parse the sensor values, and display the values in the terminal. The script uses the pyserial, the Python Serial Port Extension. This Python module encapsulates the access for the serial port.

	import logging
	import time
	from argparse import ArgumentParser
	from datetime import datetime

	import serial
	from colr import color as colr

	# LoRaWAN IoT Sensor Demo
	# Using REYAX RYLR896 transceiver modules
	# Author: Gary Stafford
	# Requirements: python3 -m pip install –user -r requirements.txt
	# To Run: python3 ./rasppi_lora_receiver.py –tty /dev/ttyAMA0 –baud-rate 115200

	# constants
	ADDRESS = 116
	NETWORK_ID = 6
	PASSWORD = "92A0ECEC9000DA0DCF0CAAB0ABA2E0EF"


	def main():
	logging.basicConfig(filename='output.log', filemode='w', level=logging.DEBUG)
	args = get_args() # get args
	payload = ""

	print("Connecting to REYAX RYLR896 transceiver module…")
	serial_conn = serial.Serial(
	port=args.tty,
	baudrate=int(args.baud_rate),
	timeout=5,
	parity=serial.PARITY_NONE,
	stopbits=serial.STOPBITS_ONE,
	bytesize=serial.EIGHTBITS
	)

	if serial_conn.isOpen():
	set_lora_config(serial_conn)
	check_lora_config(serial_conn)

	while True:
	serial_payload = serial_conn.readline() # read data from serial port
	if len(serial_payload) > 0:
	try:
	payload = serial_payload.decode(encoding="utf-8")
	except UnicodeDecodeError: # receiving corrupt data?
	logging.error("UnicodeDecodeError: {}".format(serial_payload))

	payload = payload[:-2]
	try:
	data = parse_payload(payload)
	print("\n———-")
	print("Timestamp: {}".format(datetime.now()))
	print("Payload: {}".format(payload))
	print("Sensor Data: {}".format(data))
	display_temperature(data[0])
	display_humidity(data[1])
	display_pressure(data[2])
	display_color(data[3], data[4], data[5], data[6])
	except IndexError:
	logging.error("IndexError: {}".format(payload))
	except ValueError:
	logging.error("ValueError: {}".format(payload))

	# time.sleep(2) # transmission frequency set on IoT device


	def eight_bit_color(value):
	return int(round(value / (4097 / 255), 0))


	def celsius_to_fahrenheit(value):
	return (value * 1.8) + 32


	def display_color(r, g, b, a):
	print("12-bit Color values (r,g,b,a): {},{},{},{}".format(r, g, b, a))
	r = eight_bit_color(r)
	g = eight_bit_color(g)
	b = eight_bit_color(b)
	a = eight_bit_color(a) # ambient light intensity

	print(" 8-bit Color values (r,g,b,a): {},{},{},{}".format(r, g, b, a))
	print("RGB Color")
	print(colr("\t\t", fore=(127, 127, 127), back=(r, g, b)))
	print("Light Intensity")
	print(colr("\t\t", fore=(127, 127, 127), back=(a, a, a)))


	def display_pressure(value):
	print("Barometric Pressure: {} kPa".format(round(value, 2)))


	def display_humidity(value):
	print("Humidity: {}%".format(round(value, 2)))


	def display_temperature(value):
	temperature = celsius_to_fahrenheit(value)
	print("Temperature: {}°F".format(round(temperature, 2)))


	def get_args():
	arg_parser = ArgumentParser(description="BLE IoT Sensor Demo")
	arg_parser.add_argument("–tty", required=True, help="serial tty", default="/dev/ttyAMA0")
	arg_parser.add_argument("–baud-rate", required=True, help="serial baud rate", default=1152000)
	args = arg_parser.parse_args()
	return args


	def parse_payload(payload):
	# input: +RCV=116,29,23.94\|37.71\|99.89\|16\|38\|53\|80,-61,56
	# output: [23.94, 37.71, 99.89, 16.0, 38.0, 53.0, 80.0]

	payload = payload.split(",")
	payload = payload[2].split("\|")
	payload = [float(i) for i in payload]
	return payload


	def set_lora_config(serial_conn):
	# configures the REYAX RYLR896 transceiver module

	serial_conn.write(str.encode("AT+ADDRESS=" + str(ADDRESS) + "\r\n"))
	serial_payload = (serial_conn.readline())[:-2]
	print("Address set?", serial_payload.decode(encoding="utf-8"))

	serial_conn.write(str.encode("AT+NETWORKID=" + str(NETWORK_ID) + "\r\n"))
	serial_payload = (serial_conn.readline())[:-2]
	print("Network Id set?", serial_payload.decode(encoding="utf-8"))

	serial_conn.write(str.encode("AT+CPIN=" + PASSWORD + "\r\n"))
	time.sleep(1)
	serial_payload = (serial_conn.readline())[:-2]
	print("AES-128 password set?", serial_payload.decode(encoding="utf-8"))


	def check_lora_config(serial_conn):
	serial_conn.write(str.encode("AT?\r\n"))
	serial_payload = (serial_conn.readline())[:-2]
	print("Module responding?", serial_payload.decode(encoding="utf-8"))

	serial_conn.write(str.encode("AT+ADDRESS?\r\n"))
	serial_payload = (serial_conn.readline())[:-2]
	print("Address:", serial_payload.decode(encoding="utf-8"))

	serial_conn.write(str.encode("AT+NETWORKID?\r\n"))
	serial_payload = (serial_conn.readline())[:-2]
	print("Network id:", serial_payload.decode(encoding="utf-8"))

	serial_conn.write(str.encode("AT+IPR?\r\n"))
	serial_payload = (serial_conn.readline())[:-2]
	print("UART baud rate:", serial_payload.decode(encoding="utf-8"))

	serial_conn.write(str.encode("AT+BAND?\r\n"))
	serial_payload = (serial_conn.readline())[:-2]
	print("RF frequency", serial_payload.decode(encoding="utf-8"))

	serial_conn.write(str.encode("AT+CRFOP?\r\n"))
	serial_payload = (serial_conn.readline())[:-2]
	print("RF output power", serial_payload.decode(encoding="utf-8"))

	serial_conn.write(str.encode("AT+MODE?\r\n"))
	serial_payload = (serial_conn.readline())[:-2]
	print("Work mode", serial_payload.decode(encoding="utf-8"))

	serial_conn.write(str.encode("AT+PARAMETER?\r\n"))
	serial_payload = (serial_conn.readline())[:-2]
	print("RF parameters", serial_payload.decode(encoding="utf-8"))

	serial_conn.write(str.encode("AT+CPIN?\r\n"))
	serial_payload = (serial_conn.readline())[:-2]
	print("AES128 password of the network",
	serial_payload.decode(encoding="utf-8"))


	if __name__ == "__main__":
	main()

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rasppi_lora_receiver.py

hosted with ❤ by GitHub

Prior to running the Python script, we can test and debug the connection from the Arduino Sense to the Raspberry Pi using a general application such as Minicom. Minicom is a text-based modem control and terminal emulator program. To install Minicom on the Raspberry Pi, use the following command.

sudo apt-get install minicom

To run Minicom or the Python script, we will need to know the virtual console of the serial connection (Serial1 in the script) used to communicate with the RYLR896 module, wired to the Raspberry Pi. This can found using the following command.

dmesg | grep -E --color 'serial|tty'

Search for a line, similar to the last line, shown below. Note the name of the virtual console, in my case, ttyAMA0.

[    0.000000] Kernel command line: coherent_pool=1M bcm2708_fb.fbwidth=656 bcm2708_fb.fbheight=416 bcm2708_fb.fbswap=1 vc_mem.mem_base=0x1ec00000 vc_mem.mem_size=0x20000000  dwc_otg.lpm_enable=0 console=tty1 root=PARTUUID=509d1565-02 rootfstype=ext4 elevator=deadline fsck.repair=yes rootwait quiet splash plymouth.ignore-serial-consoles
[    0.000637] console [tty1] enabled
[    0.863147] uart-pl011 20201000.serial: cts_event_workaround enabled
[    0.863289] 20201000.serial: ttyAMA0 at MMIO 0x20201000 (irq = 81, base_baud = 0) is a PL011 rev2

To view the data received from the Arduino Sense, using Minicom, use the following command, substituting the virtual console value, found above.

minicom -b 115200 -o -D /dev/ttyAMA0

If successful, we should see output similar to the lower right terminal window. Data is being transmitted by the Arduino Sense and being received by the Raspberry Pi, via LoRaWAN. In the below example, the AES 128-bit data encryption is not enabled on the Arduino, yet. With encryption turned on the sensor data (the payload) would appear garbled.

IoT Gateway Python Script

To run the Python script on the Raspberry Pi, use the following command, substituting the name of the virtual console (e.g., /dev/ttyAMA0).

python3 ./rasppi_lora_receiver.py \
  --tty /dev/ttyAMA0 --baud-rate 115200

The script starts by configuring the RYLR896 and outputting that configuration to the terminal. If successful, we should see the following informational output.

Connecting to REYAX RYLR896 transceiver module...

Address set? +OK
Network Id set? +OK
AES-128 password set? +OK
Module responding? +OK

Address: +ADDRESS=116
Firmware version: +VER=RYLR89C_V1.2.7
Network Id: +NETWORKID=6
UART baud rate: +IPR=115200
RF frequency +BAND=915000000
RF output power +CRFOP=15
Work mode +MODE=0
RF parameters +PARAMETER=12,7,1,4
AES-128 password of the network +CPIN=92A0ECEC9000DA0DCF0CAAB0ABA2E0EF

Once configured, the script will receive the data from the Arduino Sense, decrypt the data, parse the sensor values, and format and display the values within the terminal.

The following screen recording shows a parallel view of both the Arduino Serial Monitor (upper right window) and the Raspberry Pi’s terminal output (lower right window). The Raspberry Pi (receiver) receives data from the Arduino (transmitter). The Raspberry Pi successfully reads, decrypts, interprets, and displays the sensor data, including displaying color swatches for the RGB and light intensity sensor readings.

Conclusion

In this post, we explored the use of the LoRa and LoRaWAN protocols to transmit environmental sensor data from an IoT device to an IoT gateway. Given its low energy consumption, long-distance transmission capabilities, and well-developed protocols, LoRaWAN is an ideal long-range wireless protocol for IoT devices.

This blog represents my own viewpoints and not of my employer, Amazon Web Services (AWS). All product names, logos, and brands are the property of their respective owners.

Arduino, IIoT, IoT, LoRa, LoRaWAN, LPWAN, Raspberry Pi, RaspPi

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BLE and GATT for IoT: Getting Started with Bluetooth Low Energy and the Generic Attribute Profile Specification for IoT

Posted by Gary A. Stafford in C++ Development, Python, Raspberry Pi, Software Development, Technology Consulting on August 4, 2020

Gary Stafford · Getting Started with Bluetooth Low Energy and Generic Attribute Profile Specification for IoT

Introduction

According to Wikipedia, Bluetooth is a wireless technology standard used for exchanging data between fixed and mobile devices over short distances. Bluetooth Low Energy (Bluetooth LE or BLE) is a wireless personal area network (WPAN) technology designed and marketed by the Bluetooth Special Interest Group (Bluetooth SIG). According to the Bluetooth SIG, BLE is designed for very low power operation. BLE supports data rates from 125 Kb/s to 2 Mb/s, with multiple power levels from 1 milliwatt (mW) to 100 mW. Several key factors influence the effective range of a reliable Bluetooth connection, which can vary from a kilometer down to less than a meter. The newer generation Bluetooth 5 provides a theoretical 4x range improvement over Bluetooth 4.2, from approximately 200 feet (60 meters) to 800 feet (240 meters).

Wikipedia currently lists 36 definitions of Bluetooth profiles defined and adopted by the Bluetooth SIG, including the Generic Attribute Profile (GATT) Specification. According to the Bluetooth SIG, GATT is built on top of the Attribute Protocol (ATT) and establishes common operations and a framework for the data transported and stored by the ATT. GATT provides profile discovery and description services for the BLE protocol. It defines how ATT attributes are grouped together into sets to form services.

Given its low energy consumption and well-developed profiles, such as GATT, BLE is an ideal short-range wireless protocol for Internet of Things (IoT) devices, when compared to competing protocols, such as ZigBee, Bluetooth classic, and Wi-Fi. In this post, we will explore the use of BLE and the GATT specification to transmit environmental sensor data from an IoT Sensor to an IoT Gateway.

IoT Sensor

In this post, we will use an Arduino single-board microcontroller to serve as an IoT sensor, actually an array of sensors. The 3.3V AI-enabled Arduino Nano 33 BLE Sense board, released in August 2019, comes with the powerful nRF52840 processor from Nordic Semiconductors, a 32-bit ARM Cortex-M4 CPU running at 64 MHz, 1MB of CPU Flash Memory, 256KB of SRAM, and a NINA-B306 stand-alone Bluetooth 5 low energy module.

The Sense also contains an impressive array of embedded sensors:

9-axis Inertial Sensor (LSM9DS1): 3D digital linear acceleration sensor, a 3D digital
angular rate sensor, and a 3D digital magnetic sensor
Humidity and Temperature Sensor (HTS221): Capacitive digital sensor for relative humidity and temperature
Barometric Sensor (LPS22HB): MEMS nano pressure sensor: 260–1260 hectopascal (hPa) absolute digital output barometer
Microphone (MP34DT05): MEMS audio sensor omnidirectional digital microphone
Gesture, Proximity, Light Color, and Light Intensity Sensor (APDS9960): Advanced Gesture detection, Proximity detection, Digital Ambient Light Sense (ALS), and Color Sense (RGBC).

The Sense is an excellent, low-cost single-board microcontroller for learning about collecting and transmitting IoT sensor data.

IoT Gateway

In this post, we will use a recent Raspberry Pi 3 Model B+ single-board computer (SBC), to serve as an IoT Gateway. This Raspberry Pi model features a 1.4GHz Cortex-A53 (ARMv8) 64-bit quad-core processor System on a Chip (SoC), 1GB LPDDR2 SDRAM, dual-band wireless LAN, Bluetooth 4.2 BLE, and Gigabit Ethernet. To follow along with the post, you could substitute the Raspberry Pi for any Linux-based machine to run the included sample Python script.

The Arduino will transmit IoT sensor telemetry, over BLE, to the Raspberry Pi. The Raspberry Pi, using Wi-Fi or Ethernet, is then able to securely transmit the sensor telemetry data to the Cloud. In Bluetooth terminology, the Bluetooth Peripheral device (aka GATT Server), which is the Arduino, will transmit data to the Bluetooth Central device (aka GATT Client), which is the Raspberry Pi.

Arduino Sketch

For those not familiar with Arduino, a sketch is the name that Arduino uses for a program. It is the unit of code that is uploaded into non-volatile flash memory and runs on an Arduino board. The Arduino language is a set of C/C++ functions. All standard C and C++ constructs supported by the avr-g++ compiler should work in Arduino.

For this post, the sketch, combo_sensor_ble.ino, contains all the code necessary to collect environmental sensor telemetry, including temperature, relative humidity, barometric pressure, and ambient light and RGB color. All code for this post, including the sketch, can be found on GitHub.

The sensor telemetry will be advertised by the Sense, over BLE, as a GATT Environmental Sensing Service (GATT Assigned Number 0x181A) with multiple GATT Characteristics. Each Characteristic represents a sensor reading and contains the most current sensor value(s), for example, Temperature (0x2A6E) or Humidity (0x2A6F).

Each GATT Characteristic defines how the data should be represented. To represent the data accurately, the sensor readings need to be modified. For example, using ArduinoHTS221 library, the temperature is captured with two decimal points of precision (e.g., 22.21 °C). However, the Temperature GATT Characteristic (0x2A6E) requires a signed 16-bit value (-32,768–32,767). To maintain precision, the captured value (e.g., 22.21 °C) is multiplied by 100 to convert it to an integer (e.g., 2221). The Raspberry Pi will then handle converting the value back to the original value with the correct precision.

The GATT specification has no current predefined Characteristic representing ambient light and RGB color. Therefore, I have created a custom Characteristic for the color values and assigned it a universally unique identifier (UUID).

According to the documentation, ambient light and RGB color are captured as 16-bit values (a range of 0–65,535). However, using the ArduinoAPDS9960 library, I have found the scale of the readings to be within a range of 0–4097. Without diving into the weeds, the maximum count (or saturation) value is variable. It can be calculated based upon the integration time and the size of the count register (e.g., 16-bits). The ADC integration time appears to be set to 10 ms in the library’s file, Arduino_APDS9960.cpp.

RGB values are typically represented as 8-bit color. We could convert the values to 8-bit before sending or handle it later on the Raspberry Pi IoT Gateway. For the sake of demonstration purposes versus data transfer efficiency, the sketch concatenates the 12-bit values together as a string (e.g., 4097,2811,1500,4097). The string will be converted from 12-bit to 8-bit on the Raspberry Pi (e.g., 255,175,93,255).

	/*
	Description: Transmits Arduino Nano 33 BLE Sense sensor readings over BLE,
	including temperature, humidity, barometric pressure, and color,
	using the Bluetooth Generic Attribute Profile (GATT) Specification
	Author: Gary Stafford
	Reference: Source code adapted from `Nano 33 BLE Sense Getting Started`
	Adapted from Arduino BatteryMonitor example by Peter Milne
	*/

	/*
	Generic Attribute Profile (GATT) Specifications
	GATT Service: Environmental Sensing Service (ESS) Characteristics

	Temperature
	sint16 (decimalexponent -2)
	Unit is in degrees Celsius with a resolution of 0.01 degrees Celsius
	https://www.bluetooth.com/xml-viewer/?src=https://www.bluetooth.com/wp-content/uploads/Sitecore-Media-Library/Gatt/Xml/Characteristics/org.bluetooth.characteristic.temperature.xml

	Humidity
	uint16 (decimalexponent -2)
	Unit is in percent with a resolution of 0.01 percent
	https://www.bluetooth.com/xml-viewer/?src=https://www.bluetooth.com/wp-content/uploads/Sitecore-Media-Library/Gatt/Xml/Characteristics/org.bluetooth.characteristic.humidity.xml

	Barometric Pressure
	uint32 (decimalexponent -1)
	Unit is in pascals with a resolution of 0.1 Pa
	https://www.bluetooth.com/xml-viewer/?src=https://www.bluetooth.com/wp-content/uploads/Sitecore-Media-Library/Gatt/Xml/Characteristics/org.bluetooth.characteristic.pressure.xml
	*/

	#include <ArduinoBLE.h>
	#include <Arduino_HTS221.h>
	#include <Arduino_LPS22HB.h>
	#include <Arduino_APDS9960.h>

	const int UPDATE_FREQUENCY = 2000; // Update frequency in ms
	const float CALIBRATION_FACTOR = -4.0; // Temperature calibration factor (Celsius)

	int previousTemperature = 0;
	unsigned int previousHumidity = 0;
	unsigned int previousPressure = 0;
	String previousColor = "";
	int r, g, b, a;
	long previousMillis = 0; // last time readings were checked, in ms

	BLEService environmentService("181A"); // Standard Environmental Sensing service

	BLEIntCharacteristic tempCharacteristic("2A6E", // Standard 16-bit Temperature characteristic
	BLERead \| BLENotify); // Remote clients can read and get updates

	BLEUnsignedIntCharacteristic humidCharacteristic("2A6F", // Unsigned 16-bit Humidity characteristic
	BLERead \| BLENotify);

	BLEUnsignedIntCharacteristic pressureCharacteristic("2A6D", // Unsigned 32-bit Pressure characteristic
	BLERead \| BLENotify); // Remote clients can read and get updates

	BLECharacteristic colorCharacteristic("936b6a25-e503-4f7c-9349-bcc76c22b8c3", // Custom Characteristics
	BLERead \| BLENotify, 24); // 1234,5678,

	BLEDescriptor colorLabelDescriptor("2901", "16-bit ints: r, g, b, a");

	void setup() {
	Serial.begin(9600); // Initialize serial communication
	// while (!Serial); // only when connected to laptop

	if (!HTS.begin()) { // Initialize HTS221 sensor
	Serial.println("Failed to initialize humidity temperature sensor!");
	while (1);
	}

	if (!BARO.begin()) { // Initialize LPS22HB sensor
	Serial.println("Failed to initialize pressure sensor!");
	while (1);
	}

	// Avoid bad readings to start bug
	// https://forum.arduino.cc/index.php?topic=660360.0
	BARO.readPressure();
	delay(1000);

	if (!APDS.begin()) { // Initialize APDS9960 sensor
	Serial.println("Failed to initialize color sensor!");
	while (1);
	}

	pinMode(LED_BUILTIN, OUTPUT); // Initialize the built-in LED pin

	if (!BLE.begin()) { // Initialize NINA B306 BLE
	Serial.println("starting BLE failed!");
	while (1);
	}

	BLE.setLocalName("ArduinoNano33BLESense"); // Set name for connection
	BLE.setAdvertisedService(environmentService); // Advertise environment service

	environmentService.addCharacteristic(tempCharacteristic); // Add temperature characteristic
	environmentService.addCharacteristic(humidCharacteristic); // Add humidity characteristic
	environmentService.addCharacteristic(pressureCharacteristic); // Add pressure characteristic
	environmentService.addCharacteristic(colorCharacteristic); // Add color characteristic

	colorCharacteristic.addDescriptor(colorLabelDescriptor); // Add color characteristic descriptor

	BLE.addService(environmentService); // Add environment service

	tempCharacteristic.setValue(0); // Set initial temperature value
	humidCharacteristic.setValue(0); // Set initial humidity value
	pressureCharacteristic.setValue(0); // Set initial pressure value
	colorCharacteristic.setValue(""); // Set initial color value

	BLE.advertise(); // Start advertising
	Serial.print("Peripheral device MAC: ");
	Serial.println(BLE.address());
	Serial.println("Waiting for connections…");
	}

	void loop() {
	BLEDevice central = BLE.central(); // Wait for a BLE central to connect

	// If central is connected to peripheral
	if (central) {
	Serial.print("Connected to central MAC: ");
	Serial.println(central.address()); // Central's BT address:

	digitalWrite(LED_BUILTIN, HIGH); // Turn on the LED to indicate the connection

	while (central.connected()) {
	long currentMillis = millis();
	// After UPDATE_FREQUENCY ms have passed, check temperature & humidity
	if (currentMillis – previousMillis >= UPDATE_FREQUENCY) {
	previousMillis = currentMillis;
	updateReadings();
	}
	}

	digitalWrite(LED_BUILTIN, LOW); // When the central disconnects, turn off the LED
	Serial.print("Disconnected from central MAC: ");
	Serial.println(central.address());
	}
	}

	int getTemperature(float calibration) {
	// Get calibrated temperature as signed 16-bit int for BLE characteristic
	return (int) (HTS.readTemperature() * 100) + (int) (calibration * 100);
	}

	unsigned int getHumidity() {
	// Get humidity as unsigned 16-bit int for BLE characteristic
	return (unsigned int) (HTS.readHumidity() * 100);
	}

	unsigned int getPressure() {
	// Get humidity as unsigned 32-bit int for BLE characteristic
	return (unsigned int) (BARO.readPressure() * 1000 * 10);
	}

	void getColor() {
	// check if a color reading is available
	while (!APDS.colorAvailable()) {
	delay(5);
	}

	// Get color as (4) unsigned 16-bit ints
	int tmp_r, tmp_g, tmp_b, tmp_a;
	APDS.readColor(tmp_r, tmp_g, tmp_b, tmp_a);
	r = tmp_r;
	g = tmp_g;
	b = tmp_b;
	a = tmp_a;
	}

	void updateReadings() {
	int temperature = getTemperature(CALIBRATION_FACTOR);
	unsigned int humidity = getHumidity();
	unsigned int pressure = getPressure();
	getColor();

	if (temperature != previousTemperature) { // If reading has changed
	Serial.print("Temperature: ");
	Serial.println(temperature);
	tempCharacteristic.writeValue(temperature); // Update characteristic
	previousTemperature = temperature; // Save value
	}

	if (humidity != previousHumidity) { // If reading has changed
	Serial.print("Humidity: ");
	Serial.println(humidity);
	humidCharacteristic.writeValue(humidity);
	previousHumidity = humidity;
	}

	if (pressure != previousPressure) { // If reading has changed
	Serial.print("Pressure: ");
	Serial.println(pressure);
	pressureCharacteristic.writeValue(pressure);
	previousPressure = pressure;
	}

	// Get color reading everytime
	// e.g. "12345,45678,89012,23456"
	String stringColor = "";
	stringColor += r;
	stringColor += ",";
	stringColor += g;
	stringColor += ",";
	stringColor += b;
	stringColor += ",";
	stringColor += a;

	if (stringColor != previousColor) { // If reading has changed

	byte bytes[stringColor.length() + 1];
	stringColor.getBytes(bytes, stringColor.length() + 1);

	Serial.print("r, g, b, a: ");
	Serial.println(stringColor);

	colorCharacteristic.writeValue(bytes, sizeof(bytes));
	previousColor = stringColor;
	}
	}

view raw

combo_sensor_ble.ino

hosted with ❤ by GitHub

Previewing and Debugging BLE Device Services

Before looking at the code running on the Raspberry Pi, we can use any number of mobile applications to preview and debug the Environmental Sensing service running on the Arduino and being advertised over BLE. A commonly recommended application is Nordic Semiconductor’s nRF Connect for Mobile, available on Google Play. I have found the Android version works better at correctly interpreting and displaying GATT Characteristic values than the iOS version of the app.

Below, we see a scan of my local vicinity for BLE devices being advertised, using the Android version of the nRF Connect mobile application. Note the BLE device, ArduinoNano33BLESense (indicated in red). Also, note the media access control address (MAC address) of that BLE device, in my case, d1:aa:89:0c:ee:82. The MAC address will be required later on the IoT Gateway.

Connecting to the device, we see three Services. The Environmental Sensing Service (indicated in red) contains the sensor readings.

Drilling down into the Environmental Sensing Service (0x181A), we see the four expected Characteristics: Temperature (0x2A6E), Humidity (0x2A6F), Pressure (0x2A6D), and Unknown Characteristic (936b6a25-e503-4f7c-9349-bcc76c22b8c3). Since nRF Connect cannot recognize the color sensor reading as a registered GATT Characteristic (no GATT Assigned Number), it is displayed as an Unknown Characteristic. Whereas the temperature, humidity, and pressure values (indicated in red) are interpreted and displayed correctly, the color sensor reading is left as raw hexadecimal text (e.g., 30-2c-30-2c-30-2c-30-00 or 0,0,0,0).

These results indicate everything is working as expected.

BLE Client Python Code

To act as the BLE Client (aka central device), the Raspberry Pi runs a Python script. The script, rasppi_ble_receiver.py, uses the bluepy Python module for interfacing with BLE devices through Bluez, on Linux.

	import sys
	import time
	from argparse import ArgumentParser

	from bluepy import btle # linux only (no mac)
	from colr import color as colr


	# BLE IoT Sensor Demo
	# Author: Gary Stafford
	# Reference: https://elinux.org/RPi_Bluetooth_LE
	# Requirements: python3 -m pip install –user -r requirements.txt
	# To Run: python3 ./rasppi_ble_receiver.py d1:aa:89:0c:ee:82 <- MAC address – change me!


	def main():
	# get args
	args = get_args()

	print("Connecting…")
	nano_sense = btle.Peripheral(args.mac_address)

	print("Discovering Services…")
	_ = nano_sense.services
	environmental_sensing_service = nano_sense.getServiceByUUID("181A")

	print("Discovering Characteristics…")
	_ = environmental_sensing_service.getCharacteristics()

	while True:
	print("\n")
	read_temperature(environmental_sensing_service)
	read_humidity(environmental_sensing_service)
	read_pressure(environmental_sensing_service)
	read_color(environmental_sensing_service)

	# time.sleep(2) # transmission frequency set on IoT device


	def byte_array_to_int(value):
	# Raw data is hexstring of int values, as a series of bytes, in little endian byte order
	# values are converted from bytes -> bytearray -> int
	# e.g., b'\xb8\x08\x00\x00' -> bytearray(b'\xb8\x08\x00\x00') -> 2232

	# print(f"{sys._getframe().f_code.co_name}: {value}")

	value = bytearray(value)
	value = int.from_bytes(value, byteorder="little")
	return value


	def split_color_str_to_array(value):
	# e.g., b'2660,2059,1787,4097\x00' -> 2660,2059,1787,4097 ->
	# [2660, 2059, 1787, 4097] -> 166.0,128.0,111.0,255.0

	# print(f"{sys._getframe().f_code.co_name}: {value}")

	# remove extra bit on end ('\x00')
	value = value[0:-1]

	# split r, g, b, a values into array of 16-bit ints
	values = list(map(int, value.split(",")))

	# convert from 16-bit ints (2^16 or 0-65535) to 8-bit ints (2^8 or 0-255)
	# values[:] = [int(v) % 256 for v in values]

	# actual sensor is reading values are from 0 – 4097
	print(f"12-bit Color values (r,g,b,a): {values}")

	values[:] = [round(int(v) / (4097 / 255), 0) for v in values]

	return values


	def byte_array_to_char(value):
	# e.g., b'2660,2058,1787,4097\x00' -> 2659,2058,1785,4097
	value = value.decode("utf-8")
	return value


	def decimal_exponent_two(value):
	# e.g., 2350 -> 23.5
	return value / 100


	def decimal_exponent_one(value):
	# e.g., 988343 -> 98834.3
	return value / 10


	def pascals_to_kilopascals(value):
	# 1 Kilopascal (kPa) is equal to 1000 pascals (Pa)
	# to convert kPa to pascal, multiply the kPa value by 1000
	# 98834.3 -> 98.8343
	return value / 1000


	def celsius_to_fahrenheit(value):
	return (value * 1.8) + 32


	def read_color(service):
	color_char = service.getCharacteristics("936b6a25-e503-4f7c-9349-bcc76c22b8c3")[0]
	color = color_char.read()
	color = byte_array_to_char(color)
	color = split_color_str_to_array(color)
	print(f" 8-bit Color values (r,g,b,a): {color[0]},{color[1]},{color[2]},{color[3]}")
	print("RGB Color")
	print(colr('\t\t', fore=(127, 127, 127), back=(color[0], color[1], color[2])))
	print("Light Intensity")
	print(colr('\t\t', fore=(127, 127, 127), back=(color[3], color[3], color[3])))


	def read_pressure(service):
	pressure_char = service.getCharacteristics("2A6D")[0]
	pressure = pressure_char.read()
	pressure = byte_array_to_int(pressure)
	pressure = decimal_exponent_one(pressure)
	pressure = pascals_to_kilopascals(pressure)
	print(f"Barometric Pressure: {round(pressure, 2)} kPa")


	def read_humidity(service):
	humidity_char = service.getCharacteristics("2A6F")[0]
	humidity = humidity_char.read()
	humidity = byte_array_to_int(humidity)
	humidity = decimal_exponent_two(humidity)
	print(f"Humidity: {round(humidity, 2)}%")


	def read_temperature(service):
	temperature_char = service.getCharacteristics("2A6E")[0]
	temperature = temperature_char.read()
	temperature = byte_array_to_int(temperature)
	temperature = decimal_exponent_two(temperature)
	temperature = celsius_to_fahrenheit(temperature)
	print(f"Temperature: {round(temperature, 2)}°F")


	def get_args():
	arg_parser = ArgumentParser(description="BLE IoT Sensor Demo")
	arg_parser.add_argument('mac_address', help="MAC address of device to connect")
	args = arg_parser.parse_args()
	return args


	if __name__ == "__main__":
	main()

view raw

rasppi_ble_receiver.py

hosted with ❤ by GitHub

To run the Python script, execute the following command, substituting the MAC address argument for your own BLE device’s advertised MAC address.

python3 ./rasppi_ble_receiver.py d1:aa:89:0c:ee:82

Unlike the nRF Connect app, the bluepy Python module is not capable of correctly interpreting and displaying the GATT Characteristic values. Hence, the script takes the raw, incoming hexadecimal text from the Arduino and coerces it to the correct values. For example, a temperature reading must be transformed from bytes, b'\xb8\x08\x00\x00', to a byte array, bytearray(b'\xb8\x08\x00\x00'), then to an integer, 2232, then to a decimal, 22.32, and finally to the Fahrenheit scale, 72.18°F.

Sensor readings are retrieved from the BLE device every two seconds. In addition to displaying the numeric sensor readings, the Python script also displays a color swatch of the 8-bit RGB color, as well as a grayscale swatch representing the light intensity using the colr Python module.

The following screen recording shows a parallel view of both the Arduino Serial Monitor and the Raspberry Pi’s terminal output. The Raspberry Pi (central device) connects to the Arduino (peripheral device) when the Python script is started. The Raspberry Pi successfully reads and interprets the telemetry data from the Environmental Sensing Service.

Conclusion

In this post, we explored the use of BLE and the GATT specification to transmit environmental sensor data from a peripheral device to a central device. Given its low energy consumption and well-developed profiles, such as GATT, Bluetooth Low Energy (BLE) is an ideal short-range wireless protocol for IoT devices.

This blog represents my own viewpoints and not of my employer, Amazon Web Services.

Arduino, BLE, Bluetooth, Bluetooth Low Energy, IIoT, Internet of Things, IoT, Raspberry Pi

1 Comment

Prevent Motion From Running Without a Camera Connected

Posted by Gary A. Stafford in Bash Scripting, Raspberry Pi, Software Development on July 19, 2013

Introduction

If you read my post, Raspberry Pi-Powered Dashboard Video Camera Using Motion and FFmpeg, you know Motion with FFmpeg on a Raspberry Pi makes an ideal dashboard camera system. However, an issue I still struggled with when using the dash-cam was Motion running without a webcam connected.

When I start my car, the Raspberry Pi boots-up, and subsequently, Motion starts. No interaction with the Pi is required. The dash-cam starts capturing images and making the time-lapse video. However, when I get home and plug my Pi back into my local network, Motion starts up again and starts recording blank images and creating the time-lapse video, even though there is no webcam connected.

To get prevent Motion from starting up without a webcam connected, I’ve added a simple function to the Motion startup script. When the system calls Motion on startup, the new function checks if a webcam is connected. If not, it immediately exits the script, without ever starting Motion. No blank images or empty time-lapse videos are created. This saves a lot of wasted processing on the Pi. It also saves a lot of wasted time moving videos and images off the Pi that end up being blank, because no webcam was connected.

Find Your Webcam

First, attach your webcam to the Raspberry Pi. Run the following command to list the USB devices connected to the Pi:

lsusb

You should see similar output to the example below. Note your webcam’s ID(s). I ran the command twice in this example, to identify both of my webcams.

Identifying Webcams with lsusb Command

There are several ways to detect your webcam, depending on you Linux distro. I found this post particularly helpful, The Webcam HOWTO.

Modify Motion

Next, open the Motion startup script, using the following command:

sudo nano /etc/init.d/motion

Add the following ‘check_for_webcam ()’ function to the top of the script, adjacent to the existing ‘check_daemon_enabled()’ function:

# Check if specific webcam(s) are connected to Pi
check_for_webcam () {
    if lsusb | grep -s -q -e 0000:ABCD
    then
        echo "Webcam found. Continuing..."
        return 0
    else
        echo "No webcam found? Shutting down Motion!"
        return 1
    fi
}

You will need to modify the function, changing the string ‘0000:ABCD’, to match your webcam’s ID. If you change your webcam model, remember to update the ID you entered in this function.

Next add the following statement to the beginning of the ‘start’ function. This code calls the new function when Motion’s ‘start’ command is executed. If no webcam is found, the Motion script exits without starting.

if ! check_for_webcam; then
    exit 1
fi

In my example below, I have two possible webcams that might be connected, so I search (grep) for either ID.

Modifying Motion Startup Script

Testing the Script Change

Save and close the Motion script. To test the script is working, run the following command to stop Motion:

sudo /etc/init.d/motion stop

Unplug your webcam from the Raspberry Pi. Then, run the following command to start Motion:

sudo /etc/init.d/motion start

You should see the following output:

No webcam found? Shutting down Motion!

Now, plug your webcam back in and run the ‘start’ command, again. You should now see the following output:

Webcam found. Continuing...

Starting Motion With and Without Webcam

Conclusion

Now, when you start the Raspberry Pi and don’t have a web-cam connected, Motion will no longer automatically start. Just remember, if you don’t have a way to interact directly with your Pi, you will need to restart the Pi to get Motion running again after connecting a webcam.

Dash-Cam, dashboard camera video, dashboard-camera, FFmpeg, Motion, Raspberry Pi, RaspPi, RPi, webcam

Travel-Size Wireless Router for Your Raspberry Pi

Posted by Gary A. Stafford in Bash Scripting, Raspberry Pi, Software Development on July 15, 2013

Use a low-cost nano-size wireless router to connect to your Raspberry Pi while traveling. Set up your own private wireless network in your vehicle, hotel, or coffee shop.

Introduction

Recently, I purchased a USB-powered wireless router for to use with my Raspberry Pi when travelling. In an earlier post, Raspberry Pi-Powered Dashboard Video Camera Using Motion and FFmpeg, I discussed the use of the Raspberry Pi, combined with a webcam, Motion, and FFmpeg, to create a low-cost dashboard video camera. Like many, I find one the big challenges with the Raspberry Pi, is how to connect and interact with it. Being in my car, and usually out of range of my home’s wireless network, except maybe in the garage, this becomes even more of an issue. That’s where adding an inexpensive travel-size router to my vehicle comes in handy.

I chose the TP-LINK TL-WR702N Wireless N150 Travel Router, sold by Amazon. The TP-LINK router, described as ‘nano size’, measures only 2.2 inches square by 0.7 inches wide. It has several modes of operation, including as a router, access point, client, bridge, or repeater. It operates at wireless speeds up to 150Mpbs and is compatible with IEEE 802.11b/g/n networks. It supports several common network security protocols, including WEP, WPA/WPA2, WPA-PSK/WPA2-PSK encryption. For $22 USD, what more could you ask for!

My goal with the router was to do the following:

Have the Raspberry Pi auto-connect to the new TP-LINK router’s wireless network when in range, just like my home network.
Since I might still be in range of my home network, have the Raspberry Pi try to connect to the TP-LINK first, before falling back to my home network.
Ensure the network was relatively secure, since I would be exposed to many more potential threats when traveling.

My vehicle has two power outlets. I plug my Raspberry Pi into one outlet and the router into the other. You could daisy chain the router off the Pi. However, my Pi’s ports are in use my the USB wireless adapter and the USB webcam. Using the TP-LINK router, I can easily connect to the Raspberry Pi with my mobile phone or tablet, using an SSH client.

Using Fing to Locate the Pi on the TP-LINK Wireless Network

When I arrive at my destination, I log into the Pi and do a proper shutdown. This activates my shutdown script (see my last post), which moves the newly created Motion/FFmpeg time-lapse dash-cam videos to a secure folder on my Pi, before powering down.

Using SSH Terminal for iOS to Shutdown the Pi

Of course there are many other uses for the router. For example, I can remove the Pi and router from my car and plug it back in at the hotel while traveling, or power the router from my laptop while at work or the coffee shop. I now have my own private wireless network wherever I am to use the Raspberry Pi, or work with other users. Remember the TP-LINK can act as a router, access point, client, bridge, or a repeater.

The Raspberry Pi and Router both fit in a Small Container for Travel

Network Security

Before configuring your Raspberry Pi, the first thing you should do is change all the default security related settings for the router. Start with the default SSID and the PSK password. Both these default values are printed right on the router. That’s motivation enough to change!

Additionally, change the default IP address of the router and the username and password for the browser-based Administration Console.

Lastly, pick the most secure protocol possible. I chose ‘WPA-PSK/WPA2-PSK’. All these changes are done through the TP-LINK’s browser-based Administration Console.

Configuring Multiple Wireless Networks

In an earlier post, Installing a Miniature WiFi Module on the Raspberry Pi (w/ Roaming Enabled), I detailed the installation and configuration of a Miniature WiFi Module, from Adafruit Industries, on a Pi running Soft-float Debian “wheezy”. I normally connect my Pi to my home wireless network. I wanted to continue to do this in the house, but connect the new router when traveling.

Based on the earlier post, I was already using Jouni Malinen’s wpa_supplicant, the WPA Supplicant for Linux, BSD, Mac OS X, and Windows with support for WPA and WPA2. This made network configuration relatively simple. If you use wpa_supplicant, your ‘/etc/network/interfaces’ file should look like the following. If you’re not familiar with configuring the interfaces file for wpa_supplicant, this post on NoWiresSecurity.com is a good starting point.

Note that in this example, I am using DHCP for all wireless network connections. If you chose to use static IP addresses for any of the networks, you will have to change the interfaces file accordingly. Once you add multiple networks, configuring static IP addresses for each network, becomes more complex. That is my next project…

First, I generated a new pre-shared key (PSK) for the router’s SSID configuration using the following command. Substitute your own SSID (‘your_ssid’) and passphrase (‘your_passphrase’).

wpa_passphrase your_ssid your_passphrase

Based your SSID and passphrase, this command will generate a pre-shared key (PSK), similar to the following. Save or copy the PSK to the clipboard. We will need the PSK in the next step.

Then, I modified my wpa_supplicant configuration file with the following command:

sudo nano /etc/wpa_supplicant/wpa_supplicant.conf

I added the second network configuration, similar to the existing configuration for my home wireless network, using the newly generated PSK. Below is an example of what mine looks like (of course, not the actual PSKs).

Depending on your Raspberry Pi and router configurations, your wpa_supplicant configuration will look slightly different. You may wish to add more settings. Don’t consider my example the absolute right way for your networks.

Wireless Network Priority

Note the priority of the TP-LINK router is set to 2, while my home NETGEAR router is set to 1. This ensures wpa_supplicant will attempt to connect to the TP-LINK network first, before attempting the home network. The higher number gets priority. The best resource I’ve found, which explains all the configuration options is detail, is here. In this example wpa_supplicant configuration file, priority is explained this way, ‘by default, all networks will get same priority group (0). If some of the networks are more desirable, this field can be used to change the order in which wpa_supplicant goes through the networks when selecting a BSS. The priority groups will be iterated in decreasing priority (i.e., the larger the priority value, the sooner the network is matched against the scan results). Within each priority group, networks will be selected based on security policy, signal strength, etc.’

Conclusion

If you want an easy, inexpensive, secure way to connect to your Raspberry Pi, in the vehicle or other location, a travel-size wireless router is a great solution. Best of all, configuring it for your Raspberry Pi is simple if you use wpa_supplicant.

Dash-Cam, dashboard-camera, Debian, FFmpeg, Linux, Motion, Nano Router, Raspberry Pi, RaspPi, RPi, Travel Router, WiFi, Wireless, wpa_supplicant

6 Comments

Using a Startup Script to Save Motion/FFmpeg Videos and Images on The Raspberry Pi

Posted by Gary A. Stafford in Bash Scripting, Raspberry Pi, Software Development on July 9, 2013

Use a start-up script to overcome limitations of Motion/FFmpeg and save multiple Raspberry Pi dashboard camera timelapse videos and images, automatically.

Introduction

In my last post, Raspberry Pi-Powered Dashboard Video Camera Using Motion and FFmpeg, I demonstrated how the Raspberry Pi can be used as a low-cost dashboard video camera. One of the challenges I faced in that post was how to save the timelapse videos and individual images (frames) created by Motion and FFmpeg when the Raspberry Pi is turned on and off. Each time the car starts, the Raspberry Pi boots up, and Motion begins to run, the previous images and video, stored in the default ‘/tmp/motion/’ directory are removed and new images and video, created.

Take the average daily commute, we drive to and from work. Maybe we stop for a morning coffee, or stop at the store on the way home to pick up dinner. Maybe we use our car go out for lunch. Our car starts, stops, starts, stops, starts, and stops. Our daily commute actually encompasses a series small trips, and therefore multiple dash-cam timelapse videos. If you are only interested in keeping the latest timelapse video in case of an accident, then this may not be a problem. When the accident occurs, simply pull the SDHC card from the Raspberry Pi and copy the video and images off to your laptop.

However, if you are interested in capturing and preserving series of dash-cam videos, such as in the daily commute example above, then the default behavior of Motion is insufficient. To preserve each video segment or series of images, we need a way to preserve the content created by Motion and FFmpeg, before they are overwritten. In this post, I will present a solution to overcome this limitation.

The process involves the following steps:

Change the default location where Motion stores timelapse videos and images to somewhere other than a temporary directory;
Create a startup script that will move the video and images to a safe location when restarting the Pi;
Configure the Pi’s Debian operating system to run this script at startup (and optionally shutdown), before Motion starts;

Sounds pretty simple. However, understanding how startup scripts work with Debian’s Init program, making sure the new move script run before Motion starts, and knowing how to move a huge number of files, all required forethought.

Change Motion’s Default Location for Video and Images

To start, change the default location where Motion stores timelapse video and images, from ‘/tmp/motion/’ to a location outside the ‘/tmp’ directory. I chose to create a directory called ‘/motiontmp’. Make sure you set the permissions on the new ‘/motiontmp’ directory, so Motion can write to it:

sudo chmod -R 777 /motiontmp

To have Motion use this location, we need to modify the Motion configuration file:

sudo nano /etc/motion/motion.conf

Change the following setting (in bold below). Note when Motion starts for the first time, it will create the ‘motion’ sub folder inside ‘motiontmp’. You do not have to create it yourself.

# Target base directory for pictures and films
# Recommended to use absolute path. (Default: current working directory)
target_dir /motiontmp/motion

Create the Startup Script to Move Video and Images

Next, create the new shell script that will run at startup to move Motion’s video and images. The script creates a timestamped folder in new ‘motiontmp’ directory for each series of images and video. The script then copies all files from the ‘motion’ directory to the new timestamped directory. Before copying, the script deletes any zero-byte jpegs, which are images that did not fully process prior to the Raspberry Pi being shut off when the car stopped. To create the new script, run the following command.

sudo nano /etc/init.d/motionStartup.sh

Copy the following contents into the script and save it.

### BEGIN INIT INFO
# Provides:          motionStartup
# Required-Start:    $remote_fs $syslog
# Required-Stop:     $remote_fs $syslog
# Default-Start:     2 3 4 5
# Default-Stop:      0 1 6
# Short-Description: Move motion files at startup.
# Description:       Move motion files at startup.
# X-Start-Before:    motion
### END INIT INFO

#! /bin/sh
# /etc/init.d/motionStartup
#

# Some things that run always
#touch /var/lock/motionStartup
logger -s "Script motionStartup called"

# Carry out specific functions when asked to by the system
case "$1" in
  start)
    logger -s "Script motionStartup started"
    TIMESTAMP=$(date +%Y%m%d%H%M%S | sed 's/ //g') # No spaces
    logger -s "Script motionStartup $TIMESTAMP"
    sudo mkdir /motiontmp/$TIMESTAMP || logger -s "Error mkdir start"
    find /motiontmp/motion/. -type f -size 0 -print0 -delete
    find /motiontmp/motion/. -maxdepth 1 -type f | \
        xargs -I '{}' sudo mv {} /motiontmp/$TIMESTAMP
    ;;
  stop)
    logger -s "Script motionStartup stopped"
    ;;
  *)
    echo "Usage: /etc/init.d/motionStartup {start|stop}"
    exit 1
    ;;
esac

exit 0

Note the ‘X-Start-Before’ setting at the top of the script (in bold). An explanation of this setting is found on the Debian Wiki website. According to the site, ”There is no such standard-defined header, but there is a proposed extension implemented in the insserv package (since version 1.09.0-8). Use the X-Start-Before and X-Stop-After headers proposed by SuSe.” To make sure you have a current version of ‘insserv‘, you can run the following command:

dpkg -l insserv

Also, note how the files are moved by the script:

find /motiontmp/motion/. -maxdepth 1 -type f | \
        xargs -I '{}' sudo mv {} /motiontmp/$TIMESTAMP

It’s not as simple as using ‘mv *.*’ when you have a few thousand files. This will likely throw a ‘Argument list too long’ exception. According to one stackoverflow, the exception is because bash actually expands the asterisk to every matching file, producing a very long command line. Using the ‘find’ combined with ‘xargs’ gets around these problem. The ‘xargs’ command splits up the list and issues several commands if necessary. This issue applies to several commands, including rm, cp, and mv.

Lastly, note the use of the ‘logger‘ commands throughout the script. These are optional and may be removed. I like to log the script’s progress for troubleshooting purposes. Using the above ‘logger’ commands, I can easily pinpoint issues by looking at the log with grep, such as:

tail -500 /var/log/messages | grep 'motionStartup' | grep 'logger:'

You can test the script by running the following command:

/etc/init.d/./motionStartup.sh start

You should see a series of three messages output to the screen by the script, confirming the script is working. Note the new timestamped folder created by the script, below.

Below, is an example of the how the directory structure should look after a few videos are created by Motion, and the Raspberry Pi cycled off and on. You need to complete the rest of the steps in this post for this to work automatically.

Shutdown Script?

I know, the name of the post clearly says ‘Startup Script’. Well, a little tip, if you copy the code from the ‘start’ method and paste it in the ‘stop’ method, this now also works at shutdown. If you do a proper shutdown (like ‘sudo reboot’), the Raspberry Pi’s OS will call the script’s ‘stop’ method. The ‘start’ method is more useful to use for us in the car, where we may not be able to do a proper shutdown; we just turn the car off and kill power to the Pi. However, if you are shutting down from mobile device via ssh, or using a micro keyboard and LCD monitor, the script will do it’s work on the way down.

Configure Debian OS to Run the New Startup Script

To have our new script run on startup, install it by running the following command:

sudo update-rc.d motionStartup.sh defaults

A full explanation of this command is to complex for this brief post. A good overview of creating startup scripts and installing them in Debian is found on the Debian Administration website. This is the source I used to start to understand runlevels. There are also a few links at the end of the post. To tell which runlevel (state) you running at, use the following command:

runlevel

To make sure the startup script was installed properly, run the following command. This will display the contents of each ‘rc*.d’ folder. Each folder corresponds to a runlevel – 0, 1, 2, etc. Each folder contains symbolic links to the actual scripts. The links are named by order of executed (S01…, S02…, S03…):

ls /etc/rc*.d

Look for the new script listed under the appropriate runlevel(s). The new script should be listed before ‘motion’, as shown below.

If for any reason you need to uninstall the new script (not delete/remove), run the following command. This not a common task, but necessary to change the order of execution of the scripts or rename a script.

sudo update-rc.d -f motionStartup.sh remove

Copy and Remove Files from the Raspberry Pi

Once the startup script is working and we are capturing images and timelapse video, the next thing we will probably want to do is copy files off the Raspberry Pi. To do this over your WiFi network, use a ‘scp’ command from a remote machine. The below script copies all directories, stating with ‘2013’, and their contents to remote machine, preserving the directory structure.

scp -rp user@ip_address_of_pi:/motiontmp/2013* ~/local_directory/

Maybe you just want all the timelapse videos Motion/FFmpeg creates; you don’t care about the images. The following command copies just the MPEG videos from all ‘2013’ folders to a single directory on the your remote machine. The directory structure is ignored during the copy. This is the quickest way to just store all the videos.

scp -rp user@ip_address_of_pi:/motiontmp/2013*/*.mpg ~/local_directory/

If you are going to save all the MPEG timelapse videos in one location, I recommend changing the naming convention of the videos in the motion.conf file. I have added the hour, minute, and seconds to mine. This will ensure the names don’t conflict when moved to a common directory:

# File path for timelapse mpegs relative to target_dir
# Default: %Y%m%d-timelapse
# Default value is near equivalent to legacy oldlayout option
# For Motion 3.0 compatible mode choose: %Y/%m/%d-timelapse
# File extension .mpg is automatically added so do not include this
timelapse_filename %Y%m%d%H%M%S-timelapse

To remove all the videos and images once they have been moved off the Pi and are no longer needed, you can run a rm command. Using the ‘-rf’ options make sure the directories and their contents are removed.

sudo rm -rf /motiontmp/2013*

Conclusion

The only issue I have yet to overcome is maintaining the current time on the Raspberry Pi. The Pi lacks a Real Time Clock (RTC). Therefore, turning the Pi on and off in the car causes it to loose the current time. Since the Pi is not always on a WiFi network, it can’t sync to the current time when restarted. The only side-effects I’ve seen so far caused by this, the videos occasionally contain more than one driving event and the time displayed in the videos is not always correct. Otherwise, the process works pretty well.

Resources

The following are some useful resources on this topic:

Debian Reference: Chapter 3. The system initialization

How-To: Managing services with update-rc.d

Files and scripts that execute on boot

Making scripts run at boot time with Debian

Finding all files and move to new directory from shell prompt

Shell scripting: Write message to a syslog / log file

“Argument list too long”: Beyond Arguments and Limitations

Linux / Unix Command: date (used for TIMESTAMP)

Time / Date Commands

To have our new script run on startup, install it by running the following command:

dashboard-camera, Debian, FFmpeg, Linux, Motion, Rasp Pi, Raspberry Pi, runlevel, start-up script, startup script, video

3 Comments

Raspberry Pi-Powered Dashboard Video Camera Using Motion and FFmpeg

Posted by Gary A. Stafford in Bash Scripting, Raspberry Pi, Software Development on June 30, 2013

Demonstrate the use of the Raspberry Pi and a basic webcam, along with Motion and FFmpeg, to build low-cost dashboard video camera for your daily commute.

Dashboard Video Cameras

Most of us remember the proliferation of dashboard camera videos of the February 2013 meteor racing across the skies of Russia. This rare astronomical event was captured on many Russian motorist’s dashboard cameras. Due to the dangerous driving conditions in Russia, many drivers rely on dashboard cameras for insurance and legal purposes. In the United States, we are more use to seeing dashboard cameras used by law-enforcement. Who hasn’t seen those thrilling police videos of car crashes, drunk drivers, and traffic stops gone wrong.

Although driving in the United States is not as dangerous as in Russia, there is reason we can’t also use dashboard cameras. In case you are involved in an accident, you will have a video record of the event for your insurance company. If you witness an accident or other dangerous situation, your video may help law enforcement and other emergency responders. Maybe you just want to record a video diary of your next road trip.

A wide variety of dashboard video cameras, available for civilian vehicles, can be seen on Amazon’s website. They range in price and quality from less that $50 USD to well over $300 USD or more, depending on their features. In a popular earlier post, Remote Motion-Activated Web-Based Surveillance with Raspberry Pi, I demonstrated the use of the Raspberry Pi and a webcam, along with Motion and FFmpeg, to provide low-cost web-based, remote surveillance. There are many other uses for this combination of hardware and software, including as a dashboard video camera.

Methods for Creating Dashboard Camera Videos

I’ve found two methods for capturing dashboard camera videos. The first and easiest method involves configuring Motion to use FFmpeg to create a video. FFmpeg creates a video from individual images (frames) taken at regular intervals while driving. The upside of the FFmpeg option, it gives you a quick ready-made video. The downside of FFmpeg option, your inability to fully control the high-level of video compression and high frame-rate (fps). This makes it hard to discern fine details when viewing the video.

Alternately, you can capture individual JPEG images and combine them using FFmpeg from the command line or using third-party movie-editing tools. The advantage of combining the images yourself, you have more control over the quality and frame-rate of the video. Altering the frame-rate, alters your perception of the speed of the vehicle recording the video. The only disadvantage of combining the images yourself, you have the extra steps involved to process the images into a video.

At one frame every two seconds (.5 fps), a 30 minute commute to work will generate 30 frames/minute x 30 minutes, or 900 jpeg images. At 640 x 480 pixels, depending on your jpeg compression ratio, that’s a lot of data to move around and crunch into a video. If you just want a basic record of your travels, use FFmpeg. If you want a higher-quality record of trip, maybe for a video-diary, combining the frames yourself is a better way to go.

Configuring Motion for a Dashboard Camera

The installation and setup of FFmpeg and Motion are covered in my earlier post so I won’t repeat that here. Below are several Motion settings I recommend starting with for use with a dashboard video camera. To configure Motion, open it’s configuration file, by entering the following command on your Raspberry Pi:

sudo nano /etc/motion/motion.conf

To use FFmpeg, the first method, find the ‘FFMPEG related options’ section of the configuration and locate ‘Use ffmpeg to encode a timelapse movie’. Enter a number for the ‘ffmpeg_timelapse’ setting. This is the rate at which images are captured and combined into a video. I suggest starting with 2 seconds. With a dashboard camera, you are trying to record important events as you drive. In as little as 2-3 seconds at 55 mph, you can miss a lot of action. Moving the setting down to 1 second will give more detail, but you will chew up a lot of disk space, if that is an issue for you. I would experiment with different values:

# Use ffmpeg to encode a timelapse movie
# Default value 0 = off - else save frame every Nth second
ffmpeg_timelapse 2

To use the ‘do-it-yourself’ FFmpeg method, locate the ‘Snapshots’ section. Find ‘Make automated snapshot every N seconds (default: 0 = disabled)’. Change the ‘snapshot_interval’ setting, using the same logic as the ‘ffmpeg_timelapse’ setting, above:

# Make automated snapshot every N seconds (default: 0 = disabled)
snapshot_interval 2

Irregardless of which method you choose (or use them both), you will want to tweak some more settings. In the ‘Text Display’ section, locate ‘Set to ‘preview’ will only draw a box in preview_shot pictures.’ Change the ‘locate’ setting to ‘off’. As shown in the video frame below, since you are moving in your vehicle most of the time, there is no sense turning on this option. Motion cannot differentiate between the highway zipping by the camera and the approaching vehicles. Everything is in motion to the camera, the box just gets in the way:

# Set to 'preview' will only draw a box in preview_shot pictures.
locate off

Optionally, I recommend turning on the time-stamp option. This is found right below the ‘locate’ setting. Especially in the event of an accident, you want an accurate time-stamp on the video or still images (make sure you Raspberry Pi’s time is correct):

# Draws the timestamp using same options as C function strftime(3)
# Default: %Y-%m-%d\n%T = date in ISO format and time in 24 hour clock
# Text is placed in lower right corner
text_right %Y-%m-%d\n%T-%q

Starting with the largest, best quality images will ensure the video quality is optimal. Start with a large size capture and reduce it only if you are having trouble capturing the video quickly enough. These settings are found in the ‘Capture device options’ section:

# Image width (pixels). Valid range: Camera dependent, default: 352
width 640

# Image height (pixels). Valid range: Camera dependent, default: 288
height 480

Similarly, I suggest starting with a low amount of jpeg compression to maximize quality and only lower if necessary. This setting is found in the ‘Image File Output’ section:

# The quality (in percent) to be used by the jpeg compression (default: 75)
quality 90

Once you have completed the configuration of Motion, restart Motion for the changes to take effect:

sudo /etc/init.d/motion restart

Since you will be powering on your Raspberry Pi in your vehicle, and may have no way to reach Motion from a command line, you will want Motion to start capturing video and images for you automatically at startup. To enable Motion (the motion daemon) on start-up, edit the /etc/default/motion file.

sudo nano /etc/default/motion

Change the ‘start_motion_daemon‘ setting to ‘yes’. If you decide to stop using the Raspberry Pi for capturing video, remember to disable this option. Motion will keep generating video and images, even without a camera connected, if the daemon process is running.

Capturing Dashboard Video

Although taking dashboard camera videos with your Raspberry Pi sounds easy, it presents several challenges. How will you mount your camera? How will you adjust your camera’s view? How will you power your Raspberry Pi in the vehicle? How will you power-down your Raspberry Pi from the vehicle? How will you make sure Motion is running? How will you get the video and images off the Raspberry Pi? Do you have one a mini keyboard and LCD monitor to use in your vehicle? Or, is your Raspberry Pi on your wireless network? If so, do you know how to bring up the camera’s view and Motion’s admin site on your smartphone’s web-browser?

My start-up process is as follows:

Start my car.
Plug the webcam and the power cable into the Raspberry Pi.
Let the Raspberry Pi boot up fully and allow Motion to start. This takes less than one minute.
Open the http address Motion serves up using my mobile browser.
(Since my Raspberry Pi has a wireless USB adapter installed and I’m still able to connect from my garage).
Adjust the camera using the mobile browser view from the camera.
Optionally, use Motion’s ‘HTTP Based Control’ feature to adjust any Motion configurations, on-the-fly (great option).

Logitech Webcam C210 Webcam Mounted on Car Sun Visor

Raspberry Pi in Vehicle with iPhone Preview of Dashboard Camera

Adjusting Dashboard Camera using iPhone Preview over LAN Connection to Raspberry Pi

Adjusting Camera using iPhone WiFi Connection to Raspberry Pi

Using Motion’s HTTP Based Control on iPhone Mobile Web Browser

Once I reach my destination, I copy the video and/or still image frames off the Raspberry Pi:

Let the car run for at least 1-2 minutes after you stop. The Raspberry Pi is still processing the images and video.
Copy the files off the Raspberry Pi over the local network, right from car (if in range of my LAN).
Alternately, shut down the Raspberry Pi by using a SSH mobile app on your smartphone, or just shut the car off (this not the safest method!).
Place the Pi’s SDHC card into my laptop and copy the video and/or still image frames.

Shutting Down Raspberry Pi Using SSH Terminal iPhone App

Here are some tips I’ve found to make creating dashboard camera video’s easier and better quality:

Leave your camera in your vehicle once you mount and position it.
Make sure your camera is secure so the vehicle’s vibrations while driving don’t create bouncy-images or change the position of the camera field of view.
Clean your vehicle’s front window, inside and out. Bugs or other dirt are picked up by the camera and may affect the webcam’s focus.
Likewise, film on the window from smoking or dirt will soften the details of the video and create harsh glare when driving on sunny days.
Similarly, make sure your camera’s lens is clean.
Keep your dashboard clear of objects such as paper, as it reflects on the window and will obscure the dashboard camera’s video.
Constantly stopping your Raspberry Pi by shutting the vehicle off can potential damage the Raspberry Pi and/or corrupt the operating system.
Make sure to keep your Raspberry Pi out of sight of potential thieves and the direct sun when you are not driving.
Backup your Raspberry Pi’s SDHC card before using for dashboard camera, see Duplicating Your Raspberry Pi’s SDHC Card.

Creating Video from Individual Dashboard Camera Images

FFmpeg

If you choose the second method for capturing dashboard camera videos, the easiest way to combine the individual dashboard camera images is by calling FFmpeg from the command line. To create the example #3 video, shown below, I ran two commands from a Linux Terminal prompt. The first command is a bash command to rename all the images to four-digit incremented numbers (‘0001.jpg’, ‘0002.jpg’, ‘0003.jpg’, etc.). This makes it easier to execute the second command. I found this script on stackoverflow. It requires Gawk (‘sudo apt-get install gawk’). If you are unsure about running this command, make a copy of the original images in case something goes wrong.

The second command is a basic FFmpeg command to combine the images into a 20 fps MPEG-4 video file. More information on running FFmpeg can be found on their website. There is a huge number of options available with FFmpeg from the command line. Running this command, FFmpeg processed 4,666 frames at 640 x 480 pixels in 233.30 seconds, outputting a 147.5 Mb MPEG-4 video file.

find -name '*.jpg' | sort | gawk '{ printf "mv %s %04d.jpg\n", $0, NR }' | bash 
ffmpeg -r 20 -qscale 2  -i %04d.jpg output.mp4

FFmpeg Command Line Video Creation Output

Example #3 – FFmpeg Video from Command Line

If you want to compress the video, you can chain a second FFmpeg command to the first one, similar to the one below. In my tests, this reduced the video size to 20-25% of the original uncompressed version.

ffmpeg -r 20 -qscale 2 -i %04d.jpg output.mp4 && ffmpeg -i output.mp4 -vcodec mpeg2video output_compressed.mp4

If your images are to dark (early morning or overcast) or have a color-cast (poor webcam or tinted-windows), you can use programs like ImageMagick to adjust all the images as a single batch. In example #5 below, I pre-processed all the images prior to making the video. With one ImageMagick command, I adjusting their levels to make them lighter and less flat.

mogrify -level 12%,98%,1.79 *.jpg

Example #5 – FFmpeg Uncompressed Video from Command Line

Windows MovieMaker

Using Windows MovieMaker was not my first choice, but I’ve had a tough time finding an equivalent Linux gui-based application. If you are going to create your own video from the still images, you need to be able to import and adjust thousands of images quickly and easily. I can import, create, and export a typical video of a 30 minute trip in 10 minutes with MovieMaker. With MovieMaker, you can also add titles, special effects, and so forth.

Single Images Combined in Windows MovieMaker

Sample Videos

Below are a few dashboard video examples using a variety of methods. In the first two examples, I captured still images and created the FFmpeg video at the same time. You can compare quality of Method #1 to #2.

Example #2a – Motion/FFmpeg Video

Example #2b – Windows MovieMaker

Example #5 – FFmpeg Compressed Video from Command Line

Example #6 – FFmpeg Compressed Video from Command Line

Useful Links

Renaming files in a folder to sequential numbers

Useful FFmpeg Syntax Examples

ImageMagick: Command-line Options

ImageMagick: Mogrify — in-place batch processing

Duplicating Your Raspberry Pi’s SDHC Card

dashboard camera video, dashboard-camera, Debian, FFmpeg, Linux, Motion, Raspberry Pi, RaspPi, RPi, Surveillance, webcam

12 Comments

Duplicating Your Raspberry Pi’s SDHC Card

Posted by Gary A. Stafford in Software Development on February 12, 2013

There are a few reasons you might want to duplicate (clone/copy) your Raspberry Pi’s Secure Digital High-Capacity (SDHC) card. I had two, backup and a second Raspberry Pi. I spent untold hours installing and configuring software on your Raspberry Pi with Java, OpenCV, Motion, etc. Having a backup of all my work seemed like a good idea.

Second reason, a second Raspberry Pi. I wanted to set up a second Raspberry Pi, but didn’t want to spend the time to duplicate my previous efforts. Nor, could I probably ever duplicate the first Pi’s configuration, exactly. To ensure consistency across multiple Raspberry Pi’s, duplicating my first Raspberry Pi’s SDHC card made a lot of sense.

I found several posts on the web about duplicating an SDHC card. One of the best articles was on the PIXHAWK website. It only took me a few simple steps to backup my original 8 GB SDHC card, and then create a clone by copying the backup to a new 8 GB SDHC card, as follows:

1) Remove the original SDHC card from Raspberry Pi and insert it into a card reader on your computer. I strongly suggest locking the card to protect it against any mistakes while backing up.

2) Locate where the SDHC card is mounted on your computer. This can be done using GParted, or in a terminal window, using the ‘blkid’ (block device attributes) command. My Raspberry Pi’s SDHC card, with its three separate partitions was found at ‘/dev/sdb’.

GParted View of SDHC Card

Terminal Window View of Partitions

3) Use the ‘dd’ (convert and copy a file) command to duplicate the contents of the SDHC card to your computer. This can take a while and there is no progress bar. The command I used to back up the card to my computer’s $HOME directory was:

sudo dd if=/dev/sdb of=~/sdhc-card-bu.bin

4) Unmount and unlock the original SDHC card. Mount the new SDHC card. It should mount in the same place.

5) Reverse the process by copying the backup file, ‘sdhc-card-bu.bin’, to the new SDHC card. Again, this can take a while and there is no progress bar. The command I used was:

sudo dd if=~/sdhc-card-bu.bin of=/dev/sdb

Using ‘dd’, backups and restores the entire SDHC card, partitions and all. I was able to insert the card into a brand new Raspberry Pi and boot it up, without any problems.

Obviously, there are some things you may want to change on a cloned Raspberry Pi. For example, you should change the cloned Raspberry Pi’s host name, so it doesn’t conflict with the original Raspberry Pi on the network. This is easily done:

sudo nano /etc/hostname
sudo /etc/init.d/hostname.sh start

Also, changing the cloned Raspberry Pi’s root password is a wise idea for both security and sanity, especially if you have more than one Pi on your network. This guarantees you know which one you are logging into. This is easily done using the ‘passwd’ command:

Changing the Root Password on Raspberry Pi

Back Up, Clone, dd, Duplicate, Linux, Raspberry Pi, RaspberryPi, RaspPi, RPi, SDHC

8 Comments

Object Tracking on the Raspberry Pi with C++, OpenCV, and cvBlob

Posted by Gary A. Stafford in Bash Scripting, C++ Development, Raspberry Pi on February 9, 2013

Use C++ with OpenCV and cvBlob to perform image processing and object tracking on the Raspberry Pi, using a webcam.

Source code and compiled samples are now available on GitHub. The below post describes the original code on the ‘Master’ branch. As of May 2014, there is a revised and improved version of the project on the ‘rev05_2014’ branch, on GitHub. The README.md details the changes and also describes how to install OpenCV, cvBlob, and all dependencies!

Introduction

As part of a project with a local FIRST Robotics Competition (FRC) Team, I’ve been involved in developing a Computer Vision application for use on the Raspberry Pi. Our FRC team’s goal is to develop an object tracking and target acquisition application that could be run on the Raspberry Pi, as opposed to the robot’s primary embedded processor, a National Instrument’s NI cRIO-FRC II. We chose to work in C++ for its speed, We also decided to test two popular open-source Computer Vision (CV) libraries, OpenCV and cvBlob.

Due to its single ARM1176JZF-S 700 MHz ARM processor, a significant limitation of the Raspberry Pi is the ability to perform complex operations in real-time, such as image processing. In an earlier post, I discussed Motion to detect motion with a webcam on the Raspberry Pi. Although the Raspberry Pi was capable of running Motion, it required a greatly reduced capture size and frame-rate. And even then, the Raspberry Pi’s ability to process the webcam’s feed was very slow. I had doubts it would be able to meet the processor-intense requirements of this project.

Development for the Raspberry Pi

Using C++ in NetBeans 7.2.1 on Ubuntu 12.04.1 LTS and 12.10, I wrote several small pieces of code to demonstrate the Raspberry Pi’s ability to perform basic image processing and object tracking. Parts of the follow code are based on several OpenCV and cvBlob code examples, found in my research. Many of those examples are linked on the end of this article. Examples of cvBlob are especially hard to find.

Project in NetBeans

The Code

There are five files: ‘main.cpp’, ‘testfps.cpp (testfps.h)’, and ‘testcvblob.cpp (testcvblob.h)’. The main.cpp file’s main method calls the test methods in the other two files. The cvBlob library only works with the pre-OpenCV 2.0. Therefore, I wrote all the code using the older objects and methods. The code is not written using the latest OpenCV 2.0 conventions. For example, cvBlob uses 1.0’s ‘IplImage’ image type instead 2.0’s newer ‘CvMat’ image type. My next projects is to re-write the cvBlob code to use OpenCV 2.0 conventions and/or find a newer library. The cvBlob library offered so many advantages, I felt not using the newer OpenCV 2.0 features was still worthwhile.

Main Program Method (main.cpp)

	/*
	* File: main.cpp
	* Author: Gary Stafford
	* Description: Program entry point
	* Created: February 3, 2013
	*/

	#include <stdio.h>
	#include <sstream>
	#include <stdlib.h>
	#include <iostream>

	#include "testfps.hpp"
	#include "testcvblob.hpp"

	using namespace std;

	int main(int argc, char* argv[]) {
	int captureMethod = 0;
	int captureWidth = 0;
	int captureHeight = 0;

	if (argc == 4) { // user input parameters with call
	captureMethod = strtol(argv[1], NULL, 0);
	captureWidth = strtol(argv[2], NULL, 0);
	captureHeight = strtol(argv[3], NULL, 0);

	} else { // user did not input parameters with call
	cout << endl << "Demonstrations/Tests: " << endl;
	cout << endl << "(1) Test OpenCV - Show Webcam" << endl;
	cout << endl << "(2) Test OpenCV - No Webcam" << endl;
	cout << endl << "(3) Test cvBlob - Show Image" << endl;
	cout << endl << "(4) Test cvBlob - No Image" << endl;
	cout << endl << "(5) Test Blob Tracking - Show Webcam" << endl;
	cout << endl << "(6) Test Blob Tracking - No Webcam" << endl;
	cout << endl << "Input test # (1-6): ";
	cin >> captureMethod;

	// test 3 and 4 don't require width and height parameters
	if (captureMethod != 3 && captureMethod != 4) {
	cout << endl << "Input capture width (pixels): ";
	cin >> captureWidth;
	cout << endl << "Input capture height (pixels): ";
	cin >> captureHeight;
	cout << endl;

	if (!captureWidth > 0) {
	cout << endl << "Width value incorrect" << endl;
	return -1;
	}

	if (!captureHeight > 0) {
	cout << endl << "Height value incorrect" << endl;
	return -1;
	}
	}
	}

	switch (captureMethod) {
	case 1:
	TestFpsShowVideo(captureWidth, captureHeight);
	case 2:
	TestFpsNoVideo(captureWidth, captureHeight);
	break;
	case 3:
	DetectBlobsShowStillImage();
	break;
	case 4:
	DetectBlobsNoStillImage();
	break;
	case 5:
	DetectBlobsShowVideo(captureWidth, captureHeight);
	break;
	case 6:
	DetectBlobsNoVideo(captureWidth, captureHeight);
	break;
	default:
	break;
	}
	return 0;
	}

view raw main.cpp hosted with ❤ by GitHub

Tests 1-2 (testcvblob.hpp)

	// -- C++ --
	/*
	* File: testcvblob.hpp
	* Author: Gary Stafford
	* Created: February 3, 2013
	*/

	#ifndef TESTCVBLOB_HPP
	#define TESTCVBLOB_HPP

	int DetectBlobsNoStillImage();
	int DetectBlobsShowStillImage();
	int DetectBlobsNoVideo(int captureWidth, int captureHeight);
	int DetectBlobsShowVideo(int captureWidth, int captureHeight);

	#endif /* TESTCVBLOB_HPP */

view raw testcvblob.hpp hosted with ❤ by GitHub

Tests 1-2 (testcvblob.cpp)

	/*
	* File: testcvblob.cpp
	* Author: Gary Stafford
	* Description: Track blobs using OpenCV and cvBlob
	* Created: February 3, 2013
	*/

	#include <cv.h>
	#include <highgui.h>
	#include <cvblob.h>

	#include "testcvblob.hpp"

	using namespace cvb;
	using namespace std;

	// Test 3: OpenCV and cvBlob (w/ webcam feed)
	int DetectBlobsNoStillImage() {
	/// Variables /////////////////////////////////////////////////////////
	CvSize imgSize;
	IplImage image, segmentated, *labelImg;
	CvBlobs blobs;

	unsigned int result = 0;
	///////////////////////////////////////////////////////////////////////

	image = cvLoadImage("colored_balls.jpg");

	if (image == NULL) {
	return -1;
	}

	imgSize = cvGetSize(image);
	cout << endl << "Width (pixels): " << image->width;
	cout << endl << "Height (pixels): " << image->height;
	cout << endl << "Channels: " << image->nChannels;
	cout << endl << "Bit Depth: " << image->depth;
	cout << endl << "Image Data Size (kB): "
	<< image->imageSize / 1024 << endl << endl;

	segmentated = cvCreateImage(imgSize, 8, 1);

	cvInRangeS(image, CV_RGB(155, 0, 0), CV_RGB(255, 130, 130), segmentated);

	labelImg = cvCreateImage(cvGetSize(image), IPL_DEPTH_LABEL, 1);

	result = cvLabel(segmentated, labelImg, blobs);

	cvFilterByArea(blobs, 500, 1000000);

	cout << endl << "Blob Count: " << blobs.size();
	cout << endl << "Pixels Labeled: " << result << endl << endl;

	cvReleaseBlobs(blobs);

	cvReleaseImage(&labelImg);
	cvReleaseImage(&segmentated);
	cvReleaseImage(&image);

	return 0;
	}

	// Test 4: OpenCV and cvBlob (w/o webcam feed)
	int DetectBlobsShowStillImage() {
	/// Variables /////////////////////////////////////////////////////////
	CvSize imgSize;
	IplImage image, frame, segmentated, labelImg;
	CvBlobs blobs;

	unsigned int result = 0;
	bool quit = false;
	///////////////////////////////////////////////////////////////////////

	cvNamedWindow("Processed Image", CV_WINDOW_AUTOSIZE);
	cvMoveWindow("Processed Image", 750, 100);
	cvNamedWindow("Image", CV_WINDOW_AUTOSIZE);
	cvMoveWindow("Image", 100, 100);

	image = cvLoadImage("colored_balls.jpg");

	if (image == NULL) {
	return -1;
	}

	imgSize = cvGetSize(image);
	cout << endl << "Width (pixels): " << image->width;
	cout << endl << "Height (pixels): " << image->height;
	cout << endl << "Channels: " << image->nChannels;
	cout << endl << "Bit Depth: " << image->depth;
	cout << endl << "Image Data Size (kB): "
	<< image->imageSize / 1024 << endl << endl;

	frame = cvCreateImage(imgSize, image->depth, image->nChannels);

	cvConvertScale(image, frame, 1, 0);

	segmentated = cvCreateImage(imgSize, 8, 1);

	cvInRangeS(image, CV_RGB(155, 0, 0), CV_RGB(255, 130, 130), segmentated);
	cvSmooth(segmentated, segmentated, CV_MEDIAN, 7, 7);

	labelImg = cvCreateImage(cvGetSize(frame), IPL_DEPTH_LABEL, 1);

	result = cvLabel(segmentated, labelImg, blobs);
	cvFilterByArea(blobs, 500, 1000000);
	cvRenderBlobs(labelImg, blobs, frame, frame,
	CV_BLOB_RENDER_BOUNDING_BOX \| CV_BLOB_RENDER_TO_STD, 1.);

	cvShowImage("Image", frame);
	cvShowImage("Processed Image", segmentated);

	while (!quit) {
	char k = cvWaitKey(10)&0xff;
	switch (k) {
	case 27:
	case 'q':
	case 'Q':
	quit = true;
	break;
	}
	}
	cvReleaseBlobs(blobs);

	cvReleaseImage(&labelImg);
	cvReleaseImage(&segmentated);
	cvReleaseImage(&frame);
	cvReleaseImage(&image);

	cvDestroyAllWindows();

	return 0;
	}

	// Test 5: Blob Tracking (w/ webcam feed)
	int DetectBlobsNoVideo(int captureWidth, int captureHeight) {
	/// Variables /////////////////////////////////////////////////////////
	CvCapture *capture;
	CvSize imgSize;

	IplImage image, frame, segmentated, labelImg;
	int picWidth, picHeight;

	CvTracks tracks;
	CvBlobs blobs;
	CvBlob* blob;

	unsigned int result = 0;

	bool quit = false;
	///////////////////////////////////////////////////////////////////////

	capture = cvCaptureFromCAM(-1);
	cvSetCaptureProperty(capture, CV_CAP_PROP_FRAME_WIDTH, captureWidth);
	cvSetCaptureProperty(capture, CV_CAP_PROP_FRAME_HEIGHT, captureHeight);
	cvGrabFrame(capture);
	image = cvRetrieveFrame(capture);

	if (image == NULL) {
	return -1;
	}

	imgSize = cvGetSize(image);
	cout << endl << "Width (pixels): " << image->width;
	cout << endl << "Height (pixels): " << image->height << endl << endl;

	frame = cvCreateImage(imgSize, image->depth, image->nChannels);

	while (!quit && cvGrabFrame(capture)) {
	image = cvRetrieveFrame(capture);

	cvConvertScale(image, frame, 1, 0);

	segmentated = cvCreateImage(imgSize, 8, 1);

	cvInRangeS(image, CV_RGB(155, 0, 0), CV_RGB(255, 130, 130), segmentated);

	//Can experiment either or both
	cvSmooth(segmentated, segmentated, CV_MEDIAN, 7, 7);
	cvSmooth(segmentated, segmentated, CV_GAUSSIAN, 9, 9);

	labelImg = cvCreateImage(cvGetSize(frame), IPL_DEPTH_LABEL, 1);

	result = cvLabel(segmentated, labelImg, blobs);
	cvFilterByArea(blobs, 500, 1000000);
	cvRenderBlobs(labelImg, blobs, frame, frame, 0x000f, 1.);
	cvUpdateTracks(blobs, tracks, 200., 5);
	cvRenderTracks(tracks, frame, frame, 0x000f, NULL);

	picWidth = frame->width;
	picHeight = frame->height;

	if (cvGreaterBlob(blobs)) {
	blob = blobs[cvGreaterBlob(blobs)];

	cout << "Blobs found: " << blobs.size() << endl;
	cout << "Pixels labeled: " << result << endl;
	cout << "center-x: " << blob->centroid.x
	<< " center-y: " << blob->centroid.y
	<< endl;
	cout << "offset-x: " << ((picWidth / 2)-(blob->centroid.x))
	<< " offset-y: " << (picHeight / 2)-(blob->centroid.y)
	<< endl;
	cout << "\n";
	}

	char k = cvWaitKey(10)&0xff;
	switch (k) {
	case 27:
	case 'q':
	case 'Q':
	quit = true;
	break;
	}
	}

	cvReleaseBlobs(blobs);

	cvReleaseImage(&labelImg);
	cvReleaseImage(&segmentated);
	cvReleaseImage(&frame);
	cvReleaseImage(&image);

	cvDestroyAllWindows();

	cvReleaseCapture(&capture);

	return 0;
	}

	// Test 6: Blob Tracking (w/o webcam feed)
	int DetectBlobsShowVideo(int captureWidth, int captureHeight) {
	/// Variables /////////////////////////////////////////////////////////
	CvCapture *capture;
	CvSize imgSize;

	IplImage image, frame, segmentated, labelImg;
	CvPoint pt1, pt2, pt3, pt4, pt5, pt6;
	CvScalar red, green, blue;
	int picWidth, picHeight, thickness;

	CvTracks tracks;
	CvBlobs blobs;
	CvBlob* blob;

	unsigned int result = 0;

	bool quit = false;
	///////////////////////////////////////////////////////////////////////

	cvNamedWindow("Processed Video Frames", CV_WINDOW_AUTOSIZE);
	cvMoveWindow("Processed Video Frames", 750, 400);
	cvNamedWindow("Webcam Preview", CV_WINDOW_AUTOSIZE);
	cvMoveWindow("Webcam Preview", 200, 100);

	capture = cvCaptureFromCAM(1);
	cvSetCaptureProperty(capture, CV_CAP_PROP_FRAME_WIDTH, captureWidth);
	cvSetCaptureProperty(capture, CV_CAP_PROP_FRAME_HEIGHT, captureHeight);
	cvGrabFrame(capture);
	image = cvRetrieveFrame(capture);

	if (image == NULL) {
	return -1;
	}

	imgSize = cvGetSize(image);
	cout << endl << "Width (pixels): " << image->width;
	cout << endl << "Height (pixels): " << image->height << endl << endl;

	frame = cvCreateImage(imgSize, image->depth, image->nChannels);

	while (!quit && cvGrabFrame(capture)) {
	image = cvRetrieveFrame(capture);
	cvFlip(image, image, 1);

	cvConvertScale(image, frame, 1, 0);

	segmentated = cvCreateImage(imgSize, 8, 1);

	//Blue paper
	cvInRangeS(image, CV_RGB(49, 69, 100), CV_RGB(134, 163, 216), segmentated);

	//Green paper
	//cvInRangeS(image, CV_RGB(45, 92, 76), CV_RGB(70, 155, 124), segmentated);

	//Can experiment either or both
	cvSmooth(segmentated, segmentated, CV_MEDIAN, 7, 7);
	cvSmooth(segmentated, segmentated, CV_GAUSSIAN, 9, 9);

	labelImg = cvCreateImage(cvGetSize(frame), IPL_DEPTH_LABEL, 1);

	result = cvLabel(segmentated, labelImg, blobs);
	cvFilterByArea(blobs, 500, 1000000);
	cvRenderBlobs(labelImg, blobs, frame, frame, CV_BLOB_RENDER_COLOR, 0.5);
	cvUpdateTracks(blobs, tracks, 200., 5);
	cvRenderTracks(tracks, frame, frame, CV_TRACK_RENDER_BOUNDING_BOX, NULL);

	red = CV_RGB(250, 0, 0);
	green = CV_RGB(0, 250, 0);
	blue = CV_RGB(0, 0, 250);

	thickness = 1;
	picWidth = frame->width;
	picHeight = frame->height;

	pt1 = cvPoint(picWidth / 2, 0);
	pt2 = cvPoint(picWidth / 2, picHeight);
	cvLine(frame, pt1, pt2, red, thickness);

	pt3 = cvPoint(0, picHeight / 2);
	pt4 = cvPoint(picWidth, picHeight / 2);
	cvLine(frame, pt3, pt4, red, thickness);

	cvShowImage("Webcam Preview", frame);
	cvShowImage("Processed Video Frames", segmentated);

	if (cvGreaterBlob(blobs)) {
	blob = blobs[cvGreaterBlob(blobs)];
	pt5 = cvPoint(picWidth / 2, picHeight / 2);
	pt6 = cvPoint(blob->centroid.x, blob->centroid.y);
	cvLine(frame, pt5, pt6, green, thickness);
	cvCircle(frame, pt6, 3, green, 2, CV_FILLED, 0);

	cvShowImage("Webcam Preview", frame);
	cvShowImage("Processed Video Frames", segmentated);

	cout << "Blobs found: " << blobs.size() << endl;
	cout << "Pixels labeled: " << result << endl;
	cout << "center-x: " << blob->centroid.x
	<< " center-y: " << blob->centroid.y
	<< endl;
	cout << "offset-x: " << ((picWidth / 2)-(blob->centroid.x))
	<< " offset-y: " << (picHeight / 2)-(blob->centroid.y)
	<< endl;
	cout << "\n";
	}

	char k = cvWaitKey(10)&0xff;
	switch (k) {
	case 27:
	case 'q':
	case 'Q':
	quit = true;
	break;
	}
	}

	cvReleaseBlobs(blobs);

	cvReleaseImage(&labelImg);
	cvReleaseImage(&segmentated);
	cvReleaseImage(&frame);
	cvReleaseImage(&image);

	cvDestroyAllWindows();

	cvReleaseCapture(&capture);

	return 0;
	}

view raw testcvblob.cpp hosted with ❤ by GitHub

Tests 2-6 (testfps.hpp)

	// -- C++ --
	/*
	* File: testfps.hpp
	* Author: Gary Stafford
	* Created: February 3, 2013
	*/

	#ifndef TESTFPS_HPP
	#define TESTFPS_HPP

	int TestFpsNoVideo(int captureWidth, int captureHeight);
	int TestFpsShowVideo(int captureWidth, int captureHeight);

	#endif /* TESTFPS_HPP */

view raw testfps.hpp hosted with ❤ by GitHub

Tests 2-6 (testfps.cpp)

	/*
	* File: testfps.cpp
	* Author: Gary Stafford
	* Description: Test the fps of a webcam using OpenCV
	* Created: February 3, 2013
	*/

	#include <cv.h>
	#include <highgui.h>
	#include <time.h>
	#include <stdio.h>

	#include "testfps.hpp"

	using namespace std;

	// Test 1: OpenCV (w/ webcam feed)
	int TestFpsNoVideo(int captureWidth, int captureHeight) {
	IplImage* frame;
	CvCapture* capture = cvCreateCameraCapture(-1);
	cvSetCaptureProperty(capture, CV_CAP_PROP_FRAME_WIDTH, captureWidth);
	cvSetCaptureProperty(capture, CV_CAP_PROP_FRAME_HEIGHT, captureHeight);

	time_t start, end;
	double fps, sec;
	int counter = 0;
	char k;

	time(&start);

	while (1) {
	frame = cvQueryFrame(capture);
	time(&end);
	++counter;
	sec = difftime(end, start);
	fps = counter / sec;
	printf("FPS = %.2f\n", fps);

	if (!frame) {
	printf("Error");
	break;
	}

	k = cvWaitKey(10)&0xff;
	switch (k) {
	case 27:
	case 'q':
	case 'Q':
	break;
	}
	}

	cvReleaseCapture(&capture);

	return 0;
	}

	// Test 2: OpenCV (w/o webcam feed)
	int TestFpsShowVideo(int captureWidth, int captureHeight) {
	IplImage* frame;
	CvCapture* capture = cvCreateCameraCapture(-1);
	cvSetCaptureProperty(capture, CV_CAP_PROP_FRAME_WIDTH, captureWidth);
	cvSetCaptureProperty(capture, CV_CAP_PROP_FRAME_HEIGHT, captureHeight);
	cvNamedWindow("Webcam Preview", CV_WINDOW_AUTOSIZE);
	cvMoveWindow("Webcam Preview", 300, 200);

	time_t start, end;
	double fps, sec;
	int counter = 0;
	char k;

	time(&start);

	while (1) {
	frame = cvQueryFrame(capture);
	time(&end);
	++counter;
	sec = difftime(end, start);
	fps = counter / sec;
	printf("FPS = %.2f\n", fps);

	if (!frame) {
	printf("Error");
	break;
	}

	cvShowImage("Webcam Preview", frame);

	k = cvWaitKey(10)&0xff;
	switch (k) {
	case 27:
	case 'q':
	case 'Q':
	break;
	}
	}

	cvDestroyWindow("Webcam Preview");
	cvReleaseCapture(&capture);

	return 0;
	}

view raw testfps.cpp hosted with ❤ by GitHub

Compiling Locally on the Raspberry Pi

After writing the code, the first big challenge was cross-compiling the native C++ code, written on Intel IA-32 and 64-bit x86-64 processor-based laptops, to run on the Raspberry Pi’s ARM architecture. After failing to successfully cross-compile the C++ source code using crosstools-ng, mostly due to my lack of cross-compiling experience, I resorted to using g++ to compile the C++ source code directly on the Raspberry Pi.

First, I had to properly install the various CV libraries and the compiler on the Raspberry Pi, which itself is a bit daunting.

Compiling OpenCV 2.4.3, from the source-code, on the Raspberry Pi took an astounding 8 hours. Even though compiling the C++ source code takes longer on the Raspberry Pi, I could be assured the complied code would run locally. Below are the commands that I used to transfer and compile the C++ source code on my Raspberry Pi.

Copy and Compile Commands

	scp .jpg .cpp *.h {your-pi-user}@{your.ip.address}:your/file/path/
	ssh {your-pi-user}@{your.ip.address}
	cd ~/your/file/path/
	g++ `pkg-config opencv cvblob --cflags --libs` testfps.cpp testcvblob.cpp main.cpp -o FpsTest -v
	./FpsTest

view raw compile-on-rasppi.sh hosted with ❤ by GitHub

Compiling Program on Raspberry Pi

Special Note About cvBlob on ARM

At first I had given up on cvBlob working on the Raspberry Pi. All the cvBlob tests I ran, no matter how simple, continued to hang on the Raspberry Pi after working perfectly on my laptop. I had narrowed the problem down to the ‘cvLabel’ method, but was unable to resolve. However, I recently discovered a documented bug on the cvBlob website. It concerned cvBlob and the very same ‘cvLabel’ method on ARM-based devices (ARM = Raspberry Pi!). After making a minor modification to cvBlob’s ‘cvlabel.cpp’ source code, as directed in the bug post, and re-compiling on the Raspberry Pi, the test worked perfectly.

Testing OpenCV and cvBlob

The code contains three pairs of tests (six total), as follows:

OpenCV (w/ live webcam feed)
Determine if OpenCV is installed and functioning properly with the complied C++ code. Capture a webcam feed using OpenCV, and display the feed and frame rate (fps).
OpenCV (w/o live webcam feed)
Same as Test #1, but only print the frame rate (fps). The computer doesn’t need display the video feed to process the data. More importantly, the webcam’s feed might unnecessarily tax the computer’s processor and GPU.
OpenCV and cvBlob (w/ live webcam feed)
Determine if OpenCV and cvBlob are installed and functioning properly with the complied C++ code. Detect and display all objects (blobs) in a specific red color range, contained in a static jpeg image.
OpenCV and cvBlob (w/o live webcam feed)
Same as Test #3, but only print some basic information about the static image and number of blobs detected. Again, the computer doesn’t need display the video feed to process the data.
Blob Tracking (w/ live webcam feed)
Detect, track, and display all objects (blobs) in a specific blue color range, along with the largest blob’s positional data. Captured with a webcam, using OpenCV and cvBlob.
Blob Tracking (w/o live webcam feed)
Same as Test #5, but only display the largest blob’s positional data. Again, the computer doesn’t need the display the webcam feed, to process the data. The feed taxes the computer’s processor unnecessarily, which is being consumed with detecting and tracking the blobs. The blob’s positional data it sent to the robot and used by its targeting system to position its shooting platform.

The Program

There are two ways to run this program. First, from the command line you can call the application and pass in three parameters. The parameters include:

Test method you want to run (1-6)
Width of the webcam capture window in pixels
Height of the webcam capture window in pixels.

An example would be ‘./TestFps 2 640 480’ or ‘./TestFps 5 320 240’.

The second method to run the program and not pass in any parameters. In that case, the program will prompt you to input the test number and other parameters on-screen.

Input Options for Application

Test 1: Laptop versus Raspberry Pi

Test 1: Displaying Webcam Feed using OpenCV (laptop)

Test 1: Displaying Webcam Feed using OpenCV (Raspberry Pi)

Test 3: Laptop versus Raspberry Pi

Test 3: Detecting Red Color Range in Static Image using OpenCV and cvBlob (laptop)

Test 3: Detecting Red Color Range in Static Image using OpenCV and cvBlob (Raspberry Pi)

Test 5: Detecting Objects within Blue Color Range using OpenCV and cvBlob (laptop)

Test 5: Laptop versus Raspberry Pi

Test 5: Detecting Objects within Blue Color Range using OpenCV and cvBlob (Raspberry Pi)

The Results

Each test was first run on two Linux-based laptops, with Intel 32-bit and 64-bit architectures, and with two different USB webcams. The laptops were used to develop and test the code, as well as provide a baseline for application performance. Many factors can dramatically affect the application’s ability do image processing. They include the computer’s processor(s), RAM, HDD, GPU, USB, Operating System, and the webcam’s video capture size, compression ratio, and frame-rate. There are significant differences in all these elements when comparing an average laptop to the Raspberry Pi.

Frame-rates on the Intel processor-based Ubuntu laptops easily performed at or beyond the maximum 30 fps rate of the webcams, at 640 x 480 pixels. On a positive note, the Raspberry Pi was able to compile and execute the tests of OpenCV and cvBlob (see bug noted at end of article). Unfortunately, at least in my tests, the Raspberry Pi could not achieve more than 1.5 – 2 fps at most, even in the most basic tests, and at a reduced capture size of 320 x 240 pixels. This can be seen in the first and second screen-grabs of Test #1, above. Although, I’m sure there are ways to improve the code and optimize the image capture, the results were much to slow to provide accurate, real-time data to the robot’s targeting system.

Links of Interest

Static Test Images Free from: http://www.rgbstock.com/

Great Website for OpenCV Samples: http://opencv-code.com/

Another Good Website for OpenCV Samples: http://opencv-srf.blogspot.com/2010/09/filtering-images.html

cvBlob Code Sample: https://code.google.com/p/cvblob/source/browse/samples/red_object_tracking.cpp

Detecting Blobs with cvBlob: http://8a52labs.wordpress.com/2011/05/24/detecting-blobs-using-cvblobs-library/

Best Post/Script to Install OpenCV on Ubuntu and Raspberry Pi: http://jayrambhia.wordpress.com/2012/05/02/install-opencv-2-3-1-and-simplecv-in-ubuntu-12-04-precise-pangolin-arch-linux/

Measuring Frame-rate with OpenCV: http://8a52labs.wordpress.com/2011/05/19/frames-per-second-in-opencv/

OpenCV and Raspberry Pi: http://mitchtech.net/raspberry-pi-opencv/

ARM, Blob, Computer Vision, cplusplus, cpp, cross-compile, CV, cvBlob, image processing, Linux, motion detection, NetBeans, Object Tracking, OpenCV, Raspberry Pi, RaspberryPi, RaspPi, RPi, Ubuntu, webcam, x86

52 Comments

Remote Motion-Activated Web-Based Surveillance with Raspberry Pi

Posted by Gary A. Stafford in Bash Scripting, Raspberry Pi on January 1, 2013

Introduction

Want to keep an eye on your home or business while you’re away? Maybe observe wildlife close-up without disturbing them? Or, keep an eye on your kids playing in the backyard? Low-end wireless IP cameras start at $50-$75 USD. Higher-end units can run into the hundreds of dollars. Add motion detection and the price raises even further. How about a lower-cost solution? Using a Raspberry Pi with an inexpensive webcam, a wireless WiFi Module, and an optional battery pack, you can have a remote, motion-activated camera solution, at a fraction of the cost. Best of all, you won’t need to write a single line of code or hack any electronics to get started.

Motion

There are many posts on the Internet, demonstrating how to build a Raspberry Pi-powered motion-activated camera system. One of the more frequently used off-the-shelf applications for these projects is Motion. According to their website, ‘Motion is a program that monitors the video signal from one or more cameras and is able to detect if a significant part of the picture has changed; in other words, it can detect motion‘. Motion uses a technique known as visual motion detection (VMD) to compare a series of sequential camera frames for differences at a pixel level. A change between a series of sequential frames is an indication of movement.

Motion has the ability to stream images from a webcam and server them from it’s built-in web server, with little or no configuration. In addition, Motion is easily configured to work with streaming video applications like the very popular FFmpeg, and save images to databases like mySQL or PostgreSQL. Motion can also execute external scripts such as python or shell. In this post, we are going to use Motion’s most basic features, motion detection and web-streaming.

Installing Motion

Firmware Update
Before installing Motion, I recommend ensuring your Raspberry Pi is up-to-date with the latest software and firmware. Updating firmware is not necessary. However, I was recently helping someone with camera issue on their Raspberry Pi. Finding a few suggestions online for similar problems, we updated the firmware on the Raspberry Pi. It fixed the problem. Installing firmware can sound a bit intimidating. However, Liam McLoughlin (hexxeh) has made the process easy with rpi-update. I have used it successfully on multiple Raspberry Pi’s. Three commands is all it takes to update your Raspberry Pi to the latest firmware.

Software Update
You should also update your Raspberry Pi’s existing software. To update your Raspberry Pi’s software, execute the following apt-get commands:

sudo apt-get update && sudo apt-get upgrade

If you don’t do this on a regular basis, as recommended, these could take up to several minutes. Watch for errors. If there are any errors, try to run the command again. Sometimes the Raspberry Pi cannot connect to all code repositories for updates.

Installing Motion
Once the updates are complete, install Motion by issuing the following command:

sudo apt-get install motion

Enabling Motion

As the installation completes, you should see a warning in the command shell about Motion being disabled by default.

...
Adding user `motion' to group `video' ...
Adding user motion to group video
Done.
[warn] Not starting motion daemon, disabled via /etc/default/motion ... (warning).
Setting up ffmpeg (6:0.8.4-1) ...
pi@garyrasppi ~ $

To enable Motion (the motion daemon), we need to edit the /etc/default/motion file.

sudo nano /etc/default/motion

Change the ‘start_motion_daemon‘ parameter to ‘yes’.

Configuring Motion

Motion is easy to customize with loads of parameters you can tweak based on your needs. Motion has no GUI. All configuration is all done through Motion’s configuration file (/etc/motion/motion.conf). Before editing the configuration file, we need to change the permissions on it, so Motion can get access to it. While we are at it, we will also change permissions on the folder where Motion stores captured images.

sudo chmod -R 777 /etc/motion/motion.conf
sudo chmod -R 777 /tmp/motion

After changing the permissions, to configure Motion, open the Motion’s configuration file in a text editor, as root (sudo). I like using Nano. The configuration file can be opened in Nano with the following command:

sudo nano /etc/motion/motion.conf

Motion’s configuration file is lengthy. However, it is broken down into logical sections, making finding the setting you are looking for, easy. First, we need to change the ‘Live Webcam Server’ section of configuration. Below are the default settings:

############################################################
# Live Webcam Server
############################################################

# The mini-http server listens to this port for requests (default: 0 = disabled)
webcam_port 8081

# Quality of the jpeg (in percent) images produced (default: 50)
webcam_quality 50

# Output frames at 1 fps when no motion is detected and increase to the
# rate given by webcam_maxrate when motion is detected (default: off)
webcam_motion off

# Maximum framerate for webcam streams (default: 1)
webcam_maxrate 1

# Restrict webcam connections to localhost only (default: on)
webcam_localhost on

# Limits the number of images per connection (default: 0 = unlimited)
# Number can be defined by multiplying actual webcam rate by desired number of seconds
# Actual webcam rate is the smallest of the numbers framerate and webcam_maxrate
webcam_limit 0

The first thing you will want to change is Motion’s default setting that restricts image streaming to ‘localhost‘, only ( ‘webcam_localhost on‘). This means you can only view images in a web browser on the Raspberry Pi, not remotely over your network. Change that line of code to read ‘webcam_localhost off‘.

The next setting I recommend changing for security purposes is the default port Motion’s web server uses to stream images, 8081. Security through obscurity is better than no security at all. Change port 8081 to a different arbitrary port, for example, 6789 (‘webcam_port 6789‘). Just make sure you don’t pick a port already in use by another service or application. Having made this change, if your Raspberry Pi’s local IP address is 192.168.1.9, images from the webcam should be accessible at 192.168.1.9:6789.

The other two settings in this section you can play with are the webcam quality and maximum frame-rate. You will have to adjust this based on your network speed and the processing power of your Raspberry Pi. The default settings are a good place to start. I changed my quality from the default of 50 to 80 (‘webcam_quality 80‘), and changed my max frame-rate to 2 (‘webcam_maxrate 2‘).

Speaking of quality, the other two settings you may want to change are the width and height of the image being captured by Motion. The ‘Capture device options’ section is where we change these settings. As the configuration’s comments suggest, these settings are dependent on your camera. Check the camera’s available image sizes; you will need to use one of those size combinations. I have mine set to an average size of 352 x 288. This is a good size for those of us with a slower network, or when streaming video over the Internet to mobile web browser. Conversely, a larger image is better for viewing over your local network.

Image size, like compression quality, and frame-rate are dependent on processing power of your Raspberry Pi and it’s OS (Raspbian, Debian, Arch, etc.). You may need to play with these settings to get the desired results. I couldn’t stream images larger than 352 x 288 over the Internet, with my Raspberry Pi, even though my webcam could capture up to 640 x 480 pixels.

# Image width (pixels). Valid range: Camera dependent, default: 352
width 352

# Image height (pixels). Valid range: Camera dependent, default: 288
height 288

It’s important to remember, each time you make changes to Motion’s configuration file, you must restart Motion, using the following command.

sudo /etc/init.d/motion restart

Viewing Your Webcam Remotely

To view your webcam’s output from another device on your local network, point your web browser to the IP address of your Raspberry Pi, and add the port you assigned in Motion’s configuration file. Motion may take up to 15-20 seconds to start responding in the browser. If it takes longer, you probably have your image size, frame-rate, and compression settings to high for your Raspberry Pi.

Over the Internet
Enabling your webcam’s output over the Internet is relatively easy with the average home router and Internet service provider. Suppose the IP address of my Raspberry Pi, on my local network, is 192.168.1.9. Suppose I assigned port 6789 to Motion’s web server. Lastly, suppose my router’s external Internet IP address is 113.45.67.88. With this information, I can create a port-forwarding rule in my router, allowing all external HTTP traffic over TCP to 113.45.67.88:3456, to be automatically forwarded internally to 192.168.1.9:6789. The external port, 3456, is totally arbitrary, just make sure you don’t pick a port already in use.

IMPORTANT SECURITY NOTE: There are no passwords or other network protection used with this method. Make sure to keep the external IP address and port combination private, and always stop Motion, or better yet your Raspberry Pi, when not in use. Otherwise, someone could potentially be watching you!

Down at the local coffee shop, I decide to check if the mailman has delivered my new Raspberry Pi to the front porch. Having set-up port-forwarding, I enter 113.45.67.88:3456 in my smartphone’s web browser. My Internet provider routes the HTTP request to my Internet router. My router receives the request and forwards it over my local network to 192.168.1.9:6789, where Motion’s built-in web server on my Raspberry Pi is running. Motion’s web server responds by streaming still images back to my phone at the coffee shop when it detects motion. Still no sign of the mailman or my Raspberry Pi…

Static IP Addresses
I recommend using a static IP address for your Raspberry Pi, versus DHCP, if possible. Else, you will have to change your router’s port-forwarding rules each time your Raspberry Pi’s DHCP lease is renewed and its local IP address changes. There are some ways to prevent addressed from changing frequently with DHCP, if your router supports it. Look for configurable lease times or reservations options in your router’s configuration; these may be able to be extended.

Locating Your External Internet IP Address
What is your router’s external Internet IP address? To find mine, I looked in Netgear’s Router Status window. You can also use a ‘tracert’ from the command line, if you know what to look for in the output.

Since I do not pay my Internet-provider for a static external Internet IP address, the address my provider assigns to my router is dynamic. It can and will change, sometimes almost never, or sometimes daily. The frequency of change depends on your provider. To view your webcam’s images, you will need to know your router’s current external Internet IP address.

Motion Example

Here are some example from a Microsoft LifeCam VX-500 and Logitech Webcam C210 webcams. The highest quality I could consistently stream over the Internet, from my Raspberry Pi 512Mb Model B, with both Soft-float Debian “wheezy” and Raspbian “wheezy”, was 352 x 288 at 80% compression and 2 fsp max. Locally on my LAN, I could reach a frame size of 640 x 480 pixels.

In the first example, I’ve placed the Raspberry Pi in a plastic container to protect it, and mounted the webcam in a flower box. Viewing the feed over my local network, we are able to watch the hummingbirds without scaring them.

In the next two images, I’ve turned on Motion’s ‘locate box’ option, which tracks the exact area within the image that is moving. As the person come into view of the camera mounted near the front door, Motion detects and outlines the area of the images where it detects movement.

In the next video, you see the view from a Google Nexus 7 tablet. My wife and I use the Raspberry Pi surveillance system to watch our backyard when our kids are outside (the camera is no substitute for adult supervision when the kids are in the pool).

This last image is from my iPhone, while shopping at the local grocery store. My wife was impressed with my port-forwarding knowledge. OK, not really, but she did enjoy showing off the Christmas tree to friends, remotely, even if it wasn’t in motion.

Useful Links

Here are a few links to other useful articles on the use of Motion with the Raspberry Pi:

Raspberry Pi-Powered Dashboard Video Camera Using Motion and FFmpeg

Setup a webcam security system with Ubuntu Linux and Motion

Guest blog #7: Bird table webcam by Francis Agius

Raspberry Pi webcam

motion(1) – Linux man page (good source for understand Motion config)

Linux UVC Supported Devices (a good starting point for buying a webcam)

FFmpeg, Linux, Miniature WiFi Module, Motion, Motion-Activated, Networking, Raspberry Pi, RaspberryPi, RaspPi, Remote, Streaming, Streaming Video, Surveillance, webcam, WiFi, Wireless

56 Comments

Installing a Miniature WiFi Module on the Raspberry Pi (w/ Roaming Enabled)

Posted by Gary A. Stafford in Raspberry Pi on December 30, 2012

Background

In a earlier post, Installing a Miniature WiFi Module on the Raspberry Pi (w/o Roaming Enabled), I detailed the installation and configuration of a Miniature WiFi Module, from Adafruit Industries, on a RaspPi running Soft-float Debian “wheezy”. As I mentioned in that post, there was more than one method of configuring the WiFi Module (WNIC) on a WLAN, based on the research I did. I chose the simple method of hard-coding a single WLAN configuration into the ‘/etc/interfaces’ file.

Recently, while installing the same type WiFi Module (WNIC) on a RaspPi running Raspbian “wheezy”, I chose the alternate method. This involves adding the WLAN configuration to the wpa_supplicant configuration file (‘/etc/wpa_supplicant/wpa_supplicant.conf’). You can add multiple WLAN configurations to the wpa_supplicant configuration file. This allowing the RaspPi to roam from networks to network, automatically connecting to those that are configured.

If you’re not comfortable configuring networks from the command shell, you can also use the wpa_gui application (aka wpa_suppicant user interface) from the RaspPi’s desktop. It allows you to edit the same configuration from a gui, just as we will do manually in the command shell.

Installing the WiFi Module Driver

Copy the ‘Linux and Android’ Realtek driver folder from the CD, supplied by the manufacturer, to the ‘tmp’ folder on the RaspPi using WinSCP. Then, run the following commands:

cd /
cd /tmp/Linux\ and\ Android
chmod +x install.sh
sudo ./install.sh

Remember to select #1 when asked to choose a card type:

...
Please select card type(1/2):
1) RTL8192cu
2) RTL8192du
#? 1

You can insert the WiFi Module at this point in the process.

Installing Wireless LAN Security Protocol Software

As detailed in the earlier post, we need to install software that allows us to configure and connect to our WPA/WPA2-secured wireless network. The particular software is referred to as ‘wpa_supplicant’. To install ‘wpa_supplicant’ and the ‘wpagui’, enter the following commands. Note this will check for any upgrades to the RaspPi’s existing software, first. This is a commonly-recommended step. The upgrade command might take a few minutes if you haven’t run this on your RaspPi in a while.

sudo apt-get update && sudo apt-get upgrade
sudo apt-get install wpasupplicant wpagui

Configuring the New WiFi Adapter

Examine the contents of the ‘/etc/networks/interfaces’ file, by entering the following command:

sudo cat /etc/network/interfaces

Unlike in the first post, we will make no changes to this file. The ‘/etc/networks/interfaces’ file should have the default settings for both the current NIC (eth0) as well as for the WNIC (wlan0), as shown below. Note the reference to the ‘/etc/wpa_supplicant/wpa_supplicant.conf’ file. Why are the file’s contents different than in the first post? Because we installed ‘wpagui’.

WPA Supplicant Configuration
Enter the following command, substituting your own SSID (‘your_ssid’) and passphrase (‘your_passphrase’).

wpa_passphrase your_ssid your_passphrase

Based your SSID and passphrase, this command will generate a pre-shared key (PSK), similar to the following. Save or copy the PSK to the clipboard; we will need it in the next step.

Next, open the ‘/etc/wpa_supplicant/wpa_supplicant.conf’ file using Nano, by entering the following command:

sudo nano /etc/wpa_supplicant/wpa_supplicant.conf

Add the following code at the end of the file. Remember to substitute your_ssid and your_psk_or_passphrase. Note the following settings are specific to my WPA2-secured network. If you are using WPA, refer to this post for the correct WPA settings.

network={
        ssid=&amp;quot;your_ssid&amp;quot;
        proto=RSN
        key_mgmt=WPA-PSK
        pairwise=CCMP
        group=CCMP
        psk=&amp;quot;your_psk_or_passphrase&amp;quot;
}

Your final file should look similar to this:

Save the file and exit Nano. Lastly, execute the following series of commands to assign an IP address to the new WNIC.

sudo wpa_supplicant -d -c /etc/wpa_supplicant/wpa_supplicant.conf -i wlan0 -D wext
sudo ifconfig wlan0 up
sudo dhclient wlan0
sudo wpa_supplicant -B -c /etc/wpa_supplicant/wpa_supplicant.conf -i wlan0 -D wext 
ip addr show wlan0

You should see an IP Address for ‘wlan0’ displayed. That it, shutdown the RaspPi, remove the Ethernet cable, and restart the RaspPi. Use a program like ‘Advanced IP Scanner’ for Windows, or ‘Fing’ for iOS, to discover the wireless IP address of the RaspPi. The RaspPi will show up with the WiFi chipset manufacturer’s name, ‘REALTEK SEMICONDUCTOR’ or ‘REALTEK SEMICONDUCTOR CORP.’. Use this address to re-connect to the RaspPi.

Need to add another network’s configuration? Simply enter the information in the ‘/etc/wpa_supplicant/wpa_supplicant.conf’ and restart. Here are a few good articles I found on configuring a WiFi Module on the RaspPi with roaming:

http://hostap.epitest.fi/wpa_supplicant/

http://www.cyberciti.biz/faq/linux-ndiswrapper-wpa_supplicant-howto/

http://linux.die.net/man/5/wpa_supplicant.conf

http://ubuntuforums.org/showthread.php?t=1259003

http://ubuntuforums.org/showthread.php?t=318539

http://unix.stackexchange.com/questions/7817/how-to-find-out-which-wi-fi-driver-is-installed

Adafruit, Advanced IP Scanner, Miniature WiFi Module, Networking, OURLiNK, PuTTY, Raspberry Pi, RaspberryPi, RaspPi, Realtek, SSH, WiFi, WinSCP, Wireless, WLAN, WNIC, WPA, WPA2

Posts Tagged Raspberry Pi

Introduction

LoRa

LoRaWAN

Recommended Hardware

IoT Device with Embedded Sensors

IoT Gateway

LoRa Transceiver Modules

Security

USB to TTL Serial Converter Adapter

Arduino Sketch

AT Commands

Configure, Test, and Debug

Receiving Data on IoT Gateway

IoT Gateway Python Script

Conclusion

Introduction

IoT Sensor

IoT Gateway

Arduino Sketch

Previewing and Debugging BLE Device Services

BLE Client Python Code

Conclusion

Introduction

Find Your Webcam

Modify Motion

Testing the Script Change

Conclusion

Introduction

Network Security

Configuring Multiple Wireless Networks

Wireless Network Priority

Conclusion

Introduction

Change Motion’s Default Location for Video and Images

Create the Startup Script to Move Video and Images

Shutdown Script?

Configure Debian OS to Run the New Startup Script

Copy and Remove Files from the Raspberry Pi

Conclusion

Resources

Dashboard Video Cameras

Methods for Creating Dashboard Camera Videos

Configuring Motion for a Dashboard Camera

Capturing Dashboard Video

Creating Video from Individual Dashboard Camera Images

Sample Videos

Useful Links

Introduction

Development for the Raspberry Pi

The Code

Compiling Locally on the Raspberry Pi

Testing OpenCV and cvBlob

The Program

The Results

Links of Interest

Introduction

Motion

Installing Motion

Enabling Motion

Configuring Motion

Viewing Your Webcam Remotely

Motion Example

Useful Links

Background

Installing the WiFi Module Driver

Installing Wireless LAN Security Protocol Software

Configuring the New WiFi Adapter

Gary Stafford

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