Lemon Difficult

In which we use OpenCV to track a laser.

Building on my article about using HSV thresholding to isolate features in video frames, below is a video showing the use of OpenCV to track a red laser.  The majority of the algorithm is the same as presented in the previous article; the only addition is actually determining the area of interest after thresholding.  In order to preventing spoiling anyone's fun, I am not going to post the code as of now.

Hint: think about how to find the largest area in white after thresholding.

If you would like to compare algorithms or just want to know what I did, drop me a note via the contact form.

In preliminary testing, the algorithm seems robust enough to handle changing lighting conditions as well as differing background colors; however, the angle of reflection of the laser can cause issues.  For best results, the laser should be relatively perpendicular to the image plane.  As the laser approaches being parallel to the image plane, less laser light is reflected back to the camera.  The same applies to irregular objects or surfaces that scatter the laser at angles away from the camera.  This can be seen at the end of the video when the laser is reflected off the lamp shade.

Testing was done using a Logitech C920.  Depending on the laser used and ambient lighting conditions, turning off auto white balance may prevent the intensity of the laser from being interpreted as white.

As far as applications, I leave that to the reader.

In which we join files line by line.

Working in IT, there is no shortage of "grunt work" that is amenable to one off scripts or applications.  In an effort to prevent others (and possibly myself) from having to reimplement these, I am publishing these as useLESS tools.

useLESS tools: scripts or programs written to paper over the shortcomings or deficiencies in applications or processes.  You could use less, and they would be made useless, if you could reimplement everything in light of the experience gained by doing it the last time.

Today I give you our first useLESS tool - fjoin

At work I am currently involved in integrating with an external vendor.  As part of the process, I have been provided with pseudo realistic test data to process.  Being pseudo realistic, it doesn't really match with what our system expects and must be "massaged".  That is to say, nothing works.

In particular, I have two text files.  One text file has the IDs used by the vendor, and the other has the IDs our system expects.  I need a way to turn these into SQL update statements, and it needs to be repeatable so I can rerun it as testing progresses.  What to do?

Using notepad++ or you favorite editor, it is fairly easy to record a macro that inserts

UPDATE [TABLE] SET [FIELD] = '

before each line of one file and

' WHERE ID = 

at the beginning of each line in the other so that if you took line x from the first file and line x from the second file and joined them, you would get something like

UPDATE [TABLE] SET [FIELD] = 'OURID_123' WHERE ID = 42

The real trick is merging the two files line by line.  fjoin does just this.  It takes two or more files and joins them line by line stopping at the end of the shortest file.  The result is sent to the terminal so you can pipe this into another program or redirect to a file.

If you would like to create an executable from this code, you can use pyinstaller

pysinstaller -F fjoin.py

This will create a single (rather large) executable for you.

import sys

if len(sys.argv) == 1:
    print 'No files specified'
    sys.exit(0)

files = []
# skip the name of the program
for arg in sys.argv[1:]:
    try:
        files.append(open(arg))
    except IOError:
        print 'ERROR:', arg, 'does not exist'
        sys.exit(1)

reading = True
composite = []

while reading:
    for f in files:
        line = f.readline()
        # stop as soon as a file comes up short
        if not line:
            reading = False
            break
        else:
            # remove newline
            composite.append(line.rstrip('\n'))
    # only output if we joined across all files
    # in other words, no partial lines
    if len(composite) == len(files):
        print ''.join(composite)
    composite = []

for f in files:
    f.close()

fjoin.py (2.36 kb)

In which we optimize OpenCV on the BeagleBone Black.

If you have been paying attention to the BBB group on Google Groups, you may have discovered a lively thread on webcams [1]. As part of this thread, I have been working with Michael Darling to realize the best performance possible when using OpenCV to process an MJPG stream from a webcam on the BBB.  OpenCV relies on libjpeg when loading the MJPG frames to OpenCV Mats, and libjpeg is not the fastest of jpeg libraries.  However, there is another option - libjpeg-turbo [2].

While it is technically possible to compile OpenCV with libjpeg-turbo support on the BBB, you will have fewer issues and spend less time compiling if you use a cross compiler on a more powerful computer.  Michael has written a guide to cross compiling OpenCV.  Below you will find the guide as a webpage for online viewing or a pdf for download as well as the latest code to capture frames from a webcam and convert them to OpenCV Mats.  The guide is currently a draft, and we would appreciate any feedback you can provide.  Many thanks to Michael for taking the time to write this up.

Note: The code in the guide and the latest code are slightly different.

1. -o is used to indicate which frames to convert to OpenCV Mats and requires an integer argument.
       -o 1 will convert every frame
       -o 2 will convert every 2nd (ever other) frame, and so on.
       The default is 1.
2. -p is similar to -o in the original framegrabber.  However, it doesn't actually output anything.  It just controls if any frames are to be converted.
3. Captured count and processed count variables have been renamed and moved to the top.
4. Formatting has been corrected.
5. Setting of the Mat size now uses the width and height variables.

The guide will be updated to reflect these changes in a future release, but in the meantime, you will need to adjust the command line arguments specified in the guide, namely replace -o with -p.

Guide [DRAFT]
    BBB_30fps.html
    BBB_30fps.pdf (204.32 kb)
Code
    framegrabberCV.c (19.77 kb)

[1] problems with webcams
[2] libjpeg-turbo

In which we publish a MJPEG stream from the BeagleBone Black.

Continuing with the series of post on OpenCV, webcams, and MJPEG, today we will look at streaming an MJPEG capture from the BBB.  Before I get into it though, you should know that I did try FFMPEG/avconv and VLC to stream video from the BBB using rtp, but the several seconds of latency made it unsuitable for my needs.  You should also know that I do not claim that this is the one true way to stream video from the BBB.

The libraries used:

• ZeroMQ^[1] and CZMQ^[2] - used to create pub/sub connections between the BBB and software running on a desktop
• OpenCV^[3] - used to display the MJPEG stream

The subscriber was compiled and tested under Windows 7 using Visual Studio 2012; however, the code should compile under Linux with very few, if any, modifications.

Backgound:

I am working on a project that requires a video stream from the BBB be consumed in N places where N is a minimum of 2.  The stream will be processed using OpenCV, and because of the nature of this project, I need as little latency in the video stream as possible.

Theory of Operation:

The BBB will capture frames in MJPEG format from a webcam via a modified version of framegrabber.  The modified version of framegrabber can run indefinitely and outputs the frames as a series of ZeroMQ messages over a publish socket.  The clients will subscribe to the publish socket on the BBB using ZeroMQ and load each frame received into OpenCV.

The ZeroMQ pub/sub configuration allows many clients to connect to the published stream.  No synchronization is used between the publisher and subscriber; the the stream is treated as continuous, and the subscribers are free to connect and disconnect at will.

Results:

Single subscriber 640x480 - cpu use on the BBB ~4.3% and memory use ~0.8%

Multiple subscribers 640x480 - cpu use on BBB ~6.6% and memory use ~0.8%

Single subscriber 1920x1080 - cpu use on BBB ~23.2% and memory use ~3.5%

Using this setup, I have been able to stream frames with a resolution as high as 1920x1080 with little to no latency, but there is a limitation, the network.  When using this over Wifi with high resolutions or several clients running on one machine, I noticed the frame rate would drop the further I went from the router.  If you watch the output of the top command on the BBB as you get further from the router, you will see framegrabber's memory use begin to climb.  This is due to the publish socket buffering the data.  As you walk back towards the router, you will see the memory use drop until it, and the frame rate, stabilizes.  During this stabilization period you will probably experience delayed video that is displayed at a higher frame rate than normal as the buffer is flushed.

There are several things you can do reduce or eliminate this latency.

1. If possible, use a wired connection
2. Use an 802.11 N router and clients
3. Make sure your WiFi router is optimally located
4. Adjust the QoS settings of your router to give higher or highest priority to the traffic on the port you publish over

To reduce the amount of time it takes for subscribers to catch up once their connection has improved, the high water mark on the socket can be reduced.  This has the effect of dropping frames once too many are buffered and essentially reduces the amount of buffered data a subscriber has to process to get in sync.

The reader may find it interesting that it does not matter it the BBB publisher or the subscribers are started up first.  The publisher will simply dump data until at least one subscriber connects, and the subscribers will wait on the publisher.  In addition, you can kill the publisher while subscribers are connected, restart it with new (or the same) settings, and the subscribers will continue on.  The reader should verify this by changing the resolution after the subscriber or subscribers have connected.

Code:

framegrabberPub.c (17.96 kb) Publisher - you will need zhelpers.h

compile with

gcc framegrabberPub.c -lzmq -o framegrabberPub 

framegrabberSub.c (3.79 kb) C client

[BONUS]
framegrabberSub.py (2.52 kb) Python client

The Python client will display the stream with little latency until garbage collection occurs.  When this happens, the display will freeze and the buffered data on the BBB will increase.  Once garbage collection completes, the display will eventually synchronize much like the WiFi issue detailed above.

[UPDATE]
If your wireless router is capable of broadcasting at both 2.4 and 5 Ghz at the same time, you can improve performance when using a WiFi connection for both the publisher and the subscriber by having one connect at 2.4 Ghz and the other connect at 5 Ghz.

[UPDATE]
Added a link to zhelpers.h needed to compile the publisher.

[1] http://zeromq.org/
[2] http://czmq.zeromq.org/
[3] http://opencv.org/

In which we learn how to turn a buffer of bytes into an image OpenCV can work with.

If you followed my previous post, you may have realized you can produce, with very few modifications, a stream of MJPEG (jpeg) images captured from the webcam. You may also be left wondering how to work with these images using OpenCV. It might actually be easier than you imagine.

The little bit of Python code below will display a single image. The code loads an image from a file into a buffer, converts the buffer into an OpenCV image, and displays the image. If you are receiving the image as a buffer already, you can forgo the loading step.

import cv2
from cv2 import cv
import numpy as np

# let's load a buffer from a jpeg file.
# this could come from the output of frameGrabber
with open("IMAGE.jpg", mode='rb') as file:
    fileContent = file.read()

# convert the binary buffer to a numpy array
# this is a requirement of the OpenCV Python binding
pic = np.fromstring(fileContent, np.int8)

# here is the real magic
# OpenCV can actually decode a wide variety of image formats
img = cv2.imdecode(pic, cv.CV_LOAD_IMAGE_COLOR)

cv2.namedWindow("image")
while True:
    cv2.imshow("image", img)
    
    key = cv2.waitKey(20)
    if key == 27: # exit on ESC        
        break
    
cv2.destroyAllWindows()

For those of you working in C, you can convert a buffer into an image with this (tested in Visual Studio 2012)

#include <stdio.h>
#include <malloc.h>
#include "opencv2\core\core_c.h"
#include "opencv2\highgui\highgui_c.h"

// used to load a jpeg image into a buffer
int load_buffer(const char *filename, char **result) 
{ 
	int size = 0;
	FILE *f = fopen(filename, "rb");
	if (f == NULL) 
	{ 
		*result = NULL;
		return -1; // -1 means file opening fail 
	} 
	fseek(f, 0, SEEK_END);
	size = ftell(f);
	fseek(f, 0, SEEK_SET);
	*result = (char *)malloc(size+1);
	if (size != fread(*result, sizeof(char), size, f)) 
	{ 
		free(*result);
		return -2; // -2 means file reading fail 
	} 
	fclose(f);
	(*result)[size] = 0;
	return size;
}

int main() 
{ 
	char *buffer; 
	int size;
	CvMat mat;
	IplImage *img;

	// load jpeg file  into buffer
	// this could come from the output of frameGrabber
	size =load_buffer("IMG.jpg", &buffer);
	if (size < 0) 
	{ 
		puts("Error loading file");
		return 1;
	}

	// create a cvMat from the buffer
	// note the params: height, width, and format
	mat = cvMat(1080, 1920, CV_8UC3, (void*)buffer);
	// magic sauce, decode the image
	img = cvDecodeImage(&mat, 1);

	// show the image
	cvShowImage("image", img );

	// wait for a key
	cvWaitKey(0);

	// release the image
	cvReleaseImage(&img);


	return 0;
}

For further information see the OpenCV Docs.

OpenCVjpeg.py (2.24 kb)
OpenCVjpeg.c (2.78 kb)

In which we discover the limits of webcams connected to the BeagleBone Black.

As the previous couple of posts may have hinted, I am currently working on a computer vision application.  On the hardware side, I am using a webcam connected to a BeagleBone Black to capture and process images.  Finding the right camera and software configuration seems to be a challenge many people are trying to overcome.  The following is what I have learned through experimentation.

During my first foray into the world of webcams on the BBB, I chose the PS3Eye.  The PS3Eye has been used for many computer vision applications thanks to its ability to produce uncompressed 640x480 images at up to 60 FPS or uncompressed 320x240 at up to 120 FPS.  The ability to capture uncompressed images at high frame rates plus being available for $16.98 would normally make the PS3Eye a fantastic choice; however, we are dealing with the BBB.

If you plug a PS3Eye into the BBB and fire up an OpenCV application to capture at 640x480, you will receive "Select Timeout" errors instead of a video stream.  If you do the same but with the resolution set to 320x240, it will work.  It turns out the PS3Eye transfers data in bulk mode over USB.  In bulk mode, you are guaranteed to receive all of the transmission; however, you are not guaranteed timing.  What is essentially happening is the PS3Eye is saturating the bulk allotment on the USB.  The reason you encounter this problem at 640x480 and not 320x240 is because OpenCV with Python sets the frame rate to 30 FPS and provides no way to change it.  We can calculate the amount of data put on the bus as follows:

Height * Width * (Channels * 8) * FPS

So for our uncompressed images at 640x480 we have:

640 * 480 * (3 * 8) * 30 = 221184000 bits/s or ~26.36 MB/s

and 320x240 is ~6.59 MB/s

As OpenCV with Python does not allow you to set frame rate, I modified v4l2grab^[1] to accept frame rate as a command line argument.  With this, I discovered you can capture images from the PS3Eye at 640x480 as long as you set the frame rate to 15 FPS or less.  You can also capture images at 320x240 at up to 60 FPS.  The astute reader will notice that 640 * 480 * (8 * 3) * 15 = 320 * 240 * (8 * 3) * 60 which is ~13.2 MB/s.  In other words, the USB on the BBB taps aout at ~13.2 MB/s for bulk transfers.

At this point you might be thinking you do not have to worry about frames per second because you will only take still shots.  It turns out uvc under Linux does not support still image capture^[2].  In order to capture an image, you open the webcam the same way you would to capture a stream; however, you just grab one frame (or more if needed).

If you would like to capture 640x480 or larger images at 30 FPS or faster, all is not lost, but you will need a webcam that supports some sort of native compression.  In my case, I am using a Logitech C920.  It can compress using both H264 and MJPEG.  If you want to capture a video stream, H264 is probably your best choice as it should have fewer compression artifacts.  It you are after still shots, MJPEG will be your friend.

MJPEG typically compresses each frame as a separate jpeg^*.  Since MJPEG uses intra-frame compression, you only need to capture one image for a still shot.  H264 uses inter-frame compression - meaning it relies on information from several frames to determine how to compress the current frame.  In order to reconstruct the frame, you need all the ones involved in the compression.  I know the last two sentences are a great simplification, but they suffice for our discussion.

In order to test the different combinations of frame rates and encodings, I extended the v4l2 capture sample available from the project's website^[3].  To the base sample I added the ability to specify image dimensions, frame rate, and pixel format (ie compression).  I also added handling for CTRL-C so the webcam is not left in an inconsistent state if you kill the program with CTRL-C, and the ability to set the timeout value and maximum number of timeouts.

The program is available here framegrabber.c.

Please note this software is not finished.  I am publishing it now so others may use it to determine the capabilities of their webcams, but I will be improving and extending it in the future.  You may consider the capture timing functionality described in 1 below to be complete while the saving of frames described in 2 will change.

To compile framegrabber you must have the development files for v4l2 installed.

Compile with:

gcc framegrabber.c -o framegrabber

At this time, framegrabber is intended to be used in one of two ways.

1.  Timing frame capture rates.

To time frame capture rates, simply pass framegrabber to time, omit the -o switch, and set -c to the number of frames you would like to capture.  Omitting -o instructs the program to simply discard all captured frames.  In this mode, framegrabber will capture c number of frames from the webcam as fast as possible.

Here is the simplest case:

time ./framegrabber -c 1000

And here we set the pixel format, image dimensions, and frame rate:

time ./framegrabber -f mjpeg -H 480 -W 640 -c 1000 -I 30

Have a look at all the other command line switches to get a sense of the possibilities.

2.  Capturing frames from a webcam

As mentioned above, I extended the application v4l2grab to support setting the frame rate.  v4l2grab allows you to capture jpeg images from webcams that support the YUYV format.  It grabs frames in YUYV format and then converts the frames to jpeg.

When capturing frames with framegrabber, the raw frame is written out.  No conversion to jpeg is done.  This is mostly a proof on concept to show that frames captured in MJPEG format are individual jpegs and can be written out without further processing.  This has been tested with a Logitech C920, and the output is indeed an jpeg image.  Capturing in H264 and YUYV format will also work, but you will not be able to simply open the resulting file in your favorite image editor.

Currently there is no way to specify the filename for the frame or frames captured, and if -c is greater than one, the first c - 1 frames will be overwritten by frame_c.  To capture a frame, include the -o switch and set -c to one.  The resulting frame will be written to capture.jpg.

./framegrabber -f mjpeg -H 480 -W 640 -c 1

And now for the results of testing both the PS3Eye and Logitech C920

Here we see capturing 1000 frames from the Logitech C920 in MJPEG format takes ~33.6 seconds which is ~29.76 frames per second.

Here we see capturing 1000 frames from the Logitech C920 in YUYV format takes ~67 seconds which is ~14.92 frames per second.

Moving to the PS3Eye we see that if we try to capture at 30 FPS, we receive a select time out error, but if we set the frame rate to 15, we are successful.  If you compare the results of the PS3Eye capture with the results of the Logitech C920 YUYV test, you will see the real times are essentially the same, almost 15 frames per second.

At this point you maybe wondering why the Logitech C920 does not receive select timeouts at 30 FPS YUYV but the PS3Eye does.  If you notice, even though we set the frame rate to 30 FPS, we receive frames from the C920 at about 15 FPS.  The C920 use isochronous transfers as opposed to bulk like the PS3Eye, and isochronous guarantees transfer speed but not delivery.  It is likely that frames are getting dropped but enough make it through fast enough that we do not receive select timeouts.  I have not tested this further as of now.  For more information on USB transfers see [4].

In our final screenshot we can see that framegrabber uses very little cpu (.3%) while just grabbing frames.

I hope you find framegrabber useful.  The interested reader can extend the process_image function to do as they will with frames captured from the webcam.

[UPDATE 1]
It seems some MJPEG streams omit the Huffman table causing the resulting jpeg captures to fail to open in some programs^[5].  The error message, if any, is something along the lines of "Huffman table 0x00 was not defined".  If you cannot open the MJPEG captures, please try the Python script MJPEGpatcher.py below.  MJPEGpatcher should patch the captured jpeg with the required information.  It takes a single command line argument, -f FILENAME, and outputs FILENAME[patched].jpg.  The Logitech C920 does not exhibit this behavior.  MJPEGpatcher has been tested and works on images captured by a Toshiba webcam built into a laptop as well as an image submitted by a reader.  I would appreciate any feedback.

[UPDATE 2]
William C Bonner pointed out in the comments that I neglected to provide any timing information for the C920 for resolutions greater than 640x480.  When researching for this post, I was interested in explaining why the C920 could provide 640x480 at 30 FPS  and the PS3Eye could not.  In doing so, I focused on the greatest resolution and frame rate the two cameras had in common.  To redress my omission, here are timings for the C920 for 1920x1080 at 30 FPS in MJPEG, H264, and YUYV formats.  It can be seen below that the C920 is able to provide 1920x1080 at 30 FPS in both MJPEG and H264 formats, but YUYV tops out around 2.49 FPS

framegrabber.c (18.79 kb)
MJPEGpatcher.py (6.46 kb)

[1] https://github.com/twam/v4l2grab
[2] http://www.ideasonboard.org/uvc/
[3] http://linuxtv.org/downloads/v4l-dvb-apis/capture-example.html
[4] http://www.beyondlogic.org/usbnutshell/usb4.shtml
[5] http://www.the-labs.com/Video/odmlff2-avidef.pdf

In which we threshold video frames using HSV values.

In my previous post, I provided a tool that allows one to determine the H, S, and V values of a pixel or pixels in an image for use in setting a threshold on an image or video frame.  Today, I will show you what these values are good for, namely setting a threshold on frames in a video or webcam stream.

You may download the Python code here HSVthresholder.
The code is copyright © 2013 Matthew Witherwax and released under the BSD license.

To use the application, simply supply a command line argument for either a video file or a webcam using -v.  In the case of a webcam, specify dev# where # is the number of the device.  If you only have one webcam, you should be able to pass dev0; otherwise, you may start at dev1 and increment until you find the webcam you would like to use.  In the case of a video file, the application will loop the video indefinitely until you exit with esc.  For other options, pass -h on the command line.

The general flow of the program is:

1. Capture frame from video or webcam
2. Dilate image
3. Convert to HSV
4. Threshold image
5. Erode image
6. Display image

You can find more information about dilation and erosion here [1] and the function inRange() used for thresholding here [2].

All the interesting bits in the code are documented with comments, but I would like to draw attention to a two operations with parameters you may wish to tune.

In both dilating and erosion, a structuring element is supplied.  Possible values to adjust are the element itself which may be one of cv2.MORPH_RECT, cv2.MORPH_CROSS, or cv2.MORPH_ELLIPSE as well as the size, the second argument to cv2.getStructuringElement.  In addition, you may adjust the number of times the operation is applied by adjusting the iterations parameter.  While tuning these parameters, you might find it beneficial to create a third named window to display the frame after the dilation so you can see what effects your changes have.

Dilating

# here we dilate the image so we can better threshold the colors
if self.dilate:
	element = cv2.getStructuringElement(cv2.MORPH_CROSS,(5,5))
	frame = cv2.dilate(frame, element, iterations=5)

Erosion

# here we erode the image to remove noise
if self.erode:
	element = cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))
	frameHSV = cv2.erode(frameHSV, element, iterations=2)

And here are some screenshots that show the application isolating the red grip on a pen.  The red grip show up as white while the rest of the image is blacked out.  If you look closely, you will see some stray white dots.  Depending on your needs you may either ignore these or tune the parameters until they disappear.  Now that the grip has been isolated, you can track it, but that is a topic for another post.

[1] http://docs.opencv.org/doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.html
[2] http://docs.opencv.org/modules/core/doc/operations_on_arrays.html#cv2.inRange

HSVthresholder.py (6.38 kb)

In which we find the H, S, and V values for an image's pixels.

Recently I have been working on an application that requires me to locate a small colored dot in an image or video frame.  In order to accomplish this, I have been using OpenCV.  For reasons outside the scope of this post, I am working with the images/frames in the HSV color space.  You can find more information about HSV in general and OpenCV's HSV implementation here [1].  There you will also find a nifty tool - ColorWheelHSV - to help you visualize OpenCV's HSV implementation.  However, if you are like me, you might find it difficult to determine proper H, S, and V values by eyeballing your target and the output of ColorWheelHSV.

Enter HSVpixelpicker.

The Python script below will allow you to determine the H, S, and V values of a given pixel in an image as OpenCV sees it.  Simply pass the image file name with -f on the command line, click on the pixel you are interested in, and the H, S, and V values will be printed to the console.  Using this tool, you can check the exact value of the pixel or pixels you are interested in.  The more samples you have to test, the better the idea you will get of the H, S, and V values you are dealing with.  You can then refer to ColorWheelHSV to select your ranges.

The code is copyright © 2013 Matthew Witherwax and released under the BSD license.

from optparse import OptionParser
import cv2
from cv2 import cv

class HSVpixelpicker:
    def __init__(self):
        # original image
        self.image = None
        # image converted to HSV
        self.imageHSV = None
        
        # create window to show image
        cv2.namedWindow('image')
        # wire click handler
        cv.SetMouseCallback('image',self.on_mouse, 0);
        
    # handles left clicking on the image
    # gets the pixel under the cursor and prints its HSV values
    def on_mouse(self, event,x,y,flag,param):
      if(event==cv.CV_EVENT_LBUTTONDOWN):
        pixel = self.imageHSV[y][x]
        print 'H:',pixel[0],'\tS:',pixel[1],'\tV:',pixel[2]
        
    def open_image(self, filename):
        self.image = cv2.imread(filename)
        self.imageHSV = cv2.cvtColor(self.image,cv2.COLOR_BGR2HSV)
        
    def show(self, filename):
        self.open_image(filename)
        while True:
            cv2.imshow('image', self.image)
            
            # show image until user presses the esc key
            if cv.WaitKey(10) == 27:
                break
            
        # clean up
        cv2.destroyAllWindows()

if __name__ == "__main__":
    parser = OptionParser()
    parser.add_option('-f', '--file', action='store',
        type='string', dest='filename')
    (options, args) = parser.parse_args()

    hsv_picker = HSVpixelpicker()
    hsv_picker.show(options.filename)

[1] http://www.shervinemami.info/colorConversion.html

HSVpixelpicker.py (3.03 kb)

ALMA! Check your battery!

In which we make a MaxBotix sonar speak properly over a serial connection.

Recently I hooked up a MaxBotix MB1220 XL sonar to an FTDI breakout board to do some experimenting, and I ran in to an issue I had discovered before but thought was an artifact of the processor I was using.  You see, previously (a couple years ago) I had connected a MB1220 to a Fez Panda II and discovered a wonderful stream of gibberish flowing from my brand new sonar.  At first I assumed I had connected it improperly.  There are only three wires that need to be connected to make the sonar transmit over the serial port - power, ground, and TX - but just maybe I had accidentally hooked up to the analog voltage instead of TX.

No dice.

All connections secure? Check.
Are we using 5 volts? Check.
Is the processor 5 volt tolerant? Check.
Is the TX from the sonar connected to the RX of the processor? Check.
9600 baud 8N1? Check.

Still no luck.

Still just gibberish.

At some point while reading about TTL versus RS232 serial connections it occurred to me that perhaps

... the Pin 5 output delivers asynchronous serial with an RS232 format, except voltages are 0-Vcc.

could mean the levels were inverted rather than being out of range.  In other words, what my processor thought was high the sonar thought was low and vice versa.

Not having a hex inverter (or even a single inverter) handy, I flipped through some old notes about logic gates and found a simple inverter made from a couple of resistors and a transistor, parts I had on hand.

Below is what I threw together on a breadboard.

R1 = 1K
R2 = 10K
Q1 is a generic NPN transistor out of a pack from Radio Shack.

With this in place, R121 flashed across my LCD display.

Success!

Flash forward two years, and we are back to me hooking up the MB1220 to an FTDI breakout board.  The breakout board was set to 5v.  The sonar was being powered by 5v from the breakout board.  Putty was up and set for a serial connection with 9600 baud 8N1.

The result?

Gibberish.

I then wired up the above circuit on a breadboard, connected the sonar and the breakout board, and presto, the range information I expected to see.

If like me, you connect a MaxBotix sonar to a processor, FTDI chip, or anything else over serial and find gibberish, you probably should invert the signal with something like the circuit above… or stick to their I2C line.

On a recent business trip to Kuala Lumpur and Dubai, I had a 16 hour flight and thought I would watch some old Udacity videos.  I downloaded some lectures from Udacity^[1] for offline viewing and discovered they were in FLV format.  Unfortunately, the media player built into Windows 8, Windows Media Player, does not play FLV videos out of the box.  There are many solutions to this problem from installing various codecs and alternative video players to transcoding.  I chose to do the latter, and this is my story.

I know there are many different programs that promise to take care of transcoding for you, but I prefer to install as few unknown/untrusted bits of software as possible.  As a quick method to bulk convert the files, I wrote the Python script below. You will need two things to run this script:

1. Python - I wrote this using version 2.7.5 from the Anaconda package^[2]
2. ffmpeg^[3]

from os import listdir, remove
from os.path import isfile, join
import subprocess

vid_path = '[PATH TO VIDEOS]'
ffmpeg_path = '[PATH TO ffmpeg.exe]'

vids = [f for f in listdir(vid_path) if isfile(join(vid_path,f))]

for vid in vids:
    file_name = vid[:-4]
    subprocess.call(ffmpeg_path + ' -i ' + join(vid_path,vid)
        + ' -qscale 0 -ar 22050 ' + join(vid_path,file_name)
        + '.mp4', shell=True) 
    remove(join(vid_path,vid))

This script is quick and dirty.  It simply loops over all files (hopefully just .flv videos are present) in the vid_path directory specified and uses ffmpeg to transcode them to mp4.  Be sure to set ffmpeg_path to the path of ffmpeg.exe on your system.  After a video is transcoded, it is deleted. One very import note: beware of shell=True in subprocess.call().  In some cases, this can be a security risk.  Please see [4].

Possible improvement opportunities for the reader:

• make removing the original file conditional
• only join vid_path and vid once
• filter discovered files so that only .flv files (or the target type of your choice) are processed
• traverse nested directories looking for files
• research the arguments ffmpeg accepts and provide a way to change them

[1] Udacity
[2] Continuum Analytics
[3] ffmpeg.zeranoe
[4] subprocess.call()