Pets, Plants, and Computer Vision
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Homemade Massage Bars

July 16th, 2012 | Posted by admin in Uncategorized - (Comments Off on Homemade Massage Bars)

Fresh massage bars.

My aunt and I made soap this weekend along with massage bars. Massage bars are basically a mixture of solid, semi-solid, and liquid fats where the melt point of the resulting product is near human body temperature. In most cases you use bees wax as your solid fat, shea or coco butter as your semi solid fat, and some form of liquid oil like coconut or almond oil. With this mixture you get the moisturizing properties of the oils along with the protective properties of a wax.  While they are great for massages, I mainly use the bars on my feet, hands, knees, and elbows in the cold winter months. I also find that the bars help to smooth out my hands after a long day of  gardening.  The general recipe for massage bars couldn’t be simpler.

  • One part solid wax (like bees wax, soy wax may work too, measure this by weight).
  • One part semi-solid wax like shea of coco butter (by weight).
  • One part oil by volume (almond, olive, grape seed, coconut, etc)
  • Some small amount of essential oils.

You then heat the wax until it melts, add the other ingredients, stir, and pour into your mold.  Changing the fat types and ratios will vary the consistency of the bar. I suggest you refer to a guide on the melt temperatures of fats and oils to estimate your bar’s melt point. You can tweak the mixture a bit to find the optimal consistency for you.

For the bars I made I used the following recipe:

  • 10oz Bees wax
  • 7oz Shea butter
  • 4oz Hemp oil
  • 1oz Vitamin E oil
  • 4oz Whole coconut oil
  • 1Tbs Liquid lanolin
  • Essential oils (one batch had 2ml of jasmine essential oil and 1ml of lavender. The other batch had 2ml of cedar, 1ml of clove, and a dash of lemongrass).

Melting the bees wax over a very low flame.

Over a very low heat I first melted the beeswax and then added the other oils and fats. Tempered the mixture to improve its consistency. Tempering consists of cooling off the mixture and then reheating it. For this I used a sink with about two inches of cold water. Fats, oils, and waxes are  natural organic polymers. The tempering process is vaguely similar to annealing in metal in that it it allows the long chains of fats and oils to combine together into longer and more interesting chains. Tempering is not necessary but it is supposed to improve the properties of the finished product. Once the mixture heated up again I added the essential oils and then poured the mixture into molds.

Tempering in a cold water bath.

Reheating after tempering.

This batch turned out excellent, but I do have a few critiques. I used unrefined hemp oil and it made the mixture slightly green tinted, next time I would use refined hemp oil. If I to do it again I would also use slightly more cedar oil. Once the bars cool they pop right out of the mold. I wrap them in wax paper and then store them in a cool dry place. Be sure to cleanup promptly (while everything is hot). If you wipe off the excess mixture with a paper towel everything cleans up in the sink pretty quickly.

Massage bars in mold, cooling.

For what it is worth I get most of my supplies at either Amazon, Bramble Berry Soap, or the People’s Food Co-op in Ann Arbor.

 

Perler Bead Project

July 2nd, 2012 | Posted by admin in code | computer vision | domestic life | Fun! | Uncategorized - (Comments Off on Perler Bead Project)

So I had to run to Jo-Ann fabric for a few odds and ends. My Mom gave me a couple of 50% off coupons and on a whim I purchased some perler beads and a tray for about $10. Perler beads are this kids craft where you patiently place little plastic beads on tray and then fuse them using an iron to create various toys. I am pretty sure that the beads themselves are just a ruse by the vacuum cleaner companies to sell more powerful vacuums.

Perler Beads

Perler Bead Set

I wanted to see if I could use SimpleCV to map images to  pearler bead colors to create little coasters from my photos. I took the beads and created a square calibration grid so I could pull out the colors. I then quantized the image to a palette and saved the results.

This is what the calibration grid looks like when I quantize it to have 16 colors (note that this result is not repeatable because of the k-means calculation’s initial conditions).

To test my approach I used an input image (in this case Lena), pixelized the image to match the perler bead grid, and then re-applied the quantization to the pixelized image. The results are not that great.

Image pipeline, input, pixelized image (17×17 block size), and quantized result.

There are about five colors in the output image and it seems to lose a lot of its information. I did some digging and found that two things seem to be going on. First, the quantization step seems to have some bad initial conditions. This is to say that I take the image and try to cluster the colors in it into 16 groups using k-means. If the algorithm starts with a bad initial condition a lot of the clusters “run into one another” and I end up with less than 16 color groups. The other problem is subtler and has to do with image dithering. I anticipated that this might be a problem because gif images also use a quantized color palette (for gifs it is 256 colors) to compress the image size. Back in the old days of the web you would use a technique called dithering as part of your gif compression algorithms to make photographs look more realistic. Generally dithering is used to lessen the effect of quantization error around gradients. To illustrate this I found an image on wikipedia with a lot of colors and color gradients, here is what would come out of the naive SimpleCV quantization (top is input, bottom is output using img.palettize(bins=256)):

Quantization makes things look weird. The top is the input image and the bottom is the image quantized to only have 256 colors (just like a normal GIF image).

Now here is the same result using ImageMagick with GIF dithering turned on (specifically the command: convert test.jpg -dither Riemersma -colors 256 test.gif).

Still 256 colors, but the dithering makes the gradients around the lights less apparent.

As you can see the dithered images look way better. The effect seems to hold even when I shrink the number of colors down to 16 but still use dithering. In the two images below the top is the output from SimpleCV quantizing to 16 colors, while the bottom is ImageMagick result with added dithering (note that there may be some re-compression artifacts from when I saved the image).

Top is SimpleCV’s output when I quantize the image to have 16 colors, while the bottom image is ImageMagicks results with 16 colors and dithering.

Hopefully in the next week or two I can read up on dithering algorithms and see if I can’t add a few to SimpleCV.

Candy Sorter Demo Using Sight Machine and SimpleCV at CVPR 2012

June 21st, 2012 | Posted by kscottz in pics or it didn't happen - (Comments Off on Candy Sorter Demo Using Sight Machine and SimpleCV at CVPR 2012)

This is our candy sorting demo for our booth at CVPR

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Cool Stuff From CVPR 2012

June 21st, 2012 | Posted by admin in artificial intelligence | automation | classification | code | computer vision | Open Source | segmentation - (Comments Off on Cool Stuff From CVPR 2012)



Here are a few cool things that came up at CVPR. Today KIT released a benchmark data set for autonomous vehicles. KIT has spent a small fortune outfitting a vehicle with a Velodyne LIDAR, a stereo camera rig, GPS, INS, and all of the other goodies you would expect a DARPA urban challenge vehicle to have. KIT drove the vehicle around a city and recorded six hours of real world data. KIT then painstakingly rectified everything together and paid someone to mechanically segment and classify the data in the scenes (i.e. all pedestrians and vehicles have 3D boxes around them and are labeled in the data). The data is also registered to open street map data. This means that the world now has open source, real-world data for autonomous vehicle navigation. Since KIT provides benchmark along with the data it should be trivial to use the data and compare how your algorithms perform. This work will really serve to drive competition in the field.

Tomorrow at CVPR Kitware is hosting a Python for Computer Vision workshop. Kitware provides open source python tools for computer vision, and they have opened up the materials. You can find them here. I will report more information tomorrow after the workshop