Difference between revisions of "Computer Vision"
Aplstudent (talk | contribs) (OpenCV and Computer Vision) |
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While OpenCV is extremely useful and powerful it does most of the work under the hood, that said for those more interested in learning more about computer vision or implementing their own computer vision based code we recommend exploring those avenues as well. We've written some computer vision code, a Gaussian filter to blur images and a Sobel operator to detect edges. While we've found that this code works better for our purposes it is much slower than OpenCV's filters. For those interested in optimizing this code, one should learn how Gaussian blur filters and Sobel operators work and then update the code accordingly. The current plan for the edge detection is to use the camera to identify edges and approximate the distance of the edge to the car. Code has been written to approximate object distances based on camera pixel location but this is only useful if we can identify objects or edges. The ultimate goal will be to identify obstacles and walls and navigate around them. | While OpenCV is extremely useful and powerful it does most of the work under the hood, that said for those more interested in learning more about computer vision or implementing their own computer vision based code we recommend exploring those avenues as well. We've written some computer vision code, a Gaussian filter to blur images and a Sobel operator to detect edges. While we've found that this code works better for our purposes it is much slower than OpenCV's filters. For those interested in optimizing this code, one should learn how Gaussian blur filters and Sobel operators work and then update the code accordingly. The current plan for the edge detection is to use the camera to identify edges and approximate the distance of the edge to the car. Code has been written to approximate object distances based on camera pixel location but this is only useful if we can identify objects or edges. The ultimate goal will be to identify obstacles and walls and navigate around them. | ||
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| + | ==Sobel Operator and Edge detection== | ||
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| + | The Wikipedia page [https://en.wikipedia.org/wiki/Sobel_operator] for the Sobel operator provides a much more extensive and mathematical explanation than can be provided here but the general idea is to compare the differences in local intensities at every point in an image. To do this one needs to convolve a 3x3 kernel in the x direction and convolve a similar 3x3 kernel in the y direction with a 3x3 pixel matrix of the image. This step must be repeated at every unique 3x3 point on the image. The result of this process creates an image with brighter edges. The new image with enhanced edges can be passed through a filter which takes any pixel below a certain threshold and convert its intensity to zero. | ||
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| + | Exploring parameters and different operators may help one tailor their edge detection to the specific camera and lighting conditions they expect to use the edge detection under. Lowering the threshold of the final filter will allow for a wider range of acceptable edges. Using a Scharr operator or a Prewitt operator may prove to be more effective for edge identification with later code. It also may be advisable to explore kernels of a larger size, that is a 5x5 kernel. These types of changes give one more control over the results of the edge detection and once optimized may work better than those provided by libraries like OpenCv. | ||
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| + | ==Gaussian Filter== | ||
| + | |||
| + | Once again, Wikipedia will provide a better overview of this subject but we'll go over the general idea here. The Gaussian filter is essentially a low pass filter that reduces noise and detail. This kind of filtering is helpful as a precursor to edge detection because the reduction in noise enhances the differences in local intensity. A combination of a Gaussian Filter and Sobel operator has been written in python and the combination of the two add for additional parameters that can be changed to create the ideal level of edge detection. The Gaussian filter, for example, can have a different standard deviation applied and thus change the level of blurriness. A higher standard deviation will increase the blur. | ||
Revision as of 16:17, 2 December 2018
OpenCV
OpenCV is a python compatible open source library used for computer vision. OpenCV offers a wide range of functionality including built in image filters and AI based image/object recognition. So far, we've used OpenCV and python to to identify stop signs in real time, use image filters to correct for lens distortion on the RaspberryPi's camera, and code has been written to implement edge detection.
Object detection with OpenCV is not very difficult once you have a trained haar cascade. Right now we have a usable haar cascade for stop signs, eyes, and faces. These are three commonly identified image types and thus pre-made haar cascades are available on github and through google searches. Although, making your own haar cascade is possible and discussed in many tutorials. To learn more about using OpenCV and haar cascades I recommend the YouTube series, OpenCV with Python for Image and Video Analysis, by sentdex ([1]). This playlist contains multiple relevant videos which go over how to install OpenCV, how to write code to use OpenCV, how haar cascades are made and used with OpenCV.
Also note that currently OpenCV is not installed on the RaspberryPis and will need to be installed on both your computer and the RaspberryPis to make use of the preexisting code.
Basic Computer Vision
While OpenCV is extremely useful and powerful it does most of the work under the hood, that said for those more interested in learning more about computer vision or implementing their own computer vision based code we recommend exploring those avenues as well. We've written some computer vision code, a Gaussian filter to blur images and a Sobel operator to detect edges. While we've found that this code works better for our purposes it is much slower than OpenCV's filters. For those interested in optimizing this code, one should learn how Gaussian blur filters and Sobel operators work and then update the code accordingly. The current plan for the edge detection is to use the camera to identify edges and approximate the distance of the edge to the car. Code has been written to approximate object distances based on camera pixel location but this is only useful if we can identify objects or edges. The ultimate goal will be to identify obstacles and walls and navigate around them.
Sobel Operator and Edge detection
The Wikipedia page [2] for the Sobel operator provides a much more extensive and mathematical explanation than can be provided here but the general idea is to compare the differences in local intensities at every point in an image. To do this one needs to convolve a 3x3 kernel in the x direction and convolve a similar 3x3 kernel in the y direction with a 3x3 pixel matrix of the image. This step must be repeated at every unique 3x3 point on the image. The result of this process creates an image with brighter edges. The new image with enhanced edges can be passed through a filter which takes any pixel below a certain threshold and convert its intensity to zero.
Exploring parameters and different operators may help one tailor their edge detection to the specific camera and lighting conditions they expect to use the edge detection under. Lowering the threshold of the final filter will allow for a wider range of acceptable edges. Using a Scharr operator or a Prewitt operator may prove to be more effective for edge identification with later code. It also may be advisable to explore kernels of a larger size, that is a 5x5 kernel. These types of changes give one more control over the results of the edge detection and once optimized may work better than those provided by libraries like OpenCv.
Gaussian Filter
Once again, Wikipedia will provide a better overview of this subject but we'll go over the general idea here. The Gaussian filter is essentially a low pass filter that reduces noise and detail. This kind of filtering is helpful as a precursor to edge detection because the reduction in noise enhances the differences in local intensity. A combination of a Gaussian Filter and Sobel operator has been written in python and the combination of the two add for additional parameters that can be changed to create the ideal level of edge detection. The Gaussian filter, for example, can have a different standard deviation applied and thus change the level of blurriness. A higher standard deviation will increase the blur.
