Difference between revisions of "CV Chess Logs"
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*(6hr) Oct. 14 : In order to properly find the contours, the image needs to be properly smoothed and a a mask must be added to the image around the region of interest, this being the chess board. The mask blacks out all unnecessary background in the picture yielding only the chess squares. I was able to implement the mask by resizing, smoothing and converting the image to a gray, colorless version. Then an adaptive thresholding was applied to convert the image to strictly black and white. I then found the largest square contour in the image. Then I converted all pixels outside this largest contour to black using a black image copy. This cleaned up the image nicely. | *(6hr) Oct. 14 : In order to properly find the contours, the image needs to be properly smoothed and a a mask must be added to the image around the region of interest, this being the chess board. The mask blacks out all unnecessary background in the picture yielding only the chess squares. I was able to implement the mask by resizing, smoothing and converting the image to a gray, colorless version. Then an adaptive thresholding was applied to convert the image to strictly black and white. I then found the largest square contour in the image. Then I converted all pixels outside this largest contour to black using a black image copy. This cleaned up the image nicely. | ||
| − | [[File: | + | [[File:Adaptive_Thresholding.png|frameless|1000px]] |
*(10hr) Oct.15 : Able to find edges using canny edge detection. Found lines using Hough lines transform. lastly, found the corners of the chess board squares by finding the intersections of these lines. The next step is to bound rectangles around the found corners, this will effectively map all 64 squares of the chess board and complete the board recognition objective set for this week. | *(10hr) Oct.15 : Able to find edges using canny edge detection. Found lines using Hough lines transform. lastly, found the corners of the chess board squares by finding the intersections of these lines. The next step is to bound rectangles around the found corners, this will effectively map all 64 squares of the chess board and complete the board recognition objective set for this week. | ||
Revision as of 06:52, 16 October 2018
Contents
Weekly log:
Week 1 Sept. 9 - 15
Robert Goodloe
- (0.25hr) September 12: Created project page and weekly logs.
Team
- (1.5hr) September 13: Built night light circuit and designed print-able case. Unsure which dimensions to use exactly on case until we get the perfboard.
Week 2 Sept. 16 - 22
Robert Goodloe
- (0.75hr) September 18: Flashed raspbian wheezy operating system onto SD card to set up Raspberry Pi (rpi). Researched various ways to install/use openCV on rpi.
- (0.5hr) September 19: Updated Wiki to include the two weeks of work done up to this point.
Nhut Dang
- (1hr) September 19: Soldered night light circuit to newly obtained perfboard
Team
- (2hr) September 20: Attempted OpenCV compilation and install. An error was made by trying to compile on all 4 of the rpi's CPU cores. This led to overheating and a frozen installation.
- (1.5hr) September 22: TA Ethan Shry introduced us to python installation manager 'Pip'. Pip should allow a smaller/simpler install of OpenCV components without long compile time. Ran into issues with OpenCV modules
Week 3 Sept. 23 - 29
Robert Goodloe
- (2.5hr) September 24: Compiled OpenCv on rpi. The pip install was abandoned because to many issues arose trying to install cv2 module. OpenCV traditional compile and
install was done instead. Being ~4GB in size, a new 16GB SD card was used rather than the original 8GB. To avoid the overheating problem, the I let the rpi compile overnight using 1 CPU core. Total install time: ~4 hours. Looking to try face detection tutorial in the next few days to familiarize myself with the software.
- (1.5hr) September 26: Research: how to calibrate rpi camera, Corner detction algorithm vs line detection algorithm, related projects that already exist
- (2hr) September 27: Set up ssh for rpi. SSH was working after 15 mins, rest of time spent unsuccessfully attempting to get pi to automatically email IP address upon boot or setting a static IP address.
- (3hr) September 28: Compiled a list of tutorials for OpenCV on rpi. See refereces section of Project Page. Set Up VNC Viewer and added the execution of a python script to mail the IP address upon start. Managed to complete the equivalent of a "Hello World" tutorial for OpenCV. At its completion, the Pi could recognize faces in an image. I also discovered how to display the input from the camera to the desktop.
Nhut Dang
- (2hr) September 24: Compile the list of goods needed. Thinking about the problem with the chessboard if viewing from the bottom
- (1hr)September 26: Research about light up dance floor, designing the final product, finalizing the budget.
- (1hr) September 28: redesigning the powerpoint, updating the budget, researching about the line detection and corner detection, trying to understand the math behind the transformation.
Week 4 Sept. 30 - Oct. 6
Robert Goodloe
- (2hr) Oct. 3: Attempted Keras Convolutional neural net training on the constructed datasets. Unsuccessful. It does not seem efficient to train the neural net
on the rpi. Not powerful enough. Furthermore, the Microsoft Bing API scraped images that were mostly from the side angle of pieces while our current design is from the top down.
- (3hr) Oct. 4: Abandoned neural net idea with the Bing API. Attempted to get image difference to work unsuccessfully. This is without piece recognition so it should be simpler. Will try again in the next few days. Will also compile a directory of pictures of chess pieces from the top and will train on these.
Nhut Dang
- (1.5hr) Sep 30: Researching about existing code about line detection and corner detection. The board can be detected easier using the line detection.
- (1hr) Oct. 3: Finalizing the list of material. Brief research on existing projects which uses Corner detection.
- (4 hr) Oct 4: Researching about Harris Corner detection, Hough line transform, Fast corner detection. Reading existing code and try to understand it. Researching the math behind it to figure out which one is better for our project.
Week 5 Oct 7 - Oct. 13
Robert Goodloe
- (1hr) Oct. 11 : Watched tutorials and compiled OpenCV commands to be used at a later date.
- (4hr) Oct. 12 : Spent most of time solving rpi camera module problems. Have compiled a list of solutions for this. Ultimately, looks like the cable is damaged at the connector. Purchased a new one. Worked on image difference and corner detection without success.
- (4hr) Oct. 13 : Tried to use the code at Pyimagesearch to find shapes (squares in this case) in the code. The code did not work well, but I leaned about contours and their features. This will prove to be useful later. I also explored resizing, image smoothing, and thresholding. All of the image operations will make the analysis quicker and easier. In order to find the squares, I have been trying corner detection functions, but it does not seem to be accurate enough, nor does the smoothing get rid of enough noise to make it efficient.
Nhut Dang
- (1hr) Oct. 9 : Having all the material ready. Having the tripod design ready.
- (3hr) Oct. 11 : Researching about the chess engine, chessboard corner detection. Finding the source code for the youtube video. Analyzing the code.
https://pypi.org/project/python-chess/; https://github.com/jayhack/CVChess
- (3hr) Oct. 12 : Testing out the code. Researching on Corner detection. Attempting to adjust the code to fix the problem but no avail. Trying different approach by using a different code. Spending time diagnosing the code. Learning how to use Github on OpenCV.
https://www.meccanismocomplesso.org/en/opencv-python-harris-corner-detection-un-metodo-per-rilevare-i-vertici-in-unimmagine/ https://github.com/jayhack/CVChess https://www.edwardrosten.com/work/fast.html
Week 6 Oct 14 - Oct. 20
Robert Goodloe
- (6hr) Oct. 14 : In order to properly find the contours, the image needs to be properly smoothed and a a mask must be added to the image around the region of interest, this being the chess board. The mask blacks out all unnecessary background in the picture yielding only the chess squares. I was able to implement the mask by resizing, smoothing and converting the image to a gray, colorless version. Then an adaptive thresholding was applied to convert the image to strictly black and white. I then found the largest square contour in the image. Then I converted all pixels outside this largest contour to black using a black image copy. This cleaned up the image nicely.
- (10hr) Oct.15 : Able to find edges using canny edge detection. Found lines using Hough lines transform. lastly, found the corners of the chess board squares by finding the intersections of these lines. The next step is to bound rectangles around the found corners, this will effectively map all 64 squares of the chess board and complete the board recognition objective set for this week.
Began implementing some object-oriented programming into the project as it moves from image analysis to the game analysis.
