Week 5 Tuesday (Powerpoint)

The next few days will be spent just working on putting the rest of the information and images into my power point. I went over a lot of the research that we did to make sure we covered everything we needed to have covered to tell a complete story. Any gaps will also arise as we make the power point.

I am completely done with putting information in bullets into the slides. Now, I will find the corresponding images to help explain those bullets. Then, I will need to really practice and make sure I have everything I need there.

Week 5 Monday (Powerpoint)

I spent the majority of my time working on the presentation and mainly organizing my thoughts as to what progress we have made over the past four weeks. I looked through the power point that the freshman made, and the image above was in that power point. It clearly depicts the placement of the cameras and what portions of the hockey rink that they each capture. We will use this image in our presentation, as that information is something we definitely need to convey.

There was other valuable information in that power point, which we will apply to our presentation. Other than that, I input information into the presentation, and I will add the images later. Some of the bullet points will need to be taken out, but I want to keep them for now to practice with.

Week 4 Friday (Research Symposium)

I spent the majority of the day at the research symposium. There was a poster presentation, where I saw presentations on anything from 3d printed prosthetic to the radar detection that we see in the image above. The presentations were all very interesting and in-depth, making it challenging to understand everything that was written on their boards. However, it was easy to see the purpose and progress of the individual researchers. I also gained some valuable knowledge I can utilize in my presentation in a couple of weeks.

There was some in person presentations that I witnessed as well. They were on the relationship between space matter density and star production as well as the merging of the stars. This one was very hard to understand, but all three researched gave different presentations on the issue with some overlap.

When I came back to the lab, I spent some time looking into MNIST, but now my next focus is just on confirming whether or not pixels are the best thing to use to compare the broadcast system and the fixed focal length system. I am waiting for a response back from Mike, but I will continue working on the power point in the meantime.

Week 4 Thursday (Finalizing Data)

I had to analyze the data from the previous two days. I did this by finding the average number of pixels in each clipping that could be used to find the jersey number. I did this for the broadcast system and the fixed focal length camera system. After compiling the data, I found that on average there were about 209 more pixels per frame for the broadcast system. However, after closer inspection, it became clear that this difference was due to a multitude of 0 pixels in the fixed focal length system, potentially due to the angle of the camera. Comparatively, there were almost no situations in which 0 pixels were present for the broadcast system.

However, only 50 percent of the hockey players were in each frame, so this trade off must be accounted for in the final analysis of which system is better for player analysis. Now, I will work more on my power point presentation, but if any problems arise with the accuracy of the data, I will have to refer back to my calculations.

Week 4 Wednesday (Testing)

After working out the kinks in the method of comparison yesterday, today was simply spent working on finding and analyzing the data. I took about 30 frames from the set of fixed focal length camera data that is not filtered. The filtered data is what the RIT freshman had used to calibrate the system, so the numbers on the back of the jerseys are visible to the human eye. Using that data would inherently have skewed the data, making it seem like there are more pixels revealing jersey number than there really are.

Thus, I utilized the unfiltered data. I ended up using the clippings of other teams. This decision should not matter as the numbers are placed in the same location, and for our pixel analysis, should not vary based on team. I used the bounding polygon box on Image J and searched how Image J can find the area inside the box. Pressing [M] allows numerous data values to be shown in a results window. One of those data values is the area. I finished calculating the pixel values for the broadcast and fixed focal length camera systems for 30 frames each. Now, I will determine if that is sufficient data and the differences between the two systems.

Week 4 Tuesday (Programming)

The same issues from yesterday carried over to today. Thus, I needed Ethan's help to ensure the python program was able to grab the folders of image clippings and produce results that I could use to compare the broadcast system with the fixed focal length system.

Ethan went through the program and was able to solve many of the syntax errors and work with the program to make sure it was grabbing images from the appropriate places. During this process, he taught me quite a bit about directories. He explained that they are the places where the program is looking for the input. 

He determined that the program didn't actually find a percentage accuracy, but rather was supposed to produce images that has numbers written above them to indicate the jersey number that the computer was recognizing. Joe concurred that this was the purpose of the program.

Even after fixing the program for our specifications, the program didn't seem to accomplish any task. In fact, the original image was the same as the returned image. Thus, the program was not functional for comparing the broadcast and fixed focal length system. Due to time constraints, it became infeasible to write new code that would find specific percentages or return images with the predicted jersey numbers. In fact, finding specific percentages would require the numbers on the players to be read manually. Not all of the clippings have that quality, so using such a method would skew the data.

Therefore, we decided that I should simply work on finding the pixels that make up the numbers on each jersey. Now, I will go through image J and the two sets of clippings to find this data. 

Week 4 Monday (Troubleshooting)

I had the same difficulties that I had on Friday with the program. I looked through the folder and the wiki to see if there was any documentation I could use to help solve the problem, but I remained at a logjam. I looked back in the folder where I found the previous code, and I realized that there were three programs that the freshman made. However, I believe only one is necessary to find the accuracy that the program can identify players with.

Ethan was able to come in and help after lunch, but he spent quite a bit of time helping Emma. Thus, he didn't have enough time to help solve the problem I had. She had some problems with her syntax and with the format that her photos were in, so I tried correcting the photos from Tiff to PNG. That was the correction Ethan did for Emma at large, solving her issues. I wasn't able to do them all at once, so I will have to wait for him to help me do them all at once.

All in all, I should be able to run my program quickly if I can fix the program and access my folder of pictures appropriately. However, this seems to be a sizable problem.