Progress Report Due: 3/20/16

Progress:

Focus/Problem:

We are still in the process of adding a circle detection feature into our code. Through this, we plan for our app's camera to mask circles within range, so that all objects outside the circle are not shown.

This is the portion of our code where the circle detection has been placed:

 /// Apply the Hough Transform to find the circles

        HoughCircles( src_gray, circles, CV_HOUGH_GRADIENT, 1, src_gray.rows/8, 200, 100, 0, 0 );

        

        for( size_t i = 0; i < circles.size(); i++ )

        {

            cv::Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));

            int radius = cvRound(circles[i][2]);

            // circle center

            circle( image, center, 3, Scalar(0,255,0), -1, 8, 0 );

            // circle outline

            circle( image, center, radius, Scalar(0,0,255), 3, 8, 0 );

            Mat canny_output;

            //int x;

           // int y;

           int xc = cvRound(circles[i][0]);

           int yc = cvRound(circles[i][1]);

            

             for (int x = xc-radius; x < x+radius; x ++)

                for (int y = yc-radius; y < y+radius; y ++){

                    if(x-(cvRound(circles[i][0])-(x-(cvRound(circles[i][0]))))+(y-(cvRound(circles[i][1])-(y-(cvRound(circles[i][1])))) < radius*radius))

               

                        {

                            vector<vector<cv::Point> > contours;

                            vector<Vec4i> hierarchy;

                            /// Detect edges using canny

                            Canny( image, canny_output, thresh, thresh*2, 3 );

                            /// Find contours

                            findContours( canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0) );

                            image=canny_output;

As of right now, Xcode has not provided us a particular issue that needs fixing. The code builds as successful, but it is not working properly upon opening the application on the iPad.


Target Items for the Upcoming Week:
We will need to search for the part in the code that is causing this glitch. We will also need to camera mask the circle so that the camera's focus is solely on the circle and what is inside of it. Particularly, we need to focus on is our "if" statement within our code. Since we have to write the "if" statement ourselves, we are currently conducting more research as to how we are supposed to solve this issue.

Equipments Required:
-Mac Laptops
-Mr. Lin's iPad & Chord
-Large circles to verify the app recognizes what we want it to recognize.

Seminar (3/24 & 3/29) Abstract

We will be talking about the OpenCV function, "HoughCircles" and how it pertains to our project. We will inform our classmates on the essential function of HoughCircles and how the algorithm works to provide computer vision applications with circle detection. Additionally, we will briefly talk about our experience with HoughCircles and the difficulties we have encountered so far. We will briefly show how we have included the algorithm in our code. Our focus will mainly be placed on how the function will help us in developing our application in addition to explaining the algorithm to those unfamiliar to it. A draft of our first slide is shown below. 

Progress Report Due: 3/13/16

Progress:

Focus/Problem:

We are still in the process of adding a circle detection feature into our code. Through this, we plan for our app's camera to mask circles within range, so that all objects outside the circle are not shown. By doing this, we can focus the app's camera on a smaller portion of the frame, making it easier to eventually recognize numbers and letters within the circles.

As of right now, the application has detected the circle and has found the circle's center. Although, it is very glitchy. The circle's outline and center will not remain steady.


Target Items for the Upcoming Week:
We will need to search for the part in the code that is causing this glitch. We will also need to camera mask the circle so that the camera's focus is solely on the circle and what is inside of it. While circle detection will be beneficial to us if it succeeds as we intend, if we can implement text recognition we can start working with VoiceOver (to relay the detected signs back to the app user). Eventually we will also need to incorporate the red color detection code (that succeeded earlier) with this code once completed, so that the app's camera can mask colors and detect circles simultaneously.

Equipments Required:
-Mac Laptops
-Mr. Lin's iPad & Chord
-Large circles to verify the app recognizes what we want it to recognize.

Progress Report Due: 3/6/16

Progress Report Due: 3/6/16

Progress:

Focus/Problem:

We are in the process of adding a circle detection feature into our code. Through this, we plan for our app's camera to mask circles within range, so that all objects outside the circle are not shown. By doing this, we can focus the app's camera on a smaller portion of the frame, making it easier to eventually recognize numbers and letters within the circles.

As of right now though, the app's camera only detects edges (detecting any edge within range).



Target Items for the Upcoming Week:
Further our implementation of circle detection in order to go back and direct our focus on text recognition. While circle detection will be beneficial to us if it succeeds as we intend, if we can implement text recognition we can start working with VoiceOver (to relay the detected signs back to the app user). Eventually we will also need to incorporate the red color detection code (that succeeded earlier) with this code once completed, so that the app's camera can mask colors and detect circles simultaneously.

Equipments Required:
-Mac Laptops
-Mr. Lin's iPad & Chord
-Large circles to verify the app recognizes what we want it to recognize. No red items are needed right now since we have temporarily removed the color detection code out of our own code, in order to focus on circle detection. Eventually we hope to tie these two codes together.