# Detect and Measure Circular Objects in an Image

This example shows how to automatically detect circles or circular objects in an image and visualize the detected circles.

Read and display an image of round plastic chips of various colors. Besides having plenty of circles to detect, there are a few interesting things going on in this image from a circle detection point-of-view:

1. There are chips of different colors, which have different contrasts with respect to the background. On one end, the blue and red ones have strong contrast on this background. On the other end, some of the yellow chips do not contrast well with the background.

2. Notice how some chips are on top of each other and some others that are close together and almost touching each other. Overlapping object boundaries and object occlusion are usually challenging scenarios for object detection.

```rgb = imread("coloredChips.png"); imshow(rgb)```

### Step 2: Determine Radius Range for Searching Circles

Find the appropriate radius range of the circles using the `drawline` function. Draw a line over the approximate diameter of a chip.

`d = drawline;`

The length of the line ROI is the diameter of the chip. Typical chips have diameters in the range 40 to 50 pixels.

```pos = d.Position; diffPos = diff(pos); diameter = hypot(diffPos(1),diffPos(2))```
```diameter = 45 ```

### Step 3: Initial Attempt to Find Circles

The `imfindcircles` function searches for circles with a range of radii. Search for circles with radii in the range of 20 to 25 pixels. Before that, it is a good practice to ask whether the objects are brighter or darker than the background. To answer that question, look at the grayscale version of this image.

```gray_image = im2gray(rgb); imshow(gray_image)```

The background is quite bright and most of the chips are darker than the background. But, by default, `imfindcircles` finds circular objects that are brighter than the background. So, specify the `ObjectPolarity` name-value argument to `"dark"` in `imfindcircles` to search for dark circles.

`[centers,radii] = imfindcircles(rgb,[20 25],ObjectPolarity="dark")`
```centers = [] radii = [] ```

Note that the outputs `centers` and `radii` are empty, which means that no circles were found. This happens frequently because `imfindcircles` is a circle detector, and similar to most detectors, `imfindcircles` has an internal detection threshold that determines its sensitivity. In simple terms it means that the detector's confidence in a certain (circle) detection has to be greater than a certain level before it is considered a valid detection. `imfindcircles` has a `Sensitivity` name-value argument that you can use to control this internal threshold, and consequently, the sensitivity of the algorithm. A higher `Sensitivity` value sets the detection threshold lower and leads to detecting more circles. This is similar to the sensitivity control on the motion detectors used in home security systems.

### Step 4: Increase Detection Sensitivity

Coming back to the chip image, it is possible that at the default sensitivity level all the circles are lower than the internal threshold, which is why no circles were detected. By default, `Sensitivity`, which is a number between 0 and 1, is set to 0.85. Increase `Sensitivity` to 0.9.

```[centers,radii] = imfindcircles(rgb,[20 25],ObjectPolarity="dark", ... Sensitivity=0.9)```
```centers = 8×2 146.1895 198.5824 328.8132 135.5883 130.3134 43.8039 175.2698 297.0583 312.2831 192.3709 327.1316 297.0077 243.9893 166.4538 271.5873 280.8920 ```
```radii = 8×1 23.1604 22.5710 22.9576 23.7356 22.9551 22.9995 22.9055 23.0298 ```

This time `imfindcircles` found some circles - eight to be precise. `centers` contains the locations of circle centers and `radii` contains the estimated radii of those circles.

### Step 5: Draw the Circles on the Image

The function `viscircles` can be used to draw circles on the image. Output variables `centers` and `radii` from `imfindcircles` can be passed directly to `viscircles`.

```imshow(rgb) h = viscircles(centers,radii);```

The circle centers seem correctly positioned and their corresponding radii seem to match well to the actual chips. But still quite a few chips were missed. Try increasing `Sensitivity` even more, to 0.92.

```[centers,radii] = imfindcircles(rgb,[20 25],ObjectPolarity="dark", ... Sensitivity=0.92); length(centers)```
```ans = 16 ```

Increasing `Sensitivity` finds even more circles. Plot these circles on the image again.

```delete(h) % Delete previously drawn circles h = viscircles(centers,radii);```

### Step 6: Use the Second Method (Two-stage) for Finding Circles

This result looks better. `imfindcircles` has two different methods for finding circles. So far the default method, called the phase coding method, was used for detecting circles. There's another method, popularly called the two-stage method, that is available in `imfindcircles`. Use the two-stage method and show the results.

```[centers,radii] = imfindcircles(rgb,[20 25],ObjectPolarity="dark", ... Sensitivity=0.92,Method="twostage"); delete(h) h = viscircles(centers,radii);```

The two-stage method detects more circles at the `Sensitivity` of 0.92. In general, these two methods are complementary in that they have different strengths. The phase coding method is typically faster and slightly more robust to noise than the two-stage method. But it may also need higher `Sensitivity` levels to get the same number of detections as the two-stage method. For example, the phase coding method also finds the same chips if the `Sensitivity` level is raised higher, say to 0.95.

```[centers,radii] = imfindcircles(rgb,[20 25],ObjectPolarity="dark", ... Sensitivity=0.95); delete(h) viscircles(centers,radii);```

Note that both the methods in `imfindcircles` find the centers and radii of the partially visible (occluded) chips accurately.

### Step 7: Why Are Some Circles Still Getting Missed?

Looking at the last result, it is curious that `imfindcircles` does not find the yellow chips in the image. The yellow chips do not have strong contrast with the background. In fact they seem to have very similar intensities as the background. Is it possible that the yellow chips are not really darker than the background as was assumed? To confirm, show the grayscale version of this image again.

`imshow(gray_image)`

### Step 8: Find Bright Circles in the Image

The yellow chips are almost the same intensity, maybe even brighter, as compared to the background. Therefore, to detect the yellow chips, change `ObjectPolarity` to `"bright"`.

```[centersBright,radiiBright] = imfindcircles(rgb,[20 25], ... ObjectPolarity="bright",Sensitivity=0.92);```

### Step 9: Draw Bright Circles with Different Color

Draw the bright circles in a different color, by changing the `Color` name-value argument in `viscircles`.

```imshow(rgb) hBright = viscircles(centersBright,radiiBright,Color="b");```

Note that three of the missing yellow chips were found, but one yellow chip is still missing. These yellow chips are hard to find because they do not stand out as well as others on this background.

### Step 10: Lower the Value of `EdgeThreshold`

There is another name-value argument in `imfindcircles` which may be useful here, namely `EdgeThreshold`. To find circles, `imfindcircles` uses only the edge pixels in the image. These edge pixels are essentially pixels with high gradient value. The `EdgeThreshold` name-value argument controls how high the gradient value at a pixel has to be before it is considered an edge pixel and included in computation. A high value (closer to 1) for this parameter will allow only the strong edges (higher gradient values) to be included, whereas a low value (closer to 0) is more permissive and includes even the weaker edges (lower gradient values) in computation. In case of the missing yellow chip, since the contrast is low, some of the boundary pixels (on the circumference of the chip) are expected to have low gradient values. Therefore, lower the `EdgeThreshold` value to ensure that the most of the edge pixels for the yellow chip are included in computation.

```[centersBright,radiiBright,metricBright] = imfindcircles(rgb,[20 25], ... ObjectPolarity="bright",Sensitivity=0.92,EdgeThreshold=0.1); delete(hBright) hBright = viscircles(centersBright,radiiBright,Color="b");```

### Step 11: Draw Dark and Bright Circles Together

Now `imfindcircles` finds all of the yellow ones, and a green one too. Draw these chips in blue, together with the other chips that were found earlier (with `ObjectPolarity` set to `"dark"`), in red.

`h = viscircles(centers,radii);`

All the circles are detected. A final word - it should be noted that changing the parameters to be more aggressive in detection may find more circles, but it also increases the likelihood of detecting false circles. There is a trade-off between the number of true circles that can be found (detection rate) and the number of false circles that are found with them (false alarm rate).

Happy circle hunting!