---
# System prepended metadata

title: 'Advance Tutorial 1 - Image Processing [![made-with-HackMD](https://img.shields.io/badge/Made%20with-HackMD-1f425f.svg)](https://hackmd.io/@j1Jsew7VSb-GQ9oE3SBz8g/B1SY3ptA9)'

---

# Advance Tutorial 1 - Image Processing [![made-with-HackMD](https://img.shields.io/badge/Made%20with-HackMD-1f425f.svg)](https://hackmd.io/@j1Jsew7VSb-GQ9oE3SBz8g/B1SY3ptA9)

Author : Amrutavarsh Kinagi

Contact: askinagi@connect.ust.hk 

Author : Morris Tseng

Contact: mtseng@connect.ust.hk 

Updated by: Ho Yu Yat (yyhoai@connect.ust.hk) (2019)

Updated by: Tang Pok Lam (pltangab@connect.ust.hk) (2020)

Stolen and Updated by: Tang Cheuk Yu (cytangar@connect.ust.hk) (2021)

Stolen Again and Updated by: Kwong Cheuk Lam (clkwongaa@connect.ust.hk) (2022)

## Tutorial Video

https://youtu.be/Grgwa1Ng4yo

## Classwork

If you don't have a camera:
https://hackmd.io/@j1Jsew7VSb-GQ9oE3SBz8g/SkMA4nTJi

If you have a camera:
https://hackmd.io/@j1Jsew7VSb-GQ9oE3SBz8g/BkAEXbqAq

## What is an image?
Before we start, it is crucial to understand what an image is. A representation of an image in computer is actually very similar to a spreadsheet. In fact, you can view a image with a spreadsheet application. Take a look at [this spreadsheet](https://docs.google.com/spreadsheets/d/1iv-KJNuuUdBp-KXYhekPpTvZKWijySFD/edit?usp=sharing&ouid=100783747747020416812&rtpof=true&sd=true)  for an example.
![](https://i.imgur.com/Ic9KcYq.png)

You must be wondering, how this is possible. To find the answer, we need to zoom in to take a closer look.
![](https://i.imgur.com/EB3jpNv.png)
We can see that each cell is either red, green or blue. By using different bightness of the three colors, we can create an illusion that there are a lot of different colors.

To represent an image digitally, we need to store these cells, which are called pixels. Each pixel consist of the brightness value of the red, green and blue colors. These values in computers or robots are stored in a **2D array**. You may have direct access to this array or may only be given a **pointer**. Note that the coordinate system of an image in computing is different from the coordinate system normally used in mathematics. The origin (0, 0) is on the top-left corner of the iamge.

<img width="240" alt="coordinate system" src="http://pippin.gimp.org/image_processing/images/coord_sys.png">

Vision is one of the major perception systems in the robotics field. 
The robot receives image or video data from the camera module and stores it in the RAM.

Here is a camera module.

<img width="360" alt="camera module" src="https://upload.wikimedia.org/wikipedia/commons/4/4b/Raspberry_Pi_Camera_Module_-_Hand-Held_%2814672275231%29.jpg">

### Before Image Processing

Since the camera image is stored in pixels in a 2D array, it is important to know the concept and implementations of arrays.

Here is a 2D array with width 5 and height 3:

| [0][0] | [0][1] | [0][2] | [0][3] | [0][4] |
| ------ | ------ | ------ | ------ | ------ |
| [1][0] | [1][1] | [1][2] | [1][3] | [1][4] |
| [2][0] | [2][1] | [2][2] | [2][3] | [2][4] |

The following demonstrates the typical way to print and manipulate 2D arrays:

```c
#include <stdio.h>
#define WIDTH 5
#define HEIGHT 3
void print_array(int A[][WIDTH], int height) {
    for(int i = 0; i < height; i++) {
        for (int j = 0; j < WIDTH; j++) {
            printf("%d ", A[i][j]);
        }
        printf("\n");
    }
}
int main() {
    int myArray[HEIGHT][WIDTH];
    for (int i = 0; i < HEIGHT; i++) {
        for (int j = 0; j < WIDTH; j++) {
            myArray[i][j] = WIDTH * i + j;
        }
    }
    print_array(myArray, HEIGHT);
}
```

Output

```
0 1 2 3 4 
5 6 7 8 9 
10 11 12 13 14 
```

### 1. Image Type (Colored, Grayscale or Black&White)

Although a color mode setting may not exist in the emulator's camera, color modes are still worth learning. It is very important for real-life situations since it will affect the quality of the image you obtain.

#### Colored

<img width="360" alt="colored" src="https://user-images.githubusercontent.com/32810188/44947015-2f9d3180-ae39-11e8-854a-a32387a246ef.png">

Please note that the images are rendered instead of taking in reality, so the image shown here may not fully represent the real image that you will be getting.

#### RGB565
If we want to have colored pictures, we also need information about the color. The color depth defines how many bits are used to represent a color. The more bits (information), the more colors we can display. There are different ways to represent a color but virtually all displays use RGB, where a color is represented by its Red, Green, and Blue components.

RGB565 requires only 16 (5+6+5) bits (i.e. 2 bytes) and is commonly used with embedded screens. Each pixel occupies 2 bytes, the bits are split into 3 groups to represent RGB respectively. The value stored in each group is just the intensity of that color.


R: Occupies 5 bits, so its value ranges from 00000 to 11111.

G: Occupies 6 bits, so its value ranges from 000000 to 111111.

B: Occupies 5 bits, so its value ranges from 00000 to 11111.

We concatenate the 3 values in the order R, G, B. So if R=11011, G=110010 and B=11010, then the final RGB565 value is 1101111001011010.

<img width="480" alt="RGB565" src="https://www.newton.com.tw/img/7/e01/cGcq5SM0YDM5ADMkJGNxQmZ5QWM0UzMjJjN4AzLtVGdp9yYpB3LltWahJ2Lt92YuUHZpFmYuMmczdWbp9yL6MHc0RHa.jpg">

Note that the Green component has 6 bits, not because it needs higher values but because it needs higher resolution ([see RGB 565 - Why 6 Bits for Green Color](https://stackoverflow.com/questions/25467682/rgb-565-why-6-bits-for-green-color)).


#### Grayscale

<img width="360" alt="greyscale" src="https://user-images.githubusercontent.com/32810188/44947020-404da780-ae39-11e8-8c8b-f1113f03550c.png">

A grayscale image is essentially an image that only carries intensity information. Each pixel is just represented by a number that ranges from 0% (Total absence of light/ black) to 100% (Total presence of light/ white). Any fraction between is what we called **'gray'**.

Note that if the RGB values in a colored image pixel are equal, there would be no color information. Therefore, we can also **convert RGB565 images to grayscale images**. 


#### Average Method

Firstly, get the individual RGB components through bitwise operations.
```C++
int16_t pixel = *buffer;
int16_t red = ((pixel & 0xF800) >> 11);
int16_t green = ((pixel & 0x07E0) >> 5);
int16_t blue = (pixel & 0x001F);
```

Then, take the average value of the red, green and blue values to generate a grayscale value.

$\text{Grayscale} = \frac{red + green + blue}{3}$

However, human eyes are more sensitive to green than other colors, averging the three colors would generate an output that is less close to the original image under human perception.

#### Luminosity Method

Therefore, we combine the red, green and blue values with weights to generate a grayscale value.

A common ratio (weight) is **R: 0.2989, G: 0.5870, B: 0.1140**.

Try it yourself.


#### Pros and Cons of Using Different Image Type

- Colored: (colors are stored as RGB in the MCU)
    - Pros: Contains a lot of details
    - Cons: Hard to control
- Grayscale: (range from 0 ~ 255, where black = 0 and white = 255)  **Recommended**
    - Pros: Relatively easier to control, able to use gradient to differentiate features
    - Cons: -
- Black&White: (represented by 1 and 0, which black = 1 and white = 0)
    - Pros: Easiest to control
    - Cons: Hard to adjust thresholding for the image, has minimal information

These pros and cons are just a means to guide you. However, these rules aren't set in stone. It is general that for image processing, we would usually choose greyscale or black&white images to analyze as they provide adequate information in order to handle the dynamics of the car. The use of colored images may provide more information but it is up to you to determine if the extra information available is of any substantial benefit.

#### Black-and-White
<img width="360" alt="mediumintense" src="https://user-images.githubusercontent.com/32810188/44949432-b961f480-ae63-11e8-8896-d919740a78bd.png">

A black-and-white image is an image that just contains either black (0) or white (1) in any pixels.

Setting a simple threshold for the greyscale image can generate a black-and-white image.

### 2. Brightness & Intensity

Apart from color choices, the brightness and the intensity of the image captured also affect the quality of the image you obtain, which in turn affect the ability to extract good data from it.

**Low Intensity**

<img width="360" alt="lowintense" src="https://user-images.githubusercontent.com/32810188/44949429-b49d4080-ae63-11e8-902e-17e64f7caf88.png">

**Medium Intensity**

<img width="360" alt="mediumintense" src="https://user-images.githubusercontent.com/32810188/44949432-b961f480-ae63-11e8-8896-d919740a78bd.png">

**High Intensity**

<img width="360" alt="highintense" src="https://user-images.githubusercontent.com/32810188/44949434-bbc44e80-ae63-11e8-8e68-152058dd8f3a.png">

If you do not find the proper intensity, your image might contain noise that would deter you from processing the information accurately. It is important that your program is able to adjust the brightness and intensity based on different lighting conditions as it is against the rules to flash your program during the competition.

Here is a guideline if you want to write code to adjust the brightness and intensity of the image:

https://pippin.gimp.org/image-processing/chap_point.html

## Convolution of image

#### Introduce to Kernel

A kernel is a square matrix that specifies spatial weights of various pixels in an image. Different image processing contexts may use different kernels.

<img width="480" alt="wrxle" src="https://user-images.githubusercontent.com/32810188/45767787-afb2fc00-bc6d-11e8-81a5-29ba95f1c94a.png">

#### Convolution Process

Convolution of a matrix involves laying a matrix over another and then calculating the weighted sum of all pixel values. The example below uses a 3x3 kernel for convolution. If the input image is a mxn array, then the output array would be (m-2)x(n-2).

<img width="480" height="360" src="https://user-images.githubusercontent.com/32810188/45766774-f6ebbd80-bc6a-11e8-9aed-c7f57fe57159.JPG">

$105*(0)+102*(-1)+100*(0)+103*(-1)... = 89$

More examples:

<img width="480" height="360" src="https://user-images.githubusercontent.com/32810188/45768526-bb9fbd80-bc6f-11e8-9d93-120bb90f9175.gif">

Try it yourself:
https://setosa.io/ev/image-kernels/

The weighting of each pixel entry is intuitive. For example a sharpening filter, we want to emphasize the center pixel and increase the color difference of it with the ones surrounding it. We would create a kernel like this:
| - | - | - |
| -------- | -------- | -------- |
| 0        | -1        | 0        |
| -1       | 5        | -1        |
| 0       | -1        | 0         |

To further intensify the sharpening effect:
| - | - | - |
| -------- | -------- | -------- |
| -1        | -1        | -1        |
| -1       | 9        | -1        |
| -1       | -1        | -1         |

Note that the sum of weighting is 1.

## Application of Convolution - Reducing Noise

Usually the image generated by a camera might contain noise and will have to be **filtered/ smoothened** to prevent it from affecting the image analysis. 

There are mainly two types of noise: salt-and-pepper noise and Gaussian noise.

### Salt-and-Pepper Noise - Median Filter
Salt-and-pepper noise is a form of noise sometimes seen on images. It is also known as impulse noise. This noise can be caused by sharp and sudden disturbances in the image signal. It presents itself as sparsely occurring white and black pixels.

<img width="480" alt=" salt-and-pepper noise" src="https://static-01.hindawi.com/articles/tswj/volume-2014/826405/figures/826405.fig.0010a.jpg">

For salt-and-pepper noise, we can use a median filter.

<img width="480" alt="median filter" src="https://www.researchgate.net/profile/Vu_Hoang_Minh/publication/320547422/figure/fig4/AS:551864018141185@1508586043638/3-3-kernel-in-median-filter.png">

Use of a median filter to improve an image severely corrupted by defective pixels.

<img width="480" alt="colored" src="https://i.imgur.com/v9DjmbP.png">

Example Code:

```c++
void medianFilter(const uint8_t *cameraDataPtr, uint8_t *processedDataPtr, uint16_t width, uint16_t height) {
    uint8_t window[9];
    // Loop though entire image array
    // ignored the outer edge of the image to simplify the code
    for (uint16_t x=1; x<width-1; x++) {
        for (uint16_t y=1; y<height-1; y++) {
            // Fill the 3x3 window
	    uint8_t k = 0;
            for (int u=x-1; u<=x+1; u++) {
                for (int v=y-1; v<=y+1; v++) {
                    window[k++] = *(cameraDataPtr + width * v + u);
                }
            }
            // Find the median
            for(uint8_t i=0; i<5; i++) {
                for (uint8_t j=i+1; j<9; j++) {
                    if (window[j] < window[i]) {
                        uint8_t temp = window[i];
                        window[i] = window[j];
                        window[j] = temp;
                    }
                }
            }
            // Assign the median to the processed image array
            *(processedDataPtr + width * y + x) = window[4];
        }
    }
}
```

What if the image is too noisy and much information is missing?

![](https://i.imgur.com/PUOn0wM.png)

A larger kernel size can be used, it smoothes out the image better. However, a large kernel means the sharpness of the image is lost.


### Gaussian Noise - Gaussian Filter
Gaussian noise is statistical noise having a probability density function equal to that of the normal distribution, which is also known as the Gaussian distribution. In other words, the values that the noise can take on are Gaussian-distributed.

<img width="480" alt="gaussian noise" src="https://www.researchgate.net/publication/252066070/figure/fig2/AS:361043193810945@1463090813645/Noisy-image-Gaussian-noise-with-mean-and-variance-0005_Q640.jpg">

<img width="480" alt="gaussian noise" src="https://www.sfu.ca/sonic-studio-webdav/handbook/Graphics/Gaussian.gif">

<img width="640" alt="gaussian noise" src="https://miro.medium.com/max/3264/1*nOkI4VIQKM5FaSVBP8TiZw.png">

For Gaussian noise, we can use a Gaussian filter kernel.

<img width="240" alt="Gaussian filter kernel" src="https://4.bp.blogspot.com/-v4dH8qhFnEE/WqHaTPel8RI/AAAAAAAAI8g/AxIVu5i7mHU5UDcu6BkJQJj_UO11sMomwCLcBGAs/s1600/3x3%2BGaussian%2BKernel.png">

## Application for Convolution - Feature Dectection

Here are some images that have gone through the convolution.

<img width="480" alt="colored" src="https://i.imgur.com/T17jDCz.png">

### Edge Detection - Sobel Filter
The Sobel filter emphasises edges of an image.

<img width="640" alt="colored" src="https://slideplayer.com/slide/14419272/90/images/8/Sobel+operator+In+practice%2C+it+is+common+to+use%3A+Magnitude%3A.jpg">

Before:

<img width="360" alt="colored" src="https://upload.wikimedia.org/wikipedia/commons/thumb/3/3f/Bikesgray.jpg/300px-Bikesgray.jpg">

After:

<img width="360" alt="colored" src="https://upload.wikimedia.org/wikipedia/commons/thumb/2/24/Bikesgray_sobel.JPG/300px-Bikesgray_sobel.JPG">

#### Why it works
Take a look at gx kernel first.


| - | Gx | - |
| -------- | -------- | -------- |
| -1       | 0        | 1        |
| -2       | 0        | 2        |
| -1       | 0        | 1        |

If this kernel is place onto an area with no edge. It means that all the pixels in the area is about the same. Let's asume all the pixel have a value of **C**. The new value for the center pixel will be (-1**C** + 1**C** - 2**C** + 2**C** -1**C** + 1**C** = 0). The pixel will be black if all the pixels in the area are similar. The only place that the result pixel will be white is when the left and right side of pixels are very different (an edge).

The same can also be apply to gy. Finally, we can use the Pythagorean theorem to get the overall value. 

Convolutions can be extended to further detect other vital features of a track. Another key term to keep in mind is **Gradient**.

<img width="480" alt="colored" src="https://i.imgur.com/AbQLWej.png">




**Points to remember:**

1. Real life situations largely vary from digital replicated simulations. Application of algorithms should thus be geared to tackle the blemishes you would face.
2. Analyse features and algorithms from a mathematical and physical perspective. 
3. Keep the program robust in order to tackle the worst cases possible. 
4. Keep in mind the run time of your program during feature extraction.

### Further Pondering

- Applying convolution to the whole image is a processor-intensive task and might produce a large amount of unnecessary information. How can we only extract the information needed?
- Or even better, only *produce* the information needed? (This will be crucial in our application.)

## Camera - Ov7725
Ov7725 is will be the camera modules you will be using during the Robot Design Contest. It can output greyscale and RGB565.

The camera configuration is passed by a communication protocol called SCCB (similar to I2C). It is handled by the camera library.

Due to the limited memory size and computation power of STM32F1, the default image size is set to be 160 * 120.

You may have to implement your own method of processing the image data.

## Setting Up

All functions with the `cam_` prefix are defined in `camera.h`.

:::warning
:warning: Please read `camera.h` and `camera.c` before your RDC project :warning:
:::

### Camera Initialization 

Initialize the camera.

```c
void camera_GPIO_init(); //Init

CameraState camera_init(); //Check if init
```

Where `CameraStatus` is an enum that contains RGB565 and greyscale, also defined in `camera.h`.

### Colour Mode

```c
void cam_set_colormode(CamColorMode c); //Set colour mode

CamColorMode cam_get_colormode(void); //Read colour mode
```

Where `CamColorMode` is an enum containing the option for RGB565 and greyscale, also defined in `camera.h`.


### Frame Size

```c
void cam_set_frameSize(CamFrameSize f); //Set frame size

CamFrameSize cam_get_framesize(void); //Read current frame size

```

Where `CamFrameSize` is an enum containing various preset frame size, also defined in `camera.h`. Defaulted to 160x120.

### Brightness and Constrast

```c
void cam_set_brightness(int8_t brightness);
void cam_set_contrast(int8_t contrast);
```

You may want to tune this to suit the lighting environment.

### Setting Frame Rate

```c
void cam_set_framerate(CamFrameRate rate);
```

Where `CameraFrameRate` is an enum containing various preset fps values, also defined in `camera.h`. Defaulted to 75fps.

### Other Settings

Please read `camera.h` and `camera.c` before your RDC project.

## Usage

### 1. Check whether the image is ready to receive in external buffer

```C
uint8_t cam_is_frame_ready();
```

### 2. Receive image from the external buffer

```C
void cam_get_rgb565(uint16_t* image);
```

You need to define an array of the appropriate size to store the image data and pass into these functions.

### 3. Process the images and Print the image to TFT

```c
void image_processing(uint16_t* image); // You wil define this
void cam_rgb2printable(uint16_t* image, uint16_t* printable);
void tft_print_image(void* buf, uint32_t x, uint32_t y, uint32_t w, uint32_t h);

```
