# Documentation for `example/example_code.py`

### `remove_dirt(image)`
The `remove_dirt` function removes small objects from the input image using area closing. Here is an example of how to use the `remove_dirt` function:

```python
import skimage.morphology as morphology

# Load an image using some library (e.g. Pillow, OpenCV, etc.)
image = ...

# Remove small objects from the image
image = remove_dirt(image)
```


### `calculate_area(countour)`
The `calculate_area` function calculates the area of a contour in an image using OpenCV. Here is an example of how to use the `calculate_area` function:

```python
import numpy as np
import cv2 as cv

# Load an image using some library (e.g. Pillow, OpenCV, etc.)
image = ...

# Find contours in the image using OpenCV
contours = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)

# Calculate the area of each contour
for contour in contours:
    area = calculate_area(contour)
    print(area)
```


### `center_of_mass(X)`
The `center_of_mass` function calculates the center of mass of a 2D shape defined by a set of points. Here is an example of how to use the `center_of_mass` function:

```python
import numpy as np

# Define a set of points that define a shape
X = np.array([[0,0], [0,1], [1,1], [1,0]])

# Calculate the center of mass of the shape
com = center_of_mass(X)

# Print the center of mass
print(com)
```

In this example, the output would be `[0.5, 0.5]`, which is the center of the square defined by the points `X`.


### `center_of_mass(X)`
The `center_of_mass` function calculates the center of mass of a 2D shape defined by a set of points. Here is an example of how to use the `center_of_mass` function:

```python
import numpy as np

# Define a set of points that define a shape
X = np.array([[0,0], [0,1], [1,1], [1,0]])

# Calculate the center of mass of the shape
com = center_of_mass(X)

# Print the center of mass
print(com)
```

In this example, the output would be `[0.5, 0.5]`, which is the center of the square defined by the points `X`.


### `rg_ratio_normalize(imgarr)`
The `rg_ratio_normalize` function applies a normalization function to the red and green channels of a 2D image, then applies a camera calibration formula to the resulting normalized values and returns the resulting image. Here is an example of how to use the `rg_ratio_normalize` function:

```python
import numpy as np

# Load an image using some library (e.g. Pillow, OpenCV, etc.)
image = ...

# Convert the image to a NumPy array
imgarr = np.array(image)

# Apply the normalization and calibration to the image
imgnew, tmin, tmax = rg_ratio_normalize(imgarr)

# Print the minimum and maximum temperature values in the image
print(tmin, tmax)
```