95 lines
2.7 KiB
Markdown
95 lines
2.7 KiB
Markdown
# Documentation for `example/example_code.py`
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### `remove_dirt(image)`
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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:
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```python
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import skimage.morphology as morphology
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# Load an image using some library (e.g. Pillow, OpenCV, etc.)
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image = ...
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# Remove small objects from the image
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image = remove_dirt(image)
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```
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### `calculate_area(countour)`
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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:
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```python
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import numpy as np
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import cv2 as cv
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# Load an image using some library (e.g. Pillow, OpenCV, etc.)
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image = ...
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# Find contours in the image using OpenCV
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contours = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)
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# Calculate the area of each contour
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for contour in contours:
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area = calculate_area(contour)
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print(area)
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```
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### `center_of_mass(X)`
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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:
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```python
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import numpy as np
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# Define a set of points that define a shape
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X = np.array([[0,0], [0,1], [1,1], [1,0]])
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# Calculate the center of mass of the shape
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com = center_of_mass(X)
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# Print the center of mass
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print(com)
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```
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In this example, the output would be `[0.5, 0.5]`, which is the center of the square defined by the points `X`.
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### `center_of_mass(X)`
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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:
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```python
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import numpy as np
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# Define a set of points that define a shape
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X = np.array([[0,0], [0,1], [1,1], [1,0]])
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# Calculate the center of mass of the shape
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com = center_of_mass(X)
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# Print the center of mass
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print(com)
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```
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In this example, the output would be `[0.5, 0.5]`, which is the center of the square defined by the points `X`.
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### `rg_ratio_normalize(imgarr)`
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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:
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```python
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import numpy as np
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# Load an image using some library (e.g. Pillow, OpenCV, etc.)
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image = ...
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# Convert the image to a NumPy array
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imgarr = np.array(image)
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# Apply the normalization and calibration to the image
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imgnew, tmin, tmax = rg_ratio_normalize(imgarr)
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# Print the minimum and maximum temperature values in the image
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print(tmin, tmax)
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```
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