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:
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)
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:
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)
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:
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
.
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:
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
.
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:
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)