Let us see an example of Python code that demonstrates how to classify images using rules, based on image properties such as size and aspect ratio.
Here, we will define rules such as if the black color distribution is greater than 50% in leaves, then that is a diseased plant. Similarly, in case of detecting a bicycle with a person, if the aspect ratio of an image is greater than some threshold value, then that image has a bicycle with a person.
In computer vision and image classification, the aspect ratio refers to the ratio of the width to the height of an image or object. It is a measure of how elongated or stretched an object or image appears along its horizontal and vertical dimensions. Aspect ratio is often used as a feature or criterion in image analysis and classification. It’s worth noting that aspect ratio alone is often not sufficient for classification, and it is typically used in conjunction with other features, such as contour height, texture, and edge, to achieve accurate classification results. Image properties such as bounding boxes, polygon annotations, and polyline annotations are commonly used in computer vision tasks for object detection and image segmentation. These properties help you to label and annotate objects within an image. Here’s an explanation of each feature along with Python code examples to demonstrate how to work with them:
Bounding boxes
A bounding box is a rectangular region that encloses an object of interest within an image. It is defined by four values – (x_min, y_min) for the top-left corner and (x_max, y_max) for the bottom-right corner. Bounding boxes are often used for object detection and localization. Here is an example of Python code to create and manipulate bounding boxes:
# Define a bounding box as (x_min, y_min, x_max, y_max)
bounding_box = (100, 50, 300, 200)
# Access individual components
x_min, y_min, x_max, y_max = bounding_box
# Calculate width and height of the bounding box
width = x_max – x_min
height = y_max – y_min
# Check if a point (x, y) is inside the bounding box
x, y = 200, 150
is_inside = x_min <= x <= x_max and y_min <= y <= y_max
print(f”Width: {width}, Height: {height}, Is Inside: {is_inside}”)
Polygon annotation
A polygon annotation is a set of connected vertices that outline the shape of an object in an image. It is defined by a list of (x, y) coordinates representing the vertices. Polygon annotations are used for detailed object segmentation. Here is some example Python code to work with polygon annotations:
# Define a polygon annotation as a list of (x, y) coordinates
polygon = [(100, 50), (200, 50), (250, 150), (150, 200)]
# Calculate the area of the polygon (using shoelace formula)
def polygon_area(vertices):
n = len(vertices)
area = 0
for i in range(n):
j = (i + 1) % n
area += (vertices[i][0] * vertices[j][1]) – \
(vertices[j][0] * vertices[i][1])
area = abs(area) / 2
return area
area = polygon_area(polygon)
print(f”Polygon Area: {area}”)
Polyline annotations
A polyline annotation is a series of connected line segments defined by a list of (x, y) coordinates for each vertex. Polylines are often used to represent shapes with multiple line segments, such as roads or paths. Here is some Python code to work with polyline annotations:
# Define a polyline annotation as a list of (x, y) coordinates
polyline = [(100, 50), (200, 50), (250, 150), (150, 200)]
# Calculate the total length of the polyline
def polyline_length(vertices):
length = 0
for i in range(1, len(vertices)):
x1, y1 = vertices[i – 1]
x2, y2 = vertices[i]
length += ((x2 – x1) ** 2 + (y2 – y1) ** 2) ** 0.5
return length
length = polyline_length(polyline)
print(f”Polyline Length: {length}”)
These code examples demonstrate how to work with bounding boxes, polygon annotations, and polyline annotations in Python. You can use these concepts to create rules to label images in computer vision applications.
Now, let us see the following example of how we can use contour height to classify whether an image contains a person riding a bicycle or just shows a bicycle on its own.