Blob detection in python

When working with image processing tasks, blob detection is a common problem that arises. Blob detection refers to the process of identifying and locating regions or objects of interest in an image. In this article, we will explore different ways to solve the blob detection problem using Python.

Option 1: OpenCV

OpenCV is a popular library for computer vision tasks and provides various functions for image processing. One of the functions it offers is blob detection. To solve the blob detection problem using OpenCV, we can follow these steps:

import cv2

# Load the image
image = cv2.imread('image.jpg')

# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply thresholding to create a binary image
_, binary = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY)

# Perform blob detection
detector = cv2.SimpleBlobDetector_create()
keypoints = detector.detect(binary)

# Draw the detected blobs on the original image
image_with_blobs = cv2.drawKeypoints(image, keypoints, np.array([]), (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

# Display the result
cv2.imshow('Blobs', image_with_blobs)
cv2.waitKey(0)
cv2.destroyAllWindows()

This code snippet demonstrates how to use OpenCV’s blob detection function to detect and locate blobs in an image. It first loads the image, converts it to grayscale, applies thresholding to create a binary image, performs blob detection using the SimpleBlobDetector_create() function, and finally draws the detected blobs on the original image. The result is then displayed using OpenCV’s imshow() function.

Option 2: scikit-image

scikit-image is another popular library for image processing tasks in Python. It provides a wide range of functions and algorithms for various image processing tasks, including blob detection. To solve the blob detection problem using scikit-image, we can follow these steps:

import numpy as np
from skimage.feature import blob_dog
from skimage.io import imread
from skimage.color import rgb2gray
import matplotlib.pyplot as plt

# Load the image
image = imread('image.jpg')

# Convert the image to grayscale
gray = rgb2gray(image)

# Perform blob detection
blobs = blob_dog(gray, min_sigma=10, max_sigma=30, threshold=0.1)

# Draw the detected blobs on the original image
fig, ax = plt.subplots()
ax.imshow(image)
for blob in blobs:
    y, x, r = blob
    c = plt.Circle((x, y), r, color='red', linewidth=2, fill=False)
    ax.add_patch(c)

# Display the result
plt.show()

This code snippet demonstrates how to use scikit-image’s blob_dog() function to detect and locate blobs in an image. It first loads the image, converts it to grayscale, performs blob detection using the blob_dog() function with specified parameters, and finally draws the detected blobs on the original image using matplotlib. The result is then displayed using matplotlib’s show() function.

Option 3: Simple Blob Detection Algorithm

If you prefer a more lightweight solution without relying on external libraries, you can implement a simple blob detection algorithm from scratch. Here’s an example implementation:

import numpy as np
import cv2

# Load the image
image = cv2.imread('image.jpg')

# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply thresholding to create a binary image
_, binary = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY)

# Define the connectivity kernel
connectivity_kernel = np.array([[0, 1, 0],
                               [1, 1, 1],
                               [0, 1, 0]], dtype=np.uint8)

# Perform connected component labeling
_, labels = cv2.connectedComponents(binary, connectivity=connectivity_kernel)

# Find the centroids of the labeled blobs
centroids = []
for label in range(1, np.max(labels) + 1):
    blob_pixels = np.where(labels == label)
    centroid_x = np.mean(blob_pixels[1])
    centroid_y = np.mean(blob_pixels[0])
    centroids.append((centroid_x, centroid_y))

# Draw the detected blobs on the original image
for centroid in centroids:
    x, y = centroid
    cv2.circle(image, (int(x), int(y)), 5, (0, 0, 255), -1)

# Display the result
cv2.imshow('Blobs', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

This code snippet demonstrates a simple blob detection algorithm implemented using basic image processing operations in OpenCV. It first loads the image, converts it to grayscale, applies thresholding to create a binary image, performs connected component labeling using the connectedComponents() function, finds the centroids of the labeled blobs, and finally draws the detected blobs on the original image using OpenCV’s circle() function.

After exploring these three options, it is evident that using OpenCV’s blob detection function provides a more straightforward and efficient solution. It offers a dedicated function specifically designed for blob detection, making the code more readable and concise. Therefore, Option 1 using OpenCV is the recommended approach for solving the blob detection problem in Python.

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