Blob 是什么

一个Blob是在一张图片中共享一些诸如灰度值等属性的一组连接的像素点。在上图中，黑色的连接区域就是blob，blob detection的目标就是识别并标记这些区域。

Blob detection 工作原理

SimpleBlobDetector, 是非常简单的模块，算法使用如下参数控制，且执行之后的流程：

Thresholding: 将源图转为一些二值图，使用从minThreshold开始的阈值. 阈值从minThreshold开始，使用thresholdStep为梯度增加.
Grouping: 在每个二值图中，相邻的白色像素点是被group在一起的，称之为binary blobs。
Merging: 计算二值图片中binary blobs的中心点，blobs之间的距离比minDistBetweenBlobs更小的会被合并。
Center & Radius Calculation: 计算并返回新合并的blobs的中心和半径。

利用color, size, shape 过滤 Blobs

By Color : [ Note : This feature appears to be broken. I checked the code, and it appears to have a logical error ] First you need to set filterByColor = 1. Set blobColor = 0 to select darker blobs, and blobColor = 255 for lighter blobs.
By Size : You can filter the blobs based on size by setting the parameters filterByArea = 1, and appropriate values for minArea and maxArea. E.g. setting minArea = 100 will filter out all the blobs that have less then 100 pixels.
By Shape : Now shape has three different parameters.

Circularity

This just measures how close to a circle the blob is. E.g. a regular hexagon has higher circularity than say a square. To filter by circularity, set filterByCircularity = 1. Then set appropriate values for minCircularity and maxCircularity. Circularity is defined as

This means that a circle has a circularity of 1, circularity of a square is 0.785, and so on.

Convexity

A picture is worth a thousand words. Convexity is defined as the (Area of the Blob / Area of it’s convex hull). Now, Convex Hull of a shape is the tightest convex shape that completely encloses the shape. To filter by convexity, set filterByConvexity = 1, followed by setting 0 ≤ minConvexity ≤ 1 and maxConvexity ( ≤ 1)

Inertia Ratio¶

Don’t let this scare you. Mathematicians often use confusing words to describe something very simple. All you have to know is that this measures how elongated a shape is. E.g. for a circle, this value is 1, for an ellipse it is between 0 and 1, and for a line it is 0. To filter by inertia ratio, set filterByInertia = 1, and set 0 ≤ minInertiaRatio ≤ 1 and maxInertiaRatio (≤ 1 ) appropriately.

下例将展示各个参数对标准图块的选取影响：

import env
from mvlib import *
from mvlib.application import *

 
      # Read image
im = read_image("blob.jpg")
im_gray = gray(im)
dev_imshow(im_gray) 
     

 
      blob_detect_params = create_blob_detector_params(10, 200, minArea=1500, minCircularity=0.1, minConvexity=0.87, minInertiaRatio=0.01)
detector = create_blob_detector(blob_detect_params)

# Detect blobs.
keypoints = detector.detect(im_gray)

# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures
# the size of the circle corresponds to the size of blob
im_with_keypoints = dev_draw_keypoints(im, keypoints)
dev_imshow(im_with_keypoints) 
     

https://www.learnopencv.com/blob-detection-using-opencv-python-c/

https://blog.csdn.net/jinntaoo/article/details/82255829

Blob-Detect