How can I draw a red circle around blobs detected in image?

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I have the following image:

I want to achieve 3 outcomes in the output:

  1. Highlight the black dots/patches in the image, with a red circular outline around them.
  2. Count the number of dots/patches
  3. Print the number of dots/patches overlaid on the image.

Right now, I can only count the number of dots/patches in the image and print it:

import cv2

## convert to grayscale
gray = cv2.imread("blue.jpg", 0)

## threshold
th, threshed = cv2.threshold(gray, 100, 255,cv2.THRESH_BINARY_INV|cv2.THRESH_OTSU)

## findcontours
cnts = cv2.findContours(threshed, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[-2]

## filter by area
s1= 3
s2 = 20
xcnts = []
for cnt in cnts:
    if s1<cv2.contourArea(cnt) <s2:
        xcnts.append(cnt)

print("Number of dots: {}".format(len(xcnts)))
>>> Number of dots: 66

But I am not able to figure out how to highlight the patches on the image.

EDIT: Expected results for the following image:

would be this:


Here are some approaches:

1. Color Thresholding

The idea is to convert the image to HSV format then define a lower and upper color threshold to isolate the desired color range. This results in a mask where we can find the contours on the mask with cv2.findContours() and draw the contours using cv2.drawContours()

import numpy as np
import cv2

# Color threshold
image = cv2.imread('1.jpg')
original = image.copy()
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([0, 0, 127])
upper = np.array([179, 255, 255])
mask = cv2.inRange(hsv, lower, upper)
result = cv2.bitwise_and(original,original,mask=mask)

# Find blob contours on mask
cnts = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(original,[c], -1, (36,255,12), 2)

cv2.imshow('result', result)
cv2.imshow('original', original)
cv2.waitKey()

2. Simple Thresholding

The idea is to threshold and obtain a binary mask. Similarly, to highlight the patches in the image, we use cv2.drawContours(). To determine the number of colonies, we keep a counter while iterating through the contours. Finally, to print the number of patches onto the image, we use cv2.putText()

Colonies: 11

import numpy as np
import cv2

image = cv2.imread('2.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray, 5)
thresh = cv2.threshold(blur,100,255,cv2.THRESH_BINARY_INV)[1]

cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
colonies = 0
for c in cnts:
    cv2.drawContours(image, [c], -1, (36,255,12), 2)
    colonies += 1

print("Colonies:", colonies)
cv2.putText(image, 'Colonies: {}'.format(colonies), (0, image.shape[0] - 15), \
        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (36,255,12), 2)

cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()

Color thresholding to detect the blue blobs would also work

lower = np.array([0, 0, 0])
upper = np.array([179, 255, 84])

You can use this script to determine the HSV lower and upper color ranges

import cv2
import sys
import numpy as np

def nothing(x):
    pass

# Load in image
image = cv2.imread('1.jpg')

# Create a window
cv2.namedWindow('image')

# create trackbars for color change
cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv
cv2.createTrackbar('SMin','image',0,255,nothing)
cv2.createTrackbar('VMin','image',0,255,nothing)
cv2.createTrackbar('HMax','image',0,179,nothing)
cv2.createTrackbar('SMax','image',0,255,nothing)
cv2.createTrackbar('VMax','image',0,255,nothing)

# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)

# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0

output = image
wait_time = 33

while(1):

    # get current positions of all trackbars
    hMin = cv2.getTrackbarPos('HMin','image')
    sMin = cv2.getTrackbarPos('SMin','image')
    vMin = cv2.getTrackbarPos('VMin','image')

    hMax = cv2.getTrackbarPos('HMax','image')
    sMax = cv2.getTrackbarPos('SMax','image')
    vMax = cv2.getTrackbarPos('VMax','image')

    # Set minimum and max HSV values to display
    lower = np.array([hMin, sMin, vMin])
    upper = np.array([hMax, sMax, vMax])

    # Create HSV Image and threshold into a range.
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower, upper)
    output = cv2.bitwise_and(image,image, mask= mask)

    # Print if there is a change in HSV value
    if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
        print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
        phMin = hMin
        psMin = sMin
        pvMin = vMin
        phMax = hMax
        psMax = sMax
        pvMax = vMax

    # Display output image
    cv2.imshow('image',output)

    # Wait longer to prevent freeze for videos.
    if cv2.waitKey(wait_time) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()

Find Circles and Ellipses in an Image using OpenCV, The centers in the blob detection are found less accurately compared to those in the ground truth and some centers are not found (e.g. red circles in Figure 3). Ground truth and circles around detected fiber centers (a) Ground truth. (b) MRF. To identify circles, ellipses or in general any shape in which the pixels are connected we use the SimpleBlobDetector() function of OpenCV. In non-technical terms, a blob is understood as a thick liquid drop. Here, we are going to call all shapes as a blob. Our task is to detect and recognize whether the blob is a circle or not.


drawContours(), convexHull() or minEnclosingCircle() should work with what you want. Here is a tutorial from opencv that shows how to do what you want to do:

https://docs.opencv.org/3.4/da/d0c/tutorial_bounding_rects_circles.html

OpenCV has a lot of great tutorials so check them first when you want to learn something new :)

Image Analysis: 19th Scandinavian Conference, SCIA 2015, , HoughCircles function to effortlessly detect circles in images. A few days ago, I got an email from a PyImageSearch reader asking about circle detection. We draw the actual detected circle on Line 28 using the cv2.circle Our Python script has detected the red circle, outlined it in green, and then  From the many possible approaches to the problem of red circles detection, two seem straightforward: Detect all circles from the input image and keep only the ones that are filled with red. Threshold the input image in order to keep only the red pixels, search for circles in the result.


As Mr. @alkasm said, you can use cv2.drawContours(). So, you can add the following at the end of your code:

image = cv2.imread("blue.jpg")
cv2.drawContours(image, cnts,
        contourIdx = -1, 
        color = (0, 255, 0), #green
        thickness = 5)
cv2.imshow('Contours', image) 
cv2.waitKey()

Now, the image will look like this:

Detecting Circles in Images using OpenCV and Hough Circles , The detected blobs are displayed as red circles. The blob's size is Blob detection based on circular image region for a scene with a large viewpoint change. centers contains the locations of circle centers and radii contains the estimated radii of those circles. Step 5: Draw the Circles on the Image. The function viscircles can be used to draw circles on the image. Output variables centers and radii from imfindcircles can be passed directly to viscircles.


Towards a Common Software/Hardware Methodology for Future Advanced , In order to detect the circles, or any other geometric shape, we first need to For instance, the edge of a red ball on a white background is a circle. Draw in black and white the magnitude Either add a float around v or switch to python 3. $ python detect_circles.py --image images/simple.png We’ll start with something simple, detecting a red circle on a black background: Figure 1: Detecting a simple circle in an image using OpenCV. Not bad! Our Python script has detected the red circle, outlined it in green, and then placed an orange square at the center of it.


How to Detect Circles in Images, A Blob is a group of connected pixels in an image that share some common property ( E.g grayscale 14, # Draw detected blobs as red circles. This tutorial explains simple blob detection using OpenCV. What is a Blob ? A Blob is a group of connected pixels in an image that share some common property ( E.g grayscale value ). In the image above, the dark connected regions are blobs, and the goal of blob detection is to identify and mark […]


Blob Detection Using OpenCV ( Python, C++ ), It's much more useful to say draw a box around the found face. Fornately SimpleCV To demostrate let's make a new circle, this time red on the simplecv logo. We found everything we need, all we have to do is to draw. Create new Mat of unsigned 8-bit chars, filled with zeros. It will contain all the drawings we are going to make (rects and circles).