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

Question

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:

Answer 1

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()