#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG

"""

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

BLOB stands for Binary Large OBject and refers to a group of connected pixels in a binary image.

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 these regions.

How it works:

1. Threshold input images to binary.

2. Grouping: connected white/black pixels are grouped together.

3. Merging: blobs located closer than minDistBetweenBlobs are merged.

4. Center & Radius Calculation : The centers and radii of the new merged blobs are computed and returned.

Can be filtered by color, size or shape

"""

import matplotlib.pyplot as plt

import numpy as np

import cv2

# The input image.

image = cv2.imread("images/cast_iron1.tif", 0)

#image = cv2.imread("images/Osteosarcoma_01_small.tif")

#Extract only blue channel as DAPI / nuclear (blue) staining is the best

#channel to perform cell count.

#image=image[:,:,0]

#No need to pre-threshold as blob detector has build in threshold.

#We can supply a pre-thresholded image.

# Set up the SimpleBlobdetector with default parameters.

params = cv2.SimpleBlobDetector_Params()

# Define thresholds

#Can define thresholdStep. See documentation.

params.minThreshold = 0

params.maxThreshold = 255

# Filter by Area.

params.filterByArea = True

params.minArea = 50

params.maxArea = 10000

# Filter by Color (black=0)

params.filterByColor = False #Set true for cast_iron as we'll be detecting black regions

params.blobColor = 0

# Filter by Circularity

params.filterByCircularity = True

params.minCircularity = 0.5

params.maxCircularity = 1

# Filter by Convexity

params.filterByConvexity = True

params.minConvexity = 0.5

params.maxConvexity = 1

# Filter by InertiaRatio

params.filterByInertia = True

params.minInertiaRatio = 0

params.maxInertiaRatio = 1

# Distance Between Blobs

params.minDistBetweenBlobs = 0

# Setup the detector with parameters

detector = cv2.SimpleBlobDetector_create(params)

# Detect blobs

keypoints = detector.detect(image)

print("Number of blobs detected are : ", len(keypoints))

# Draw blobs

img_with_blobs = cv2.drawKeypoints(image, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

plt.imshow(img_with_blobs)

cv2.imshow("Keypoints", img_with_blobs)

cv2.waitKey(0)

cv2.destroyAllWindows()

# Save result

cv2.imwrite("particle_blobs.jpg", img_with_blobs)