LightestDetection

import numpy as np
import argparse
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", help = "path to the image file")
ap.add_argument("-r", "--radius", type = int,
	help = "radius of Gaussian blur; must be odd")
args = vars(ap.parse_args())

# load the image and convert it to grayscale
image = cv2.imread(args["image"])
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# perform a naive attempt to find the (x, y) coordinates of
# the area of the image with the largest intensity value
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
cv2.circle(image, maxLoc, 5, (255, 0, 0), 2)

# display the results of the naive attempt
cv2.imshow("Naive", image)

# apply a Gaussian blur to the image then find the brightest
# region
gray = cv2.GaussianBlur(gray, (args["radius"], args["radius"]), 0)
(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(gray)
image = orig.copy()
cv2.circle(image, maxLoc, args["radius"], (255, 0, 0), 2)

# display the results of our newly improved method
cv2.imshow("Robust", image)
cv2.waitKey(0)

from imutils import contours
from skimage import measure
import numpy as np
import argparse
import imutils
import cv2

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
	help="path to the image file")
args = vars(ap.parse_args())

# load the image, convert it to grayscale, and blur it
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (11, 11), 0)

# threshold the image to reveal light regions in the
# blurred image
thresh = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY)[1]

# perform a series of erosions and dilations to remove
# any small blobs of noise from the thresholded image
thresh = cv2.erode(thresh, None, iterations=2)
thresh = cv2.dilate(thresh, None, iterations=4)

# perform a connected component analysis on the thresholded
# image, then initialize a mask to store only the "large"
# components
labels = measure.label(thresh, neighbors=8, background=0)
mask = np.zeros(thresh.shape, dtype="uint8")

# loop over the unique components
for label in np.unique(labels):
	# if this is the background label, ignore it
	if label == 0:
		continue

	# otherwise, construct the label mask and count the
	# number of pixels 
	labelMask = np.zeros(thresh.shape, dtype="uint8")
	labelMask[labels == label] = 255
	numPixels = cv2.countNonZero(labelMask)

	# if the number of pixels in the component is sufficiently
	# large, then add it to our mask of "large blobs"
	if numPixels > 300:
		mask = cv2.add(mask, labelMask)

# find the contours in the mask, then sort them from left to
# right
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
	cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cnts = contours.sort_contours(cnts)[0]

# loop over the contours
for (i, c) in enumerate(cnts):
	# draw the bright spot on the image
	(x, y, w, h) = cv2.boundingRect(c)
	((cX, cY), radius) = cv2.minEnclosingCircle(c)
	cv2.circle(image, (int(cX), int(cY)), int(radius),
		(0, 0, 255), 3)
	cv2.putText(image, "#{}".format(i + 1), (x, y - 15),
		cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)

# show the output image
cv2.imshow("Image", image)
cv2.waitKey(0)