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

path = r'C:\Users\micha\PycharmProjects\NotUniversityHacks\CameraReference\WithFire.jpg'
image = cv2.imread(path)

#graying out and blurring the image
#this is "pre-processing" the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (11, 11), 0)

# threshold the image using the "normal" image (no fire = normal)
thresh = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY)[1]

# erosions and dilations to remove noise from the 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 = imutils.grab_contours(cnts)
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)
# show the output image
cv2.imshow("Image", image)
cv2.waitKey(0)



