Area of Objects detected by watershed algorithm

I am using watershed algorithm to detect tree crowns. The images were taken by a Drone and its shown below. I want to get the area (number of pixels) of each tree individually and I don't have idea of how to do that.

The code

img = cv2.imread("subset3.tif")
imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(imgray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 713, 9)

# noise removal
kernel = np.ones((9,9),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 3)

# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)

# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,3)
ret, sure_fg = cv2.threshold(dist_transform,0.005*dist_transform.max(),255,0)

# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)

# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)

# Add one to all labels so that sure background is not 0, but 1
markers = markers+1

# Now, mark the region of unknown with zero
markers[unknown==255] = 0

#Apply watershed()
markers = cv2.watershed(img,markers)
img[markers == -1] = [0,255,0]

Basically ret is the number of components and markers marks which component each pixel belongs to. So we can just count them:

area =[np.sum(markers==val) for val in range(ret)]
     

You may need to change to range(1, ret+1) since we already shifted markers