How can i count segments in an image in python? - python-3.x

Question

I am new to image processing and python. You might've seen my amateur codes on this site in the last couple of days.
I am trying to count the number of trees using aerial images. This is my code:
from PIL import Image
import cv2
import numpy as np
from skimage import io, filters, measure
from scipy import ndimage
img = Image.open("D:\\Texture analysis\\K-2.jpg")
row, col = img.size
hsvimg = img.convert('HSV')
hsvimg.mode = 'RGB'
hsvimg.save('newImage2.jpg')
npHSI = np.asarray(hsvimg) #Convert HSI Image to np image
blur = cv2.GaussianBlur(npHSI, (45, 45), 5)
assert isinstance(blur, np.ndarray) ##############################
assert len(blur.shape) == 3 #Convert np Image to HSI Image
assert blur.shape[2] == 3 ##############################
hsiBlur = Image.fromarray(blur, 'RGB')
hsiBlur.save('hsiBlur.jpg') #Save the blurred image
## Read
img = cv2.imread("D:\\Texture analysis\\hsiBlur.jpg")
## convert to hsv
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
#Threshold the image and segment the trees
mask = cv2.inRange(hsv, (36, 25, 25), (70, 255,255))
imask = mask>0
green = np.zeros_like(img, np.uint8)
green[imask] = img[imask]
## save
cv2.imwrite("green.png", green)
#Count the number of trees
im = io.imread('green.png', as_grey=True)
val = filters.threshold_otsu(im)
drops = ndimage.binary_fill_holes(im < val)
labels = measure.label(drops)
print(labels.max())
Original image:
HSI image with gaussian filter:
Segmented image:
The last part of the code returns 7, which is a wrong output. The value should be above 50. How can I properly count the number of green segments in the final segmented image?
EDIT
I converted green.png to binary and applied erosion with a 3x3 filter and iterated it 7 times to remove the noise.
This is what I did at the end. I followed this stackoverflow link
##save
cv2.imwrite("green.png", green)
#Convert to grayscale
gray = np.dot(green[...,:3], [0.299, 0.587, 0.114])
cv2.imwrite("grayScale.jpg", gray)
#Binarize the grayscale image
ret,bin_img = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
cv2.imwrite("bin_img.jpg", bin_img)
#Erosion to remove the noise
kernel = np.ones((3, 3),np.uint8)
erosion = cv2.erode(gray, kernel, iterations = 7)
cv2.imwrite("erosion.jpg", erosion)
#Count the number of trees
finalImage = cv2.imread('erosion.jpg')
finalImage = cv2.cvtColor(finalImage, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(finalImage, 127, 255, 1)
im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
cv2.drawContours(finalImage,[cnt],0,(0,0,255),1)
Saurav mentioned in his answer ... size of "contours" will give you the count. This print(contour.size())gives an error and print(contour) just prints a long 2D array. How can i get the size of contour?
PS. I didn't upload the grayscale, binary and eroded image because i felt that the images were already taking too much space, I can still upload them if anyone wants to.

Answer 1

I've found 52 trees with that script:
from PIL import Image, ImageDraw, ImageFont
image = Image.open('04uX3.jpg')
pixels = image.load()
size = image.size
draw = ImageDraw.Draw(image)
font = ImageFont.truetype('arial', 60)
i = 1
for x in range(0, size[0], 100):
for y in range(0, size[1], 100):
if pixels[x, y][1] > 200:
draw.text((x, y), str(i), (255, 0, 0), font=font)
i += 1
image.save('result.png')
You can see that some trees weren't detected and some non-trees were detected. So this is very rough calculation: