def merge_contour_images(uid,img_type,mask,centery,centerx,fieldid,imagedate):
	import numpy as np
	import cv2
	from PIL import Image
	import firebase_admin
	from firebase_admin import credentials
	from firebase_admin import db
	# Fetch the service account key JSON file contents
	n_file_name = uid+'_'+img_type+'.png'
	etci_file_name = uid+'_ETCI.png'
	n_file = cv2.imread(n_file_name)
	etci = cv2.imread(etci_file_name)
	mask_h,mask_w = mask.shape
	orig_h,orig_w,orig_rgb = etci.shape
	h,w = orig_h,orig_w
#	print(mask_h)
#	print(mask_w)
	
	new_img = np.zeros([h,w,3])
	mask_half_height = round(mask_h/2)
	mask_half_width= round(mask_w/2)
	centerx = round(w/2)
	centery = round(h/2)
	y_start_mask = centery-mask_half_height
	y_end_mask = centery+mask_half_height
	x_start_mask = centerx-mask_half_width
	x_end_mask = centerx+mask_half_width
#	print(y_start_mask)
#	print(y_end_mask)
#	print(x_start_mask)
#	print(x_end_mask)
	temp_x = 0
	temp_y = 0
#	print('hw')
#	print(h)
#	print(w)
	health_count = 0
	mask_pixel_area = 0

	for py in range(0,h):
		for px in range(0,w):			
			if (py>=y_start_mask) and (py<=y_end_mask-2) and (px>=x_start_mask) and (px<=x_end_mask-2):
				#print(px-x_start_mask)
				if mask[py-y_start_mask][px-x_start_mask]==255:
					new_img[py][px][0]=n_file[py][px][0]
					new_img[py][px][1]=n_file[py][px][1]
					new_img[py][px][2]=n_file[py][px][2]
					mask_pixel_area = mask_pixel_area+1
					if (n_file[py][px][0] == 6) and (n_file[py][px][1]==101) and (n_file[py][px][2]==61):
						health_count = health_count+1
					elif (n_file[py][px][0] == 17) and (n_file[py][px][1]==167) and (n_file[py][px][2]==95):
						health_count = health_count+0.9
					elif (n_file[py][px][0] == 129) and (n_file[py][px][1]==191) and (n_file[py][px][2]==108):
						health_count = health_count+0.8
					elif (n_file[py][px][0] == 186) and (n_file[py][px][1]==227) and (n_file[py][px][2]==131):
						health_count = health_count+0.7
					elif (n_file[py][px][0] == 230) and (n_file[py][px][1]==243) and (n_file[py][px][2]==164):
						health_count = health_count+0.6
					elif (n_file[py][px][0] == 255) and (n_file[py][px][1]==240) and (n_file[py][px][2]==181):
						health_count = health_count+0.5
					elif (n_file[py][px][0] == 251) and (n_file[py][px][1]==192) and (n_file[py][px][2]==126):
						health_count = health_count+0.4
					elif (n_file[py][px][0] == 247) and (n_file[py][px][1]==136) and (n_file[py][px][2]==90):
						health_count = health_count+0.3
					elif (n_file[py][px][0] == 234) and (n_file[py][px][1]==79) and (n_file[py][px][2]==59):
						health_count = health_count+0.2
					else:
						health_count = health_count+0.1
				else:
					new_img[py][px][0]=etci[py][px][0]
					new_img[py][px][1]=etci[py][px][1]
					new_img[py][px][2]=etci[py][px][2]
				
			else:
				new_img[py][px][0]=etci[py][px][0]
				new_img[py][px][1]=etci[py][px][1]
				new_img[py][px][2]=etci[py][px][2]
			
			
	if mask_pixel_area ==0:
		health_percentage=0
	else:		
		health_percentage = round(health_count*100/mask_pixel_area)
	db.reference('PaidMonitoredFields').child('PMF').child(uid).child(fieldid).child('Health').child(img_type).child(imagedate).set(str(health_percentage))	
	mn_file_name = uid+'_m'+img_type+'.png'
	#new_img = Image.fromarray(new_img)
	#status = cv2.imwrite(mn_file_name,new_img)
	#new_img.save(mn_file_name,"PNG")
	
	#new_img = Image.open(mn_file_name)
	#etci_orig = Image.open(etci_file_name)
	#etci_orig = etci_orig.convert("RGBA")
	#new_img = new_img.convert("RGBA")
	#new_img = new_img.resize((orig_w,orig_h),Image.NEAREST)
	#new_img = Image.blend(etci_orig,new_img,0.6)
	#new_img.save(mn_file_name,"PNG")
	return