def find_right_img(uid,fieldid,all_coords_dict):
	import cv2
	import numpy as np
	from matplotlib import pyplot as plt
	large = uid+'_TCI_large.png'
	print('right img coords')
	small = uid+fieldid+'_static_map_large.png'
	#all_coords_dict = {}
	coords_dict = {}
	#try:

	img = cv2.imread(large,0)
	img2 = img.copy()
	template = cv2.imread(small,0)
	w, h = template.shape[::-1]
	orig_w = w
	template = template[:h,round(w/2):]
	w, h = template.shape[::-1]
	cv2.imwrite('right_half.png',template)
	print(h)

	# All the 6 methods for comparison in a list
	
# All the 6 methods for comparison in a list
	methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR','cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
	ww =0
	
	for meth in methods:
		img = img2.copy()
		method = eval(meth)

		# Apply template Matching
		res = cv2.matchTemplate(img,template,method)
		min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
		# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
		if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
			top_left = min_loc
		else:
			top_left = max_loc
		bottom_right = (top_left[0] + w, top_left[1] + h)
		ystart = top_left[0]
		yend = top_left[0]+orig_w
		xstart = top_left[1]-round(orig_w/2)
		xend = top_left[1]+round(orig_w/2)
		#print('xvalues')
		#print(xstart)
		#print(xend)
		#print(ystart)
		#print(yend)
		coords_dict  = {}
		coords_dict['xstart'] =xstart
		coords_dict['xend'] =xend
		coords_dict['ystart'] =ystart
		coords_dict['yend'] =yend
		try:
			temp_coords_dict = all_coords_dict[str(xstart)]
	
			coord_count = int(temp_coords_dict['count'])
			#print(coord_count)
			coord_count = coord_count+1
			coords_dict['count']=coord_count
	
		except:
			coords_dict['count'] = 1
		all_coords_dict[str(xstart)]=coords_dict
		#print(all_coords_dict)

		temp_count = 0
	
	print(all_coords_dict)
	
	return all_coords_dict