def make_on_the_go_farm_image(study_uid, field_name,satellite, imageType,bounds, from_date, to_date,CLIENT_ID,CLIENT_SECRET,INSTANCE_ID, max_num):
	from sentinelhub import WebFeatureService, BBox, CRS, DataSource, MimeType, CRS, BBox, WmsRequest,DataCollection
	from sentinelhub import SHConfig
	from firebase_admin import credentials
	from firebase_admin import db
	from statistics import mean
	from PIL import Image, ImageFilter
	import os.path
	import PIL
	import numpy as np
	from oct2py import octave
	from io import BytesIO
	import base64
	import time
	from sentinelhub import BBoxSplitter
	from pathlib import Path
	from osgeo import gdal
	import os
	from osgeo import gdal
	from osgeo import osr
	import numpy as np
	import os,sys
	import cv2
	import csv
	import json
	from make_geotiff import make_geotiff
	import traceback
	from get_mask import get_mask

	#from scipy import ndimag
	config = SHConfig()
	print(imageType)
	img_values_arr = []
	cred = credentials.Certificate('servicekey2.json')
	access_key = 'AKIAIPCM5ZR7FRHMY3MA'
	secret_key = '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'
	if CLIENT_ID and CLIENT_SECRET:
		config.sh_client_id = CLIENT_ID
		config.sh_client_secret = CLIENT_SECRET
		config.instance_id = INSTANCE_ID
	lat_len = (bounds[3]-bounds[1])
	lng_len = bounds[2] - bounds[0]
	#if width > height

	img_width = 500
	search_bbox = BBox(bbox=bounds, crs=CRS.WGS84)
	search_time_interval = (from_date, to_date)

	avg_im = 0
	i = 0
	i_num = 0
	arr_init = 0
	iterator_wms = WebFeatureService(search_bbox,search_time_interval,data_collection=satellite,maxcc=1.0,config=config)
	images_arr = []
	prev_date = '90101010'
	for tile_info in iterator_wms:
		try:
			width = 2500
			s1_request = WmsRequest(data_collection=satellite,layer=imageType,bbox=search_bbox,time=tile_info["properties"]["date"],width = width, image_format=MimeType.TIFF,config=config)
			s1_data = s1_request.get_data()
		except:
			height = 2500
			s1_request = WmsRequest(data_collection=satellite,layer=imageType,bbox=search_bbox,time=tile_info["properties"]["date"],height = height,image_format=MimeType.TIFF,config=config)
			s1_data = s1_request.get_data()

		file_name= str(tile_info["properties"]["date"]) + str(i_num)+'.tiff'
		i_num = i_num + 1
		im = s1_data[-1]
		w,h = len(im[0]), len(im)
		im = make_rgb(im,w,h)
		im = PIL.Image.fromarray(im)
		im.save((study_uid + "/area_estimate_ndvi_" + field_name + ".png"))
		time.sleep(1)
		break
		
		
def make_rgb(im,w,h):
	import numpy as np
	new_im = np.zeros((h,w,3), dtype=np.uint8)
	p_num = 0
	q_num = 0
	for p in im:
		q_num = 0
		for q in p:
			if q/255 <=0.1:
				new_im[p_num][q_num] =[171,5,53]
			elif q/255 > 0.1 and q/255 <= 0.2:
				new_im[p_num][q_num] =[234,79,59]
			elif q/255 > 0.2 and q/255 <= 0.3:
				new_im[p_num][q_num] =[247,136,90]
			elif q/255 > 0.3 and q/255 <=0.4:
				new_im[p_num][q_num] =[251,192,126]
			elif q/255 > 0.4 and q/255 <= 0.5:
				new_im[p_num][q_num] =[255,240,181]
			elif q/255 > 0.5 and q/255 <=0.6:
				new_im[p_num][q_num] =[230,243,164]
			elif q/255 > 0.6 and q/255 <=0.7:
				new_im[p_num][q_num] =[186,227,131]
			elif q/255 > 0.7 and q/255 <=0.8:
				new_im[p_num][q_num] =[129,191,108]
			elif q/255 > 0.8 and q/255 <=0.9:
				new_im[p_num][q_num] =[17,167,95]
			else:
				new_im[p_num][q_num] =[6,101,61]
			q_num = q_num + 1
		p_num = p_num + 1
	return new_im