from PIL import Image
import numpy as np
from google.cloud import storage
import os
from firebase_admin import db
from get_mask import get_mask
import cv2
import scipy.ndimage
def make_area_estimation_image(arr, study_uid, study_field_id, threshold):
	num = 0
	row = 0
	column = 0
	m = 25
	storage_client = storage.Client()
	bucket_name = 'farmbase-b2f7e.appspot.com'

	map_fields = db.reference('PaidMonitoredFields').child('PMF').child(study_uid).child(study_field_id).get()
	coordinates = map_fields["Coordinates"]
	fieldmaxlat = map_fields["FieldMaxLat"]
	fieldminlat = map_fields["FieldMinLat"]
	fieldmaxlong = map_fields["FieldMaxLong"]
	fieldminlong = map_fields["FieldMinLong"]
	try:
		field_name = map_fields["FieldDescription"]
	except:
		field_name = map_fields["FieldAddress"]
	field_area = map_fields["FieldArea"]
	main_img_name = study_uid + "/area_estimate_ndvi_" + field_name + ".png"
	rvi_img = cv2.imread(main_img_name)
	h,w,c = rvi_img.shape
	data = np.zeros((h,w,3), dtype=np.uint8)
	new_rvi_data = data
	total_pixels = h*w
	area_pixels_arr = [0,0,0,0,0]
	area_pixels = 0
	
	#rvi_img = cv2.imread("area_estimate_rvi.tiff")

	#crops_rgb = [[0,255,255],[0,255,0],[255,0,0],[0,0,255],[0,0,0]]
	crops_rgb = [[0,0,0],[0,255,0]]
	#ranges = [[2,6],[6,10],[10,14],[14,18],[-2,2]]
	# print(max(arr))
	# print(min(arr))
	for i in arr:
		if i >= threshold:
			area_pixels_arr[1] = area_pixels_arr[1] + 1
			data[row, column] = crops_rgb[1]
			new_rvi_data[row, column] = [rvi_img[row][column][2],rvi_img[row][column][1],rvi_img[row][column][0]]
			#data[row, column] = 1
			area_pixels = area_pixels + 1
		else:
			area_pixels_arr[0] = area_pixels_arr[0] + 1
			#data[row, column] = 0
			data[row, column] = crops_rgb[0]
			new_rvi_data[row, column] = crops_rgb[0]


		# new_img[row][column] = i
		column = column + 1
#		total_pixels =  total_pixels + 1
		if column%w == 0:
		    row = row+1
		    column = 0
	#print(row, column)



	# img = Image.fromarray(data, 'RGB')
	# file_name = main_img_name
	temp_rvi_img = Image.fromarray(new_rvi_data, 'RGB')
	rvi_file_name = main_img_name
	isdir = os.path.isdir(study_uid)
	if isdir != True:
		os.mkdir(study_uid)
	#img.save(file_name)
	temp_rvi_img.save(rvi_file_name)
	latestDay = 0
	lastDayRef = db.reference('PaidMonitoredFields').child('PMF').child(study_uid).child(study_field_id).child('SensedDays').get()
	try:
		for (x,y) in lastDayRef.items():
			if int(x) > int(latestDay):
				latestDay = x
	except:
		latestDay = 20202020
	latestDay = 40404040
	db.reference('PaidMonitoredFields').child('PMF').child(study_uid).child(study_field_id).child('SensedDays').child(str(latestDay)).set("yes")
	latestDay = str(latestDay)
	destination_blob_name_dem = 'PaidMonitoredFields/'+study_uid+'/'+study_field_id+'/'+latestDay+'/hybrid'
	dem_file_name = main_img_name
	# #get_mask(study_uid,study_field_id,coordinates,fieldmaxlat,fieldminlat,fieldmaxlong,fieldminlong)
	# img = cv2.imread(dem_file_name)
	# kernel_int = 2
	# kernel = np.ones((kernel_int, kernel_int), np.uint8)
	# opening = img
	
	# rvi_img = cv2.imread(rvi_file_name)

	# opening = cv2.dilate(opening, kernel, iterations = 1)
	# rvi_img = cv2.dilate(rvi_img, kernel, iterations = 1)
	# cv2.imwrite(rvi_file_name, rvi_img)

	# cv2.imwrite(dem_file_name, opening)
	new_area_pixels, new_total_pixels = get_mask(study_uid,study_field_id,coordinates,fieldmaxlat,fieldminlat,fieldmaxlong,fieldminlong, field_name)
	#crop_area = (area_pixels_arr[1]/(area_pixels_arr[0] + area_pixels_arr[1]))*int(field_area)
	crop_area = (new_area_pixels/new_total_pixels)*int(field_area)
	crop_area = int(crop_area/10000)
	print_text = (field_name + ' Area: ' + str(int(field_area/10000)) + ' Ha, Crop Area: ' + str(crop_area) + ' Ha')
	print(print_text)
	try:
		db.reference('EgyptResults').child(field_name).set(print_text)
	except:
		awa = 1
	bucket = storage_client.get_bucket(bucket_name)
	blob = bucket.blob(destination_blob_name_dem)
	blob.upload_from_filename(dem_file_name)

	# destination_blob_name_dem = 'PaidMonitoredFields/'+study_uid+'/'+study_field_id+'/'+latestDay+'/ndvi'
	# dem_file_name = rvi_file_name
	# blob = bucket.blob(destination_blob_name_dem)
	# blob.upload_from_filename(dem_file_name)
	#print(new_img)