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_egypt_estimate_image(arr, w, h, study_uid, study_field_id, pred_vals):
	num = 0
	new_img = [[0]*w]*h
	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"]
	data = np.zeros((h,w,3), dtype=np.uint8)
	total_pixels = h*w
	area_pixels_arr = [0,0,0,0,0]
	area_pixels = 0

	#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 >=2 and i < 6:
		# 	area_pixels_arr[0] = area_pixels_arr[0] + 1
		# 	data[row, column] = crops_rgb[0]
		# 	area_pixels = area_pixels + 1
		# elif i >=6 and i < 10:
		# 	area_pixels_arr[1] = area_pixels_arr[1] + 1
		# 	data[row, column] = crops_rgb[1]
		# 	area_pixels = area_pixels + 1
		# elif i >=10 and i < 13:
		# 	area_pixels_arr[2] = area_pixels_arr[2] + 1
		# 	data[row, column] = crops_rgb[2]
		# 	area_pixels = area_pixels + 1
		# elif i >=13 and i < 18:
		# 	area_pixels_arr[3] = area_pixels_arr[3] + 1
		# 	data[row, column] = crops_rgb[3]
		# 	area_pixels = area_pixels + 1
		# else:
		# 	area_pixels_arr[4] = area_pixels_arr[4] + 1
		# 	data[row, column] = crops_rgb[4]
		# 	area_pixels = area_pixels+1
		if i >= 1.5:
			area_pixels_arr[1] = area_pixels_arr[1] + 1
			#data[row, column] = crops_rgb[1]
			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_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 = study_uid + '/area_estimation.png'
	isdir = os.path.isdir(study_uid)
	if isdir != True:
		os.mkdir(study_uid)
	img.save(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 = 10101010
	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 = file_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
	m = 5
	arr = np.array(img)
	nval = np.max(arr) + 1
	counts, _ = np.histogram(arr, bins=range(nval + 1))

	# Compute the set of neighbours for each number via binary dilation
	c = np.array([scipy.ndimage.morphology.binary_dilation(arr == i, iterations=-1)
				for i in range(nval)])

	# Loop over the set of arrays with bad count and update them to the most common
	# neighbour
	for i in filter(lambda i: counts[i] < m, range(nval)):
		arr[arr == i] = np.argmax(np.sum(c[:, arr == i], axis=1))
	im = Image.fromarray(arr)
	im.save(file_name)

	# i = 0
	# while i < 1:
	# 	opening = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
	# 	#opening = cv2.morphologyEx(opening, cv2.MORPH_OPEN, kernel)
	# 	i = i + 1
	# # kernel = np.ones((1,1), np.uint8)
#	opening = cv2.dilate(opening, kernel, iterations = 1)
	i = 0
	#while i < 1:
	 	#opening = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
#	opening = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
	#i = i + 1
	cv2.imwrite(dem_file_name, opening)
	area_pixels,total_pixels = get_mask(study_uid,study_field_id,coordinates,fieldmaxlat,fieldminlat,fieldmaxlong,fieldminlong)
	crop_area = (area_pixels/total_pixels)*int(field_area)
	crop_area = int(crop_area/10000)
	print((field_name + ' Area: ' + str(int(field_area/10000)) + ' Ha, Crop Area: ' + str(crop_area) + ' Ha'))
	bucket = storage_client.get_bucket(bucket_name)
	blob = bucket.blob(destination_blob_name_dem)
	blob.upload_from_filename(dem_file_name)
	#print(new_img)