#get the image
#get total pixels
#get colored_pixels where alpha is 1
#get field_area
#get crop field_area
from tempfile import tempdir
import firebase_admin
from firebase_admin import credentials
from firebase_admin import db
import pandas as pdf
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import statsmodels.api as sm
import seaborn as sns
sns.set()
import traceback
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
import csv
import time
import json
import numpy as np
from sklearn.cluster import KMeans
from sentinelhub import WebFeatureService, BBox, CRS, MimeType, CRS, BBox, WmsRequest,DataCollection
import traceback
from PIL import Image
import numpy as np
from google.cloud import storage
import cv2
storage_client = storage.Client()
bucket_name = 'farmbase-b2f7e.appspot.com'

cred = credentials.Certificate('servicekey.json')
x = 0.0003
y = 0.0003
fromdate = '20220701'
todate = '20220715'

s1_images = ['IW-VH-DB']
s1_images = ['NDVI']
s2_images = []
# s1_images = ["B02", "B03", "B04", "B05"]
#s2_images = ["RVI-NEW"]
dates = [ '10101010', '20202020', '30303030', '40404040']
dates = ['10101010']
try:
    firebase_admin.initialize_app(cred, {'databaseURL': 'https://farmbase-b2f7e-31c0c.firebaseio.com/'})
except:
    print('fire running')

uid = 'wEJQVaxiZZaaId2aJe6lRJ8Zd0S2'
uid = 'JgbkYJkMVsRXpSndLsDlabDomOp2'
#uid = 'a7X8A4BkmbeUkkZ5HyHLcqljyA12'
uid = 'VWoPJ2nfWDRlrYQo9dmctdLHeJN2'
uid = 'xk6kws4YMkdeBAstSWDNsd42w9m2'
fields_obj = db.reference('PaidMonitoredFields').child('PMF').child(uid).get()
crops = ['Wheat', 'Sugarbeet', 'Clover', 'Barley']
crops = ['Quinoa']
crops = ['Cotton']
dates = ['10101010']
num = 0
www = 0
for single_date in dates:
    crop = crops[num]
    for(fieldid, field_obj) in fields_obj.items():
        if www == 0:
            total_pixels, area_pixels = 0,0
            bucket = storage_client.get_bucket(bucket_name)
            destination_blob_name = 'PaidMonitoredFields/'+uid+'/'+fieldid+'/'+single_date+'/hybrid'
            print(destination_blob_name)
            blob = bucket.blob(destination_blob_name)
            file_name = 'temp_img.png'
            name = field_obj["FieldDescription"]
            field_area = int(field_obj["FieldArea"])/10000
            blob.download_to_filename(file_name)
            img =  cv2.imread(file_name, cv2.IMREAD_UNCHANGED)
            h,w,c = img.shape
            b_channel, g_channel, r_channel, alpha_channel = cv2.split(img)
            for y in range(0,h):
                for x in range(0,w):
                    if alpha_channel[y][x] == 255:
                        if g_channel[y][x] != 0 or r_channel[y][x] != 0 or b_channel[y][x] != 0:
                            area_pixels = area_pixels + 1
                        total_pixels = total_pixels + 1
            crop_area = round((area_pixels/total_pixels)*field_area)
            print([area_pixels,total_pixels, h, w, c])
            print(('Name: ' + name +  ', Area: ' + str(field_area) + ", Crop Area: " + str(crop_area)))
            #db.reference('EgyptResults').child(crop).child(name).set(crop_area)
    num = num + 1