from PIL import Image
import numpy as np
from google.cloud import storage
from firebase_admin import db
import cv2
def make_area_estimate_image(arr, w, h, study_uid, study_field_id, pred_vals):
    num = 0
    new_img = [[0]*w]*h
    row = 0
    column = 0
    storage_client = storage.Client()
    bucket_name = 'farmbase-b2f7e.appspot.com'
    data = np.zeros((h,w,3), dtype=np.uint8)
    for i in arr:
        if i in pred_vals:
            data[row, column] = [0, 255,0]
        new_img[row][column] = i
        column = column + 1
        if column%w == 0:
            row = row+1
            column = 0
        #print(row, column)
    img = Image.fromarray(data, 'RGB')
    img.save('area1.png')
    img.save('marea.png')
    latestDay = 0
    lastDayRef = db.reference('PaidMonitoredFields').child('PMF').child(study_uid).child(study_field_id).child('SensedDays').get()
    for (x,y) in lastDayRef.items():
        if int(x) > int(latestDay):
            latestDay = x
    destination_blob_name_dem = 'PaidMonitoredFields/'+study_uid+'/'+study_field_id+'/'+latestDay+'/hybrid'
    dem_file_name = 'area1.png'
    img = cv2.imread(dem_file_name)
    kernel = np.ones((5,5), np.uint8)
    opening = img
    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.erode(opening, kernel, iterations = 1)
    cv2.imwrite(dem_file_name, opening)
    bucket = storage_client.get_bucket(bucket_name)
    blob = bucket.blob(destination_blob_name_dem)
    blob.upload_from_filename(dem_file_name)
    #print(new_img)