# -*- coding: utf-8 -*-
"""Copy of mask_polygon.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/19sE5vesr0kwdEdZJfhqQUoDAdgDYEDRz
"""

#####################Pull satellite_images##################

# Commented out IPython magic to ensure Python compatibility.
# %pip install leafmap
import time
from samgeo import SamGeo, tms_to_geotiff
import firebase_admin
from firebase_admin import credentials, db

cred = credentials.Certificate("servicekey.json")
firebase_admin.initialize_app(
            cred, {"databaseURL": "https://farmbase-b2f7e-31c0c.firebaseio.com/"}
        )

def handle_snapshot(snapshot):
    data = snapshot.val()
    print("Retrieved data:", data)
    return data
    # You can process or use the retrieved data here

# Example usage:
firebase_database_url = "https://farmbase-b2f7e-31c0c.firebaseio.com//"
UID = "TCXcp5VIsfhHZrh0nm2VsgBtcGy2"
path_polygonobj = "/PaidPolygonsByPolygonID/PMF/"

polygon_inside = ['1624703953738', '1677843764871', '1684906005406', '1613456188258', '1612354256326', '1614852918378', '1620123908548', '1613365388360', '1612505885674', '1706336250255', '1613478767931', '1614838600285', '1613388991566', '1614762710873', '1711771596326', '1698477566058', '1613193157318', '1614854390976', '1612510162348', '1620191668320', '1621423920299', '1620194317180', '1704953689234', '1613718312299', '1708576356873', '1612769371940', '1620905725978', '1614838400946', '1613190872776', '1613626199423', '1614847953023', '1709112826214', '1615198187105', '1614083311003', '1612605617536', '1614083435075', '1620630188476', '1614159405784', '1620304290692', '1614922517659', '1613111802015', '1614227970144', '1620300938140', '1624882679508', '1614313349704', '1675930368286', '1614142920947', '1614763981694', '1614756881264', '1613195278584', '1624253335033', '1613970013375', '1614849073055', '1615026209073', '1612945767997', '1623912622957', '1611824857184', '1613452777862', '1613024264523', '1624705332258', '1620625798021', '1613805095432', '1614583910642', '1620710869368', '1612931099881', '1611831332781', '1621057609262', '1621338899370', '1613472636074', '1612499030535', '1613128765644', '1612506267698', '1611913320519', '1613195436926', '1612508080465', '1675747639165', '1676529494047', '1613733804099', '1620300381828', '1615200483555', '1675054638861', '1614764748811', '1621055714032', '1613020647492', '1613041148421', '1614314146016', '1614947266743', '1620710081832', '1676092185201', '1614151414246', '1674629069526', '1612336946571', '1611812833957', '1613022788592', '1615201234159', '1621317621681', '1613370839768', '1613805627222', '1697515942343', '1613639066060', '1621411835485', '1620710301521', '1615183467273', '1620714424962', '1622271746930', '1612348658985', '1677665562738', '1620975257262', '1620214384400', '1622183153675', '1614581172645', '1623990371841', '1614579162993', '1620631638556', '1674552289467', '1676444867449', '1620125953167', '1612436341586', '1615183710327', '1622272568267', '1711713693259', '1623730462323', '1711798669281', '1706340026114', '1613373661230', '1615007888207', '1620190173834', '1613456020677', '1624508211578', '1624704506421', '1613475339946', '1612847261783', '1614426642009', '1612520317069', '1711603544575', '1613215660690', '1619612491198', '1674631989366', '1615023852152', '1679740521306', '1620385444112', '1612939457530', '1613726334558', '1674794172481', '1612507455247', '1621073481761', '1709615178320', '1624680226822', '1676267296128', '1614065081380', '1613190646052', '1615187655534', '1675481880024', '1612170970924', '1613019543807', '1620715769443', '1613714119760', '1621411250298', '1700808347231', '1620299043950', '1613992002312', '1613106406215', '1612865031834', '1620102006840', '1613363876496', '1613365769097', '1613021697258', '1674457829808', '1675930428752', '1613627711456', '1614341731948', '1620718134073', '1689750218000', '1613647891476', '1614939283152', '1614595796101', '1613817324112', '1622459306802', '1614759123936', '1624882477124', '1613112270229', '1613190260264', '1704954332838', '1676357661070', '1620298602405', '1676455038371', '1704951766925', '1675417503061', '1612065785634', '1624337464217', '1613720738687', '1619757291352', '1614240115068', '1612418897820', '1623840549803', '1612511204847', '1620968051865', '1613804894809', '1623840981583', '1613453794730', '1620194391561', '1621339059251', '1612585894867', '1614237957666', '1614596602887', '1620904440159', '1698477661861', '1614401357927', '1611832512795', '1613024758259', '1613729264412', '1614235375544', '1613813310597', '1674456394378', '1677910583988', '1614148942204', '1619763820132', '1620883572771', '1614162658878', '1678426503365', '1620127620684', '1711079065249', '1612856718499', '1613387155825', '1613970766418', '1677646753707', '1620890353585', '1621484391239', '1613734283619', '1620360901304', '1678169101192', '1622458778691', '1622438937398', '1711686349604', '1613216659312', '1612603872764', '1624000066258', '1614255056341', '1613636243782', '1613114028575', '1621252770806', '1613994043603', '1612171032101', '1620879428334', '1614843448424', '1611981164281', '1676095946695', '1676545888570', '1612933607572', '1620034631531', '1612867436149', '1614922430715', '1615189824672', '1625031627515', '1614754323597', '1612762346027', '1613020408610', '1620300144553', '1614599770867', '1674629223576', '1680327064249', '1614322067010', '1622439947901', '1675481909438', '1624362483924', '1675744640742', '1615013058288', '1619845271568', '1612251685659', '1620105438989', '1620716089113', '1619754207861', '1698477535095', '1613713539444', '1619585241753', '1674639709355', '1621060111039', '1711688973281', '1614592720632', '1612434538962', '1621230092601', '1613815145229', '1706160646733', '1614332941778', '1613456902075', '1614854128599', '1614058730895', '1614660168650', '1621071091914', 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'1620880124030', '1621325256866', '1623906122907', '1621251629182', '1620705200860', '1613548502725', '1704955398157', '1620276558634', '1621249440412', '1675831579057', '1612952684313', '1612861811557', '1623839707997', '1620186799287', '1613799499966', '1612003828042', '1615197624754', '1621403570693', '1614673486316', '1614835543812', '1619696391373', '1612949963255', '1615188660778', '1615015355610', '1612440952663', '1676545659309', '1620122342035', '1701079271295', '1614579944799', '1620217403568', '1613372042516', '1612416048623', '1614235218509', '1711077686470', '1620191608460', '1612517419506', '1612953604110', '1613969141797', '1623729085646', '1709011711477', '1614851818535', '1613124177879', '1711715069976', '1709192401905', '1613112770224', '1613552879382', '1614752790125', '1612600514672', '1619680320207', '1620820041248', '1613459400247', '1617099627938', '1700206868900', '1613127300259', '1698482712217', '1614756765498', '1613626755279', '1620799431076', '1614938629455', '1612262069206', '1614404669292', '1623913694136', '1620109064609', '1615011377158', '1624703808127', '1620624732480', '1676547093233', '1614146159063', '1613807459411', '1674453590344', '1614152456949', '1614407108246', '1704950228737', '1620379563875', '1613539352817', '1707106593326', '1620123590085', '1621061775276', '1614226746079', '1614585312414', '1676356359025', '1624084550275', '1614751534132', '1613715389713', '1620798023503', '1613970303291', '1613385371883', '1615023311707', '1620625498735', '1612953508881', '1675416970489', '1620187910255', '1620300424729', '1614681424701', '1612954468128', '1613389203568', '1679549843812', '1612589455820', '1697089730853', '1613129219313', '1674453280735', '1615184147023', '1615184268583', '1613211034288', '1620277942697', '1612594613867', '1620127339403', '1675251504111', '1620715990353', '1613717244949', '1611819092032', '1711369263635', '1613987943906', '1611991718110', '1614427171838', '1621064896684', '1619850332067', '1621067969990', '1612157477392', '1614922823371', '1613542731485', '1613993948070', '1612355777647', '1620280921616', '1613388524937', '1613384367401', '1616391835816', '1612352457180', '1614761030096', '1613726698509', '1615184163475', '1614918380933', '1622179846082', '1615017736544', '1678426600984', '1621056340729', '1612784597891', '1613386286991', '1623143738198', '1612955478618', '1613729815464', '1613036470202', '1621334165553', '1614750243981', '1614057109804', '1612172929005', '1613711673488', '1620216286516', '1614597461942', '1674627985796', '1620795666769', '1612343589935', '1621340390236', '1620215024146', '1620022231267', '1615181350847', '1676442554911', '1623828057866', '1614161984967', '1613453033305', '1674626660916', '1613538938833', '1613561492295', '1614061753759', '1612503014866', '1675225021074', '1620192252534', '1613629517841', '1612509546830', '1612342586886', '1622439837999', '1615028487013', '1620798840849', '1622182799607', '1621252795706', '1711347607544', '1613633300307', '1613544918051', '1612434914980', '1615180019040', '1620121572918', '1612783700746', '1679911992317', '1622287570017', '1614919225808', '1624696879392', '1613454513640', '1614751637153', '1621249305040', '1611832637105', '1704954997310', '1612770340133', '1614412181983', '1620644109574', '1614077955789', '1612589014067', '1611823533982', '1611812314024', '1615183331640', '1621231970206', '1621075687419', '1678172060534', '1613471649593', '1620215045481', '1612412572324', '1634023236202', '1613469326430', '1674623363252', '1622441614150', '1674544810467', '1675230593934', '1612346669793', '1621508792906', '1613018299929', '1614925439143', '1613452811325', '1624958311812', '1613191420063', '1614339603083', '1613535248793', '1615179556146', '1619612324454', '1674453326900', '1612008911484', '1619696193492', '1621678114487', '1614058878961', '1612003689603', '1613974109671', '1624601707255', '1621403559151', '1705727512817', '1620185816286', '1612781049997', '1620900799752', '1620187919831', '1612941055369', '1677580196219', '1612528275782', '1622291194496', '1612158167433', '1613038913128', '1614924447718', '1677817823291', '1611818162183', '1613384183892', '1706174875851', '1613732988675', '1612414146555', '1614850662537', '1612776847785', '1620646748758', '1615030898369', '1613382728740', '1611993522981', '1614926526695', '1620631460866', '1614857134089', '1624094466033', '1623476058305', '1624683568160', '1674556497608', '1624275351740', '1674626336638', '1621483566716', '1623494394676', '1613539346477', '1620186770162', '1614928202372', '1674554640299', '1620278186181', '1711799573140', '1614400050823', '1613452223707', '1621684021543', '1614671953481', '1612779814592', '1621061246412', '1620805255932', '1676542888757', '1674820886835', '1612870708104', '1621581341057', '1619584638738', '1613539239554', '1613713709092', '1614750276099', '1613388009710', '1613712501585', '1612356492365', '1613974169749', '1706609979101', '1620281157471', '1677817402254', '1710993860096', '1710310752374', '1682672946776', '1620787803246', '1676358638306', '1622460367163', '1614148111499', '1624087338654', '1613029476389', '1675483102070', '1709115460982', '1620298181250', '1614162542630', '1614330585474', '1623924126783', '1614406647653', '1620039504193', '1612946372302', '1613190075862', '1624683762470', '1620886200498', '1620364552546', '1677652443079', '1614853116473', '1615183115550', '1614164655986', '1620275490133', '1612956767723', '1619500915072', '1620886329070', '1613365835951', '1620817569146', '1613816765732', '1619676077599', '1614590980540', '1613557380665', '1679915561608', '1615186068999', '1614677663345', '1674636056218', '1674628070810', '1676538195671', '1711609139170', '1632293847690', '1612588140187', '1705738047358', '1706342909214', '1614924828853', '1612852165020', '1679656906997', '1624704816424', '1614757486207', '1614150467135', '1613548467531', '1611830602515', '1614144267064', '1674623187408', '1614679134272', '1675832412507', '1622090657803', '1620368750366', '1614426134167', '1709641478381', '1613454239311', '1620731085402', '1676269163263', '1613193019102', '1613709509264', '1622289630011', '1620887923380', '1621401323949', '1615179424016', '1613640967128', '1613465241131', '1613474926715', '1613128381856', '1612329764296', '1620631784751', '1619587863808', '1614749584031', '1613638480905', '1614763472416', '1613632058016', '1614854071583', '1613367273667', '1613973834543', '1612265603155', '1676542975491', '1612507136014', '1612769205816', '1613132621181', '1613363980505', '1614227231972', '1613367014192', '1614576734885', '1620730135797', '1621423304751', '1705735940317', '1612005737578', '1704953774426', '1675484055265', '1621062721887', '1674622138623', '1613038469057', '1612518651148', '1612244099304', '1612501486016', '1613993635829', '1614316277823', '1615007061772', '1620732170181', '1612163667961', '1613381545988', '1614322814713', '1612514857937', '1612937709577', '1612520635477', '1622527116530', '1613380485224', '1613122352355', '1674628431316', '1612349160049', '1612430507808', '1612872604167', '1674453531475', '1619670227419', '1613365255644', '1613996212880', '1613800877055', '1613714431811', '1706349701364', '1620714714404', '1614835325886', '1706173529522', '1706348886315', '1614597428909', '1674294846444', '1612853317642', '1676624881939', '1621506459883', '1620211119044', '1614233221456', '1614232536044', '1706352297160', '1624272767368', '1620477462265', '1613207830194', '1621403377552', '1613459704013', '1614837867887', '1614754323543', '1612353363271', '1614338545937', '1613022173562', '1684905003178', '1622264502758', '1706176052007', '1624681900647', '1615181510728', '1620899516653', '1614596343731', '1620816716405', '1612586186843', '1675058740347', '1621942786946', '1614162629849', '1627469496215', '1613540621412', '1614672103588', '1679739555633', '1612329899480', '1614164454797', '1615018201164', '1614841633824', '1624886269501', '1612431739322', '1621487183584', '1612503126665', '1613624442341', '1612421861597', '1613022172752', '1620276236180', '1701079589593', '1624946466357', '1613639400027', '1613716148855', '1697786790228', '1614921400056', '1612940539205', '1676095590436', '1612242093258', '1612954261628', '1711449127902', '1613555790096', '1706537501014', '1613130607574', '1612341782621', '1613105564714', '1612423786061', '1613477903739', '1682673383086', '1613468001023', '1611817448203', '1614579857734', '1620709612808', '1676975599618', '1620281242138', '1620216876156', '1706177930709', '1613727645767', '1620298517673', '1620126159598', '1612265619894', '1611919506865', '1624001813852', '1613019192232', '1615014455691', '1620361807950', '1614150095116', '1624876183558', '1624946761049', '1612505885716', '1620643350937', '1620893757835', '1612938061463', '1613625197186', '1613130471395', '1614252425982', '1614678333552', '1614075120651', '1620804233719', '1675922212872', '1622012205439', '1620629836226', '1678942943226', '1615030604947', '1675492728198', '1614491511291', '1614916423458', '1612852882375', '1614855088943', '1614682905377', '1674792471176', '1620123400304', '1612771235411', '1621423234601', '1613190184169', '1619847638552', '1614079737274', '1623844302242', '1612501396420', '1621068680591', '1613725465625', '1620301095698', '1677653237471', '1614065338227', '1615191488855', '1612511970685', '1612522032267', '1614846861059', '1613038912036', '1674883560849', '1710995473466', '1615198192539', '1678255979453', '1680326970543', '1615025571549', '1612506157949', '1615191753184', '1624434386356', '1622013524609', '1612345238490', '1613206104162', '1622024834862', '1614839417070', '1674634329820', '1614859756607', '1619590583138', '1614337658154', '1611895463649', '1711369286499', '1613368017665', '1620188355850', '1621243600649', '1675315469543', '1614226205690', '1620629570296', '1613471944601', '1620716518578', '1620797331279', '1676703743218', '1613994636876', '1620121655998', '1614674850804', '1621248579575', '1612439188478', '1676106157047', '1623838894452', '1615179808744', '1706176773069', '1613110346197', '1675335605692']

#ref = db.reference(path_polygonobj).child(UID).get(False,True)
#polygonID_list = list(ref.keys())

from shapely.geometry import Polygon

def get_polygonobj(path_polygonobj, UID, polygonID):

    ref = db.reference(path_polygonobj).child(UID).child(polygonID)
    polygon_obj = ref.get(handle_snapshot)
    fieldID = polygon_obj[0]["fieldID"]
    #name: farmerID
    farmerID = polygon_obj[0]["Name"]
    #phone_number: landId
    landID = polygon_obj[0]["PhoneNumber"]
    #area
    #field_description =  field_obj[0]["FieldDescription"]
    field_area =  polygon_obj[0]["Area"]
    polygon_coordinates = polygon_obj[0]["Coordinates"]
    coords = [(point['Longitude'], point['Latitude']) for point in polygon_coordinates.values()]

    # Create a shapely polygon from the coordinates
    polygon = Polygon(coords)
    # Get the bounding box of the polygon
    min_lng, min_lat = min(coord[0] for coord in coords), min(coord[1] for coord in coords)
    max_lng, max_lat = max(coord[0] for coord in coords), max(coord[1] for coord in coords)

    fieldmaxlat = max_lat
    fieldmaxlong = max_lng
    fieldminlat = min_lat
    fieldminlong = min_lng

    return fieldID, farmerID, landID, field_area, polygon_coordinates, fieldmaxlat, fieldminlat, fieldmaxlong, fieldminlong



import leafmap
from shapely.geometry import Polygon

def plot_polygon_on_image(polygonID, min_lat, max_lat, min_lng, max_lng):

    # Expand the bounding box slightly to ensure coverage
    buffer = 0.00001
    min_lng -= buffer
    min_lat -= buffer
    max_lng += buffer
    max_lat += buffer
    bbox=[min_lng, min_lat, max_lng, max_lat]

    # Fetch the satellite image for the bounding box
    #image = f"{polygonID}.png"
    image = f"satellite_images/{polygonID}.png"

    tms_to_geotiff(output=image, bbox=[min_lng, min_lat, max_lng, max_lat], zoom=20, source="Satellite", overwrite=True)



import numpy as np
from PIL import Image, ImageDraw
import matplotlib.pyplot as plt

def create_polymask(uid, fieldid, w_mask, h_mask, X, Y, input_image, output_filename):
    img = Image.open(input_image)
    w_img, h_img = img.size
     # Create a new white image
    white_image = Image.new('RGBA', (w_img, h_img), color='white')
    # Create a new mask image with white background and resize it to match the original image dimensions
    mask_image = Image.new('L', (w_img, h_img), color=0)
    # Convert X and Y to tuples
    vertices = list(zip(X, Y))
    # Draw polygon mask on the resized image
    draw = ImageDraw.Draw(mask_image)
    draw.polygon(vertices, fill=255)
    # Convert the mask image to RGB mode to match the original image
    mask_image_rgb = mask_image.convert('RGBA')  # Convert mask to RGB
    white_image.paste(img, (0, 0), mask_image)
    white_image.save(output_filename,facecolor='white')
    # Plot the resulting image
    #plt.imshow(white_image)
    #plt.axis('off')
    #plt.show()
    return 1

def map_planet_coords(uid,fieldid,coordinates,fieldmaxlat,fieldminlat,fieldmaxlong,fieldminlong,input_folder, output_folder):
  import math
  import matplotlib.path
  from matplotlib.path import Path
  import numpy as np
  import cv2
  from PIL import Image
  import collections
  import firebase_admin
  from firebase_admin import credentials
  from firebase_admin import db

  cred = credentials.Certificate('servicekey.json')
  coordinates = collections.OrderedDict(sorted(coordinates.items()))
  #print(fieldid)
  #print("cooridnates",coordinates)

  tci_file_name = f"{input_folder}/{fieldid}.png"

  #tci_file_name = uid +'/' + fieldid + '_highres_num.png'
  tci = cv2.imread(tci_file_name,0)
  w_tci,h_tci = tci.shape
  #print(w_tci, h_tci)
  centerx = round(w_tci/2)
  centery = round(h_tci/2)
  zero_lat = fieldmaxlat
  zero_long = fieldminlong

  X = []
  Y = []
  #print(fieldminlat, fieldmaxlat, fieldminlong, fieldmaxlong, h_tci, w_tci)
  pixelsperlat = h_tci/abs(float(fieldmaxlong)-float(fieldminlong))
  pixelsperlong = w_tci/abs(float(fieldmaxlat)-float(fieldminlat))
  #print(pixelsperlat, pixelsperlong)
  pformatflag =0
  X = []
  Y = []
  ploc = 0
  for k,v in coordinates.items():
    pointKey = "P_" + str(ploc)
    ploc = ploc + 1
    v = coordinates[pointKey]
    temp_lat = v['Latitude']
    temp_long = v['Longitude']
    temp_lat = float(temp_lat)
    temp_long = float(temp_long)
    x = round((temp_long-zero_long)*pixelsperlat)
    y = round((zero_lat-temp_lat)*pixelsperlong)
    X.append(x)
    Y.append(y)
    output_file = f"{output_folder}/masked/{fieldid}.png"
  create_polymask(uid, fieldid, w_tci, h_tci, X, Y, tci_file_name, output_file)

import json
# Initialize an empty dictionary to store data
polygon_data = {}
#Iterate through polygonID_list
for polygonID in polygon_inside[:]:
    try:
        fieldID, farmerID, landID, field_area, polygon_coordinates, fieldmaxlat, fieldminlat, fieldmaxlong, fieldminlong = get_polygonobj(path_polygonobj, UID, polygonID)
        #Store data in the dictionary with polygonID as the key
        polygon_data[str(polygonID)] = {
            'fieldID': fieldID,
            'farmerID' : farmerID,
            'landID': landID,
            'field_area': field_area,
            'polygon_coordinates': polygon_coordinates,
            'fieldmaxlat': fieldmaxlat,
            'fieldminlat': fieldminlat,
            'fieldmaxlong': fieldmaxlong,
            'fieldminlong': fieldminlong,
                          }
    except Exception as e:
        print(f"Error processing polygonID {polygonID}: {e}")

#Define the output JSON file path
output_json_file = 'Downloads/polygon_data_all.json'

#Write the dictionary to a JSON file
with open(output_json_file, 'w') as json_file:
    json.dump(polygon_data, json_file, indent=4)

print(f"Saved polygon data to {output_json_file}")

time.sleep(10000)
text_to_remove = "1613971589362"
for item in polygon_inside[:]:  # Use a copy of the list with [:] to avoid modifying while iterating
    if text_to_remove in item:
        polygon_inside.remove(item)

len(polygon_inside)

import json
# Step 1: Read the JSON file
with open('Downloads/polygon_data_all.json', 'r') as file:
    polygon_data = json.load(file)

#polygon_data

poly_count = 0


for polygonID in polygon_inside:
    #print(poly_count, polygonID)
    #fieldID, farmerID, landID, field_area, polygon_coordinates, fieldmaxlat, fieldminlat, fieldmaxlong, fieldminlong = get_polygonobj(path_polygonobj, UID, polygonID)
    fieldmaxlat = polygon_data[f"{polygonID}"]["fieldmaxlat"]
    fieldminlat = polygon_data[f"{polygonID}"]["fieldminlat"]
    fieldmaxlong = polygon_data[f"{polygonID}"]["fieldmaxlong"]
    fieldminlong = polygon_data[f"{polygonID}"]["fieldminlong"]
    coordinates = polygon_data[f"{polygonID}"]["polygon_coordinates"]
    input_folder = "satellite_images/"
    output_folder = "satellite_images/"
    plot_polygon_on_image(polygonID, fieldminlat, fieldmaxlat, fieldminlong, fieldmaxlong)
    map_planet_coords(UID,polygonID,coordinates,fieldmaxlat,fieldminlat,fieldmaxlong,fieldminlong, input_folder, output_folder)
    poly_count = poly_count+1

######

# Comm
# Inst
# %pi