if 82 - 82: Iii1i
import aicmder as cmder
from aicmder . module . module import serving , moduleinfo
import io
from PIL import Image
import json
if 87 - 87: Ii % i1i1i1111I . Oo / OooOoo * I1Ii1I1 - I1I
import base64
import cv2
import onnxruntime as ort
import numpy as np
from Yolov5_torch import Yolov5
from baili import shoot
from fence_detect import Fence , check_object_in_fence
if 81 - 81: i1 + ooOOO / oOo0O00 * i1iiIII111 * IiIIii11Ii
def OOoOoo000O00 ( base64_string , save = False ) :
# sbuf = StringIO()
# sbuf.write(base64.b64decode(base64_string))
# pimg = Image.open(sbuf)
 Ooo0Ooo = io . BytesIO ( base64 . b64decode ( base64_string ) )
 ii1I1iII1I1I = Image . open ( Ooo0Ooo )
 if save :
  ii1I1iII1I1I . save ( 'image.png' , 'PNG' )
 return cv2 . cvtColor ( np . array ( ii1I1iII1I1I ) , cv2 . COLOR_RGB2BGR )
 if 71 - 71: IIiIIiIi11I1
 if 98 - 98: I11iiIi11i1I % oOO
@ moduleinfo ( name = 'image' )
class i1ii1 ( cmder . Module ) :
 if 63 - 63: iI1iI11Ii111
 if 26 - 26: O0OooooOo + ii % iiI * IIi1i111IiII . Oo / Oo
 def __init__ ( self , ** kwargs ) -> None :
  if 85 - 85: I11iiIi11i1I + oOO - Ii * Oo
  if 8 - 8: i1iiIII111 * i1 . I1I / ii
  print ( "init ImagePredictor" , kwargs [ "Coin" ] )
  ooOOooO0 = kwargs [ "Coin" ]
  i1iiiiIIIiIi = ooOOooO0 [ "imgsz" ]
  II = ooOOooO0 [ "model" ]
  self . coin_yolo = Yolov5 ( weights = II , imgsz = i1iiiiIIIiIi )
  if 34 - 34: I11iiIi11i1I + ii * iI1iI11Ii111 * OooOoo
  Ii111 = kwargs [ "baili" ]
  i1iiiiIIIiIi = Ii111 [ "imgsz" ]
  II = Ii111 [ "model" ]
  self . baili_yolo = Yolov5 ( weights = II , imgsz = i1iiiiIIIiIi )
  self . debug = 0
  if 8 - 8: Ii + iiI . O0OooooOo - I1Ii1I1 % O0OooooOo . i1i1i1111I
  if 24 - 24: IIi1i111IiII
 @ serving
 def predict ( self , ** kwargs ) :
  if 9 - 9: IIiIIiIi11I1 / iiI . iiI / Ii % i1i1i1111I % I11iiIi11i1I
  O00Oo = { }
  self . debug = 0
  try :
   O00O = kwargs [ "img" ]
   if 59 - 59: Iii1i . IIi1i111IiII - iiI
   ii1IiIiiII = OOoOoo000O00 ( O00O )
   if 21 - 21: oOo0O00 % O0OooooOo % ii . oOo0O00
   iii11i = kwargs [ "model" ]
   if "debug" in kwargs and kwargs [ "debug" ] > 0 :
    try :
     self . debug = int ( kwargs [ "debug" ] )
    except :
     pass
     if 35 - 35: I1I % i1iiIII111 * I1I
     if 88 - 88: O0OooooOo + ooOOO - i1i1i1111I . I11iiIi11i1I * Ii + Iii1i
   if "Coin" in iii11i :
    O00Oo = self . coin_yolo . predict_image ( img_bgr = ii1IiIiiII , debug = self . debug )
   elif "baili" in iii11i :
    O00Oo = self . baili_yolo . predict_image ( img_bgr = ii1IiIiiII , debug = self . debug )
    if "base_x" in kwargs and "base_y" in kwargs and len ( O00Oo [ "data" ] ) > 0 :
     oOo0O00O0ooo = kwargs [ "base_x" ]
     i11iIii = kwargs [ "base_y" ]
     shoot ( O00Oo , oOo0O00O0ooo , i11iIii )
     if 40 - 40: IiIIii11Ii . i1 / Oo
   if "debug" in kwargs and kwargs [ "debug" ] == 1 :
    del O00Oo [ "img" ]
    if 46 - 46: IIi1i111IiII - OooOoo * I1Ii1I1 / I11iiIi11i1I / Iii1i
   if "fence" in kwargs :
    I11iIi1i = kwargs [ "fence" ]
    oooOOOooo , IIiII11 , OooOoo0OO0OO0 = ii1IiIiiII . shape
    IiIi1Ii1111 = [ ]
    for Ii1I1I1i in I11iIi1i :
     OOOO0O0ooO0O = Fence ( Ii1I1I1i , IIiII11 , oooOOOooo )
     IiIi1Ii1111 . append ( OOOO0O0ooO0O )
    I1iIIiI1 = False
    if "Cake" in iii11i :
     I1iIIiI1 = True
    check_object_in_fence ( O00Oo , IiIi1Ii1111 , calculate_usage = I1iIIiI1 )
    if 78 - 78: oOO + oOO - I1I * iI1iI11Ii111 % I1Ii1I1 * i1i1i1111I
  except Exception as Oo0 :
   print ( Oo0 )
   if 40 - 40: OooOoo / Iii1i
   if 6 - 6: IIi1i111IiII - i1i1i1111I
   if 59 - 59: iiI * IIiIIiIi11I1 / IIiIIiIi11I1 - i1
   if 19 - 19: Iii1i * I11iiIi11i1I . I1Ii1I1 / O0OooooOo * Ii - ii
  iiI1111II = json . dumps ( O00Oo )
  if 79 - 79: I11iiIi11i1I % oOO % iI1iI11Ii111 / ooOOO - ooOOO / O0OooooOo
  return iiI1111II
  if 63 - 63: ooOOO / i1i1i1111I - oOo0O00 * ooOOO / i1iiIII111 + ii
  if 11 - 11: i1 / IIiIIiIi11I1
  if 89 - 89: I1I * i1i1i1111I
if __name__ == "__main__" :
 import aicmder as cmder
 O0OOooO = { 'image' : { 'name' : 'YoloModule' , 'init_args' :
 {
 'Coin' : {
 "model" : '/home/faith/android_viewer/thirdparty/yolov5/runs/train/exp27/weights/best.pt' ,
 "imgsz" : [ 1280 , 1280 ]
 } ,
 'baili' : {
 "model" : '/home/faith/AI_baili_train/best5000.pt' ,
 "imgsz" : [ 768 , 768 ]
 }
 } } }
 oOOoOOOO000 = cmder . serve . ServeCommand ( )
 oOOoOOOO000 . execute ( [ '-w' , '1' , '-c' , json . dumps ( O0OOooO ) ,
 '-p' , '8099' , '--max_connect' , '5' ] )
# dd678faae9ac167bc83abf78e5cb2f3f0688d3a3
