import re
from django.shortcuts import render
from django.http import HttpResponse, JsonResponse
import os
import subprocess
from datetime import datetime
import learn.yolov5.detect as DL
import learn.Darknet2Pytorch2Onnx.hy_runonnx as RONNX
from pathlib import PosixPath
import requests, os, json
import cv2
import numpy as np
from shutil import copyfile

def adb_shell(cmd):
    result = subprocess.getstatusoutput(cmd)
    return result

'''改程序执行慢，且因为C++和python在cv2中处理细节不同，返回值并不相同
def detect_brightness():
    img = cv2.imread("/home/wangchunlin/img_brightness/1.jpeg")
    gray_img= cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    a=0
    Ma = 0
    hist = np.zeros(256,dtype=int)
    print(hist.dtype)
    h, w = gray_img.shape[:2]
    print(h, w)
    for i in range(h):
        for j in range(w):
            if(float(gray_img[i,j]-128)>128):
                input(">")
            a+=float(gray_img[i,j]-128)
            hist[int(gray_img[i,j])]+=1
    da = a /float(h*w)
    print(da)
    for i in range(256):
        Ma+=abs(i-128-da)*hist[i]
    Ma /= float(h*w)
    print(Ma)
    cast=abs(da)/abs(Ma)
    return cast
'''

def upload(request):
    if request.method == 'POST':
        file = request.FILES.get('file') #获取前端上传的文件
		#以下用绝对路径存储文件，之前我用相对路径一直写不对
        curPath = os.path.abspath( os.path.dirname( __file__ ) )
        img_path = os.path.abspath(curPath+'/static/upload/'+file.name)
        print(img_path)
        f = open(img_path,'wb')
        for i in file.chunks():
            f.write(i)
        f.close()
        return JsonResponse({'upload_img_path':img_path, 'source_image_name':file.name})


def dlurl(request):
    if request.method == 'GET':
        url = request.GET["url"] #获取前端上传的文件
        m_model=request.GET["model"]
        m_class=request.GET["class"]
        model_name = request.GET.get("model_name")
        class_name = request.GET.get("class_name")
        print(request.GET["model_name"])
        print(request.GET["class_name"])
        print("m_model:",m_model)
        print("url:",url)
        if(("filename") in request.GET):
            print("上传图片模式")
            fix = datetime.now().strftime('%Y%m%d%H%M%S%f')+'1' #给文件加前缀防止文件名重复
            source_img_path=request.GET["url"]
            img_path = os.path.join(os.path.dirname(source_img_path),fix+request.GET["filename"])
            copyfile(source_img_path,img_path)   
            img_path_res = '/static/detected/'+fix+request.GET["filename"]
            curPath = os.path.abspath( os.path.dirname( __file__ ) )
        else:
            print("下载URL模式")
            name = url.split(r'/')[-1]
            if not (name.endswith(".jpg") or name.endswith(".jpeg") or name.endswith(".png") or name.endswith(".bmp")):
                return JsonResponse({'flag': False})

            fix = datetime.now().strftime('%Y%m%d%H%M%S%f')+'1' #给文件加前缀防止文件名重复
            curPath = os.path.abspath( os.path.dirname( __file__ ) )
            img_path = os.path.abspath(curPath+'/static/download/'+fix+name)
            r = requests.get(url)
            # 保存
            with open (img_path, 'wb') as f:
                f.write(r.content)
                f.close
            #返回给前端的图片路径用相对路径，前端用绝对路径反而加载不了图片
            img_path_res = '/static/detected/'+fix+name
        return inference(m_class, m_model, curPath, img_path_res, img_path, model_name, class_name)

def home(request):
    return render(request, 'home.html')

def inference(m_class, m_model, curPath, img_path_res, img_path, model_name, class_name):
    result = adb_shell("ImageDetect/build/ImageDetect {}".format(img_path))
    if result[0]==0:
        cast = result[1].split("||")[0]
        da = result[1].split("||")[1]
        satu = result[1].split("||")[2]
        CON = result[1].split("||")[3]
        ASM = result[1].split("||")[4]
        ENT = result[1].split("||")[5]
        MEAN = result[1].split("||")[6]
        sb = result[1].split("||")[7]
        lap = result[1].split("||")[8]
        dev = result[1].split("||")[9]
    print(cast, da, satu)
    mesg = "颜色分布(0-1合理):{}&nbsp;&nbsp;&nbsp;分布方差(负数偏暗):{}&nbsp;&nbsp;&nbsp;色彩饱和度(零为补光):{}<br/>对比度:{}&nbsp;&nbsp;&nbsp;角度方向二阶矩:{}&nbsp;&nbsp;&nbsp;熵:{}&nbsp;&nbsp;&nbsp;平均值:{}&nbsp;&nbsp;&nbsp;Sobel:{}&nbsp;&nbsp;&nbsp;Lap:{}&nbsp;&nbsp;&nbsp;dev:{}".format(cast, da, satu, CON, ASM, ENT, MEAN,sb,lap,dev)
    
    if(int(m_class)==1): #外破
        if(int(m_model)==1):
            print("使用所有模型检测")
        if(int(m_model)==2):
            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v3.0.pt"), source=img_path, project=(curPath+"/static/detected"))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v3.0.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
        if(int(m_model)==3):
            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v7.0.pt"), source=img_path, project=(curPath+"/static/detected"))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v7.0.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})  
        if(int(m_model)==4): 
            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v7.0m.pt"), source=img_path, project=(curPath+"/static/detected"))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v7.0m.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True}) 
        if(int(m_model)==5): 
            RR = DL.run(weights=(curPath+"/yolov5/fire_smoke_towercrane.pt"), source=img_path, project=(curPath+"/static/detected"))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"fire_smoke_towercrane.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
        if(int(m_model)==6): 
            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v7.0m6.pt"), source=img_path, project=(curPath+"/static/detected"),imgsz=(1280,1280))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v7.0m6.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
        if(int(m_model)==7): 
            RR=RONNX.main(curPath+"/Darknet2Pytorch2Onnx/hy_vehicle_4class.onnx", curPath+"/Darknet2Pytorch2Onnx/hy_vehicle_4class.names", img_path, (curPath+"/static/detected"))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"hy_vehicle_4class.onnx"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
        if(int(m_model)==8): 
            RR=RONNX.main(curPath+"/Darknet2Pytorch2Onnx/hy_vehicle_2class.onnx", curPath+"/Darknet2Pytorch2Onnx/hy_vehicle_2class.names", img_path, (curPath+"/static/detected"))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"hy_vehicle_2class.onnx"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
        
    if(int(m_class)==2): #山火
        if(int(m_model)==1):
            print("使用所有模型检测")
        if(int(m_model)==2): # 小
            RR = DL.run(weights=(curPath+"/yolov5/v3s.pt"), source=img_path, project=(curPath+"/static/detected"))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"v3s.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
        if(int(m_model)==3): # 大
            RR = DL.run(weights=(curPath+"/yolov5/v6m6.pt"), source=img_path, imgsz=(1280, 1280), project=(curPath+"/static/detected"))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"v6m6.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
        if(int(m_model)==4): # 范模型
            RR = DL.run(weights=(curPath+"/yolov5/best.pt"), source=img_path, project=(curPath+"/static/detected"))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"best.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
        
    if(int(m_class)==3): # 绝缘子
        if(int(m_model)==1):
            print("使用所有模型检测")
        if(int(m_model)==2):
            RR = DL.run(weights=(curPath+"/yolov5/birdpecked_unnormal_version1.pt"), source=img_path, project=(curPath+"/static/detected"))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"birdpecked_unnormal_version1.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
        
    if(int(m_class)==4): # 一般物体coco
        if(int(m_model)==1):
            print("使用所有模型检测")
        if(int(m_model)==2): #yolov5s
            RR = DL.run(weights=(curPath+"/yolov5/yolov5s.pt"), source=img_path, project=(curPath+"/static/detected"))
            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"yolov5s.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})