|
|
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
|
|
|
from pathlib import PosixPath
|
|
|
import requests, os, json
|
|
|
import cv2
|
|
|
import numpy as np
|
|
|
|
|
|
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
|
|
|
m_model=request.POST.get('model')
|
|
|
m_class=request.POST.get('class')
|
|
|
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
|
|
|
#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/'+fix+file.name)
|
|
|
#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+file.name
|
|
|
print(img_path)
|
|
|
f = open(img_path,'wb')
|
|
|
for i in file.chunks():
|
|
|
f.write(i)
|
|
|
f.close()
|
|
|
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):{} 分布方差(负数偏暗):{} 色彩饱和度(零为补光):{}<br/>对比度:{} 角度方向二阶矩:{} 熵:{} 平均值:{} Sobel:{} Lap:{} dev:{}".format(cast, da, satu, CON, ASM, ENT, MEAN,sb,lap,dev)
|
|
|
if(int(m_class)==1):
|
|
|
if(int(m_model)==1): #小模型
|
|
|
RR = DL.run(weights=(curPath+"/yolov5/v3s.pt"), source=img_path, project=(curPath+"/static/detected"))
|
|
|
return JsonResponse({'img_name':img_path_res,'code':"【烟火 小模型】"+RR+' <br/> '+mesg, 'flag': True})
|
|
|
if(int(m_model)==2): #大模型
|
|
|
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':"【烟火 大模型】"+RR+' <br/> '+mesg, 'flag': True})
|
|
|
if(int(m_model)==3): #泛模型
|
|
|
RR = DL.run(weights=(curPath+"/yolov5/best.pt"), source=img_path, project=(curPath+"/static/detected"))
|
|
|
return JsonResponse({'img_name':img_path_res,'code':"【烟火 泛模型】"+RR+' <br/> '+mesg, 'flag': True})
|
|
|
if(int(m_class)==2):
|
|
|
RR = DL.run(weights=(curPath+"/yolov5/yolov5s.pt"), source=img_path, project=(curPath+"/static/detected"))
|
|
|
return JsonResponse({'img_name':img_path_res,'code':"【一般物体检测】"+RR+' <br/> '+mesg, 'flag': True})
|
|
|
if(int(m_class)==3):
|
|
|
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':"【烟火+塔吊】"+RR+' <br/> '+mesg, 'flag': True})
|
|
|
|
|
|
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"]
|
|
|
print("m_model:",m_model)
|
|
|
print("url:",url)
|
|
|
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
|
|
|
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):{} 分布方差(负数偏暗):{} 色彩饱和度(零为补光):{}<br/>对比度:{} 角度方向二阶矩:{} 熵:{} 平均值:{} Sobel:{} Lap:{} dev:{}".format(cast, da, satu, CON, ASM, ENT, MEAN,sb,lap,dev)
|
|
|
if(int(m_class)==1):
|
|
|
if(int(m_model)==1): #小模型
|
|
|
RR = DL.run(weights=(curPath+"/yolov5/v3s.pt"), source=img_path, project=(curPath+"/static/detected"))
|
|
|
return JsonResponse({'img_name':img_path_res,'code':"【烟火 小模型】"+RR+' <br/> '+mesg, 'flag': True})
|
|
|
if(int(m_model)==2): #大模型
|
|
|
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':"【烟火 大模型】"+RR+' <br/> '+mesg, 'flag': True})
|
|
|
if(int(m_model)==3): #泛模型
|
|
|
RR = DL.run(weights=(curPath+"/yolov5/best.pt"), source=img_path, project=(curPath+"/static/detected"))
|
|
|
return JsonResponse({'img_name':img_path_res,'code':"【烟火 泛模型】"+RR+' <br/> '+mesg, 'flag': True})
|
|
|
if(int(m_class)==2):
|
|
|
RR = DL.run(weights=(curPath+"/yolov5/yolov5s.pt"), source=img_path, project=(curPath+"/static/detected"))
|
|
|
return JsonResponse({'img_name':img_path_res,'code':"【一般物体检测】"+RR+' <br/> '+mesg, 'flag': True})
|
|
|
if(int(m_class)==3):
|
|
|
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':"【烟火+塔吊】"+RR+' <br/> '+mesg, 'flag': True})
|
|
|
|
|
|
|
|
|
|
|
|
def home(request):
|
|
|
return render(request, 'home.html') |