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yolo.py
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import cv2
from utils.datasets import *
from utils.utils import *
import numpy as np
import colorsys
import os
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
import math
import time
import csv
import lightTest
from utils.draw import draw_boxes,pil_draw_box_2
from licence import Licence
class YOLO(object):
_defaults = {
"model_path": 'yolo4_weights.pth',
"anchors_path": 'model_data/yolo_anchors.txt',
"classes_path": 'model_data/coco_classes.txt',
"model_image_size" : (416, 416, 3),
"confidence": 0.5,
"cuda": True
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
def __init__(self, weights):
self.__dict__.update(self._defaults)
#self.class_names = self._get_class()
#self.anchors = self._get_anchors()
#self.generate()
self.dir=['UP','RIGHT','DOWN',"LEFT"]
self.currentCarID=0
self.virtureLine=[[0,0],[0,0]]
self.carCnt=0
self.motoCnt=0
self.personCnt=0
self.truckCnt=0
self.flag=False
self.trafficLine=None
self.trafficLight=[0,0,0,0]
self.curpath=0
self.trafficLightColor=None
self.device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.model=torch.load(weights,map_location=self.device)['model'].float()
self.model.to(self.device).eval()
self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names
self.colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(self.names))]
self.licence=Licence()
self.leftlight=[]
self.forwardlight=[]
self.rightlight=[]
self.leftlightColor=0
self.forwardlightColor=0
self.rightlightColor=0
self.carDirection={}
self.config=None
def detect_image(self,img,trafficline,path,idx_frame,illegal,config):
self.config=config
self.leftlight=self.config.leftlight
#print(self.leftlight)
self.forwardlight=self.config.forwardlight
self.rightlight=self.config.rightlight
self.trafficLine=trafficline
self.curpath=path
#(filepath,filename)=os.path.split(path)
self.personCnt=self.carCnt=self.motoCnt=self.truckCnt=0
im0=img.copy()
image=im0
half=self.device.type!='cpu'
if half:
self.model.half()
img = letterbox(im0,new_shape=(640,640))[0]
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(self.device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
pred = self.model(img)[0]
pred = non_max_suppression(pred, 0.4, 0.5)
cars=[]
return_boxs=[]
return_class_names=[]
return_scores=[]
for i, det in enumerate(pred): # detections per image
if det is not None and len(det):
#print(det)
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Write results
for *xyxy, conf, cls in det:
c=cls
x=int(xyxy[0])
y=int(xyxy[1])
w=int(xyxy[2]-xyxy[0])
h=int(xyxy[3]-xyxy[1])
label = '%s %.2f' % (self.names[int(cls)], conf)
if (self.config.preflag[3]==1 or self.config.preflag[4]==1 or self.config.preflag[5]==1) :
#self.flag=True
pass
else:
if c==9:
if self.flag:
if y<self.trafficLight[1]:
self.trafficLight=xyxy
w=int(xyxy[2]-xyxy[0])
h=int(xyxy[3]-xyxy[1])
if w>h:
self.config.leftlight=self.leftlight=[xyxy[0],xyxy[1],xyxy[0]+w/2,xyxy[3]]
self.config.forwardlight=self.forwardlight=[xyxy[0]+w/2,xyxy[1],xyxy[2],xyxy[3]]
continue
self.flag=True
self.trafficLight=xyxy
print(self.trafficLight)
w=int(xyxy[2]-xyxy[0])
h=int(xyxy[3]-xyxy[1])
if w>h:
self.config.leftlight=self.leftlight=[xyxy[0],xyxy[1],xyxy[0]+w/2,xyxy[3]]
self.config.forwardlight=self.forwardlight=[xyxy[0]+w/2,xyxy[1],xyxy[2],xyxy[3]]
print(self.config.leftlight)
print(self.config.forwardlight)
continue
plot_one_box(xyxy, im0, label=label, color=self.colors[int(cls)], line_thickness=3)
if c==2:
self.carCnt=self.carCnt+1
if c==0:
self.personCnt=self.personCnt+1
if os.path.exists(self.curpath+"illegal/"):
pass
else:
os.mkdir(self.curpath+"illegal")
if os.path.exists(self.curpath+"illegal/runred"):
pass
else:
os.mkdir(self.curpath+"illegal/runred")
if self.trafficLine!=None:
if self.trafficLightColor=='green' and x>=self.trafficLine[0] and x+w<=self.trafficLine[2] and conf>0.6 and (h/w>=1.6):
if idx_frame%8==0:
imgTmp=im0[y:y+h,x:x+w]
cv2.imwrite(self.curpath+"illegal/runred/"+str(idx_frame)+".jpg",imgTmp)
if isinstance(illegal.get(idx_frame,0),int):
illegal[idx_frame]={}
illegal[idx_frame].update({'runred':True})
font = ImageFont.truetype(font='model_data/simhei.ttf',size=np.floor(0.012 * np.shape(im0)[1] ).astype('int32'))
im0=pil_draw_box_2(im0,[x,y,x+w,y+h],label="闯红灯",font=font)
if c==3:
self.motoCnt=self.motoCnt+1
if c==7:
self.truckCnt=self.truckCnt+1
if c != 2 and c != 7:
continue
if(y+h<im0.shape[0]-12):
#continue
return_boxs.append([x+w/2,y+h/2,w,h])
return_class_names.append(self.names[int(cls)])
return_scores.append(conf)
if len(self.config.leftlight)>0 or len(self.config.forwardlight)>0 or len(self.config.rightlight)>0:
#print(self.config.leftlight)
if len(self.config.leftlight)>0:
x1=int(self.config.leftlight[0])
y1=int(self.config.leftlight[1])
x2=int(self.config.leftlight[2])
y2=int(self.config.leftlight[3])
imgTmp=im0[y1:y2,x1:x2]
w=x2-x1
h=y2-y1
if w>h:
imgTmp=self.rotate_bound(imgTmp,90)
#cv2.imwrite("test.jpg",imgTmp)
self.leftlightColor=lightTest.detectImg(imgTmp)
if self.leftlightColor=='green':
plot_one_box(self.leftlight, im0, label=self.leftlightColor, color=(0,255,0), line_thickness=3)
elif self.leftlightColor=='red':
plot_one_box(self.leftlight, im0, label=self.leftlightColor, color=(255,0,0), line_thickness=3)
elif self.leftlightColor=='yellow':
plot_one_box(self.leftlight, im0, label=self.leftlightColor, color=(255,255,0), line_thickness=3)
if len(self.config.forwardlight)>0:
x1=int(self.config.forwardlight[0])
y1=int(self.config.forwardlight[1])
x2=int(self.config.forwardlight[2])
y2=int(self.config.forwardlight[3])
imgTmp=im0[y1:y2,x1:x2]
w=x2-x1
h=y2-y1
#cv2.imwrite("out/"+str(idx_frame)+".jpg",imgTmp)
if w>h:
imgTmp=self.rotate_bound(imgTmp,90)
self.forwardlightColor=lightTest.detectImg(imgTmp)
if self.forwardlightColor=='green':
plot_one_box(self.forwardlight, im0, label=self.forwardlightColor, color=(0,255,0), line_thickness=3)
elif self.forwardlightColor=='red':
plot_one_box(self.forwardlight, im0, label=self.forwardlightColor, color=(255,0,0), line_thickness=3)
elif self.forwardlightColor=='yellow':
plot_one_box(self.forwardlight, im0, label=self.forwardlightColor, color=(255,255,0), line_thickness=3)
if len(self.config.rightlight)>0:
x1=int(self.config.rightlight[0])
y1=int(self.config.rightlight[1])
x2=int(self.config.rightlight[2])
y2=int(self.config.rightlight[3])
imgTmp=im0[y1:y2,x1:x2]
w=x2-x1
h=y2-y1
if w>h:
imgTmp=self.rotate_bound(imgTmp,90)
self.rightlightColor=lightTest.detectImg(imgTmp)
if self.rightlightColor=='green':
plot_one_box(self.rightlight, im0, label=self.rightlightColor, color=(0,255,0), line_thickness=3)
elif self.rightlightColor=='red':
plot_one_box(self.rightlight, im0, label=self.rightlightColor, color=(255,0,0), line_thickness=3)
elif self.rightlightColor=='yellow':
plot_one_box(self.rightlight, im0, label=self.rightlightColor, color=(255,255,0), line_thickness=3)
elif self.flag==True:
x1=int(self.trafficLight[0])
y1=int(self.trafficLight[1])
x2=int(self.trafficLight[2])
y2=int(self.trafficLight[3])
w=x2-x1
h=y2-y1
imgLight=im0[y1:y2,x1:x2]
if w>h:
imgLight=self.rotate_bound(imgLight,90)
self.trafficLightColor=lightTest.detectImg(imgLight)
if self.trafficLightColor=='green':
plot_one_box(self.trafficLight, im0, label=self.trafficLightColor, color=(0,255,0), line_thickness=3)
elif self.trafficLightColor=='red':
plot_one_box(self.trafficLight, im0, label=self.trafficLightColor, color=(255,0,0), line_thickness=3)
elif self.trafficLightColor=='yellow':
plot_one_box(self.trafficLight, im0, label=self.trafficLightColor, color=(255,255,0), line_thickness=3)
im0 = cv2.putText(im0, "moto: %d car: %d person:%d truck: %d"%(self.motoCnt,self.carCnt,self.personCnt,self.truckCnt), (0,60), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 2)
return np.array(return_boxs),np.array(return_scores),np.array(return_class_names),im0
#print(xyxy)
def rotate_bound(self,image,angle):
#获取图像的尺寸
#旋转中心
(h,w) = image.shape[:2]
(cx,cy) = (w/2,h/2)
#设置旋转矩阵
M = cv2.getRotationMatrix2D((cx,cy),-angle,1.0)
cos = np.abs(M[0,0])
sin = np.abs(M[0,1])
# 计算图像旋转后的新边界
nW = int((h*sin)+(w*cos))
nH = int((h*cos)+(w*sin))
# 调整旋转矩阵的移动距离（t_{x}, t_{y}）
M[0,2] += (nW/2) - cx
M[1,2] += (nH/2) - cy
return cv2.warpAffine(image,M,(nW,nH))
def letterbox(self,img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better test mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = new_shape
ratio = new_shape[0] / shape[1], new_shape[1] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return img, ratio, (dw, dh)
# if __name__=='__main__':
# img=cv2.imread("./bus.jpg")
# yolo=YOLO()
# yolo.detect_image(img,'weights/yolov5s.pt')