mirror of
https://github.com/we0091234/yolov8-plate.git
synced 2024-05-03 08:34:58 +08:00
247 lines
10 KiB
Python
247 lines
10 KiB
Python
import torch
|
||
import cv2
|
||
import numpy as np
|
||
import argparse
|
||
import copy
|
||
import time
|
||
import os
|
||
from ultralytics.nn.tasks import attempt_load_weights
|
||
from plate_recognition.plate_rec import get_plate_result,init_model,cv_imread
|
||
from plate_recognition.double_plate_split_merge import get_split_merge
|
||
from fonts.cv_puttext import cv2ImgAddText
|
||
|
||
def allFilePath(rootPath,allFIleList):# 读取文件夹内的文件，放到list
|
||
fileList = os.listdir(rootPath)
|
||
for temp in fileList:
|
||
if os.path.isfile(os.path.join(rootPath,temp)):
|
||
allFIleList.append(os.path.join(rootPath,temp))
|
||
else:
|
||
allFilePath(os.path.join(rootPath,temp),allFIleList)
|
||
|
||
def four_point_transform(image, pts): #透视变换得到车牌小图
|
||
# rect = order_points(pts)
|
||
rect = pts.astype('float32')
|
||
(tl, tr, br, bl) = rect
|
||
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
|
||
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
|
||
maxWidth = max(int(widthA), int(widthB))
|
||
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
|
||
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
|
||
maxHeight = max(int(heightA), int(heightB))
|
||
dst = np.array([
|
||
[0, 0],
|
||
[maxWidth - 1, 0],
|
||
[maxWidth - 1, maxHeight - 1],
|
||
[0, maxHeight - 1]], dtype = "float32")
|
||
M = cv2.getPerspectiveTransform(rect, dst)
|
||
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
|
||
return warped
|
||
|
||
|
||
def letter_box(img,size=(640,640)): #yolo 前处理 letter_box操作
|
||
h,w,_=img.shape
|
||
r=min(size[0]/h,size[1]/w)
|
||
new_h,new_w=int(h*r),int(w*r)
|
||
new_img = cv2.resize(img,(new_w,new_h))
|
||
left= int((size[1]-new_w)/2)
|
||
top=int((size[0]-new_h)/2)
|
||
right = size[1]-left-new_w
|
||
bottom=size[0]-top-new_h
|
||
img =cv2.copyMakeBorder(new_img,top,bottom,left,right,cv2.BORDER_CONSTANT,value=(114,114,114))
|
||
return img,r,left,top
|
||
|
||
def load_model(weights, device): #加载yolov8 模型
|
||
model = attempt_load_weights(weights,device=device) # load FP32 model
|
||
return model
|
||
|
||
def xywh2xyxy(det): #xywh转化为xyxy
|
||
y = det.clone()
|
||
y[:,0]=det[:,0]-det[0:,2]/2
|
||
y[:,1]=det[:,1]-det[0:,3]/2
|
||
y[:,2]=det[:,0]+det[0:,2]/2
|
||
y[:,3]=det[:,1]+det[0:,3]/2
|
||
return y
|
||
|
||
def my_nums(dets,iou_thresh): #nms操作
|
||
y = dets.clone()
|
||
y_box_score = y[:,:5]
|
||
index = torch.argsort(y_box_score[:,-1],descending=True)
|
||
keep = []
|
||
while index.size()[0]>0:
|
||
i = index[0].item()
|
||
keep.append(i)
|
||
x1=torch.maximum(y_box_score[i,0],y_box_score[index[1:],0])
|
||
y1=torch.maximum(y_box_score[i,1],y_box_score[index[1:],1])
|
||
x2=torch.minimum(y_box_score[i,2],y_box_score[index[1:],2])
|
||
y2=torch.minimum(y_box_score[i,3],y_box_score[index[1:],3])
|
||
zero_=torch.tensor(0).to(device)
|
||
w=torch.maximum(zero_,x2-x1)
|
||
h=torch.maximum(zero_,y2-y1)
|
||
inter_area = w*h
|
||
nuion_area1 =(y_box_score[i,2]-y_box_score[i,0])*(y_box_score[i,3]-y_box_score[i,1]) #计算交集
|
||
union_area2 =(y_box_score[index[1:],2]-y_box_score[index[1:],0])*(y_box_score[index[1:],3]-y_box_score[index[1:],1])#计算并集
|
||
|
||
iou = inter_area/(nuion_area1+union_area2-inter_area)#计算iou
|
||
|
||
idx = torch.where(iou<=iou_thresh)[0] #保留iou小于iou_thresh的
|
||
index=index[idx+1]
|
||
return keep
|
||
|
||
|
||
def restore_box(dets,r,left,top): #坐标还原到原图上
|
||
|
||
dets[:,[0,2]]=dets[:,[0,2]]-left
|
||
dets[:,[1,3]]= dets[:,[1,3]]-top
|
||
dets[:,:4]/=r
|
||
# dets[:,5:13]/=r
|
||
|
||
return dets
|
||
# pass
|
||
|
||
def post_processing(prediction,conf,iou_thresh,r,left,top): #后处理
|
||
|
||
prediction = prediction.permute(0,2,1).squeeze(0)
|
||
xc = prediction[:, 4:6].amax(1) > conf #过滤掉小于conf的框
|
||
x = prediction[xc]
|
||
if not len(x):
|
||
return []
|
||
boxes = x[:,:4] #框
|
||
boxes = xywh2xyxy(boxes) #中心点 宽高 变为 左上 右下两个点
|
||
score,index = torch.max(x[:,4:6],dim=-1,keepdim=True) #找出得分和所属类别
|
||
x = torch.cat((boxes,score,x[:,6:14],index),dim=1) #重新组合
|
||
|
||
score = x[:,4]
|
||
keep =my_nums(x,iou_thresh)
|
||
x=x[keep]
|
||
x=restore_box(x,r,left,top)
|
||
return x
|
||
|
||
def pre_processing(img,opt,device): #前处理
|
||
img, r,left,top= letter_box(img,(opt.img_size,opt.img_size))
|
||
# print(img.shape)
|
||
img=img[:,:,::-1].transpose((2,0,1)).copy() #bgr2rgb hwc2chw
|
||
img = torch.from_numpy(img).to(device)
|
||
img = img.float()
|
||
img = img/255.0
|
||
img =img.unsqueeze(0)
|
||
return img ,r,left,top
|
||
|
||
def det_rec_plate(img,img_ori,detect_model,plate_rec_model):
|
||
result_list=[]
|
||
img,r,left,top = pre_processing(img,opt,device) #前处理
|
||
predict = detect_model(img)[0]
|
||
outputs=post_processing(predict,0.3,0.5,r,left,top) #后处理
|
||
for output in outputs:
|
||
result_dict={}
|
||
output = output.squeeze().cpu().numpy().tolist()
|
||
rect=output[:4]
|
||
rect = [int(x) for x in rect]
|
||
label = output[-1]
|
||
roi_img = img_ori[rect[1]:rect[3],rect[0]:rect[2]]
|
||
# land_marks=np.array(output[5:13],dtype='int64').reshape(4,2)
|
||
# roi_img = four_point_transform(img_ori,land_marks) #透视变换得到车牌小图
|
||
if int(label): #判断是否是双层车牌，是双牌的话进行分割后然后拼接
|
||
roi_img=get_split_merge(roi_img)
|
||
plate_number,rec_prob,plate_color,color_conf=get_plate_result(roi_img,device,plate_rec_model,is_color=True)
|
||
|
||
result_dict['plate_no']=plate_number #车牌号
|
||
result_dict['plate_color']=plate_color #车牌颜色
|
||
result_dict['rect']=rect #车牌roi区域
|
||
result_dict['detect_conf']=output[4] #检测区域得分
|
||
# result_dict['landmarks']=land_marks.tolist() #车牌角点坐标
|
||
# result_dict['rec_conf']=rec_prob #每个字符的概率
|
||
result_dict['roi_height']=roi_img.shape[0] #车牌高度
|
||
# result_dict['plate_color']=plate_color
|
||
# if is_color:
|
||
result_dict['color_conf']=color_conf #颜色得分
|
||
result_dict['plate_type']=int(label) #单双层 0单层 1双层
|
||
result_list.append(result_dict)
|
||
return result_list
|
||
|
||
|
||
def draw_result(orgimg,dict_list,is_color=False): # 车牌结果画出来
|
||
result_str =""
|
||
for result in dict_list:
|
||
rect_area = result['rect']
|
||
|
||
x,y,w,h = rect_area[0],rect_area[1],rect_area[2]-rect_area[0],rect_area[3]-rect_area[1]
|
||
padding_w = 0.05*w
|
||
padding_h = 0.11*h
|
||
rect_area[0]=max(0,int(x-padding_w))
|
||
rect_area[1]=max(0,int(y-padding_h))
|
||
rect_area[2]=min(orgimg.shape[1],int(rect_area[2]+padding_w))
|
||
rect_area[3]=min(orgimg.shape[0],int(rect_area[3]+padding_h))
|
||
|
||
height_area = result['roi_height']
|
||
# landmarks=result['landmarks']
|
||
result_p = result['plate_no']
|
||
if result['plate_type']==0:#单层
|
||
result_p+=" "+result['plate_color']
|
||
else: #双层
|
||
result_p+=" "+result['plate_color']+"双层"
|
||
result_str+=result_p+" "
|
||
# for i in range(4): #关键点
|
||
# cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1)
|
||
cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),(0,0,255),2) #画框
|
||
|
||
labelSize = cv2.getTextSize(result_p,cv2.FONT_HERSHEY_SIMPLEX,0.5,1) #获得字体的大小
|
||
if rect_area[0]+labelSize[0][0]>orgimg.shape[1]: #防止显示的文字越界
|
||
rect_area[0]=int(orgimg.shape[1]-labelSize[0][0])
|
||
orgimg=cv2.rectangle(orgimg,(rect_area[0],int(rect_area[1]-round(1.6*labelSize[0][1]))),(int(rect_area[0]+round(1.2*labelSize[0][0])),rect_area[1]+labelSize[1]),(255,255,255),cv2.FILLED)#画文字框,背景白色
|
||
|
||
if len(result)>=6:
|
||
orgimg=cv2ImgAddText(orgimg,result_p,rect_area[0],int(rect_area[1]-round(1.6*labelSize[0][1])),(0,0,0),21)
|
||
# orgimg=cv2ImgAddText(orgimg,result_p,rect_area[0]-height_area,rect_area[1]-height_area-10,(0,255,0),height_area)
|
||
|
||
print(result_str)
|
||
return orgimg
|
||
|
||
|
||
if __name__ == "__main__":
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument('--detect_model', nargs='+', type=str, default=r'weights/yolov8s.pt', help='model.pt path(s)') #yolov8检测模型
|
||
parser.add_argument('--rec_model', type=str, default=r'weights/plate_rec_color.pth', help='model.pt path(s)')#车牌字符识别模型
|
||
parser.add_argument('--image_path', type=str, default=r'imgs', help='source') #待识别图片路径
|
||
parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)') #yolov8 网络模型输入大小
|
||
parser.add_argument('--output', type=str, default='result', help='source') #结果保存的文件夹
|
||
device =torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||
|
||
clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)]
|
||
opt = parser.parse_args()
|
||
save_path = opt.output
|
||
|
||
if not os.path.exists(save_path):
|
||
os.mkdir(save_path)
|
||
|
||
detect_model = load_model(opt.detect_model, device) #初始化yolov8识别模型
|
||
plate_rec_model=init_model(device,opt.rec_model,is_color=True) #初始化识别模型
|
||
#算参数量
|
||
total = sum(p.numel() for p in detect_model.parameters())
|
||
total_1 = sum(p.numel() for p in plate_rec_model.parameters())
|
||
print("yolov8 detect params: %.2fM,rec params: %.2fM" % (total/1e6,total_1/1e6))
|
||
|
||
detect_model.eval()
|
||
# print(detect_model)
|
||
file_list = []
|
||
allFilePath(opt.image_path,file_list)
|
||
count=0
|
||
time_all = 0
|
||
time_begin=time.time()
|
||
for pic_ in file_list:
|
||
print(count,pic_,end=" ")
|
||
time_b = time.time() #开始时间
|
||
img = cv2.imread(pic_)
|
||
img_ori = copy.deepcopy(img)
|
||
result_list=det_rec_plate(img,img_ori,detect_model,plate_rec_model)
|
||
time_e=time.time()
|
||
ori_img=draw_result(img,result_list) #将结果画在图上
|
||
img_name = os.path.basename(pic_)
|
||
save_img_path = os.path.join(save_path,img_name) #图片保存的路径
|
||
time_gap = time_e-time_b #计算单个图片识别耗时
|
||
if count:
|
||
time_all+=time_gap
|
||
count+=1
|
||
cv2.imwrite(save_img_path,ori_img) #op
|
||
# print(result_list)
|
||
print(f"sumTime time is {time.time()-time_begin} s, average pic time is {time_all/(len(file_list)-1)}")
|
||
|