onnx_infer.py

import onnxruntime
import numpy as np
import cv2
import copy
import os
import argparse
from PIL import Image, ImageDraw, ImageFont
import time

plateName=r"#京沪津渝冀晋蒙辽吉黑苏浙皖闽赣鲁豫鄂湘粤桂琼川贵云藏陕甘青宁新学警港澳挂使领民航危0123456789ABCDEFGHJKLMNPQRSTUVWXYZ险品"
mean_value,std_value=((0.588,0.193))#识别模型均值标准差

def decodePlate(preds):        #识别后处理
    pre=0
    newPreds=[]
    for i in range(len(preds)):
        if preds[i]!=0 and preds[i]!=pre:
            newPreds.append(preds[i])
        pre=preds[i]
    plate=""
    for i in newPreds:
        plate+=plateName[int(i)]
    return plate
    # return newPreds

def rec_pre_precessing(img,size=(48,168)): #识别前处理
    img =cv2.resize(img,(168,48))
    img = img.astype(np.float32)
    img = (img/255-mean_value)/std_value  #归一化 减均值 除标准差
    img = img.transpose(2,0,1)         #h,w,c 转为 c,h,w
    img = img.reshape(1,*img.shape)    #channel,height,width转为batch,channel,height,channel
    return img

def get_plate_result(img,session_rec): #识别后处理
    img =rec_pre_precessing(img)
    y_onnx = session_rec.run([session_rec.get_outputs()[0].name], {session_rec.get_inputs()[0].name: img})[0]
    # print(y_onnx[0])
    index =np.argmax(y_onnx[0],axis=1)  #找出概率最大的那个字符的序号
    # print(y_onnx[0])
    plate_no = decodePlate(index)
    # plate_no = decodePlate(y_onnx[0])
    return plate_no


def allFilePath(rootPath,allFIleList):  #遍历文件
    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 get_split_merge(img):  #双层车牌进行分割后识别
    h,w,c = img.shape
    img_upper = img[0:int(5/12*h),:]
    img_lower = img[int(1/3*h):,:]
    img_upper = cv2.resize(img_upper,(img_lower.shape[1],img_lower.shape[0]))
    new_img = np.hstack((img_upper,img_lower))
    return new_img


def order_points(pts):     # 关键点排列 按照（左上，右上，右下，左下）的顺序排列
    rect = np.zeros((4, 2), dtype = "float32")
    s = pts.sum(axis = 1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    diff = np.diff(pts, axis = 1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    return rect


def four_point_transform(image, pts):  #透视变换得到矫正后的图像，方便识别
    rect = order_points(pts)
    (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 the warped image
    return warped

def my_letter_box(img,size=(640,640)):  #
    h,w,c = img.shape
    r = min(size[0]/h,size[1]/w)
    new_h,new_w = int(h*r),int(w*r)
    top = int((size[0]-new_h)/2)
    left = int((size[1]-new_w)/2)
    
    bottom = size[0]-new_h-top
    right = size[1]-new_w-left
    img_resize = cv2.resize(img,(new_w,new_h))
    img = cv2.copyMakeBorder(img_resize,top,bottom,left,right,borderType=cv2.BORDER_CONSTANT,value=(114,114,114))
    return img,r,left,top

def xywh2xyxy(boxes):   #xywh坐标变为 左上 ，右下坐标 x1,y1  x2,y2
    xywh =copy.deepcopy(boxes)
    xywh[:,0]=boxes[:,0]-boxes[:,2]/2
    xywh[:,1]=boxes[:,1]-boxes[:,3]/2
    xywh[:,2]=boxes[:,0]+boxes[:,2]/2
    xywh[:,3]=boxes[:,1]+boxes[:,3]/2
    return xywh
 
def my_nms(boxes,iou_thresh):         #nms
    index = np.argsort(boxes[:,4])[::-1]
    keep = []
    while index.size >0:
        i = index[0]
        keep.append(i)
        x1=np.maximum(boxes[i,0],boxes[index[1:],0])
        y1=np.maximum(boxes[i,1],boxes[index[1:],1])
        x2=np.minimum(boxes[i,2],boxes[index[1:],2])
        y2=np.minimum(boxes[i,3],boxes[index[1:],3])
        
        w = np.maximum(0,x2-x1)
        h = np.maximum(0,y2-y1)

        inter_area = w*h
        union_area = (boxes[i,2]-boxes[i,0])*(boxes[i,3]-boxes[i,1])+(boxes[index[1:],2]-boxes[index[1:],0])*(boxes[index[1:],3]-boxes[index[1:],1])
        iou = inter_area/(union_area-inter_area)
        idx = np.where(iou<=iou_thresh)[0]
        index = index[idx+1]
    return keep

def restore_box(boxes,r,left,top):  #返回原图上面的坐标
    boxes[:,[0,2,5,7,9,11]]-=left
    boxes[:,[1,3,6,8,10,12]]-=top

    boxes[:,[0,2,5,7,9,11]]/=r
    boxes[:,[1,3,6,8,10,12]]/=r
    return boxes

def detect_pre_precessing(img,img_size):  #检测前处理
    img,r,left,top=my_letter_box(img,img_size)
    # cv2.imwrite("1.jpg",img)
    img =img[:,:,::-1].transpose(2,0,1).copy().astype(np.float32)
    img=img/255
    img=img.reshape(1,*img.shape)
    return img,r,left,top

def post_precessing(dets,r,left,top,conf_thresh=0.3,iou_thresh=0.5):#检测后处理
    choice = dets[:,:,4]>conf_thresh
    dets=dets[choice]
    dets[:,13:15]*=dets[:,4:5]
    box = dets[:,:4]
    boxes = xywh2xyxy(box)
    score= np.max(dets[:,13:15],axis=-1,keepdims=True)
    index = np.argmax(dets[:,13:15],axis=-1).reshape(-1,1)
    output = np.concatenate((boxes,score,dets[:,5:13],index),axis=1) 
    reserve_=my_nms(output,iou_thresh) 
    output=output[reserve_] 
    output = restore_box(output,r,left,top)
    return output

def rec_plate(outputs,img0,session_rec):  #识别车牌
    dict_list=[]
    for output in outputs:
        result_dict={}
        rect=output[:4].tolist()
        land_marks = output[5:13].reshape(4,2)
        roi_img = four_point_transform(img0,land_marks)
        label = int(output[-1])
        score = output[4]
        if label==1:  #代表是双层车牌
            roi_img = get_split_merge(roi_img)
        plate_no = get_plate_result(roi_img,session_rec)
        result_dict['rect']=rect
        result_dict['landmarks']=land_marks.tolist()
        result_dict['plate_no']=plate_no
        result_dict['roi_height']=roi_img.shape[0]
        dict_list.append(result_dict)
    return dict_list

def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20):  #将识别结果画在图上
    if (isinstance(img, np.ndarray)):  #判断是否OpenCV图片类型
        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    draw = ImageDraw.Draw(img)
    fontText = ImageFont.truetype(
        "fonts/platech.ttf", textSize, encoding="utf-8")
    draw.text((left, top), text, textColor, font=fontText)
    return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)

def draw_result(orgimg,dict_list):
    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]=min(orgimg.shape[1],int(y-padding_h))
        rect_area[2]=max(0,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 = result['plate_no']
        result_str+=result+" "
        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
        if len(result)>=1:
            orgimg=cv2ImgAddText(orgimg,result,rect_area[0]-height_area,rect_area[1]-height_area-10,(255,0,0),height_area)
    print(result_str)
    return orgimg

if __name__ == "__main__":
    begin = time.time()
    parser = argparse.ArgumentParser()
    parser.add_argument('--detect_model',type=str, default=r'weights/plate_detect.onnx', help='model.pt path(s)')  #检测模型
    parser.add_argument('--rec_model', type=str, default='weights/plate_rec.onnx', help='model.pt path(s)')#识别模型
    parser.add_argument('--image_path', type=str, default='imgs', help='source') 
    parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--output', type=str, default='result1', help='source') 
    opt = parser.parse_args()
    file_list = []
    allFilePath(opt.image_path,file_list)
    providers =  ['CPUExecutionProvider']
    clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)]
    img_size = (opt.img_size,opt.img_size)
    session_detect = onnxruntime.InferenceSession(opt.detect_model, providers=providers )
    session_rec = onnxruntime.InferenceSession(opt.rec_model, providers=providers )
    if not os.path.exists(opt.output):
        os.mkdir(opt.output)
    save_path = opt.output
    count = 0
    for pic_ in file_list:
        count+=1
        print(count,pic_,end=" ")
        img=cv2.imread(pic_)
        img0 = copy.deepcopy(img)
        img,r,left,top = detect_pre_precessing(img,img_size) #检测前处理
        # print(img.shape)
        y_onnx = session_detect.run([session_detect.get_outputs()[0].name], {session_detect.get_inputs()[0].name: img})[0]
        outputs = post_precessing(y_onnx,r,left,top) #检测后处理
        result_list=rec_plate(outputs,img0,session_rec)
        ori_img = draw_result(img0,result_list)
        img_name = os.path.basename(pic_)
        save_img_path = os.path.join(save_path,img_name)
        cv2.imwrite(save_img_path,ori_img)
    print(f"总共耗时{time.time()-begin} s")