mirror of
https://github.com/we0091234/yolov7_plate.git
synced 2025-09-26 21:01:13 +08:00
260 lines
10 KiB
Python
260 lines
10 KiB
Python
import onnxruntime
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import numpy as np
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import cv2
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import copy
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import os
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import argparse
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from PIL import Image, ImageDraw, ImageFont
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import time
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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"
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mean_value,std_value=((0.588,0.193))#识别模型均值标准差
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def decodePlate(preds): #识别后处理
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pre=0
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newPreds=[]
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for i in range(len(preds)):
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if preds[i]!=0 and preds[i]!=pre:
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newPreds.append(preds[i])
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pre=preds[i]
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plate=""
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for i in newPreds:
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plate+=plateName[int(i)]
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return plate
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# return newPreds
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def rec_pre_precessing(img,size=(48,168)): #识别前处理
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img =cv2.resize(img,(168,48))
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img = img.astype(np.float32)
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img = (img/255-mean_value)/std_value #归一化 减均值 除标准差
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img = img.transpose(2,0,1) #h,w,c 转为 c,h,w
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img = img.reshape(1,*img.shape) #channel,height,width转为batch,channel,height,channel
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return img
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def get_plate_result(img,session_rec): #识别后处理
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img =rec_pre_precessing(img)
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y_onnx = session_rec.run([session_rec.get_outputs()[0].name], {session_rec.get_inputs()[0].name: img})[0]
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# print(y_onnx[0])
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index =np.argmax(y_onnx[0],axis=1) #找出概率最大的那个字符的序号
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# print(y_onnx[0])
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plate_no = decodePlate(index)
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# plate_no = decodePlate(y_onnx[0])
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return plate_no
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def allFilePath(rootPath,allFIleList): #遍历文件
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fileList = os.listdir(rootPath)
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for temp in fileList:
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if os.path.isfile(os.path.join(rootPath,temp)):
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allFIleList.append(os.path.join(rootPath,temp))
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else:
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allFilePath(os.path.join(rootPath,temp),allFIleList)
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def get_split_merge(img): #双层车牌进行分割后识别
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h,w,c = img.shape
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img_upper = img[0:int(5/12*h),:]
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img_lower = img[int(1/3*h):,:]
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img_upper = cv2.resize(img_upper,(img_lower.shape[1],img_lower.shape[0]))
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new_img = np.hstack((img_upper,img_lower))
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return new_img
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def order_points(pts): # 关键点排列 按照（左上，右上，右下，左下）的顺序排列
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rect = np.zeros((4, 2), dtype = "float32")
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s = pts.sum(axis = 1)
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rect[0] = pts[np.argmin(s)]
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rect[2] = pts[np.argmax(s)]
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diff = np.diff(pts, axis = 1)
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rect[1] = pts[np.argmin(diff)]
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rect[3] = pts[np.argmax(diff)]
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return rect
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def four_point_transform(image, pts): #透视变换得到矫正后的图像，方便识别
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rect = order_points(pts)
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(tl, tr, br, bl) = rect
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widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
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widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
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maxWidth = max(int(widthA), int(widthB))
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heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
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heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
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maxHeight = max(int(heightA), int(heightB))
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dst = np.array([
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[0, 0],
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[maxWidth - 1, 0],
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[maxWidth - 1, maxHeight - 1],
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[0, maxHeight - 1]], dtype = "float32")
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M = cv2.getPerspectiveTransform(rect, dst)
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warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
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# return the warped image
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return warped
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def my_letter_box(img,size=(640,640)): #
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h,w,c = img.shape
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r = min(size[0]/h,size[1]/w)
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new_h,new_w = int(h*r),int(w*r)
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top = int((size[0]-new_h)/2)
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left = int((size[1]-new_w)/2)
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bottom = size[0]-new_h-top
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right = size[1]-new_w-left
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img_resize = cv2.resize(img,(new_w,new_h))
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img = cv2.copyMakeBorder(img_resize,top,bottom,left,right,borderType=cv2.BORDER_CONSTANT,value=(114,114,114))
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return img,r,left,top
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def xywh2xyxy(boxes): #xywh坐标变为 左上 ，右下坐标 x1,y1 x2,y2
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xywh =copy.deepcopy(boxes)
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xywh[:,0]=boxes[:,0]-boxes[:,2]/2
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xywh[:,1]=boxes[:,1]-boxes[:,3]/2
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xywh[:,2]=boxes[:,0]+boxes[:,2]/2
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xywh[:,3]=boxes[:,1]+boxes[:,3]/2
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return xywh
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def my_nms(boxes,iou_thresh): #nms
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index = np.argsort(boxes[:,4])[::-1]
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keep = []
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while index.size >0:
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i = index[0]
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keep.append(i)
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x1=np.maximum(boxes[i,0],boxes[index[1:],0])
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y1=np.maximum(boxes[i,1],boxes[index[1:],1])
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x2=np.minimum(boxes[i,2],boxes[index[1:],2])
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y2=np.minimum(boxes[i,3],boxes[index[1:],3])
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w = np.maximum(0,x2-x1)
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h = np.maximum(0,y2-y1)
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inter_area = w*h
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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])
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iou = inter_area/(union_area-inter_area)
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idx = np.where(iou<=iou_thresh)[0]
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index = index[idx+1]
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return keep
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def restore_box(boxes,r,left,top): #返回原图上面的坐标
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boxes[:,[0,2,5,7,9,11]]-=left
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boxes[:,[1,3,6,8,10,12]]-=top
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boxes[:,[0,2,5,7,9,11]]/=r
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boxes[:,[1,3,6,8,10,12]]/=r
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return boxes
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def detect_pre_precessing(img,img_size): #检测前处理
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img,r,left,top=my_letter_box(img,img_size)
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# cv2.imwrite("1.jpg",img)
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img =img[:,:,::-1].transpose(2,0,1).copy().astype(np.float32)
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img=img/255
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img=img.reshape(1,*img.shape)
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return img,r,left,top
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def post_precessing(dets,r,left,top,conf_thresh=0.3,iou_thresh=0.45):#检测后处理
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num_cls=2
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choice = dets[:,:,4]>conf_thresh
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dets=dets[choice]
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dets[:,5:5+num_cls]*=dets[:,4:5] #5::7是2分类类别分数
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box = dets[:,:4]
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boxes = xywh2xyxy(box)
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score= np.max(dets[:,5:5+num_cls],axis=-1,keepdims=True)
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index = np.argmax(dets[:,5:5+num_cls],axis=-1).reshape(-1,1)
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kpt_b=5+num_cls
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landmarks=dets[:,[kpt_b,kpt_b+1,kpt_b+3,kpt_b+4,kpt_b+6,kpt_b+7,kpt_b+9,kpt_b+10]] #yolov7关键有三个数，x,y,score，这里我们只需要x,y
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output = np.concatenate((boxes,score,landmarks,index),axis=1)
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reserve_=my_nms(output,iou_thresh)
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output=output[reserve_]
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output = restore_box(output,r,left,top)
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return output
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def rec_plate(outputs,img0,session_rec): #识别车牌
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dict_list=[]
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for output in outputs:
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result_dict={}
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rect=output[:4].tolist()
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land_marks = output[5:13].reshape(4,2)
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# landmarks = [landmarks[0],landmarks[1],landmarks[3],landmarks[4],landmarks[6],landmarks[7],landmarks[9],landmarks[10]]
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roi_img = four_point_transform(img0,land_marks)
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label = int(output[-1])
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score = output[4]
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if label==1: #代表是双层车牌
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roi_img = get_split_merge(roi_img)
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plate_no = get_plate_result(roi_img,session_rec)
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result_dict['rect']=rect
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result_dict['landmarks']=land_marks.tolist()
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result_dict['plate_no']=plate_no
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result_dict['roi_height']=roi_img.shape[0]
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dict_list.append(result_dict)
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return dict_list
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def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20): #将识别结果画在图上
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if (isinstance(img, np.ndarray)): #判断是否OpenCV图片类型
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img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
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draw = ImageDraw.Draw(img)
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fontText = ImageFont.truetype(
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"fonts/platech.ttf", textSize, encoding="utf-8")
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draw.text((left, top), text, textColor, font=fontText)
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return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
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def draw_result(orgimg,dict_list):
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result_str =""
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for result in dict_list:
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rect_area = result['rect']
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x,y,w,h = rect_area[0],rect_area[1],rect_area[2]-rect_area[0],rect_area[3]-rect_area[1]
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padding_w = 0.05*w
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padding_h = 0.11*h
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rect_area[0]=max(0,int(x-padding_w))
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rect_area[1]=min(orgimg.shape[1],int(y-padding_h))
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rect_area[2]=max(0,int(rect_area[2]+padding_w))
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rect_area[3]=min(orgimg.shape[0],int(rect_area[3]+padding_h))
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height_area = result['roi_height']
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landmarks=result['landmarks']
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result = result['plate_no']
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result_str+=result+" "
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for i in range(4): #关键点
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cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1)
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cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),(0,0,255),2) #画框
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if len(result)<0:
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orgimg=cv2ImgAddText(orgimg,result,rect_area[0]-height_area,rect_area[1]-height_area-10,(255,0,0),height_area)
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print(result_str)
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return orgimg
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--detect_model',type=str, default=r'weights/plate_detect.onnx', help='model.pt path(s)') #检测模型
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parser.add_argument('--rec_model', type=str, default='weights/plate_rec.onnx', help='model.pt path(s)')#识别模型
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parser.add_argument('--image_path', type=str, default=r'imgs', help='source')
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parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)')
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parser.add_argument('--output', type=str, default='result', help='source')
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opt = parser.parse_args()
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file_list = []
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allFilePath(opt.image_path,file_list)
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providers = ['CPUExecutionProvider']
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clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)]
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img_size = (opt.img_size,opt.img_size)
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session_detect = onnxruntime.InferenceSession(opt.detect_model, providers=providers )
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session_rec = onnxruntime.InferenceSession(opt.rec_model, providers=providers )
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if not os.path.exists(opt.output):
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os.mkdir(opt.output)
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save_path = opt.output
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count = 0
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begin = time.time()
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for pic_ in file_list:
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count+=1
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print(count,pic_,end=" ")
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img=cv2.imread(pic_)
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img0 = copy.deepcopy(img)
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img,r,left,top = detect_pre_precessing(img,img_size) #检测前处理
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# print(img.shape)
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y_onnx = session_detect.run([session_detect.get_outputs()[0].name], {session_detect.get_inputs()[0].name: img})[0]
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outputs = post_precessing(y_onnx,r,left,top) #检测后处理
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result_list=rec_plate(outputs,img0,session_rec)
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ori_img = draw_result(img0,result_list)
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img_name = os.path.basename(pic_)
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save_img_path = os.path.join(save_path,img_name)
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cv2.imwrite(save_img_path,ori_img)
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print(f"总共耗时{time.time()-begin} s")
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