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detect_rec_plate.py
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detect_rec_plate.py
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import argparse
import time
import os
import copy
import cv2
import torch
import numpy as np
import torch.backends.cudnn as cudnn
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords
from plate_recognition.plate_rec import get_plate_result,allFilePath,init_model,cv_imread
from plate_recognition.double_plate_split_merge import get_split_merge
from utils.datasets import letterbox
from utils.cv_puttext import cv2ImgAddText
def cv_imread(path):
img=cv2.imdecode(np.fromfile(path,dtype=np.uint8),-1)
return img
clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)]
def order_points(pts): #关键点按照（左上，右上，右下，左下）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
# 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 get_plate_rec_landmark(img, xyxy, conf, landmarks, class_num,device,plate_rec_model):
h,w,c = img.shape
result_dict={}
tl = 1 or round(0.002 * (h + w) / 2) + 1 # line/font thickness
x1 = int(xyxy[0])
y1 = int(xyxy[1])
x2 = int(xyxy[2])
y2 = int(xyxy[3])
height=y2-y1
landmarks_np=np.zeros((4,2))
rect=[x1,y1,x2,y2]
for i in range(4):
point_x = int(landmarks[2 * i])
point_y = int(landmarks[2 * i + 1])
landmarks_np[i]=np.array([point_x,point_y])
class_label= int(class_num) #车牌的的类型0代表单牌，1代表双层车牌
roi_img = four_point_transform(img,landmarks_np) #透视变换得到车牌小图
# cv2.imwrite("roi.jpg",roi_img)
# roi_img_h = roi_img.shape[0]
# roi_img_w = roi_img.shape[1]
# if roi_img_w/roi_img_h<3:
# class_label=
# h_w_r = roi_img_w/roi_img_h
if class_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) #对车牌小图进行识别
result_dict['rect']=rect
result_dict['landmarks']=landmarks_np.tolist()
result_dict['plate_no']=plate_number
result_dict['rec_conf']=rec_prob #每个字符的概率
result_dict['plate_color']=plate_color
result_dict['color_conf']=color_conf
result_dict['roi_height']=roi_img.shape[0]
result_dict['score']=conf
result_dict['label']=class_label
return result_dict
def detect_Recognition_plate(model, orgimg, device,plate_rec_model,img_size):
conf_thres = 0.3
iou_thres = 0.5
dict_list=[]
im0 = copy.deepcopy(orgimg)
imgsz=(img_size,img_size)
img = letterbox(im0, new_shape=imgsz)[0]
img = img[:, :, ::-1].transpose(2, 0, 1).copy() # BGR to RGB, to 3x640X640
img = torch.from_numpy(img).to(device)
img = 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 = model(img)[0]
pred = non_max_suppression(pred, conf_thres=conf_thres, iou_thres=iou_thres, kpt_label=4,agnostic=True)
for i, det in enumerate(pred):
if len(det):
# Rescale boxes from img_size to im0 size
scale_coords(img.shape[2:], det[:, :4], im0.shape, kpt_label=False)
scale_coords(img.shape[2:], det[:, 6:], im0.shape, kpt_label=4, step=3)
for j in range(det.size()[0]):
xyxy = det[j, :4].view(-1).tolist()
conf = det[j, 4].cpu().numpy()
landmarks = det[j, 6:].view(-1).tolist()
landmarks = [landmarks[0],landmarks[1],landmarks[3],landmarks[4],landmarks[6],landmarks[7],landmarks[9],landmarks[10]]
class_num = det[j, 5].cpu().numpy()
result_dict = get_plate_rec_landmark(orgimg, xyxy, conf, landmarks, class_num,device,plate_rec_model)
dict_list.append(result_dict)
return dict_list
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]=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))
rect_area = [int(x) for x in rect_area]
height_area = result['roi_height']
landmarks=result['landmarks']
result_p = result['plate_no']+" "+result['plate_color']
result_str+=result_p+" "
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)
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)
if len(result)>1:
for i in range(4): #关键点
cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1)
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,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='weights/yolov7-lite-s.pt', help='model.pt path(s)') #检测模型
parser.add_argument('--rec_model', type=str, default='weights/plate_rec_color.pth', help='model.pt path(s)') #车牌识别 +颜色识别
parser.add_argument('--source', type=str, default='imgs', help='source') # file/folder, 0 for webcam
# parser.add_argument('--img-size', nargs= '+', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--output', type=str, default='result', help='source')
parser.add_argument('--kpt-label', type=int, default=4, help='number of keypoints')
device =torch.device("cuda" if torch.cuda.is_available() else "cpu")
# device = torch.device("cpu")
opt = parser.parse_args()
print(opt)
model = attempt_load(opt.detect_model, map_location=device)
# torch.save()
plate_rec_model=init_model(device,opt.rec_model)
if not os.path.exists(opt.output):
os.mkdir(opt.output)
file_list=[]
allFilePath(opt.source,file_list)
time_b = time.time()
for pic_ in file_list:
print(pic_,end=" ")
img = cv_imread(pic_)
if img.shape[-1]==4:
img=cv2.cvtColor(img,cv2.COLOR_BGRA2BGR)
# img = my_letter_box(img)
dict_list=detect_Recognition_plate(model, img, device,plate_rec_model,opt.img_size)
ori_img=draw_result(img,dict_list)
img_name = os.path.basename(pic_)
save_img_path = os.path.join(opt.output,img_name)
cv2.imwrite(save_img_path,ori_img)
print(f"elasted time is {time.time()-time_b} s")