223 lines
8.4 KiB
Python
223 lines
8.4 KiB
Python
# -*- coding: UTF-8 -*-
|
||
import argparse
|
||
import time
|
||
import os
|
||
import cv2
|
||
import torch
|
||
from numpy import random
|
||
import copy
|
||
import numpy as np
|
||
from models.experimental import attempt_load
|
||
from utils.datasets import letterbox
|
||
from utils.general import check_img_size, non_max_suppression_face, scale_coords
|
||
|
||
from utils.torch_utils import time_synchronized
|
||
from utils.cv_puttext import cv2ImgAddText
|
||
from plate_recognition.plate_rec import get_plate_result,allFilePath,cv_imread
|
||
|
||
from plate_recognition.double_plate_split_merge import get_split_merge
|
||
|
||
clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)]
|
||
|
||
def load_model(weights, device):
|
||
model = attempt_load(weights, map_location=device) # load FP32 model
|
||
return model
|
||
|
||
|
||
def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None):
|
||
# Rescale coords (xyxy) from img1_shape to img0_shape
|
||
if ratio_pad is None: # calculate from img0_shape
|
||
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
|
||
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
|
||
else:
|
||
gain = ratio_pad[0][0]
|
||
pad = ratio_pad[1]
|
||
|
||
coords[:, [0, 2, 4, 6]] -= pad[0] # x padding
|
||
coords[:, [1, 3, 5, 7]] -= pad[1] # y padding
|
||
coords[:, :10] /= gain
|
||
#clip_coords(coords, img0_shape)
|
||
coords[:, 0].clamp_(0, img0_shape[1]) # x1
|
||
coords[:, 1].clamp_(0, img0_shape[0]) # y1
|
||
coords[:, 2].clamp_(0, img0_shape[1]) # x2
|
||
coords[:, 3].clamp_(0, img0_shape[0]) # y2
|
||
coords[:, 4].clamp_(0, img0_shape[1]) # x3
|
||
coords[:, 5].clamp_(0, img0_shape[0]) # y3
|
||
coords[:, 6].clamp_(0, img0_shape[1]) # x4
|
||
coords[:, 7].clamp_(0, img0_shape[0]) # y4
|
||
# coords[:, 8].clamp_(0, img0_shape[1]) # x5
|
||
# coords[:, 9].clamp_(0, img0_shape[0]) # y5
|
||
return coords
|
||
|
||
|
||
|
||
|
||
def get_plate_rec_landmark(img, xyxy, conf, landmarks, class_num,device):
|
||
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])
|
||
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
|
||
result_dict['rect']=rect
|
||
result_dict['landmarks']=landmarks_np.tolist()
|
||
result_dict['class']=class_label
|
||
return result_dict
|
||
|
||
|
||
|
||
def detect_plate(model, orgimg, device,img_size):
|
||
# Load model
|
||
# img_size = opt_img_size
|
||
conf_thres = 0.3
|
||
iou_thres = 0.5
|
||
dict_list=[]
|
||
# orgimg = cv2.imread(image_path) # BGR
|
||
img0 = copy.deepcopy(orgimg)
|
||
assert orgimg is not None, 'Image Not Found '
|
||
h0, w0 = orgimg.shape[:2] # orig hw
|
||
r = img_size / max(h0, w0) # resize image to img_size
|
||
if r != 1: # always resize down, only resize up if training with augmentation
|
||
interp = cv2.INTER_AREA if r < 1 else cv2.INTER_LINEAR
|
||
img0 = cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolation=interp)
|
||
|
||
imgsz = check_img_size(img_size, s=model.stride.max()) # check img_size
|
||
|
||
img = letterbox(img0, new_shape=imgsz)[0]
|
||
# img =process_data(img0)
|
||
# Convert
|
||
img = img[:, :, ::-1].transpose(2, 0, 1).copy() # BGR to RGB, to 3x416x416
|
||
|
||
# Run inference
|
||
t0 = time.time()
|
||
|
||
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)
|
||
|
||
# Inference
|
||
t1 = time_synchronized()
|
||
pred = model(img)[0]
|
||
t2=time_synchronized()
|
||
# print(f"infer time is {(t2-t1)*1000} ms")
|
||
|
||
# Apply NMS
|
||
pred = non_max_suppression_face(pred, conf_thres, iou_thres)
|
||
|
||
# print('img.shape: ', img.shape)
|
||
# print('orgimg.shape: ', orgimg.shape)
|
||
|
||
# Process detections
|
||
for i, det in enumerate(pred): # detections per image
|
||
if len(det):
|
||
# Rescale boxes from img_size to im0 size
|
||
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], orgimg.shape).round()
|
||
|
||
# Print results
|
||
for c in det[:, -1].unique():
|
||
n = (det[:, -1] == c).sum() # detections per class
|
||
|
||
det[:, 5:13] = scale_coords_landmarks(img.shape[2:], det[:, 5:13], orgimg.shape).round()
|
||
|
||
for j in range(det.size()[0]):
|
||
xyxy = det[j, :4].view(-1).tolist()
|
||
conf = det[j, 4].cpu().numpy()
|
||
landmarks = det[j, 5:13].view(-1).tolist()
|
||
class_num = det[j, 13].cpu().numpy()
|
||
result_dict = get_plate_rec_landmark(orgimg, xyxy, conf, landmarks, class_num,device)
|
||
dict_list.append(result_dict)
|
||
return dict_list
|
||
# cv2.imwrite('result.jpg', orgimg)
|
||
|
||
|
||
|
||
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))
|
||
|
||
|
||
landmarks=result['landmarks']
|
||
label=result['class']
|
||
# 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]),clors[label],2) #画框
|
||
cv2.putText(img,str(label),(rect_area[0],rect_area[1]),cv2.FONT_HERSHEY_SIMPLEX,0.5,clors[label],2)
|
||
# orgimg=cv2ImgAddText(orgimg,label,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/plate_detect.pt', help='model.pt path(s)') #检测模型
|
||
parser.add_argument('--image_path', type=str, default=r'D:\Project\ChePai\test\images\val', 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')
|
||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||
# device =torch.device("cpu")
|
||
opt = parser.parse_args()
|
||
print(opt)
|
||
save_path = opt.output
|
||
count=0
|
||
if not os.path.exists(save_path):
|
||
os.mkdir(save_path)
|
||
|
||
detect_model = load_model(opt.detect_model, device) #初始化检测模型
|
||
time_all = 0
|
||
time_begin=time.time()
|
||
if not os.path.isfile(opt.image_path): #目录
|
||
file_list=[]
|
||
allFilePath(opt.image_path,file_list)
|
||
for img_path in file_list:
|
||
|
||
print(count,img_path)
|
||
time_b = time.time()
|
||
img =cv_imread(img_path)
|
||
|
||
if img is None:
|
||
continue
|
||
if img.shape[-1]==4:
|
||
img=cv2.cvtColor(img,cv2.COLOR_BGRA2BGR)
|
||
# detect_one(model,img_path,device)
|
||
dict_list=detect_plate(detect_model, img, device,opt.img_size)
|
||
ori_img=draw_result(img,dict_list)
|
||
img_name = os.path.basename(img_path)
|
||
save_img_path = os.path.join(save_path,img_name)
|
||
time_e=time.time()
|
||
time_gap = time_e-time_b
|
||
if count:
|
||
time_all+=time_gap
|
||
cv2.imwrite(save_img_path,ori_img)
|
||
count+=1
|
||
else: #单个图片
|
||
print(count,opt.image_path,end=" ")
|
||
img =cv_imread(opt.image_path)
|
||
if img.shape[-1]==4:
|
||
img=cv2.cvtColor(img,cv2.COLOR_BGRA2BGR)
|
||
# detect_one(model,img_path,device)
|
||
dict_list=detect_plate(detect_model, img, device,opt.img_size)
|
||
ori_img=draw_result(img,dict_list)
|
||
img_name = os.path.basename(opt.image_path)
|
||
save_img_path = os.path.join(save_path,img_name)
|
||
cv2.imwrite(save_img_path,ori_img)
|
||
print(f"sumTime time is {time.time()-time_begin} s, average pic time is {time_all/(len(file_list)-1)}") |