如果你认为车牌只是车子的‘名字’，那么是时候让你见识一下，当科技赋予它‘超能力’时会发生什么？
上效果图；
这就是车牌识别的力量，下面是主函数代码：
# -*- coding: UTF-8 -*-
import argparse
import time
from pathlib import Path
import os
import cv2
import torch
import torch.backends.cudnn as cudnn
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, apply_classifier, scale_coords, xyxy2xywh, \
strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
from utils.cv_puttext import cv2ImgAddText
from plate_recognition.plate_rec import get_plate_result, allFilePath, init_model, cv_imread
# from plate_recognition.plate_cls import 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)]
danger = ['危', '险']
def order_points(pts): # 四个点按照左上 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 load_model(weights, device): # 加载检测模型
model = attempt_load(weights, map_location=device) # load FP32 model
return model
def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None): # 返回到原图坐标
# 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[:, :8] /= 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, plate_rec_model,
is_color=False): # 获取车牌坐标以及四个角点坐标并获取车牌号
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
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)
if not is_color:
plate_number, rec_prob = get_plate_result(roi_img, device, plate_rec_model, is_color=is_color) # 对车牌小图进行识别
else:
plate_number, rec_prob, plate_color, color_conf = get_plate_result(roi_img, device, plate_rec_model,
is_color=is_color)
# cv2.imwrite("roi.jpg",roi_img)
result_dict['rect'] = rect # 车牌roi区域
result_dict['detect_conf'] = conf # 检测区域得分
result_dict['landmarks'] = landmarks_np.tolist() # 车牌角点坐标
result_dict['plate_no'] = plate_number # 车牌号
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'] = ""
if is_color:
result_dict['plate_color'] = plate_color # 车牌颜色
result_dict['color_conf'] = color_conf # 颜色得分
result_dict['plate_type'] = class_label # 单双层 0单层 1双层
return result_dict
def detect_Recognition_plate(model, orgimg, device, plate_rec_model, img_size, is_color=False): # 获取车牌信息
# Load model
# img_size = opt_img_size
conf_thres = 0.3 # 得分阈值
iou_thres = 0.5 # nms的iou值
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] # 检测前处理，图片长宽变为32倍数，比如变为640X640
# img =process_data(img0)
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1).copy() # BGR to RGB, to 3x416x416 图片的BGR排列转为RGB,然后将图片的H,W,C排列变为C,H,W排列
# 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, plate_rec_model,
is_color=is_color)
dict_list.append(result_dict)
return dict_list
# cv2.imwrite('result.jpg', orgimg)
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) >= 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_p,rect_area[0]-height_area,rect_area[1]-height_area-10,(0,255,0),height_area)
print(result_str)
return orgimg
def get_second(capture):
if capture.isOpened():
rate = capture.get(5) # 帧速率
FrameNumber = capture.get(7) # 视频文件的帧数
duration = FrameNumber / rate # 帧速率/视频总帧数 是时间，除以60之后单位是分钟
return int(rate), int(FrameNumber), int(duration)
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('--rec_model', type=str, default='weights/plate_rec_color.pth',
help='model.pt path(s)') # 车牌识别+颜色识别模型
parser.add_argument('--is_color', type=bool, default=True, help='plate color') # 是否识别颜色
parser.add_argument('--image_path', type=str, default='imgs', help='source') # 图片路径
parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)') # 网络输入图片大小
parser.add_argument('--output', type=str, default='result', help='source') # 图片结果保存的位置
parser.add_argument('--video', type=str, default='video/1.mp4', help='source') # 视频的路径
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 使用gpu还是cpu进行识别
# 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
plate_rec_model = init_model(device, opt.rec_model, is_color=opt.is_color) # 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("detect params: %.2fM,rec params: %.2fM" % (total / 1e6, total_1 / 1e6))
# plate_color_model =init_color_model(opt.color_model,device)
time_all = 0
time_begin = time.time()
video_name = opt.video
capture = cv2.VideoCapture(video_name)
print(video_name)
# 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_Recognition_plate(detect_model, img, device, plate_rec_model, opt.img_size,
# is_color=opt.is_color)
# 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)
