做钓鱼网站违法,中国银行全球门户网站,郑州网站建设口碑好,太原建站模板厂家文章目录 车牌检测算法检测实现1.环境布置2.数据处理2.1 CCPD数据集介绍2.1.1 ccpd2019及20202.1.2 文件名字解析 2.2数据集处理2.2.1 CCPD数据处理2.2.2 CPRD数据集处理 2.3 检测算法2.3.1 数据配置car_plate.yaml2.3.2 模型配置2.3.3 train.py2.3.4 训练结果 2.4 部署2.4.1 p… 文章目录 车牌检测算法检测实现1.环境布置2.数据处理2.1 CCPD数据集介绍2.1.1 ccpd2019及20202.1.2 文件名字解析 2.2数据集处理2.2.1 CCPD数据处理2.2.2 CPRD数据集处理 2.3 检测算法2.3.1 数据配置car_plate.yaml2.3.2 模型配置2.3.3 train.py2.3.4 训练结果 2.4 部署2.4.1 pth推理detect.py2.4.2 onnx推理2.4.3 trt推理2.4.4 numpy版本trt推理2.4.5 推理结果展示 参考开源 车牌检测算法检测实现
1.环境布置
torch1.8.1torchvision0.9.1
pip install -r requirements.txt2.数据处理
2.1 CCPD数据集介绍
2.1.1 ccpd2019及2020
CCPD数据集主要采集于安徽某停车场一段时间内的数据 所有图片尺寸固定为720×1160w×h 大约包含25w的各种场景图片如下图所示 类别描述图片数CCPD-Base通用车牌图片200kCCPD-FN车牌离摄像头拍摄位置相对较近或较远20kCCPD-DB车牌区域亮度较亮、较暗或者不均匀20kCCPD-Rotate车牌水平倾斜 20 到 50 度竖直倾斜-10到 10 度10kCCPD-Tilt车牌水平倾斜 15 到 45 度竖直倾斜 15 到 45 度10kCCPD-Weather车牌在雨雪雾天气拍摄得到10kCCPD-Challenge在车牌检测识别任务中较有挑战性的图片10kCCPD-Blur由于摄像机镜头抖动导致的模楜车牌图片5kCCPD-NP没有安装车牌的新车图片5k
2.1.2 文件名字解析 1、01:车牌占整个界面比例一般没用可忽略
2、86_91: 车牌的水平角度和垂直角度
3、298\341_449\414: 车牌标注框左上角和右下角的坐标
4、458\394_308\410_304\357_454\341车牌四个顶点的坐标顺序为右下、左下、左上、右上
5、0_0_14_28_24_26_29: 这个代表着和省份 (第一位)、地市 (第二位)、车牌号 (剩余部分) 的映射关系
6、124: 亮度值越大亮度越高仅供参考
7、24模糊度值越小越模糊仅供参考)2.2数据集处理
2.2.1 CCPD数据处理
1.解压ccpd,cprd数据集2019,ccpd文件太大解压慢利用程序进行解压
import tarfile
# 指定tar文件路径
tar_path r.\CCPD2019\CCPD2019.tar
# 打开tar文件
with tarfile.open(tar_path, r) as tar:# 解压所有文件到指定目录tar.extractall(r.\ccpd)
# 解压完成
print(解压完成)ccpd数据集处理方法
import os
import shutil
import cv2
import numpy as np
from tqdm import tqdmdef allFilePath(rootPath, allFIleList):获取指定目录下所有以.jpg结尾的文件的路径并将这些路径存储在一个列表中。fileList os.listdir(rootPath)for temp in fileList:if os.path.isfile(os.path.join(rootPath, temp)):if temp.endswith(.jpg):allFIleList.append(os.path.join(rootPath, temp))else:allFilePath(os.path.join(rootPath, temp), allFIleList)def order_points(pts):对给定的坐标点进行排序使得列表中的第一个点是左上角第二个点是右上角第三个点是右下角第四个点是左下角。返回排序后的坐标点列表。# initialzie a list of coordinates that will be ordered# such that the first entry in the list is the top-left,# the second entry is the top-right, the third is the# bottom-right, and the fourth is the bottom-leftpts pts[:4, :]rect np.zeros((5, 2), dtypefloat32)# the top-left point will have the smallest sum, whereas# the bottom-right point will have the largest sums pts.sum(axis1)rect[0] pts[np.argmin(s)]rect[2] pts[np.argmax(s)]# now, compute the difference between the points, the# top-right point will have the smallest difference,# whereas the bottom-left will have the largest differencediff np.diff(pts, axis1)rect[1] pts[np.argmin(diff)]rect[3] pts[np.argmax(diff)]# return the ordered coordinatesreturn rectdef get_rect_and_landmarks(img_path):该函数用于从图像文件路径中解析出矩形框和关键点的坐标并返回解析后的结果。file_name img_path.split(/)[-1].split(-)landmarks_np np.zeros((5, 2))rect file_name[2].split(_)landmarks file_name[3].split(_)rect_str .join(rect)landmarks_str .join(landmarks)rect rect_str.split()landmarks landmarks_str.split()rect [int(x) for x in rect]landmarks [int(x) for x in landmarks]for i in range(4):landmarks_np[i][0] landmarks[2 * i]landmarks_np[i][1] landmarks[2 * i 1]# middle_landmark_w int((landmarks[4]landmarks[6])/2)# middle_landmark_h int((landmarks[5]landmarks[7])/2)# landmarks.append(middle_landmark_w)# landmarks.append(middle_landmark_h)landmarks_np_new order_points(landmarks_np)# landmarks_np_new[4]np.array([middle_landmark_w,middle_landmark_h])return rect, landmarks, landmarks_np_newdef x1x2y1y2_yolo(rect, landmarks, img):h, w, c img.shaperect[0] max(0, rect[0])rect[1] max(0, rect[1])rect[2] min(w - 1, rect[2] - rect[0])rect[3] min(h - 1, rect[3] - rect[1])annotation np.zeros((1, 14))annotation[0, 0] (rect[0] rect[2] / 2) / w # cxannotation[0, 1] (rect[1] rect[3] / 2) / h # cyannotation[0, 2] rect[2] / w # wannotation[0, 3] rect[3] / h # hannotation[0, 4] landmarks[0] / w # l0_xannotation[0, 5] landmarks[1] / h # l0_yannotation[0, 6] landmarks[2] / w # l1_xannotation[0, 7] landmarks[3] / h # l1_yannotation[0, 8] landmarks[4] / w # l2_xannotation[0, 9] landmarks[5] / h # l2_yannotation[0, 10] landmarks[6] / w # l3_xannotation[0, 11] landmarks[7] / h # l3_y# annotation[0, 12] landmarks[8] / w # l4_x# annotation[0, 13] landmarks[9] / h # l4_yreturn annotationdef xywh2yolo(rect, landmarks_sort, img):h, w, c img.shaperect[0] max(0, rect[0])rect[1] max(0, rect[1])rect[2] min(w - 1, rect[2] - rect[0])rect[3] min(h - 1, rect[3] - rect[1])annotation np.zeros((1, 12))annotation[0, 0] (rect[0] rect[2] / 2) / w # cxannotation[0, 1] (rect[1] rect[3] / 2) / h # cyannotation[0, 2] rect[2] / w # wannotation[0, 3] rect[3] / h # hannotation[0, 4] landmarks_sort[0][0] / w # l0_xannotation[0, 5] landmarks_sort[0][1] / h # l0_yannotation[0, 6] landmarks_sort[1][0] / w # l1_xannotation[0, 7] landmarks_sort[1][1] / h # l1_yannotation[0, 8] landmarks_sort[2][0] / w # l2_xannotation[0, 9] landmarks_sort[2][1] / h # l2_yannotation[0, 10] landmarks_sort[3][0] / w # l3_xannotation[0, 11] landmarks_sort[3][1] / h # l3_y# annotation[0, 12] landmarks_sort[4][0] / w # l4_x# annotation[0, 13] landmarks_sort[4][1] / h # l4_yreturn annotation
def yolo2x1y1x2y2(annotation, img):h, w, c img.shaperect annotation[:, 0:4].squeeze().tolist()landmarks annotation[:, 4:].squeeze().tolist()rect_w w * rect[2]rect_h h * rect[3]rect_x int(rect[0] * w - rect_w / 2)rect_y int(rect[1] * h - rect_h / 2)new_rect [rect_x, rect_y, rect_x rect_w, rect_y rect_h]for i in range(5):landmarks[2 * i] landmarks[2 * i] * wlandmarks[2 * i 1] landmarks[2 * i 1] * hreturn new_rect, landmarksdef update_txt(file_root rI:/CCPD2019/ccpd,save_img_pathrH:\data\images,save_txt_pathH:\data\labels):print(file_root, start!!!!!)file_list []count 0allFilePath(file_root, file_list)# print(file_list)# exit()for img_path in file_list:count 1# img_path rccpd_yolo_test/02-90_85-173466_452541-452553_176556_178463_454460-0_0_6_26_15_26_32-68-53.jpgtext_path img_path.replace(.jpg, .txt)# 读取图片img cv2.imread(img_path)rect, landmarks, landmarks_sort get_rect_and_landmarks(img_path)# annotationx1x2y1y2_yolo(rect,landmarks,img)annotation xywh2yolo(rect, landmarks_sort, img)str_label 0 for i in range(len(annotation[0])):str_label str_label str(annotation[0][i])str_label str_label.replace([, ).replace(], )str_label str_label.replace(,, ) \n# if os.path.exists(text_path):# continue# else:shutil.move(img_path,os.path.join(os.path.join(save_img_path,os.path.basename(img_path))))text_path_save os.path.join(save_txt_path,os.path.basename(text_path))# print(text_path_save)# exit()with open(text_path_save, w) as f:f.write(str_label)print(text_path,finished!)# print(count, img_path)print(os.getpid(),end!!!)def delete_non_jpg_images(image_folder):for filename in os.listdir(image_folder):if not filename.endswith(.jpg):file_path os.path.join(image_folder, filename)os.remove(file_path)print(删除完毕)def move_files_to_folders(images_folder, folders_folder, labels_folder):for filename in os.listdir(images_folder):if filename.endswith(.jpg):image_path os.path.join(images_folder, filename)label_path os.path.join(images_folder, os.path.splitext(filename)[0] .txt)folder_path os.path.join(folders_folder, filename)labels_folder_path os.path.join(labels_folder, os.path.splitext(filename)[0] .txt)if not os.path.exists(folder_path) and not os.path.exists(labels_folder_path) and os.path.exists(label_path):# 不存在同名shutil.move(image_path, folder_path)shutil.move(label_path, labels_folder_path)if __name__ __main__:# 1. 处理ccpd文件夹import multiprocessingpool multiprocessing.Pool(processes14) # 这里使用4个进程files []for dir in os.listdir(rI:/CCPD2019/ccpd):files.append(os.path.join(rI:/CCPD2019/ccpd,dir))# 使用进程池执行任务results pool.map(update_txt,files)# 关闭进程池防止新任务被提交pool.close()# 等待所有任务完成pool.join()# 2. 清理异常文件夹# 调用删除非jpg图像的函数image_folder rH:\data\images# 删除文件delete_non_jpg_images(image_folder)# 3.加入一些新增文件夹文件# 指定文件夹路径# images_folder rsingle_yellow_val# folders_folder rH:\data\images# labels_folder rH:\data\labels# # 调用移动文件的函数# move_files_to_folders(images_folder, folders_folder, labels_folder)处理后文件,共计329499个文件
处理后为类别左上角右下角4个角点 2.2.2 CPRD数据集处理
Unified Chinese License Plate Detection and Recognition with High Efficiency(Arxiv 2022) 这段话描述了标签文件夹中的txt文件的注释格式。每个txt文件的文件名与相应图像的名称相同。 在txt文件中每一行表示图像中的一个车牌LP的注释。 一个LP的注释格式如下
x1y1x2y2x3y3x4y4typecontent前八个数字表示边界四边形的坐标。 type注释表示LP的类型0表示蓝色车牌1表示黄色单线车牌2表示黄色双线车牌3表示白色车牌。 content注释表示LP的内容。
2.3 检测算法
YOLOv5-Face是YOLOv5的一个改进版本特别针对人脸检测任务。它添加了一个5-Point Landmark Regression Head关键点回归并对Landmark Regression Head使用了Wing loss进行约束。此外YOLOv5-Face还设计了不同模型尺寸的检测器从大模型到超小模型以实现在嵌入式或移动设备上的实时检测。在WiderFace数据集上的实验结果表明YOLOv5-Face在几乎所有的Easy、Medium和Hard子集上都能达到最先进的性能超过了特定设计的人脸检测器。与许多其他的人脸检测器不同YOLOv5-Face把人脸检测作为一个一般的目标检测任务来看待。
这里将yolov5-face 修改为四个关键点做目标检测任务
2.3.1 数据配置car_plate.yaml
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
# Train command: python train.py --data voc.yaml
# Default dataset location is next to /yolov5:
# /parent_folder
# /VOC
# /yolov5# download command/URL (optional)
download: bash data/scripts/get_voc.sh# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: E:/data/train/images
val: E:/data/val_detect/val
# number of classes
nc: 2# class names
names: [ single_plate,double_plate]
2.3.2 模型配置
# parameters
nc: 2 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 0.5 # layer channel multiple# anchors
anchors:- [4,5, 8,10, 13,16] # P3/8- [23,29, 43,55, 73,105] # P4/16- [146,217, 231,300, 335,433] # P5/32# YOLOv5 backbone
backbone:# [from, number, module, args][[-1, 1, StemBlock, [32, 3, 2]], # 0-P2/4[-1, 1, ShuffleV2Block, [128, 2]], # 1-P3/8[-1, 3, ShuffleV2Block, [128, 1]], # 2[-1, 1, ShuffleV2Block, [256, 2]], # 3-P4/16[-1, 7, ShuffleV2Block, [256, 1]], # 4[-1, 1, ShuffleV2Block, [512, 2]], # 5-P5/32[-1, 3, ShuffleV2Block, [512, 1]], # 6]# YOLOv5 head
head:[[-1, 1, Conv, [128, 1, 1]],[-1, 1, nn.Upsample, [None, 2, nearest]],[[-1, 4], 1, Concat, [1]], # cat backbone P4[-1, 1, C3, [128, False]], # 10[-1, 1, Conv, [128, 1, 1]],[-1, 1, nn.Upsample, [None, 2, nearest]],[[-1, 2], 1, Concat, [1]], # cat backbone P3[-1, 1, C3, [128, False]], # 14 (P3/8-small)[-1, 1, Conv, [128, 3, 2]],[[-1, 11], 1, Concat, [1]], # cat head P4[-1, 1, C3, [128, False]], # 17 (P4/16-medium)[-1, 1, Conv, [128, 3, 2]],[[-1, 7], 1, Concat, [1]], # cat head P5[-1, 1, C3, [128, False]], # 20 (P5/32-large)[[14, 17, 20], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)]
2.3.3 train.py parser argparse.ArgumentParser()# 权重parser.add_argument(--weights, typestr, defaultbest.pt, helpinitial weights path)# 默认配置文件parser.add_argument(--cfg, typestr, defaultmodels/car_plate.yaml, helpmodel.yaml path)parser.add_argument(--data, typestr, defaultdata/car_plate.yaml, helpdata.yaml path)parser.add_argument(--hyp, typestr, defaultdata/hyp.scratch.yaml, helphyperparameters path)parser.add_argument(--epochs, typeint, default10)parser.add_argument(--batch-size, typeint, default64, helptotal batch size for all GPUs)parser.add_argument(--img-size, nargs, typeint, default[640, 640], help[train, test] image sizes)parser.add_argument(--rect, actionstore_true, helprectangular training)parser.add_argument(--resume, nargs?, constTrue, defaultFalse, helpresume most recent training)parser.add_argument(--nosave, actionstore_true, helponly save final checkpoint)parser.add_argument(--notest, actionstore_true, helponly test final epoch)parser.add_argument(--noautoanchor, actionstore_true, helpdisable autoanchor check)parser.add_argument(--evolve, actionstore_true, helpevolve hyperparameters)parser.add_argument(--bucket, typestr, default, helpgsutil bucket)parser.add_argument(--cache-images, actionstore_true, helpcache images for faster training)parser.add_argument(--image-weights, actionstore_true, helpuse weighted image selection for training)parser.add_argument(--device, default, helpcuda device, i.e. 0 or 0,1,2,3 or cpu)parser.add_argument(--multi-scale, actionstore_true, defaultTrue, helpvary img-size /- 50%%)parser.add_argument(--single-cls, actionstore_true, helptrain multi-class data as single-class)parser.add_argument(--adam, actionstore_true, helpuse torch.optim.Adam() optimizer)parser.add_argument(--sync-bn, actionstore_true, helpuse SyncBatchNorm, only available in DDP mode)parser.add_argument(--local_rank, typeint, default-1, helpDDP parameter, do not modify)parser.add_argument(--log-imgs, typeint, default2, helpnumber of images for WB logging, max 100)parser.add_argument(--log-artifacts, actionstore_true, helplog artifacts, i.e. final trained model)parser.add_argument(--workers, typeint, default4, helpmaximum number of dataloader workers)parser.add_argument(--project, defaultruns/train, helpsave to project/name)parser.add_argument(--name, defaultexp, helpsave to project/name)parser.add_argument(--exist-ok, actionstore_true, helpexisting project/name ok, do not increment)2.3.4 训练结果 2.4 部署
2.4.1 pth推理detect.py
#!/usr/bin/env python
import warnings
warnings.filterwarnings(ignore)
import os
import cv2
import numpy as np
import time
import torch
import copy
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_synchronizedclors [(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_locationdevice) # load FP32 modelreturn model
def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_padNone):# Rescale coords (xyxy) from img1_shape to img0_shapeif ratio_pad is None: # calculate from img0_shapegain min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain old / newpad (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh paddingelse:gain ratio_pad[0][0]pad ratio_pad[1]coords[:, [0, 2, 4, 6]] - pad[0] # x paddingcoords[:, [1, 3, 5, 7]] - pad[1] # y paddingcoords[:, :10] / gain# clip_coords(coords, img0_shape)coords[:, 0].clamp_(0, img0_shape[1]) # x1coords[:, 1].clamp_(0, img0_shape[0]) # y1coords[:, 2].clamp_(0, img0_shape[1]) # x2coords[:, 3].clamp_(0, img0_shape[0]) # y2coords[:, 4].clamp_(0, img0_shape[1]) # x3coords[:, 5].clamp_(0, img0_shape[0]) # y3coords[:, 6].clamp_(0, img0_shape[1]) # x4coords[:, 7].clamp_(0, img0_shape[0]) # y4# coords[:, 8].clamp_(0, img0_shape[1]) # x5# coords[:, 9].clamp_(0, img0_shape[0]) # y5return coords
def get_plate_rec_landmark(img, xyxy, conf, landmarks, class_num, device):h, w, c img.shaperesult_dict {}tl 1 or round(0.002 * (h w) / 2) 1 # line/font thicknessx1 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[box] rectresult_dict[landmarks] landmarks_np.tolist()result_dict[class] class_labelreturn result_dict
class Detect:def __init__(self, conf_thres0.6, iou_thres0.5, img_size640):self.conf_thres conf_thresself.iou_thres iou_thresself.img_size img_sizeself.device cuda if torch.cuda.is_available() else cpuself.detect_model load_model(weights/best.pt, self.device)def detect(self, orgimg):dict_list []if orgimg is None:return []if orgimg.shape[-1] 4:orgimg cv2.cvtColor(orgimg, cv2.COLOR_BGRA2BGR)h0, w0 orgimg.shape[:2] # orig hwimg0 copy.deepcopy(orgimg)r self.img_size / max(h0, w0) # resize image to img_sizeif r ! 1: # always resize down, only resize up if training with augmentationinterp cv2.INTER_AREA if r 1 else cv2.INTER_LINEARimg0 cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolationinterp)imgsz check_img_size(self.img_size, sself.detect_model.stride.max()) # check img_sizeimg letterbox(img0, new_shapeimgsz,autoFalse)[0]# Convertimg img[:, :, ::-1].transpose(2, 0, 1).copy() # BGR to RGB, to 3x416x416# Run inferencet0 time.time()img torch.from_numpy(img).to(self.device)img img.float() # uint8 to fp16/32img / 255.0 # 0 - 255 to 0.0 - 1.0if img.ndimension() 3:img img.unsqueeze(0)# Inferencet1 time_synchronized()pred self.detect_model(img)[0]print(pred.shape)t2 time_synchronized()print(finfer time is {(t2-t1)*1000} ms)# Apply NMSpred non_max_suppression_face(pred, self.conf_thres, self.iou_thres)# print(pred.shape)# Process detectionsfor i, det in enumerate(pred): # detections per imageif len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] scale_coords(img.shape[2:], det[:, :4], orgimg.shape).round()# Print resultsfor c in det[:, -1].unique():n (det[:, -1] c).sum() # detections per classdet[:, 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, self.device)dict_list.append(result_dict)return dict_listdef draw_result(self, orgimg, dict_list):返回绘制之后的原图for result in dict_list:rect_area result[box]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 * wpadding_h 0.11 * hrect_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_strresult 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)return orgimg
if __name__ __main__:det Detect()img_dir imagessave_dir resultif not os.path.exists(save_dir):os.makedirs(save_dir)for file in os.listdir(img_dir):img_path os.path.join(img_dir,file)img cv2.imdecode(np.fromfile(img_path, dtypenp.uint8), -1)dict_list det.detect(img)# print(dict_list)result_img det.draw_result(img, dict_list)save_dir_path os.path.join(save_dir,file)cv2.imwrite(save_dir_path,result_img)
2.4.2 onnx推理
import argparse
import time, os
import torch
from detect import scale_coords_landmarks, get_plate_rec_landmark
from torch2trt.trt_model import TrtModel
from utils.general import non_max_suppression_face, scale_coords, check_img_size
import cv2
import copy
from utils.torch_utils import time_synchronized
import numpy as npclors [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]def letterbox(img, size(640, 640)):h, w, c img.shape# 缩放因子ratio min(size[0] / h, size[1] / w)new_h, new_w int(h * ratio), int(w * ratio)top int((size[0] - new_h) / 2)left int((size[1] - new_w) / 2)bottom size[0] - new_h - topright size[1] - new_w - leftimg_resize cv2.resize(img, (new_w, new_h))img cv2.copyMakeBorder(img_resize, top, bottom, left, right, borderTypecv2.BORDER_CONSTANT,value(114, 114, 114))return img, ratio, left, topdef nms(boxes, iou_thresh):# numpy nmsindex 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 * hunion_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 keepdef restore_box(boxes, r, left, top): # 返回原图上面的坐标boxes[:, [0, 2, 5, 7, 9, 11]] - leftboxes[:, [1, 3, 6, 8, 10, 12]] - topboxes[:, [0, 2, 5, 7, 9, 11]] / rboxes[:, [1, 3, 6, 8, 10, 12]] / rreturn boxesdef post_precessing(dets, ratio, left, top, conf_thresh0.3, iou_thresh0.5): # 检测后处理# 选取大于置信度的choice dets[:, :, 4] conf_threshdets dets[choice]dets[:, 13:15] * dets[:, 4:5]box dets[:, :4]boxes xywh2xyxy(box)score np.max(dets[:, 13:15], axis-1, keepdimsTrue)index np.argmax(dets[:, 13:15], axis-1).reshape(-1, 1)output np.concatenate((boxes, score, dets[:, 5:13], index), axis1)reserve_ nms(output, iou_thresh)output output[reserve_]output restore_box(output, ratio, left, top)return outputdef xywh2xyxy(boxes): # xywh坐标变为 左上 右下坐标 x1,y1 x2,y2xywh copy.deepcopy(boxes)xywh[:, 0] boxes[:, 0] - boxes[:, 2] / 2xywh[:, 1] boxes[:, 1] - boxes[:, 3] / 2xywh[:, 2] boxes[:, 0] boxes[:, 2] / 2xywh[:, 3] boxes[:, 1] boxes[:, 3] / 2return xywhclass Detect:def __init__(self, conf_thres0.6, iou_thres0.5, img_size640, trt_pathweights/best.trt):self.conf_thres conf_thresself.iou_thres iou_thresself.img_size img_sizeself.device cuda if torch.cuda.is_available() else cpuself.detect_model TrtModel(trt_path)def detect_processing(self, img, img_size(640, 640)):img, ratio, left, top letterbox(img, sizeimg_size)img img[:, :, ::-1].transpose(2, 0, 1).copy().astype(np.float32)img img / 255img img.reshape(1, *img.shape)return img, ratio, left, topdef detect(self, orgimg):dict_list []if orgimg is None:return []if orgimg.shape[-1] 4:orgimg cv2.cvtColor(orgimg, cv2.COLOR_BGRA2BGR)h0, w0 orgimg.shape[:2] # orig hwimg0 copy.deepcopy(orgimg)# imgsz check_img_size(self.img_size, s32) # check img_sizeimg, ratio, left, top self.detect_processing(img0)if img.ndim 3:img img[None, ...]# Inferencet1 time_synchronized()pred self.detect_model(img).reshape([1, 25200, 15])output post_precessing(pred, ratio, left, top, conf_threshself.conf_thres, iou_threshself.iou_thres)for output in output:result_dict {}rect output[:4].astype(int).tolist()land_marks output[5:13].astype(int).reshape(4, 2)conf output[4].astype(int).tolist()result_dict[box] rectresult_dict[class] confresult_dict[landmarks] land_marks.tolist()dict_list.append(result_dict)return dict_listdef draw_result(self, orgimg, dict_list):返回绘制之后的原图for result in dict_list:rect_area result[box]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 * wpadding_h 0.11 * hrect_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_strresult 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)return orgimgdef __del__(self):self.detect_model.destroy()if __name__ __main__:# 可视化# img_vis(img, orgimg, pred)det Detect()img_dir imagessave_dir resultif not os.path.exists(save_dir):os.makedirs(save_dir)for file in os.listdir(img_dir):img_path os.path.join(img_dir, file)img cv2.imdecode(np.fromfile(img_path, dtypenp.uint8), -1)dict_list det.detect(img)print(dict_list)result_img det.draw_result(img, dict_list)save_dir_path os.path.join(save_dir, file)cv2.imwrite(save_dir_path, result_img)
2.4.3 trt推理
import argparse
import time,os
import torch
from detect import scale_coords_landmarks, get_plate_rec_landmark
from torch2trt.trt_model import TrtModel
from utils.datasets import letterbox
from utils.general import non_max_suppression_face, scale_coords, check_img_size
import cv2
import copy
from utils.torch_utils import time_synchronized
import numpy as np
clors [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]
def img_process(img_path,long_side640,stride_max32):图像预处理orgimgcv2.imread(img_path)img0 copy.deepcopy(orgimg)h0, w0 orgimg.shape[:2] # orig hwr long_side/ max(h0, w0) # resize image to img_sizeif r ! 1: # always resize down, only resize up if training with augmentationinterp cv2.INTER_AREA if r 1 else cv2.INTER_LINEARimg0 cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolationinterp)imgsz check_img_size(long_side, sstride_max) # check img_sizeimg letterbox(img0, new_shapeimgsz,autoFalse)[0] # auto True最小矩形 False固定尺度# Convertimg img[:, :, ::-1].transpose(2, 0, 1).copy() # BGR to RGB, to 3x416x416img torch.from_numpy(img)img img.float() # uint8 to fp16/32img / 255.0 # 0 - 255 to 0.0 - 1.0if img.ndimension() 3:img img.unsqueeze(0)return img,orgimgclass Detect:def __init__(self, conf_thres0.6, iou_thres0.5, img_size640,trt_pathweights/best.trt):self.conf_thres conf_thresself.iou_thres iou_thresself.img_size img_sizeself.device cuda if torch.cuda.is_available() else cpuself.detect_model TrtModel(trt_path)def detect(self, orgimg):dict_list []if orgimg is None:return []if orgimg.shape[-1] 4:orgimg cv2.cvtColor(orgimg, cv2.COLOR_BGRA2BGR)h0, w0 orgimg.shape[:2] # orig hwimg0 copy.deepcopy(orgimg)r self.img_size / max(h0, w0) # resize image to img_sizeif r ! 1: # always resize down, only resize up if training with augmentationinterp cv2.INTER_AREA if r 1 else cv2.INTER_LINEARimg0 cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolationinterp)imgsz check_img_size(self.img_size, s32) # check img_sizeimg letterbox(img0, new_shapeimgsz,autoFalse)[0]# Convertimg img[:, :, ::-1].transpose(2, 0, 1).copy() # BGR to RGB, to 3x416x416# Run inferencet0 time.time()# img torch.from_numpy(img).to(self.device)img img.astype(float) # uint8 to fp16/32img / 255.0 # 0 - 255 to 0.0 - 1.0if img.ndim 3:img img[None,...]# Inferencet1 time_synchronized()pred self.detect_model(img).reshape([1, 25200, 15])t2 time_synchronized()print(finfer time is {(t2-t1)*1000} ms)# Apply NMS# pred torch.tensor(pred)pred non_max_suppression_face(torch.tensor(pred), self.conf_thres, self.iou_thres)# print(type(pred))# Process detectionsfor i, det in enumerate(pred): # detections per imageif len(det):# Rescale boxes from img_size to im0 size# print(type(det))det[:, :4] scale_coords(img.shape[2:], det[:, :4], orgimg.shape).round()# Print resultsfor c in det[:, -1].unique():n (det[:, -1] c).sum() # detections per classdet[:, 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, self.device)dict_list.append(result_dict)return dict_listdef draw_result(self, orgimg, dict_list):返回绘制之后的原图for result in dict_list:rect_area result[box]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 * wpadding_h 0.11 * hrect_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_strresult 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)return orgimgdef __del__(self):self.detect_model.destroy()if __name__ __main__:# 可视化# img_vis(img, orgimg, pred)det Detect()img_dir imagessave_dir resultif not os.path.exists(save_dir):os.makedirs(save_dir)for file in os.listdir(img_dir):img_path os.path.join(img_dir, file)img cv2.imdecode(np.fromfile(img_path, dtypenp.uint8), -1)dict_list det.detect(img)print(dict_list)result_img det.draw_result(img, dict_list)save_dir_path os.path.join(save_dir, file)cv2.imwrite(save_dir_path, result_img)
2.4.4 numpy版本trt推理
import argparse
import time, os
import torch
from detect import scale_coords_landmarks, get_plate_rec_landmark
from torch2trt.trt_model import TrtModel
from utils.general import non_max_suppression_face, scale_coords, check_img_size
import cv2
import copy
from utils.torch_utils import time_synchronized
import numpy as np
clors [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]
def letterbox(img, size(640, 640)):h, w, c img.shape# 缩放因子ratio min(size[0] / h, size[1] / w)new_h, new_w int(h * ratio), int(w * ratio)top int((size[0] - new_h) / 2)left int((size[1] - new_w) / 2)bottom size[0] - new_h - topright size[1] - new_w - leftimg_resize cv2.resize(img, (new_w, new_h))img cv2.copyMakeBorder(img_resize, top, bottom, left, right, borderTypecv2.BORDER_CONSTANT,value(114, 114, 114))return img, ratio, left, topdef nms(boxes, iou_thresh):# numpy nmsindex 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 * hunion_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 keepdef restore_box(boxes, r, left, top): # 返回原图上面的坐标boxes[:, [0, 2, 5, 7, 9, 11]] - leftboxes[:, [1, 3, 6, 8, 10, 12]] - topboxes[:, [0, 2, 5, 7, 9, 11]] / rboxes[:, [1, 3, 6, 8, 10, 12]] / rreturn boxesdef post_precessing(dets, ratio, left, top, conf_thresh0.3, iou_thresh0.5): # 检测后处理# 选取大于置信度的choice dets[:, :, 4] conf_threshdets dets[choice]dets[:, 13:15] * dets[:, 4:5]box dets[:, :4]boxes xywh2xyxy(box)score np.max(dets[:, 13:15], axis-1, keepdimsTrue)index np.argmax(dets[:, 13:15], axis-1).reshape(-1, 1)output np.concatenate((boxes, score, dets[:, 5:13], index), axis1)reserve_ nms(output, iou_thresh)output output[reserve_]output restore_box(output, ratio, left, top)return outputdef xywh2xyxy(boxes): # xywh坐标变为 左上 右下坐标 x1,y1 x2,y2xywh copy.deepcopy(boxes)xywh[:, 0] boxes[:, 0] - boxes[:, 2] / 2xywh[:, 1] boxes[:, 1] - boxes[:, 3] / 2xywh[:, 2] boxes[:, 0] boxes[:, 2] / 2xywh[:, 3] boxes[:, 1] boxes[:, 3] / 2return xywhclass Detect:def __init__(self, conf_thres0.6, iou_thres0.5, img_size640, trt_pathweights/best.trt):self.conf_thres conf_thresself.iou_thres iou_thresself.img_size img_sizeself.device cuda if torch.cuda.is_available() else cpuself.detect_model TrtModel(trt_path)def detect_processing(self, img, img_size(640, 640)):img, ratio, left, top letterbox(img, sizeimg_size)img img[:, :, ::-1].transpose(2, 0, 1).copy().astype(np.float32)img img / 255img img.reshape(1, *img.shape)return img, ratio, left, topdef detect(self, orgimg):dict_list []if orgimg is None:return []if orgimg.shape[-1] 4:orgimg cv2.cvtColor(orgimg, cv2.COLOR_BGRA2BGR)h0, w0 orgimg.shape[:2] # orig hwimg0 copy.deepcopy(orgimg)# imgsz check_img_size(self.img_size, s32) # check img_sizeimg, ratio, left, top self.detect_processing(img0)if img.ndim 3:img img[None, ...]# Inferencet1 time_synchronized()pred self.detect_model(img).reshape([1, 25200, 15])output post_precessing(pred, ratio, left, top, conf_threshself.conf_thres, iou_threshself.iou_thres)for output in output:result_dict {}rect output[:4].astype(int).tolist()land_marks output[5:13].astype(int).reshape(4, 2)conf output[4].astype(int).tolist()result_dict[box] rectresult_dict[class] confresult_dict[landmarks] land_marks.tolist()dict_list.append(result_dict)return dict_listdef draw_result(self, orgimg, dict_list):返回绘制之后的原图for result in dict_list:rect_area result[box]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 * wpadding_h 0.11 * hrect_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_strresult 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)return orgimgdef __del__(self):self.detect_model.destroy()if __name__ __main__:# 可视化# img_vis(img, orgimg, pred)det Detect()img_dir imagessave_dir resultif not os.path.exists(save_dir):os.makedirs(save_dir)for file in os.listdir(img_dir):img_path os.path.join(img_dir, file)img cv2.imdecode(np.fromfile(img_path, dtypenp.uint8), -1)dict_list det.detect(img)print(dict_list)result_img det.draw_result(img, dict_list)save_dir_path os.path.join(save_dir, file)cv2.imwrite(save_dir_path, result_img)
2.4.5 推理结果展示 参考开源
Chinese_license_plate_detection_recognition
