基于Yolov5的二维码QR码识别
1.QR code介绍
一个 QR 码可以分为两个部分：功能图形和编码区域。
数据集 大小10,85张
1.1 通过split_train_val.py得到trainval.txt、val.txt、test.txt
# coding:utf-8import os
import random
import argparseparser = argparse.ArgumentParser()
#xml文件的地址，根据自己的数据进行修改 xml一般存放在Annotations下
parser.add_argument('--xml_path', default='Annotations', type=str, help='input xml label path')
#数据集的划分，地址选择自己数据下的ImageSets/Main
parser.add_argument('--txt_path', default='ImageSets/Main', type=str, help='output txt label path')
opt = parser.parse_args()trainval_percent = 0.9
train_percent = 0.8
xmlfilepath = opt.xml_path
txtsavepath = opt.txt_path
total_xml = os.listdir(xmlfilepath)
if not os.path.exists(txtsavepath):os.makedirs(txtsavepath)num = len(total_xml)
list_index = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list_index, tv)
train = random.sample(trainval, tr)file_trainval = open(txtsavepath + '/trainval.txt', 'w')
file_test = open(txtsavepath + '/test.txt', 'w')
file_train = open(txtsavepath + '/train.txt', 'w')
file_val = open(txtsavepath + '/val.txt', 'w')for i in list_index:name = total_xml[i][:-4] + '\\n'if i in trainval:file_trainval.write(name)if i in train:file_train.write(name)else:file_val.write(name)else:file_test.write(name)file_trainval.close()
file_train.close()
file_val.close()
file_test.close()1.2 通过voc_label.py得到适合yolov5训练需要的
# -*- coding: utf-8 -*-
import xml.etree.ElementTree as ET
import os
from os import getcwdsets = ['train', 'val']
classes = ["QR"] # 改成自己的类别
abs_path = os.getcwd()
print(abs_path)def convert(size, box):dw = 1. / (size[0])dh = 1. / (size[1])x = (box[0] + box[1]) / 2.0 - 1y = (box[2] + box[3]) / 2.0 - 1w = box[1] - box[0]h = box[3] - box[2]x = x * dww = w * dwy = y * dhh = h * dhreturn x, y, w, hdef convert_annotation(image_id):in_file = open('Annotations/%s.xml' % (image_id), encoding='UTF-8')out_file = open('labels/%s.txt' % (image_id), 'w')tree = ET.parse(in_file)root = tree.getroot()size = root.find('size')w = int(size.find('width').text)h = int(size.find('height').text)for obj in root.iter('object'):difficult = obj.find('difficult').text#difficult = obj.find('Difficult').textcls = obj.find('name').textif cls not in classes or int(difficult) == 1:continuecls_id = classes.index(cls)xmlbox = obj.find('bndbox')b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),float(xmlbox.find('ymax').text))b1, b2, b3, b4 = b# 标注越界修正if b2 > w:b2 = wif b4 > h:b4 = hb = (b1, b2, b3, b4)bb = convert((w, h), b)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\\n')wd = getcwd()
for image_set in sets:if not os.path.exists('labels/'):os.makedirs('labels/')image_ids = open('ImageSets/Main/%s.txt' % (image_set)).read().strip().split()list_file = open('%s.txt' % (image_set), 'w')for image_id in image_ids:list_file.write(abs_path + '/images/%s.jpg\\n' % (image_id))convert_annotation(image_id)list_file.close()2.基于yolov5的QR码检测
2.1配置 QR.yaml
# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
train: data/QR/train.txt # 16551 images
val: data/QR/val.txt # 4952 images# number of classes
nc: 1# class names
names: ['QR']2.2 修改yolov5s_QR.yaml
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license# Parameters
nc: 1 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
anchors:- [10,13, 16,30, 33,23] # P3/8- [30,61, 62,45, 59,119] # P4/16- [116,90, 156,198, 373,326] # P5/32# YOLOv5 v6.0 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2[-1, 1, Conv, [128, 3, 2]], # 1-P2/4[-1, 3, C3, [128]],[-1, 1, Conv, [256, 3, 2]], # 3-P3/8[-1, 6, C3, [256]],[-1, 1, Conv, [512, 3, 2]], # 5-P4/16[-1, 9, C3, [512]],[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SPPF, [1024, 5]], # 9]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]], # cat backbone P4[-1, 3, C3, [512, False]], # 13[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]], # cat backbone P3[-1, 3, C3, [256, False]], # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 14], 1, Concat, [1]], # cat head P4[-1, 3, C3, [512, False]], # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]], # cat head P5[-1, 3, C3, [1024, False]], # 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)]
2.3 训练QR码检测模型
parser = argparse.ArgumentParser()parser.add_argument('--weights', type=str, default=ROOT / 'weights/yolov5s.pt', help='initial weights path')parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path')parser.add_argument('--data', type=str, default=ROOT / 'data/QR.yaml', help='dataset.yaml path')parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')parser.add_argument('--epochs', type=int, default=100, help='total training epochs')parser.add_argument('--batch-size', type=int, default=8, help='total batch size for all GPUs, -1 for autobatch')parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')parser.add_argument('--rect', action='store_true', help='rectangular training')parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')parser.add_argument('--noval', action='store_true', help='only validate final epoch')parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')parser.add_argument('--noplots', action='store_true', help='save no plot files')parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk')parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')parser.add_argument('--workers', type=int, default=0, help='max dataloader workers (per RANK in DDP mode)')parser.add_argument('--project', default=ROOT / 'runs/train_QR', help='save to project/name')parser.add_argument('--name', default='exp', help='save to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')parser.add_argument('--quad', action='store_true', help='quad dataloader')parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')parser.add_argument('--seed', type=int, default=0, help='Global training seed')parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')开启python train.py
3.性能评价
map 为0.962
检测结果图：
4.QR码识别
4.1 转成onnx模型
python export.py --weights runs/train_QR/exp3/weights/best.pt --include onnx engine --device cpu4.2 基于opencv的QR码识别
import cv2
import time
import sys
import numpy as npdet = cv2.QRCodeDetector()def build_model(is_cuda):net = cv2.dnn.readNet("best.onnx") if is_cuda: print("Attempty to use CUDA")net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA_FP16)else:print("Running on CPU")net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)return netINPUT_WIDTH = 640
INPUT_HEIGHT = 640
SCORE_THRESHOLD = 0.2
NMS_THRESHOLD = 0.4
CONFIDENCE_THRESHOLD = 0.4def detect(image, net): blob = cv2.dnn.blobFromImage(image, 1/255.0, (INPUT_WIDTH, INPUT_HEIGHT), swapRB=True, crop=False)net.setInput(blob)preds = net.forward()return predsdef load_classes(): class_list = []with open("classes.txt", "r") as f:class_list = [cname.strip() for cname in f.readlines()]return class_listclass_list = load_classes()def wrap_detection(input_image, output_data): class_ids = [] confidences = [] boxes = [] rows = output_data.shape[0] image_width, image_height, _ = input_image.shapex_factor = image_width / INPUT_WIDTH y_factor = image_height / INPUT_HEIGHTfor r in range(rows):row = output_data[r]confidence = row[4]if confidence >= 0.4: classes_scores = row[5:]_, _, _, max_indx = cv2.minMaxLoc(classes_scores)class_id = max_indx[1]if (classes_scores[class_id] > .25):confidences.append(confidence)class_ids.append(class_id)x, y, w, h = row[0].item(), row[1].item(), row[2].item(), row[3].item() left = int((x - 0.5 * w) * x_factor)top = int((y - 0.5 * h) * y_factor)width = int(w * x_factor)height = int(h * y_factor)box = np.array([left, top, width, height])boxes.append(box)indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.25, 0.45) result_class_ids = []result_confidences = []result_boxes = []for i in indexes:result_confidences.append(confidences[i])result_class_ids.append(class_ids[i])result_boxes.append(boxes[i])return result_class_ids, result_confidences, result_boxesdef format_yolov5(frame): row, col, _ = frame.shape_max = max(col, row)result = np.zeros((_max, _max, 3), np.uint8)result[0:row, 0:col] = framereturn resultcolors = [(255, 255, 0), (0, 255, 0), (0, 255, 255), (255, 0, 0)] is_cuda = len(sys.argv) > 1 and sys.argv[1] == "cuda"net = build_model(is_cuda) start = time.time_ns()
frame_count = 0
total_frames = 0
fps = -1frame = cv2.imread('QR-00345.jpg')
inputImage = format_yolov5(frame)
outs = detect(inputImage, net) class_ids, confidences, boxes = wrap_detection(inputImage, outs[0]) for (classid, confidence, box) in zip(class_ids, confidences, boxes): color = colors[int(classid) % len(colors)]ROI=frame[(box[1]):(box[1]+box[3]),(box[0]):(box[0]+box[2])] data, pts, st_code = det.detectAndDecode(ROI) print(data)cv2.rectangle(frame, box, color, 2) #cv2.rectangle(frame, (box[0], box[1] - 20), (box[0] + box[2], box[1]), color, -1)#cv2.putText(frame, class_list[classid], (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0))cv2.putText(frame, str(data), (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, .3, (0,0,0))cv2.imshow("output", frame)
cv2.waitKey(10000)检测结果
4.3 基于zbar的QR码识别
import cv2
import time
import sys
import numpy as np
import zxing
from pyzbar import pyzbardef build_model(is_cuda):net = cv2.dnn.readNet("best.onnx")if is_cuda:print("Attempty to use CUDA")net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA_FP16)else:print("Running on CPU")net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)return netINPUT_WIDTH = 640
INPUT_HEIGHT = 640
SCORE_THRESHOLD = 0.2
NMS_THRESHOLD = 0.4
CONFIDENCE_THRESHOLD = 0.4def detect(image, net):blob = cv2.dnn.blobFromImage(image, 1/255.0, (INPUT_WIDTH, INPUT_HEIGHT), swapRB=True, crop=False)net.setInput(blob)preds = net.forward()return predsdef load_capture():capture = cv2.VideoCapture("sample.mp4")return capturedef load_classes():class_list = []with open("classes.txt", "r") as f:class_list = [cname.strip() for cname in f.readlines()]return class_listclass_list = load_classes()def wrap_detection(input_image, output_data):class_ids = []confidences = []boxes = []rows = output_data.shape[0]image_width, image_height, _ = input_image.shapex_factor = image_width / INPUT_WIDTHy_factor = image_height / INPUT_HEIGHTfor r in range(rows):row = output_data[r]confidence = row[4]if confidence >= 0.4:classes_scores = row[5:]_, _, _, max_indx = cv2.minMaxLoc(classes_scores)class_id = max_indx[1]if (classes_scores[class_id] > .25):confidences.append(confidence)class_ids.append(class_id)x, y, w, h = row[0].item(), row[1].item(), row[2].item(), row[3].item() left = int((x - 0.5 * w) * x_factor)top = int((y - 0.5 * h) * y_factor)width = int(w * x_factor)height = int(h * y_factor)box = np.array([left, top, width, height])boxes.append(box)indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.25, 0.45) result_class_ids = []result_confidences = []result_boxes = []for i in indexes:result_confidences.append(confidences[i])result_class_ids.append(class_ids[i])result_boxes.append(boxes[i])return result_class_ids, result_confidences, result_boxesdef format_yolov5(frame):row, col, _ = frame.shape_max = max(col, row)result = np.zeros((_max, _max, 3), np.uint8)result[0:row, 0:col] = framereturn resultcolors = [(255, 255, 0), (0, 255, 0), (0, 255, 255), (255, 0, 0)]is_cuda = len(sys.argv) > 1 and sys.argv[1] == "cuda"net = build_model(is_cuda)
capture = load_capture()start = time.time_ns()
frame_count = 0
total_frames = 0
fps = -1frame = cv2.imread('QR-00345.jpg')
inputImage = format_yolov5(frame)
outs = detect(inputImage, net)class_ids, confidences, boxes = wrap_detection(inputImage, outs[0])for (classid, confidence, box) in zip(class_ids, confidences, boxes):color = colors[int(classid) % len(colors)]ROI=frame[(box[1]):(box[1]+box[3]),(box[0]):(box[0]+box[2])]#barcode = zx.decode(ROI)#print(barcode.parsed)data = pyzbar.decode(ROI)print(data)cv2.rectangle(frame, box, color, 2)#cv2.rectangle(frame, (box[0], box[1] - 20), (box[0] + box[2], box[1]), color, -1)#cv2.putText(frame, class_list[classid], (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, .5, (0,0,0))cv2.putText(frame, str(data), (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, .3, (0,0,0))cv2.imshow("output", frame)
cv2.waitKey(10000)