YOLO를 사용하여 단일 이미지에서 여러 QR 코드 디코딩을 위한 매개변수를 최적화하는 방법

설치

Dynamsoft 바코드 리더 Python

pip install dbr

pip install opencv-python

git clone https://github.com/AlexeyAB/darknet.git

Dynamsoft Barcode SDK의 사전 구성된 매개변수 템플릿을 가져오고 사용하는 방법

모드를 선택한 다음 Advanced Settings 를 클릭합니다.

Template 섹션까지 아래로 스크롤합니다. ResultCoordinateType를 Pixel로 변경 후 복사 버튼을 클릭합니다.

import cv2 as cv
import numpy as np
import time
from dbr import *
import os

reader = BarcodeReader()
# Apply for a trial license: https://www.dynamsoft.com/customer/license/trialLicense?product=dbr
license_key = "LICENSE-KEY"
reader.init_license(license_key)

def decode(filename, template_name):
    frame = cv.imread(filename)

    template_path = os.path.dirname(os.path.abspath(__file__)) + os.path.sep + template_name
    settings = reader.reset_runtime_settings() 
    error = reader.init_runtime_settings_with_file(template_path, EnumConflictMode.CM_OVERWRITE)

    before = time.time()
    results = reader.decode_buffer(frame)
    after = time.time()

    COLOR_RED = (0,0,255)
    thickness = 2
    if results != None:
        found = len(results)
        for result in results:
            text = result.barcode_text 
            points = result.localization_result.localization_points
            data = np.array([[points[0][0], points[0][1]], [points[1][0], points[1][1]], [points[2][0], points[2][1]], [points[3][0], points[3][1]]])
            cv.drawContours(image=frame, contours=[data], contourIdx=-1, color=COLOR_RED, thickness=thickness, lineType=cv.LINE_AA)
            cv.putText(frame, result.barcode_text, points[0], cv.FONT_HERSHEY_SIMPLEX, 0.5, COLOR_RED)

        cv.putText(frame, '%.2f s, Qr found: %d' % (after - before, found), (20, 20), cv.FONT_HERSHEY_SIMPLEX, 0.5, COLOR_RED)
    else:
        cv.putText(frame, '%.2f s, Qr found: %d' % (after - before, 0), (20, 20), cv.FONT_HERSHEY_SIMPLEX, 0.5, COLOR_RED)

    cv.imshow(template_name, frame)

decode("test.jpg", "l1.json")
decode("test.jpg", "l2.json")
decode("test.jpg", "l3.json")
decode("test.jpg", "l4.json")
decode("test.jpg", "l5.json")
cv.waitKey(0)

YOLOv4로 QR 코드 감지기 교육

batch=64              # line 6
subdivisions=16       # line 7
width=640             # line 8
height=640            # line 9

max_batches = 6000    # line 20

steps=4800,5400       # line 22

filters=18            # 212
classes=1             # 220

filters=18            # 263
classes=1             # 269

QR_CODE

classes = 1
train  = data/train.txt
valid  = data/test.txt
names = data/obj.names
backup = backup/

import os
import re
from shutil import copyfile
import argparse
import math
import random

def iterate_dir(source, ratio):
    source = source.replace('\\', '/')
    train_dir = 'data/obj/train'
    test_dir = 'data/obj/test'

    if not os.path.exists(train_dir):
        os.makedirs(train_dir)
    if not os.path.exists(test_dir):
        os.makedirs(test_dir)

    images = [f for f in os.listdir(source)
              if re.search(r'([a-zA-Z0-9\s_\\.\-\(\):])+(?i)(.jpg|.jpeg|.png)$', f)]

    num_images = len(images)
    num_test_images = math.ceil(ratio*num_images)

    image_files = []

    for i in range(num_test_images):
        idx = random.randint(0, len(images)-1)
        filename = images[idx]
        image_files.append("data/obj/test/" + filename)
        copyfile(os.path.join(source, filename),
                os.path.join(test_dir, filename))
        txt_filename = os.path.splitext(filename)[0]+'.txt'
        copyfile(os.path.join(source, txt_filename),
                os.path.join(test_dir, txt_filename))

        images.remove(images[idx])

    with open("data/test.txt", "w") as outfile:
        for image in image_files:
            outfile.write(image)
            outfile.write("\n")
        outfile.close()

    image_files = []

    for filename in images:
        image_files.append("data/obj/train/" + filename)
        copyfile(os.path.join(source, filename),
                os.path.join(train_dir, filename))
        txt_filename = os.path.splitext(filename)[0]+'.txt'
        copyfile(os.path.join(source, txt_filename),
                os.path.join(train_dir, txt_filename))

    with open("data/train.txt", "w") as outfile:
        for image in image_files:
            outfile.write(image)
            outfile.write("\n")
        outfile.close()

def main():
    parser = argparse.ArgumentParser(description="Partition dataset of images into training and testing sets",
                                    formatter_class=argparse.RawTextHelpFormatter)
    parser.add_argument(
        '-i', '--imageDir',
        help='Path to the folder where the image dataset is stored. If not specified, the CWD will be used.',
        type=str,
        default=os.getcwd()
    )
    parser.add_argument(
        '-r', '--ratio',
        help='The ratio of the number of test images over the total number of images. The default is 0.1.',
        default=0.1,
        type=float)
    args = parser.parse_args()
    iterate_dir(args.imageDir, args.ratio)

if __name__ == '__main__':
    main()

python partition_dataset.py -i ../images -r 0.1

darknet detector test data/obj.data yolov4-tiny-custom.cfg backup/yolov4-tiny-custom_last.weights sample/test.png

darknet detector test data/obj.data yolov4-tiny-custom.cfg backup/yolov4-tiny-custom_last.weights sample/test.png

import cv2 as cv
import numpy as np
import time
from dbr import *
import os

# Initialize Dynamsoft Barcode Reader
reader = BarcodeReader()
# Apply for a trial license: https://www.dynamsoft.com/customer/license/trialLicense
license_key = "LICENSE-KEY"
reader.init_license(license_key)

# Load YOLOv4-tiny model
class_names = open('obj.names').read().strip().split('\n')
net = cv.dnn.readNetFromDarknet('yolov4-tiny-custom.cfg', 'yolov4-tiny-custom_last.weights')
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)

model = cv.dnn_DetectionModel(net)
width = 640
height = 640

CONFIDENCE_THRESHOLD = 0.2
NMS_THRESHOLD = 0.4
COLOR_RED = (0,0,255)
COLOR_BLUE = (255,0,0)

def decode(filename, template_name):
    frame = cv.imread(filename)

    if frame.shape[1] > 1024 or frame.shape[0] > 1024:
        width = 1024
        height = 1024
    model.setInputParams(size=(width, height), scale=1/255, swapRB=True)

    template_path = os.path.dirname(os.path.abspath(__file__)) + os.path.sep + template_name
    settings = reader.reset_runtime_settings() 
    error = reader.init_runtime_settings_with_file(template_path, EnumConflictMode.CM_OVERWRITE)

    # YOLO detection
    yolo_start = time.time()
    classes, scores, boxes = model.detect(frame, CONFIDENCE_THRESHOLD, NMS_THRESHOLD)
    yolo_end = time.time()
    print("YOLO detection time: %.2f s" % (yolo_end - yolo_start))

    index = 0
    dbr_found = 0
    total_dbr_time = 0
    for (classid, score, box) in zip(classes, scores, boxes):
        label = "%s : %f" % (class_names[classid], score) 
        tmp = frame[box[1]:box[1] + box[3], box[0]: box[0] + box[2]]

        # Set parameters for DBR
        settings = reader.get_runtime_settings()
        settings.expected_barcodes_count = 1
        settings.barcode_format_ids = EnumBarcodeFormat.BF_QR_CODE
        reader.update_runtime_settings(settings)

        before = time.time()
        results = reader.decode_buffer(tmp)
        after = time.time()

        total_dbr_time += after - before

        if results != None:
            found = len(results)
            for result in results:
                text = result.barcode_text 
                dbr_found += 1
                points = result.localization_result.localization_points
                data = np.array([[points[0][0], points[0][1]], [points[1][0], points[1][1]], [points[2][0], points[2][1]], [points[3][0], points[3][1]]])
                cv.drawContours(image=tmp, contours=[data], contourIdx=-1, color=(0, 0, 255), thickness=2, lineType=cv.LINE_AA)
                cv.putText(frame, text, (box[0], box[1] + 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, COLOR_RED)
        else:
            found = 0

        index += 1
        cv.rectangle(frame, box, COLOR_BLUE, 2)
        cv.putText(frame, label, (box[0], box[1] - 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, COLOR_BLUE, 2)

    cv.putText(frame, 'DBR+YOLO %.2f s, DBR found: %d, YOLO found: %d' % (yolo_end - yolo_start + total_dbr_time, dbr_found, len(classes)), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, COLOR_RED)
    cv.imshow(template_name, frame)

decode("test.jpg", "l1.json")
decode("test.jpg", "l2.json")
decode("test.jpg", "l3.json")
decode("test.jpg", "l4.json")
decode("test.jpg", "l5.json")
cv.waitKey(0)

Boofcv QR 이미지 세트에 대한 벤치마크

소스 코드