driving_annotation_datasetarmas-23-_jpg.rf.6e8b0bb60cbdf978a59abb82118c52ba.jpg6406403
			gunsUnspecified0014941537332

0 0.50703125 0.21875 0.09375 0.06953125

import os
import random
trainval_percent = 0.9
train_percent = 0.9
xmlfilepath = 'data/Annotations'
txtsavepath = 'data/ImageSets'
total_xml = os.listdir(xmlfilepath)
num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)
ftrainval = open('data/ImageSets/trainval.txt', 'w')
ftest = open('data/ImageSets/test.txt', 'w')
ftrain = open('data/ImageSets/train.txt', 'w')
fval = open('data/ImageSets/val.txt', 'w')
for i in list:
    name = total_xml[i][:-4] + '\n'
    if i in trainval:
        ftrainval.write(name)
        if i in train:
            ftrain.write(name)
        else:
            fval.write(name)
    else:
        ftest.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()

import xml.etree.ElementTree as ET
import os
from os import getcwd
sets = ['train', 'val', 'test']
classes = ['guns', 'knife'] # 根據標籤名稱填寫類別
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 - 1
    y = (box[2] + box[3]) / 2.0 - 1
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x * dw
    w = w * dw
    y = y * dh
    h = h * dh
    return x, y, w, h
def convert_annotation(image_id):
    in_file = open('data/Annotations/%s.xml' % (image_id), encoding='UTF-8')
    out_file = open('data/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
        cls = obj.find('name').text
        if cls not in classes or int(difficult) == 1:
            continue
        cls_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
        # 標註越界修正
        if b2 > w:
            b2 = w
        if b4 > h:
            b4 = h
        b = (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('data/labels/'):
        os.makedirs('data/labels/')
    image_ids = open('data/ImageSets/%s.txt' % (image_set)).read().strip().split()
    list_file = open('data/%s.txt' % (image_set), 'w')
    for image_id in image_ids:
        list_file.write(abs_path + '/data/images/%s.jpg\n' % (image_id))
        convert_annotation(image_id)
    list_file.close()

train: data/train.txt
val: data/val.txt
test: data/test.txt
nc: 2
names: ['guns', 'knife']

model = YOLO('yolov8s.pt')
results = model.train(data='data.yaml', epochs=200, imgsz=640, batch=16, workers=0)

yolo train data=data.yaml model=yolov8s.pt epochs=200 imgsz=640 batch=16 workers=0 device=0

import cv2
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("./best.pt") # 自定義預測模型加載路徑
# Open the video file
video_path = "./demo.mp4" # 自定義預測視頻路徑
cap = cv2.VideoCapture(video_path)
# Get the video properties
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Be sure to use lower case
out = cv2.VideoWriter('./outputs.mp4', fourcc, fps, (frame_width, frame_height)) # 自定義輸出視頻路徑
# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()
    if success:
        # Run YOLOv8 inference on the frame
        # results = model(frame)
        results = model.predict(source=frame, save=True, imgsz=640, conf=0.5)
        results[0].names[0] = "道路積水"
        # Visualize the results on the frame
        annotated_frame = results[0].plot()
        # Write the annotated frame to the output file
        out.write(annotated_frame)
        # Display the annotated frame (optional)
        cv2.imshow("YOLOv8 Inference", annotated_frame)
        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break
# Release the video capture and writer objects
cap.release()
out.release()
cv2.destroyAllWindows()

import warnings
warnings.filterwarnings('ignore')
from ultralytics import YOLO
if __name__ == '__main__':
    model = YOLO('models/best.pt')
    model.predict(source='test_pic',
                  imgsz=640,
                  save=True,
                  conf=0.25
                  )

yolo predict model="best.pt" source='demo.jpg'

conda create -n ultralytics-env python=3.10
conda activate ultralytics-env
pip install -r requirements.txt

python app.py

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    <script src="/static/app.js"></script>