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After Width: | Height: | Size: 218 KiB |
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# Default ignored files
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/shelf/
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/workspace.xml
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# Editor-based HTTP Client requests
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/httpRequests/
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# Datasource local storage ignored files
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/dataSources/
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/dataSources.local.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="Flask">
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<option name="enabled" value="false" />
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</component>
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$">
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<excludeFolder url="file://$MODULE_DIR$/.venv" />
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</content>
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<orderEntry type="jdk" jdkName="Python 3.12" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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</module>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="DataSourceManagerImpl" format="xml" multifile-model="true">
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<data-source source="LOCAL" name="@localhost" uuid="ace78a80-69b0-472d-b69d-3f3c0c3ba70f">
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<driver-ref>mysql.8</driver-ref>
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<synchronize>true</synchronize>
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<jdbc-driver>com.mysql.cj.jdbc.Driver</jdbc-driver>
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<jdbc-url>jdbc:mysql://localhost:3306</jdbc-url>
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<working-dir>$ProjectFileDir$</working-dir>
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</data-source>
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</component>
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</project>
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@ -0,0 +1,6 @@
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<component name="InspectionProjectProfileManager">
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<settings>
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="Black">
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<option name="sdkName" value="Python 3.12 (FireDetect)" />
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12" project-jdk-type="Python SDK" />
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/FireDetect.iml" filepath="$PROJECT_DIR$/.idea/FireDetect.iml" />
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</modules>
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</component>
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</project>
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import os
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import time
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from flask import Flask, render_template, Response, request
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import cv2
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import numpy as np
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from datetime import datetime
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import threading
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from opt_einsum.backends import torch
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from ultralytics.utils.ops import scale_coords
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from models.experimental import attempt_load
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from utils.general import non_max_suppression
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from utils.torch_utils import select_device
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# 初始化 Flask 应用
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app = Flask(__name__, template_folder='./templates')
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# 加载预训练的 YOLOv5 模型
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model = attempt_load('runs/train/exp8/weights/best.pt')
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device = select_device('cpu')
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model.to(device).eval()
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# 其他全局变量
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capture = 0
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rec_frame = None
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grey = 0
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neg = 0
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face = 0
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switch = 1
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rec = 0
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# 创建 shots 目录以保存图片
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try:
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os.mkdir('./shots')
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except OSError as error:
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pass
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# 打开摄像头
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camera = cv2.VideoCapture(0)
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# 定义 record 函数以在后台线程中记录视频
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def record(out):
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global rec_frame
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while rec:
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time.sleep(0.05)
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out.write(rec_frame)
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# 定义 detect_fire 函数以使用 YOLOv5 模型检测
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def detect_fire(frame):
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global net, device
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# 将 BGR 帧转换为 RGB
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# 转换为 PyTorch 张量
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img = torch.from_numpy(frame_rgb).permute(2, 0, 1).unsqueeze(0).to(device)
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img = img.float() # uint8 to fp32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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# 推理
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pred = model(img, augment=False, visualize=False)[0]
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pred = non_max_suppression(pred, 0.25, 0.45, classes=None, agnostic=False)
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# 绘制检测结果
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for det in pred:
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if len(det):
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], frame.shape).round()
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for *xyxy, conf, cls in reversed(det):
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label = f'{model.names[int(cls)]} {conf:.2f}'
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cv2.rectangle(frame, (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3])), (0, 0, 255), 2)
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cv2.putText(frame, label, (int(xyxy[0]), int(xyxy[1] - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
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return frame
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def gen_frames(): # generate frame by frame from camera
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global out, capture,rec_frame
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while True:
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success, frame = camera.read()
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if success:
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if(face):
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frame= detect_fire(frame)
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if(grey):
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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if(neg):
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frame=cv2.bitwise_not(frame)
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if(capture):
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capture=0
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now = datetime.datetime.now()
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p = os.path.sep.join(['shots', "shot_{}.png".format(str(now).replace(":",''))])
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cv2.imwrite(p, frame)
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if(rec):
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rec_frame=frame
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frame= cv2.putText(cv2.flip(frame,1),"Recording...", (0,25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255),4)
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frame=cv2.flip(frame,1)
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try:
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ret, buffer = cv2.imencode('.jpg', cv2.flip(frame,1))
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frame = buffer.tobytes()
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yield (b'--frame\r\n'
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b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
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except Exception as e:
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pass
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else:
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pass
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@app.route('/')
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def index():
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return render_template('index.html')
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@app.route('/video_feed')
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def video_feed():
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return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
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@app.route('/requests',methods=['POST','GET'])
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def tasks():
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global switch,camera
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if request.method == 'POST':
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if request.form.get('click') == 'Capture':
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global capture
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capture=1
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elif request.form.get('grey') == 'Grey':
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global grey
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grey=not grey
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elif request.form.get('neg') == 'Negative':
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global neg
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neg=not neg
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elif request.form.get('face') == 'Face Only':
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global face
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face=not face
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if(face):
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time.sleep(4)
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elif request.form.get('stop') == 'Stop/Start':
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if(switch==1):
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switch=0
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camera.release()
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cv2.destroyAllWindows()
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else:
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camera = cv2.VideoCapture(0)
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switch=1
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elif request.form.get('rec') == 'Start/Stop Recording':
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global rec, out
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rec= not rec
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if(rec):
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now=datetime.datetime.now()
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fourcc = cv2.VideoWriter_fourcc(*'XVID')
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out = cv2.VideoWriter('vid_{}.avi'.format(str(now).replace(":",'')), fourcc, 20.0, (640, 480))
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#Start new thread for recording the video
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thread = threading.Thread(target = record, args=[out, ])
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thread.start()
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elif(rec==False):
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out.release()
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elif request.method=='GET':
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return render_template('index.html')
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return render_template('index.html')
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||||
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||||
if __name__ == '__main__':
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app.run()
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camera.release()
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||||
cv2.destroyAllWindows()
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||||
@ -0,0 +1,72 @@
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||||
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
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||||
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
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||||
# Example usage: python train.py --data Argoverse.yaml
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# parent
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||||
# ├── yolov5
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# └── datasets
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||||
# └── Argoverse ← downloads here (31.3 GB)
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||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/Argoverse # dataset root dir
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train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
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val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
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||||
test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
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# Classes
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names:
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0: person
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1: bicycle
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||||
2: car
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||||
3: motorcycle
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||||
4: bus
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||||
5: truck
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||||
6: traffic_light
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||||
7: stop_sign
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||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
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||||
download: |
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||||
import json
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||||
from tqdm import tqdm
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from utils.general import download, Path
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||||
def argoverse2yolo(set):
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labels = {}
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a = json.load(open(set, "rb"))
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for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
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img_id = annot['image_id']
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img_name = a['images'][img_id]['name']
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img_label_name = f'{img_name[:-3]}txt'
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cls = annot['category_id'] # instance class id
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x_center, y_center, width, height = annot['bbox']
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x_center = (x_center + width / 2) / 1920.0 # offset and scale
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y_center = (y_center + height / 2) / 1200.0 # offset and scale
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width /= 1920.0 # scale
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height /= 1200.0 # scale
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img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
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if not img_dir.exists():
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img_dir.mkdir(parents=True, exist_ok=True)
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k = str(img_dir / img_label_name)
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if k not in labels:
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labels[k] = []
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labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
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for k in labels:
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with open(k, "w") as f:
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f.writelines(labels[k])
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# Download
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dir = Path(yaml['path']) # dataset root dir
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urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
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download(urls, dir=dir, delete=False)
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# Convert
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annotations_dir = 'Argoverse-HD/annotations/'
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(dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
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for d in "train.json", "val.json":
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argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
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# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
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# Example usage: python train.py --data GlobalWheat2020.yaml
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# parent
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# ├── yolov5
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# └── datasets
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# └── GlobalWheat2020 ← downloads here (7.0 GB)
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/GlobalWheat2020 # dataset root dir
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train: # train images (relative to 'path') 3422 images
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- images/arvalis_1
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- images/arvalis_2
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- images/arvalis_3
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- images/ethz_1
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- images/rres_1
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- images/inrae_1
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- images/usask_1
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val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
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- images/ethz_1
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test: # test images (optional) 1276 images
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- images/utokyo_1
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- images/utokyo_2
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- images/nau_1
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- images/uq_1
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# Classes
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names:
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0: wheat_head
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# Download script/URL (optional) ---------------------------------------------------------------------------------------
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download: |
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from utils.general import download, Path
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# Download
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dir = Path(yaml['path']) # dataset root dir
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urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
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'https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip']
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download(urls, dir=dir)
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# Make Directories
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for p in 'annotations', 'images', 'labels':
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(dir / p).mkdir(parents=True, exist_ok=True)
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# Move
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for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
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'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
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(dir / p).rename(dir / 'images' / p) # move to /images
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f = (dir / p).with_suffix('.json') # json file
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if f.exists():
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f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,30 @@
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
|
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# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
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# Example usage: python classify/train.py --data imagenet
|
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# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── imagenet10 ← downloads here
|
||||
|
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/imagenet10 # dataset root dir
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||||
train: train # train images (relative to 'path') 1281167 images
|
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val: val # val images (relative to 'path') 50000 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
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names:
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0: tench
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1: goldfish
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2: great white shark
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3: tiger shark
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4: hammerhead shark
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5: electric ray
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6: stingray
|
||||
7: cock
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8: hen
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9: ostrich
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|
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# Download script/URL (optional)
|
||||
download: data/scripts/get_imagenet10.sh
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@ -0,0 +1,119 @@
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
|
||||
# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
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# Example usage: python classify/train.py --data imagenet
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# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
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# └── imagenet100 ← downloads here
|
||||
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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path: ../datasets/imagenet100 # dataset root dir
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||||
train: train # train images (relative to 'path') 1281167 images
|
||||
val: val # val images (relative to 'path') 50000 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
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||||
names:
|
||||
0: tench
|
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1: goldfish
|
||||
2: great white shark
|
||||
3: tiger shark
|
||||
4: hammerhead shark
|
||||
5: electric ray
|
||||
6: stingray
|
||||
7: cock
|
||||
8: hen
|
||||
9: ostrich
|
||||
10: brambling
|
||||
11: goldfinch
|
||||
12: house finch
|
||||
13: junco
|
||||
14: indigo bunting
|
||||
15: American robin
|
||||
16: bulbul
|
||||
17: jay
|
||||
18: magpie
|
||||
19: chickadee
|
||||
20: American dipper
|
||||
21: kite
|
||||
22: bald eagle
|
||||
23: vulture
|
||||
24: great grey owl
|
||||
25: fire salamander
|
||||
26: smooth newt
|
||||
27: newt
|
||||
28: spotted salamander
|
||||
29: axolotl
|
||||
30: American bullfrog
|
||||
31: tree frog
|
||||
32: tailed frog
|
||||
33: loggerhead sea turtle
|
||||
34: leatherback sea turtle
|
||||
35: mud turtle
|
||||
36: terrapin
|
||||
37: box turtle
|
||||
38: banded gecko
|
||||
39: green iguana
|
||||
40: Carolina anole
|
||||
41: desert grassland whiptail lizard
|
||||
42: agama
|
||||
43: frilled-necked lizard
|
||||
44: alligator lizard
|
||||
45: Gila monster
|
||||
46: European green lizard
|
||||
47: chameleon
|
||||
48: Komodo dragon
|
||||
49: Nile crocodile
|
||||
50: American alligator
|
||||
51: triceratops
|
||||
52: worm snake
|
||||
53: ring-necked snake
|
||||
54: eastern hog-nosed snake
|
||||
55: smooth green snake
|
||||
56: kingsnake
|
||||
57: garter snake
|
||||
58: water snake
|
||||
59: vine snake
|
||||
60: night snake
|
||||
61: boa constrictor
|
||||
62: African rock python
|
||||
63: Indian cobra
|
||||
64: green mamba
|
||||
65: sea snake
|
||||
66: Saharan horned viper
|
||||
67: eastern diamondback rattlesnake
|
||||
68: sidewinder
|
||||
69: trilobite
|
||||
70: harvestman
|
||||
71: scorpion
|
||||
72: yellow garden spider
|
||||
73: barn spider
|
||||
74: European garden spider
|
||||
75: southern black widow
|
||||
76: tarantula
|
||||
77: wolf spider
|
||||
78: tick
|
||||
79: centipede
|
||||
80: black grouse
|
||||
81: ptarmigan
|
||||
82: ruffed grouse
|
||||
83: prairie grouse
|
||||
84: peacock
|
||||
85: quail
|
||||
86: partridge
|
||||
87: grey parrot
|
||||
88: macaw
|
||||
89: sulphur-crested cockatoo
|
||||
90: lorikeet
|
||||
91: coucal
|
||||
92: bee eater
|
||||
93: hornbill
|
||||
94: hummingbird
|
||||
95: jacamar
|
||||
96: toucan
|
||||
97: duck
|
||||
98: red-breasted merganser
|
||||
99: goose
|
||||
# Download script/URL (optional)
|
||||
download: data/scripts/get_imagenet100.sh
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,436 @@
|
||||
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
# Objects365 dataset https://www.objects365.org/ by Megvii
|
||||
# Example usage: python train.py --data Objects365.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/Objects365 # dataset root dir
|
||||
train: images/train # train images (relative to 'path') 1742289 images
|
||||
val: images/val # val images (relative to 'path') 80000 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: Person
|
||||
1: Sneakers
|
||||
2: Chair
|
||||
3: Other Shoes
|
||||
4: Hat
|
||||
5: Car
|
||||
6: Lamp
|
||||
7: Glasses
|
||||
8: Bottle
|
||||
9: Desk
|
||||
10: Cup
|
||||
11: Street Lights
|
||||
12: Cabinet/shelf
|
||||
13: Handbag/Satchel
|
||||
14: Bracelet
|
||||
15: Plate
|
||||
16: Picture/Frame
|
||||
17: Helmet
|
||||
18: Book
|
||||
19: Gloves
|
||||
20: Storage box
|
||||
21: Boat
|
||||
22: Leather Shoes
|
||||
23: Flower
|
||||
24: Bench
|
||||
25: Potted Plant
|
||||
26: Bowl/Basin
|
||||
27: Flag
|
||||
28: Pillow
|
||||
29: Boots
|
||||
30: Vase
|
||||
31: Microphone
|
||||
32: Necklace
|
||||
33: Ring
|
||||
34: SUV
|
||||
35: Wine Glass
|
||||
36: Belt
|
||||
37: Monitor/TV
|
||||
38: Backpack
|
||||
39: Umbrella
|
||||
40: Traffic Light
|
||||
41: Speaker
|
||||
42: Watch
|
||||
43: Tie
|
||||
44: Trash bin Can
|
||||
45: Slippers
|
||||
46: Bicycle
|
||||
47: Stool
|
||||
48: Barrel/bucket
|
||||
49: Van
|
||||
50: Couch
|
||||
51: Sandals
|
||||
52: Basket
|
||||
53: Drum
|
||||
54: Pen/Pencil
|
||||
55: Bus
|
||||
56: Wild Bird
|
||||
57: High Heels
|
||||
58: Motorcycle
|
||||
59: Guitar
|
||||
60: Carpet
|
||||
61: Cell Phone
|
||||
62: Bread
|
||||
63: Camera
|
||||
64: Canned
|
||||
65: Truck
|
||||
66: Traffic cone
|
||||
67: Cymbal
|
||||
68: Lifesaver
|
||||
69: Towel
|
||||
70: Stuffed Toy
|
||||
71: Candle
|
||||
72: Sailboat
|
||||
73: Laptop
|
||||
74: Awning
|
||||
75: Bed
|
||||
76: Faucet
|
||||
77: Tent
|
||||
78: Horse
|
||||
79: Mirror
|
||||
80: Power outlet
|
||||
81: Sink
|
||||
82: Apple
|
||||
83: Air Conditioner
|
||||
84: Knife
|
||||
85: Hockey Stick
|
||||
86: Paddle
|
||||
87: Pickup Truck
|
||||
88: Fork
|
||||
89: Traffic Sign
|
||||
90: Balloon
|
||||
91: Tripod
|
||||
92: Dog
|
||||
93: Spoon
|
||||
94: Clock
|
||||
95: Pot
|
||||
96: Cow
|
||||
97: Cake
|
||||
98: Dinning Table
|
||||
99: Sheep
|
||||
100: Hanger
|
||||
101: Blackboard/Whiteboard
|
||||
102: Napkin
|
||||
103: Other Fish
|
||||
104: Orange/Tangerine
|
||||
105: Toiletry
|
||||
106: Keyboard
|
||||
107: Tomato
|
||||
108: Lantern
|
||||
109: Machinery Vehicle
|
||||
110: Fan
|
||||
111: Green Vegetables
|
||||
112: Banana
|
||||
113: Baseball Glove
|
||||
114: Airplane
|
||||
115: Mouse
|
||||
116: Train
|
||||
117: Pumpkin
|
||||
118: Soccer
|
||||
119: Skiboard
|
||||
120: Luggage
|
||||
121: Nightstand
|
||||
122: Tea pot
|
||||
123: Telephone
|
||||
124: Trolley
|
||||
125: Head Phone
|
||||
126: Sports Car
|
||||
127: Stop Sign
|
||||
128: Dessert
|
||||
129: Scooter
|
||||
130: Stroller
|
||||
131: Crane
|
||||
132: Remote
|
||||
133: Refrigerator
|
||||
134: Oven
|
||||
135: Lemon
|
||||
136: Duck
|
||||
137: Baseball Bat
|
||||
138: Surveillance Camera
|
||||
139: Cat
|
||||
140: Jug
|
||||
141: Broccoli
|
||||
142: Piano
|
||||
143: Pizza
|
||||
144: Elephant
|
||||
145: Skateboard
|
||||
146: Surfboard
|
||||
147: Gun
|
||||
148: Skating and Skiing shoes
|
||||
149: Gas stove
|
||||
150: Donut
|
||||
151: Bow Tie
|
||||
152: Carrot
|
||||
153: Toilet
|
||||
154: Kite
|
||||
155: Strawberry
|
||||
156: Other Balls
|
||||
157: Shovel
|
||||
158: Pepper
|
||||
159: Computer Box
|
||||
160: Toilet Paper
|
||||
161: Cleaning Products
|
||||
162: Chopsticks
|
||||
163: Microwave
|
||||
164: Pigeon
|
||||
165: Baseball
|
||||
166: Cutting/chopping Board
|
||||
167: Coffee Table
|
||||
168: Side Table
|
||||
169: Scissors
|
||||
170: Marker
|
||||
171: Pie
|
||||
172: Ladder
|
||||
173: Snowboard
|
||||
174: Cookies
|
||||
175: Radiator
|
||||
176: Fire Hydrant
|
||||
177: Basketball
|
||||
178: Zebra
|
||||
179: Grape
|
||||
180: Giraffe
|
||||
181: Potato
|
||||
182: Sausage
|
||||
183: Tricycle
|
||||
184: Violin
|
||||
185: Egg
|
||||
186: Fire Extinguisher
|
||||
187: Candy
|
||||
188: Fire Truck
|
||||
189: Billiards
|
||||
190: Converter
|
||||
191: Bathtub
|
||||
192: Wheelchair
|
||||
193: Golf Club
|
||||
194: Briefcase
|
||||
195: Cucumber
|
||||
196: Cigar/Cigarette
|
||||
197: Paint Brush
|
||||
198: Pear
|
||||
199: Heavy Truck
|
||||
200: Hamburger
|
||||
201: Extractor
|
||||
202: Extension Cord
|
||||
203: Tong
|
||||
204: Tennis Racket
|
||||
205: Folder
|
||||
206: American Football
|
||||
207: earphone
|
||||
208: Mask
|
||||
209: Kettle
|
||||
210: Tennis
|
||||
211: Ship
|
||||
212: Swing
|
||||
213: Coffee Machine
|
||||
214: Slide
|
||||
215: Carriage
|
||||
216: Onion
|
||||
217: Green beans
|
||||
218: Projector
|
||||
219: Frisbee
|
||||
220: Washing Machine/Drying Machine
|
||||
221: Chicken
|
||||
222: Printer
|
||||
223: Watermelon
|
||||
224: Saxophone
|
||||
225: Tissue
|
||||
226: Toothbrush
|
||||
227: Ice cream
|
||||
228: Hot-air balloon
|
||||
229: Cello
|
||||
230: French Fries
|
||||
231: Scale
|
||||
232: Trophy
|
||||
233: Cabbage
|
||||
234: Hot dog
|
||||
235: Blender
|
||||
236: Peach
|
||||
237: Rice
|
||||
238: Wallet/Purse
|
||||
239: Volleyball
|
||||
240: Deer
|
||||
241: Goose
|
||||
242: Tape
|
||||
243: Tablet
|
||||
244: Cosmetics
|
||||
245: Trumpet
|
||||
246: Pineapple
|
||||
247: Golf Ball
|
||||
248: Ambulance
|
||||
249: Parking meter
|
||||
250: Mango
|
||||
251: Key
|
||||
252: Hurdle
|
||||
253: Fishing Rod
|
||||
254: Medal
|
||||
255: Flute
|
||||
256: Brush
|
||||
257: Penguin
|
||||
258: Megaphone
|
||||
259: Corn
|
||||
260: Lettuce
|
||||
261: Garlic
|
||||
262: Swan
|
||||
263: Helicopter
|
||||
264: Green Onion
|
||||
265: Sandwich
|
||||
266: Nuts
|
||||
267: Speed Limit Sign
|
||||
268: Induction Cooker
|
||||
269: Broom
|
||||
270: Trombone
|
||||
271: Plum
|
||||
272: Rickshaw
|
||||
273: Goldfish
|
||||
274: Kiwi fruit
|
||||
275: Router/modem
|
||||
276: Poker Card
|
||||
277: Toaster
|
||||
278: Shrimp
|
||||
279: Sushi
|
||||
280: Cheese
|
||||
281: Notepaper
|
||||
282: Cherry
|
||||
283: Pliers
|
||||
284: CD
|
||||
285: Pasta
|
||||
286: Hammer
|
||||
287: Cue
|
||||
288: Avocado
|
||||
289: Hamimelon
|
||||
290: Flask
|
||||
291: Mushroom
|
||||
292: Screwdriver
|
||||
293: Soap
|
||||
294: Recorder
|
||||
295: Bear
|
||||
296: Eggplant
|
||||
297: Board Eraser
|
||||
298: Coconut
|
||||
299: Tape Measure/Ruler
|
||||
300: Pig
|
||||
301: Showerhead
|
||||
302: Globe
|
||||
303: Chips
|
||||
304: Steak
|
||||
305: Crosswalk Sign
|
||||
306: Stapler
|
||||
307: Camel
|
||||
308: Formula 1
|
||||
309: Pomegranate
|
||||
310: Dishwasher
|
||||
311: Crab
|
||||
312: Hoverboard
|
||||
313: Meat ball
|
||||
314: Rice Cooker
|
||||
315: Tuba
|
||||
316: Calculator
|
||||
317: Papaya
|
||||
318: Antelope
|
||||
319: Parrot
|
||||
320: Seal
|
||||
321: Butterfly
|
||||
322: Dumbbell
|
||||
323: Donkey
|
||||
324: Lion
|
||||
325: Urinal
|
||||
326: Dolphin
|
||||
327: Electric Drill
|
||||
328: Hair Dryer
|
||||
329: Egg tart
|
||||
330: Jellyfish
|
||||
331: Treadmill
|
||||
332: Lighter
|
||||
333: Grapefruit
|
||||
334: Game board
|
||||
335: Mop
|
||||
336: Radish
|
||||
337: Baozi
|
||||
338: Target
|
||||
339: French
|
||||
340: Spring Rolls
|
||||
341: Monkey
|
||||
342: Rabbit
|
||||
343: Pencil Case
|
||||
344: Yak
|
||||
345: Red Cabbage
|
||||
346: Binoculars
|
||||
347: Asparagus
|
||||
348: Barbell
|
||||
349: Scallop
|
||||
350: Noddles
|
||||
351: Comb
|
||||
352: Dumpling
|
||||
353: Oyster
|
||||
354: Table Tennis paddle
|
||||
355: Cosmetics Brush/Eyeliner Pencil
|
||||
356: Chainsaw
|
||||
357: Eraser
|
||||
358: Lobster
|
||||
359: Durian
|
||||
360: Okra
|
||||
361: Lipstick
|
||||
362: Cosmetics Mirror
|
||||
363: Curling
|
||||
364: Table Tennis
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils.general import Path, check_requirements, download, np, xyxy2xywhn
|
||||
|
||||
check_requirements('pycocotools>=2.0')
|
||||
from pycocotools.coco import COCO
|
||||
|
||||
# Make Directories
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
for p in 'images', 'labels':
|
||||
(dir / p).mkdir(parents=True, exist_ok=True)
|
||||
for q in 'train', 'val':
|
||||
(dir / p / q).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Train, Val Splits
|
||||
for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
|
||||
print(f"Processing {split} in {patches} patches ...")
|
||||
images, labels = dir / 'images' / split, dir / 'labels' / split
|
||||
|
||||
# Download
|
||||
url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
|
||||
if split == 'train':
|
||||
download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json
|
||||
download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
|
||||
elif split == 'val':
|
||||
download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json
|
||||
download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
|
||||
download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
|
||||
|
||||
# Move
|
||||
for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
|
||||
f.rename(images / f.name) # move to /images/{split}
|
||||
|
||||
# Labels
|
||||
coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
|
||||
names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
|
||||
for cid, cat in enumerate(names):
|
||||
catIds = coco.getCatIds(catNms=[cat])
|
||||
imgIds = coco.getImgIds(catIds=catIds)
|
||||
for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
|
||||
width, height = im["width"], im["height"]
|
||||
path = Path(im["file_name"]) # image filename
|
||||
try:
|
||||
with open(labels / path.with_suffix('.txt').name, 'a') as file:
|
||||
annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=False)
|
||||
for a in coco.loadAnns(annIds):
|
||||
x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
|
||||
xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
|
||||
x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
|
||||
file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
@ -0,0 +1,51 @@
|
||||
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
|
||||
# Example usage: python train.py --data SKU-110K.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── SKU-110K ← downloads here (13.6 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/SKU-110K # dataset root dir
|
||||
train: train.txt # train images (relative to 'path') 8219 images
|
||||
val: val.txt # val images (relative to 'path') 588 images
|
||||
test: test.txt # test images (optional) 2936 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: object
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import shutil
|
||||
from tqdm import tqdm
|
||||
from utils.general import np, pd, Path, download, xyxy2xywh
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
parent = Path(dir.parent) # download dir
|
||||
urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
|
||||
download(urls, dir=parent, delete=False)
|
||||
|
||||
# Rename directories
|
||||
if dir.exists():
|
||||
shutil.rmtree(dir)
|
||||
(parent / 'SKU110K_fixed').rename(dir) # rename dir
|
||||
(dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir
|
||||
|
||||
# Convert labels
|
||||
names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names
|
||||
for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
|
||||
x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations
|
||||
images, unique_images = x[:, 0], np.unique(x[:, 0])
|
||||
with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
|
||||
f.writelines(f'./images/{s}\n' for s in unique_images)
|
||||
for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
|
||||
cls = 0 # single-class dataset
|
||||
with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
|
||||
for r in x[images == im]:
|
||||
w, h = r[6], r[7] # image width, height
|
||||
xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
|
||||
f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label
|
||||
@ -0,0 +1,98 @@
|
||||
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
|
||||
# Example usage: python train.py --data VOC.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── VOC ← downloads here (2.8 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/VOC
|
||||
train: # train images (relative to 'path') 16551 images
|
||||
- images/train2012
|
||||
- images/train2007
|
||||
- images/val2012
|
||||
- images/val2007
|
||||
val: # val images (relative to 'path') 4952 images
|
||||
- images/test2007
|
||||
test: # test images (optional)
|
||||
- images/test2007
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: aeroplane
|
||||
1: bicycle
|
||||
2: bird
|
||||
3: boat
|
||||
4: bottle
|
||||
5: bus
|
||||
6: car
|
||||
7: cat
|
||||
8: chair
|
||||
9: cow
|
||||
10: diningtable
|
||||
11: dog
|
||||
12: horse
|
||||
13: motorbike
|
||||
14: person
|
||||
15: pottedplant
|
||||
16: sheep
|
||||
17: sofa
|
||||
18: train
|
||||
19: tvmonitor
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
from tqdm import tqdm
|
||||
from utils.general import download, Path
|
||||
|
||||
|
||||
def convert_label(path, lb_path, year, image_id):
|
||||
def convert_box(size, box):
|
||||
dw, dh = 1. / size[0], 1. / size[1]
|
||||
x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
|
||||
return x * dw, y * dh, w * dw, h * dh
|
||||
|
||||
in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
|
||||
out_file = open(lb_path, '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)
|
||||
|
||||
names = list(yaml['names'].values()) # names list
|
||||
for obj in root.iter('object'):
|
||||
cls = obj.find('name').text
|
||||
if cls in names and int(obj.find('difficult').text) != 1:
|
||||
xmlbox = obj.find('bndbox')
|
||||
bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
|
||||
cls_id = names.index(cls) # class id
|
||||
out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
|
||||
urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
|
||||
f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
|
||||
f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
|
||||
download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
|
||||
|
||||
# Convert
|
||||
path = dir / 'images/VOCdevkit'
|
||||
for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
|
||||
imgs_path = dir / 'images' / f'{image_set}{year}'
|
||||
lbs_path = dir / 'labels' / f'{image_set}{year}'
|
||||
imgs_path.mkdir(exist_ok=True, parents=True)
|
||||
lbs_path.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
|
||||
image_ids = f.read().strip().split()
|
||||
for id in tqdm(image_ids, desc=f'{image_set}{year}'):
|
||||
f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
|
||||
lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
|
||||
f.rename(imgs_path / f.name) # move image
|
||||
convert_label(path, lb_path, year, id) # convert labels to YOLO format
|
||||
@ -0,0 +1,68 @@
|
||||
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||||
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
|
||||
# Example usage: python train.py --data VisDrone.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# └── datasets
|
||||
# └── VisDrone ← downloads here (2.3 GB)
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/VisDrone # dataset root dir
|
||||
train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
|
||||
val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
|
||||
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: pedestrian
|
||||
1: people
|
||||
2: bicycle
|
||||
3: car
|
||||
4: van
|
||||
5: truck
|
||||
6: tricycle
|
||||
7: awning-tricycle
|
||||
8: bus
|
||||
9: motor
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from utils.general import download, os, Path
|
||||
|
||||
def visdrone2yolo(dir):
|
||||
from PIL import Image
|
||||
from tqdm import tqdm
|
||||
|
||||
def convert_box(size, box):
|
||||
# Convert VisDrone box to YOLO xywh box
|
||||
dw = 1. / size[0]
|
||||
dh = 1. / size[1]
|
||||
return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
|
||||
|
||||
(dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
|
||||
pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
|
||||
for f in pbar:
|
||||
img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
|
||||
lines = []
|
||||
with open(f, 'r') as file: # read annotation.txt
|
||||
for row in [x.split(',') for x in file.read().strip().splitlines()]:
|
||||
if row[4] == '0': # VisDrone 'ignored regions' class 0
|
||||
continue
|
||||
cls = int(row[5]) - 1
|
||||
box = convert_box(img_size, tuple(map(int, row[:4])))
|
||||
lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
|
||||
with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
|
||||
fl.writelines(lines) # write label.txt
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
urls = ['https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip',
|
||||
'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip',
|
||||
'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip',
|
||||
'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-challenge.zip']
|
||||
download(urls, dir=dir, curl=True, threads=4)
|
||||
|
||||
# Convert
|
||||
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
|
||||
visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
|
||||
@ -0,0 +1,26 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
<filename>000003.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000003.jpg</path>
|
||||
<source>
|
||||
<database>Unknown</database>
|
||||
</source>
|
||||
<size>
|
||||
<width>1280</width>
|
||||
<height>720</height>
|
||||
<depth>3</depth>
|
||||
</size>
|
||||
<segmented>0</segmented>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>445</xmin>
|
||||
<ymin>4</ymin>
|
||||
<xmax>1000</xmax>
|
||||
<ymax>428</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
</annotation>
|
||||
@ -0,0 +1,38 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
<filename>000005(2).jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000005(2).jpg</path>
|
||||
<source>
|
||||
<database>Unknown</database>
|
||||
</source>
|
||||
<size>
|
||||
<width>1080</width>
|
||||
<height>1440</height>
|
||||
<depth>3</depth>
|
||||
</size>
|
||||
<segmented>0</segmented>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>453</xmin>
|
||||
<ymin>862</ymin>
|
||||
<xmax>1037</xmax>
|
||||
<ymax>1262</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>214</xmin>
|
||||
<ymin>250</ymin>
|
||||
<xmax>993</xmax>
|
||||
<ymax>855</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
</annotation>
|
||||
@ -0,0 +1,38 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
<filename>000005.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000005.jpg</path>
|
||||
<source>
|
||||
<database>Unknown</database>
|
||||
</source>
|
||||
<size>
|
||||
<width>1920</width>
|
||||
<height>1080</height>
|
||||
<depth>3</depth>
|
||||
</size>
|
||||
<segmented>0</segmented>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>884</xmin>
|
||||
<ymin>485</ymin>
|
||||
<xmax>1252</xmax>
|
||||
<ymax>665</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>1323</xmin>
|
||||
<ymin>513</ymin>
|
||||
<xmax>1735</xmax>
|
||||
<ymax>668</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
</annotation>
|
||||
@ -0,0 +1,26 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
<filename>000007.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000007.jpg</path>
|
||||
<source>
|
||||
<database>Unknown</database>
|
||||
</source>
|
||||
<size>
|
||||
<width>1280</width>
|
||||
<height>720</height>
|
||||
<depth>3</depth>
|
||||
</size>
|
||||
<segmented>0</segmented>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>438</xmin>
|
||||
<ymin>5</ymin>
|
||||
<xmax>1027</xmax>
|
||||
<ymax>429</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
</annotation>
|
||||
@ -0,0 +1,38 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
<filename>000008.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000008.jpg</path>
|
||||
<source>
|
||||
<database>Unknown</database>
|
||||
</source>
|
||||
<size>
|
||||
<width>1920</width>
|
||||
<height>1080</height>
|
||||
<depth>3</depth>
|
||||
</size>
|
||||
<segmented>0</segmented>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>708</xmin>
|
||||
<ymin>307</ymin>
|
||||
<xmax>1011</xmax>
|
||||
<ymax>603</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>863</xmin>
|
||||
<ymin>669</ymin>
|
||||
<xmax>984</xmax>
|
||||
<ymax>718</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
</annotation>
|
||||
@ -0,0 +1,26 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
<filename>000014.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000014.jpg</path>
|
||||
<source>
|
||||
<database>Unknown</database>
|
||||
</source>
|
||||
<size>
|
||||
<width>1920</width>
|
||||
<height>1080</height>
|
||||
<depth>3</depth>
|
||||
</size>
|
||||
<segmented>0</segmented>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>738</xmin>
|
||||
<ymin>730</ymin>
|
||||
<xmax>1098</xmax>
|
||||
<ymax>1060</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
</annotation>
|
||||
@ -0,0 +1,26 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
<filename>000020.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000020.jpg</path>
|
||||
<source>
|
||||
<database>Unknown</database>
|
||||
</source>
|
||||
<size>
|
||||
<width>256</width>
|
||||
<height>256</height>
|
||||
<depth>3</depth>
|
||||
</size>
|
||||
<segmented>0</segmented>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>97</xmin>
|
||||
<ymin>81</ymin>
|
||||
<xmax>187</xmax>
|
||||
<ymax>213</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
</annotation>
|
||||
@ -0,0 +1,26 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
<filename>000023.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000023.jpg</path>
|
||||
<source>
|
||||
<database>Unknown</database>
|
||||
</source>
|
||||
<size>
|
||||
<width>720</width>
|
||||
<height>480</height>
|
||||
<depth>3</depth>
|
||||
</size>
|
||||
<segmented>0</segmented>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>347</xmin>
|
||||
<ymin>145</ymin>
|
||||
<xmax>421</xmax>
|
||||
<ymax>234</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
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||||
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<annotation>
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|
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<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
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|
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||||
<annotation>
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||||
<folder>JPEGImages</folder>
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||||
<filename>000037(2).jpg</filename>
|
||||
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|
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|
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@ -0,0 +1,26 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
<filename>000037.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000037.jpg</path>
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|
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|
||||
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|
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<annotation>
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<folder>JPEGImages</folder>
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||||
<filename>000039.jpg</filename>
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<path>C:\Users\pink\Desktop\voc\JPEGImages\000039.jpg</path>
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<annotation>
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<folder>JPEGImages</folder>
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||||
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<path>C:\Users\pink\Desktop\voc\JPEGImages\000041.jpg</path>
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||||
@ -0,0 +1,26 @@
|
||||
<annotation>
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<folder>JPEGImages</folder>
|
||||
<filename>000042.jpg</filename>
|
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<path>C:\Users\pink\Desktop\voc\JPEGImages\000042.jpg</path>
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||||
<annotation>
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||||
<folder>JPEGImages</folder>
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||||
<filename>000050.jpg</filename>
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<path>C:\Users\pink\Desktop\voc\JPEGImages\000050.jpg</path>
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||||
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||||
@ -0,0 +1,26 @@
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||||
<annotation>
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||||
<folder>JPEGImages</folder>
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||||
<filename>000051.jpg</filename>
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||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000051.jpg</path>
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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@ -0,0 +1,26 @@
|
||||
<annotation>
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||||
<folder>JPEGImages</folder>
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
@ -0,0 +1,26 @@
|
||||
<annotation>
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||||
<folder>JPEGImages</folder>
|
||||
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||||
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||||
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||||
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||||
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||||
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@ -0,0 +1,26 @@
|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
@ -0,0 +1,26 @@
|
||||
<annotation>
|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
@ -0,0 +1,26 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
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|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000079(2).jpg</path>
|
||||
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|
||||
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|
||||
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|
||||
<size>
|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
@ -0,0 +1,26 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
<filename>000079.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000079.jpg</path>
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||||
<source>
|
||||
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||||
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|
||||
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|
||||
<width>1920</width>
|
||||
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||||
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||||
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||||
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||||
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||||
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@ -0,0 +1,26 @@
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||||
<annotation>
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<folder>JPEGImages</folder>
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||||
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@ -0,0 +1,26 @@
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<annotation>
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||||
<folder>JPEGImages</folder>
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||||
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<path>C:\Users\pink\Desktop\voc\JPEGImages\000106.jpg</path>
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||||
<annotation>
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||||
<folder>JPEGImages</folder>
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||||
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||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000107.jpg</path>
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<annotation>
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||||
<folder>JPEGImages</folder>
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||||
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||||
<annotation>
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||||
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||||
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||||
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@ -0,0 +1,26 @@
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||||
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||||
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||||
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||||
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||||
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||||
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@ -0,0 +1,26 @@
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||||
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||||
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@ -0,0 +1,26 @@
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||||
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||||
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|
||||
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||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000123.jpg</path>
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||||
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||||
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||||
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||||
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||||
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@ -0,0 +1,26 @@
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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@ -0,0 +1,38 @@
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||||
<annotation>
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||||
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||||
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||||
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||||
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<annotation>
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<folder>JPEGImages</folder>
|
||||
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|
||||
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<annotation>
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||||
<folder>JPEGImages</folder>
|
||||
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|
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<path>C:\Users\pink\Desktop\voc\JPEGImages\000155.jpg</path>
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|
||||
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@ -0,0 +1,26 @@
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||||
<annotation>
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||||
<folder>JPEGImages</folder>
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||||
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||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000162.jpg</path>
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|
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|
||||
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|
||||
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||||
<annotation>
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||||
<folder>JPEGImages</folder>
|
||||
<filename>000186.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000186.jpg</path>
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||||
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@ -0,0 +1,26 @@
|
||||
<annotation>
|
||||
<folder>JPEGImages</folder>
|
||||
<filename>000187.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000187.jpg</path>
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||||
<annotation>
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||||
<folder>JPEGImages</folder>
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||||
<filename>000189.jpg</filename>
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||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000189.jpg</path>
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||||
<database>Unknown</database>
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||||
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||||
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||||
<annotation>
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||||
<folder>JPEGImages</folder>
|
||||
<filename>000192.jpg</filename>
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||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000192.jpg</path>
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||||
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||||
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||||
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||||
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||||
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||||
@ -0,0 +1,26 @@
|
||||
<annotation>
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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@ -0,0 +1,26 @@
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
@ -0,0 +1,26 @@
|
||||
<annotation>
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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@ -0,0 +1,26 @@
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||||
<annotation>
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||||
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||||
<filename>000211.jpg</filename>
|
||||
<path>C:\Users\pink\Desktop\voc\JPEGImages\000211.jpg</path>
|
||||
<source>
|
||||
<database>Unknown</database>
|
||||
</source>
|
||||
<size>
|
||||
<width>720</width>
|
||||
<height>480</height>
|
||||
<depth>3</depth>
|
||||
</size>
|
||||
<segmented>0</segmented>
|
||||
<object>
|
||||
<name>smoke</name>
|
||||
<pose>Unspecified</pose>
|
||||
<truncated>0</truncated>
|
||||
<difficult>0</difficult>
|
||||
<bndbox>
|
||||
<xmin>523</xmin>
|
||||
<ymin>196</ymin>
|
||||
<xmax>594</xmax>
|
||||
<ymax>289</ymax>
|
||||
</bndbox>
|
||||
</object>
|
||||
</annotation>
|
||||
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Reference in new issue