initial commit

.dockerignore

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+env
+.git
+**/__pycache__

.gitignore

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+env
+**/__pycache__

Dockerfile

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+FROM python:3
+
+ENV PYTHONUNBUFFERED=value
+
+WORKDIR /usr/src/app
+
+COPY requirements.txt ./
+
+RUN pip install --no-cache-dir --extra-index-url https://download.pytorch.org/whl/cpu -r requirements.txt
+
+RUN apt update && apt install -y ffmpeg libsm6 libxext6
+
+COPY src .
+COPY best.pt /best.pt
+
+CMD [ "gunicorn", "-b 0.0.0.0:80", "app:app" ]

docker-compose.yml

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+version: '3'
+
+services:
+  alv.cx-glass:
+    build: .
+    restart: unless-stopped
+    ports:
+      - 8759:8000

index.html

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+<img id="img" src="" />
+
+<p id="statusline">submit an image to get started!</p>
+<form id="form" action="http://localhost:5000/submit" method="post">
+<input type="file" id="job" name="image" accept="image/png,image/jpeg" /><br>
+<button type="submit" id="submit">submit</button>
+</form>
+
+<script>
+  const form = document.getElementById("form");
+  const img = document.getElementById("img");
+  const statusline = document.getElementById("statusline");
+
+  async function handleSubmit(ev) {
+    ev.preventDefault();
+    const form = ev.currentTarget;
+    const data = new FormData(form);
+    const url = form.action;
+    const fetchOptions = {
+      method: form.method,
+      body: data
+    }
+
+    const resp = await fetch(url, fetchOptions);
+    const id = (await resp.json()).id;
+    console.log(id);
+
+    while (true) {
+      const resp = await fetch('http://localhost:5000/state/' + id);
+      const state = (await resp.json()).state;
+      statusline.innerHTML = state;
+      if (state == "READY") {
+        img.src = 'http://localhost:5000/result/' + id
+        return;
+      }
+    }
+  }
+
+  form.addEventListener('submit', handleSubmit)
+</script>

requirements.txt

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+blinker==1.7.0
+certifi==2022.12.7
+charset-normalizer==2.1.1
+click==8.1.7
+contourpy==1.2.0
+cycler==0.12.1
+filelock==3.9.0
+Flask==3.0.0
+fonttools==4.47.0
+fsspec==2023.4.0
+gunicorn==21.2.0
+idna==3.4
+itsdangerous==2.1.2
+Jinja2==3.1.2
+kiwisolver==1.4.5
+MarkupSafe==2.1.3
+matplotlib==3.8.2
+mpmath==1.3.0
+networkx==3.0
+numpy==1.24.1
+opencv-python==4.8.1.78
+packaging==23.2
+pandas==2.1.4
+Pillow==9.3.0
+psutil==5.9.7
+py-cpuinfo==9.0.0
+pyparsing==3.1.1
+python-dateutil==2.8.2
+pytz==2023.3.post1
+PyYAML==6.0.1
+requests==2.28.1
+scipy==1.11.4
+seaborn==0.13.0
+six==1.16.0
+sympy==1.12
+thop==0.1.1.post2209072238
+torch==2.1.2+cpu
+torchaudio==2.1.2+cpu
+torchvision==0.16.2+cpu
+tqdm==4.66.1
+typing_extensions==4.4.0
+tzdata==2023.3
+ultralytics==8.0.230
+urllib3==1.26.13
+Werkzeug==3.0.1

src/app.py

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+#!/usr/bin/env python3
+
+import os
+import uuid
+import json
+import time
+import cv2
+import queue
+from pathlib import Path as P
+from PIL import Image
+from flask import Flask, request, send_from_directory
+from flask_cors import CORS
+from threading import Thread
+
+app = Flask(__name__)
+CORS(app)
+
+QUEUE_STATE_IN_QUEUE = 'IN_QUEUE'
+QUEUE_STATE_PROCESSING = 'PROCESSING'
+QUEUE_STATE_READY = 'READY'
+
+q = queue.Queue(maxsize=1000)
+state = {}
+root = P("./q").absolute()
+root.mkdir(parents=True, exist_ok=True)
+
+
+def process():
+    print("importing ultralytics")
+    from ultralytics import YOLO
+    print("creating model")
+    model = YOLO(os.environ.get("MODEL_PATH", "/best.pt"))
+    print("created model")
+    last = 0
+    while True:
+        if time.time() < last + 10:
+            print("aa")
+            time.sleep(0.5)
+            continue
+
+        print("BB")
+
+        print("waiting for/getting job")
+        job = q.get()
+        filepath = root.joinpath(job)
+        print("got job")
+        state[job] = {
+            'state': QUEUE_STATE_PROCESSING,
+        }
+        print(f"processing job: {job=}")
+        img = cv2.imread(str(filepath))
+        results = model.predict(img)
+
+        for r in results:
+            im_array = r.plot()
+            im = Image.fromarray(im_array[..., ::-1])
+            im.save(str(filepath.with_suffix(".png")))
+            state[job] = {
+                'state': QUEUE_STATE_READY,
+            }
+
+        filepath.unlink()
+
+
+@app.route("/submit", methods=['POST'])
+def render():
+    id = str(uuid.uuid4())
+    request.files['image'].save(root.joinpath(id))
+    q.put(id)
+    state[id] = {'state': QUEUE_STATE_IN_QUEUE}
+    return json.dumps({'id': id})
+
+
+@app.route("/state/<id>")
+def get_state(id):
+    return json.dumps(state[id])
+
+
+@app.route("/result/<i>")
+def get_result(i):
+    print(root)
+    print(i + '.png')
+    return send_from_directory(root, i + '.png')
+
+
+if __name__ == '__main__':
+    p = Thread(target=process)
+    p.start()
+    app.run(debug=True)
+    p.join()