niten
ed962240b3
When more than a single request was coming in at a time, the server would crash when trying to write the output file. Each worker had a single detector with one DN network, so if more than <WORKER#> requests came in at once, one network would be used multiple times--and it's not threadsafe. I've added a queue of nets to pull from, so each request has exclusive access to a specific network.
126 lines
3.8 KiB
Python
126 lines
3.8 KiB
Python
#!/usr/bin/env python3
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from detector import Detector
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from fastapi import FastAPI, HTTPException, Request, UploadFile, File
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from fastapi.responses import FileResponse
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from os import listdir, remove
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from os.path import getatime, splitext, basename, isfile
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from pathlib import Path
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from threading import Thread
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from time import sleep
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import hashlib
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import os
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import re
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import tempfile
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def get_envvar(name):
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return os.environ.get(name)
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def get_envvar_or_fail(name):
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result = get_envvar(name)
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if result:
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return result
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else:
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raise EnvironmentError('Missing required environment variable: ' + name)
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def to_int(input_int):
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if input_int:
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return int(input_int)
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else:
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return None
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yolo_config = get_envvar_or_fail('OBJECTIFIER_YOLOV3_CONFIG')
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yolo_weights = get_envvar_or_fail('OBJECTIFIER_YOLOV3_WEIGHTS')
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yolo_labels_file = get_envvar_or_fail('OBJECTIFIER_YOLOV3_LABELS')
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buffer_size = to_int(get_envvar('OBJECTIFIER_BUFFER_SIZE')) or 524288
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max_file_age = to_int(get_envvar('OBJECTIFIER_CLEANUP_MAX_AGE'))
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file_cleanup_delay = to_int(get_envvar('OBJECTIFIER_CLEANUP_DELAY'))
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detection_timeout = to_int(get_envvar('OBJECTIFIER_TIMEOUT')) or 5
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pool_size = to_int(get_envvar('OBJECTIFIER_POOL_SIZE')) or 10
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yolo_labels = []
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with open(yolo_labels_file, "r") as f:
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yolo_labels = [line.strip() for line in f.readlines()]
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incoming_dir = Path(get_envvar_or_fail('CACHE_DIRECTORY'))
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outgoing_dir = Path(get_envvar_or_fail('STATE_DIRECTORY'))
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detector = Detector(
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weights=yolo_weights,
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cfg=yolo_config,
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classes=yolo_labels,
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tempdir=outgoing_dir,
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pool_size=pool_size)
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app = FastAPI()
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analyzed_images = {}
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img_formats = [ "png", "jpg", "gif", "bmp" ]
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def cleanup_old_files(path, extensions, max_age):
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for filename in listdir(path):
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if (splitext(filename) in extensions) and (getatime(filename) - time.time() > max_age):
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print("removing old output file: " + filename)
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remove(filename)
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def run_cleanup_thread(path, extensions, age, delay):
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while True:
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cleanup_old_files(path, extensions, age)
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sleep(delay)
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cleanup_thread = Thread(
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target=run_cleanup_thread,
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args=(outgoing_dir, img_formats, max_file_age, file_cleanup_delay))
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cleanup_thread.daemon = True
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cleanup_thread.start()
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def detection_to_dict(d):
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return {
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"label": d.label,
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"confidence": d.confidence,
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"box": {
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"x": d.box[0],
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"y": d.box[1],
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"width": d.box[2],
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"height": d.box[3],
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},
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}
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def result_to_dict(res, base_url):
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return {
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"labels": [detection.label for detection in res.detections],
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"detections": [detection_to_dict(detection) for detection in res.detections],
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"output": base_url + basename(res.outfile),
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}
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@app.post("/images")
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def analyze_image(request: Request, image: UploadFile):
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base_url = re.sub(r'\/images\/?$', '/analyzed_images/', str(request.url))
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infile = incoming_dir / image.filename
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file_hash = hashlib.sha256()
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with open(infile, "wb") as f:
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chunk = image.file.read(buffer_size)
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while chunk:
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file_hash.update(chunk)
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print ("writing " + str(buffer_size) + " bytes")
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f.write(chunk)
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chunk = image.file.read(buffer_size)
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result = detector.detect_objects(
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infile,
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str(outgoing_dir / (file_hash.hexdigest() + ".png")),
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detection_timeout)
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remove(infile)
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return result_to_dict(result, base_url)
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@app.get("/analyzed_images/{image_name}", response_class=FileResponse)
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def get_analyzed_image(image_name: str):
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filename = outgoing_dir / image_name
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if isfile(filename):
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return str(filename)
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else:
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raise HTTPException(status_code=404, detail="file not found: " + str(filename))
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