mirror of
https://github.com/azaion/detections.git
synced 2026-04-22 22:16:31 +00:00
Oleksandr Bezdieniezhnykh
27f4aceb52
- Updated the `Inference` class to replace the `get_onnx_engine_bytes` method with `download_model`, allowing for dynamic model loading based on a specified filename. - Modified the `convert_and_upload_model` method to accept `source_bytes` instead of `onnx_engine_bytes`, enhancing flexibility in model conversion. - Introduced a new property `engine_name` to the `Inference` class for better access to engine details. - Adjusted the `AIRecognitionConfig` structure to include a new method pointer `from_dict`, improving configuration handling. - Updated various test cases to reflect changes in model paths and timeout settings, ensuring consistency and reliability in testing.
107 lines
3.2 KiB
Python
107 lines
3.2 KiB
Python
import json
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import os
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import threading
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import time
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import uuid
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import pytest
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_MEDIA = os.environ.get("MEDIA_DIR", "/media")
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def _ai_config_video() -> dict:
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return {
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"probability_threshold": 0.25,
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"tracking_intersection_threshold": 0.6,
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"altitude": 400,
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"focal_length": 24,
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"sensor_width": 23.5,
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"paths": [f"{_MEDIA}/video_test01.mp4"],
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"frame_period_recognition": 4,
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"frame_recognition_seconds": 2,
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}
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def _ai_config_image() -> dict:
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return {
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"probability_threshold": 0.25,
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"altitude": 400,
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"focal_length": 24,
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"sensor_width": 23.5,
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"paths": [f"{_MEDIA}/image_small.jpg"],
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}
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def test_ft_p08_immediate_async_response(
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warm_engine, http_client, jwt_token
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):
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media_id = f"async-{uuid.uuid4().hex}"
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body = _ai_config_image()
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headers = {"Authorization": f"Bearer {jwt_token}"}
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t0 = time.monotonic()
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r = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
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elapsed = time.monotonic() - t0
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assert elapsed < 2.0
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assert r.status_code == 200
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assert r.json() == {"status": "started", "mediaId": media_id}
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@pytest.mark.slow
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@pytest.mark.timeout(300)
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def test_ft_p09_sse_event_delivery(
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warm_engine, http_client, jwt_token, sse_client_factory
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):
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media_id = f"sse-{uuid.uuid4().hex}"
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body = _ai_config_video()
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headers = {"Authorization": f"Bearer {jwt_token}"}
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collected: list[dict] = []
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thread_exc: list[BaseException] = []
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done = threading.Event()
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def _listen():
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try:
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with sse_client_factory() as sse:
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time.sleep(0.3)
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for event in sse.events():
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if not event.data or not str(event.data).strip():
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continue
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data = json.loads(event.data)
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if data.get("mediaId") != media_id:
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continue
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collected.append(data)
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if (
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data.get("mediaStatus") == "AIProcessed"
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and data.get("mediaPercent") == 100
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):
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break
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except BaseException as e:
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thread_exc.append(e)
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finally:
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done.set()
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th = threading.Thread(target=_listen, daemon=True)
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th.start()
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time.sleep(0.5)
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r = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
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assert r.status_code == 200
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ok = done.wait(timeout=290)
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assert ok, "SSE listener did not finish within 290s"
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th.join(timeout=5)
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assert not thread_exc, thread_exc
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assert collected, "no SSE events received"
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final = collected[-1]
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assert final.get("mediaStatus") == "AIProcessed"
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assert final.get("mediaPercent") == 100
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def test_ft_n04_duplicate_media_id_409(
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warm_engine, http_client, jwt_token
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):
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media_id = "dup-test"
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body = _ai_config_image()
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headers = {"Authorization": f"Bearer {jwt_token}"}
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r1 = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
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assert r1.status_code == 200
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r2 = http_client.post(f"/detect/{media_id}", json=body, headers=headers)
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assert r2.status_code == 409
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