From 57e4b4c3fc0489b05c1e3721ae783bc33bdd6b4c Mon Sep 17 00:00:00 2001 From: Mateo Date: Tue, 16 Jul 2024 08:41:18 +0200 Subject: [PATCH] Switch to ncnn (#217) * use async core * update sensor * break at night * update run * temp * vision using ultralytics * fix deps * update dep * fix path * fix path 2 * indent * fix path * imgsz * fix model loading * fix test vision * use temp folder * fix engine * adapt test core * add test async * mypy * split build * docstring * drop nvidia * style * fix mypy * crop unused * header * fix import * unused import * fix day time * put back alerts * use ultralytics image * update deps * missing deps * fix version * fix vision sha * drop git install * 2 digits * get back to 30s * update install * fix docker * fix path * drop cpu limit * imprrove vision test * add missing tests * unused variable * missing blank line --- Dockerfile | 19 +- Makefile | 1 - docker-compose.yml | 4 - pyproject.toml | 8 +- pyroengine/core.py | 184 ++++--- pyroengine/engine.py | 11 +- pyroengine/sensors.py | 33 ++ pyroengine/utils.py | 57 +-- pyroengine/vision.py | 157 +++--- src/poetry.lock | 1111 ++++++++++++++++++++++++++++++++++++++++- src/pyproject.toml | 2 +- src/requirements.txt | 36 +- src/run.py | 19 +- tests/test_core.py | 177 ++----- tests/test_engine.py | 1 - tests/test_sensors.py | 55 ++ tests/test_vision.py | 172 +++---- 17 files changed, 1548 insertions(+), 499 deletions(-) diff --git a/Dockerfile b/Dockerfile index 90740461..f7db3fff 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,25 +1,24 @@ FROM python:3.8.16-slim # set environment variables -ENV PYTHONPATH "${PYTHONPATH}:/usr/src/app" -ENV PATH /usr/local/bin:$PATH -ENV LANG C.UTF-8 -ENV PYTHONUNBUFFERED 1 -ENV PYTHONDONTWRITEBYTECODE 1 - +ENV PATH="/usr/local/bin:$PATH" +ENV LANG="C.UTF-8" +ENV PYTHONUNBUFFERED=1 +ENV PYTHONDONTWRITEBYTECODE=1 # set work directory WORKDIR /usr/src/app -COPY ./README.md /tmp/README.md COPY ./setup.py /tmp/setup.py # install git RUN apt-get update && apt-get install git -y -COPY ./src/requirements.txt /tmp/requirements.txt + RUN apt-get update && apt-get install ffmpeg libsm6 libxext6 -y\ - && pip install --upgrade pip setuptools wheel \ - && pip install --default-timeout=500 -r /tmp/requirements.txt \ + && pip install --upgrade pip setuptools wheel + +COPY ./src/requirements.txt /tmp/requirements.txt +RUN pip install --default-timeout=500 -r /tmp/requirements.txt \ && pip cache purge \ && rm -rf /root/.cache/pip diff --git a/Makefile b/Makefile index 3f09854a..d740a3c0 100644 --- a/Makefile +++ b/Makefile @@ -40,7 +40,6 @@ build-optional-lib: # Run the engine wrapper run: - bash scripts/setup-docker-compose.sh docker build . -t pyronear/pyro-engine:latest docker compose up -d diff --git a/docker-compose.yml b/docker-compose.yml index ed13cbba..1b416755 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -13,10 +13,6 @@ services: - ./data:/usr/src/app/data restart: always network_mode: host - deploy: - resources: - limits: - cpus: "3" logging: driver: "json-file" options: diff --git a/pyproject.toml b/pyproject.toml index f3ff323e..79214e25 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,12 +30,9 @@ classifiers = [ ] dynamic = ["version"] dependencies = [ - "Pillow>=8.4.0", - "onnxruntime>=1.10.0,<2.0.0", - "numpy>=1.19.5,<2.0.0", + "ultralytics==8.2.50", "pyroclient @ git+https://github.com/pyronear/pyro-api.git@767be30a781b52b29d68579d543e3f45ac8c4713#egg=pyroclient&subdirectory=client", "requests>=2.20.0,<3.0.0", - "opencv-python==4.5.5.64", "tqdm>=4.62.0", "huggingface_hub==0.23.1", ] @@ -43,6 +40,7 @@ dependencies = [ [project.optional-dependencies] test = [ "pytest>=5.3.2", + "pytest-asyncio>=0.14.0", "coverage[toml]>=4.5.4", "requests>=2.20.0,<3.0.0", "python-dotenv>=0.15.0", @@ -66,6 +64,7 @@ docs = [ dev = [ # test "pytest>=5.3.2", + "pytest-asyncio>=0.14.0", "coverage[toml]>=4.5.4", "requests>=2.20.0,<3.0.0", # style @@ -93,7 +92,6 @@ zip-safe = true [tool.setuptools.packages.find] exclude = ["docs*", "scripts*", "tests*", "src*"] - [tool.mypy] files = "pyroengine/" show_error_codes = true diff --git a/pyroengine/core.py b/pyroengine/core.py index 8789acca..8ad82f38 100644 --- a/pyroengine/core.py +++ b/pyroengine/core.py @@ -3,24 +3,20 @@ # This program is licensed under the Apache License 2.0. # See LICENSE or go to for full license details. +import asyncio import logging -import signal +import time from datetime import datetime -from multiprocessing import Manager, Pool -from multiprocessing import Queue as MPQueue -from types import FrameType -from typing import List, Optional, Tuple, cast +from typing import Any, List import numpy as np import urllib3 -from PIL import Image from .engine import Engine from .sensors import ReolinkCamera __all__ = ["SystemController", "is_day_time"] - urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) # Configure logging @@ -28,26 +24,28 @@ def is_day_time(cache, frame, strategy, delta=0): - """This function allows to know if it is daytime or not. We have two strategies. - The first one is to take the current time and compare it to the sunset time. - The second is to see if we have a color image. The ir cameras switch to ir mode at night and - therefore produce black and white images. This function can use one or more strategies depending on the use case. + """ + Determines if it is daytime using specified strategies. + + Strategies: + 1. Time-based: Compares the current time with sunrise and sunset times. + 2. IR-based: Analyzes the color of the image; IR cameras produce black and white images at night. Args: - cache (Path): cache folder where sunset_sunrise.txt is located - frame (PIL image): frame to analyze with ir strategy - strategy (str): Strategy to define day time [None, time, ir or both] - delta (int): delta before and after sunset / sunrise in sec + cache (Path): Cache folder where `sunset_sunrise.txt` is located. + frame (PIL.Image): Frame to analyze with the IR strategy. + strategy (str): Strategy to define daytime ("time", "ir", or "both"). + delta (int): Time delta in seconds before and after sunrise/sunset. Returns: - bool: is day time + bool: True if it is daytime, False otherwise. """ is_day = True if strategy in ["both", "time"]: with open(cache.joinpath("sunset_sunrise.txt")) as f: lines = f.readlines() - sunrise = datetime.strptime(lines[0][:-1], "%H:%M") - sunset = datetime.strptime(lines[1][:-1], "%H:%M") + sunrise = datetime.strptime(lines[0].strip(), "%H:%M") + sunset = datetime.strptime(lines[1].strip(), "%H:%M") now = datetime.strptime(datetime.now().isoformat().split("T")[1][:5], "%H:%M") if (now - sunrise).total_seconds() < -delta or (sunset - now).total_seconds() < -delta: is_day = False @@ -60,95 +58,89 @@ def is_day_time(cache, frame, strategy, delta=0): return is_day -def handler(signum: int, frame: Optional[FrameType]) -> None: - """ - Signal handler for timeout. - - Args: - signum (int): The signal number. - frame (Optional[FrameType]): The current stack frame (or None). - """ - raise Exception("Analyze stream timeout") - - -def capture_camera_image(args: Tuple[ReolinkCamera, MPQueue]) -> None: +async def capture_camera_image(camera: ReolinkCamera, image_queue: asyncio.Queue) -> None: """ Captures an image from the camera and puts it into a queue. Args: - args (tuple): A tuple containing the camera instance and a queue. + camera (ReolinkCamera): The camera instance. + image_queue (asyncio.Queue): The queue to put the captured image. """ - camera, queue = args - cam_id = camera.ip_address try: if camera.cam_type == "ptz": - for pose_id in camera.cam_poses: + for idx, pose_id in enumerate(camera.cam_poses): cam_id = f"{camera.ip_address}_{pose_id}" - frame = camera.capture(pose_id) + frame = camera.capture() + # Move camera to the next pose to avoid waiting + next_pos_id = camera.cam_poses[(idx + 1) % len(camera.cam_poses)] + camera.move_camera("ToPos", idx=int(next_pos_id), speed=50) if frame is not None: - queue.put((cam_id, frame)) + await image_queue.put((cam_id, frame)) + await asyncio.sleep(0) # Yield control else: frame = camera.capture() if frame is not None: - queue.put((cam_id, frame)) + await image_queue.put((cam_id, frame)) + await asyncio.sleep(0) # Yield control except Exception as e: logging.exception(f"Error during image capture from camera {cam_id}: {e}") class SystemController: """ - Implements the full system controller for capturing and analyzing camera streams. + Controls the system for capturing and analyzing camera streams. Attributes: engine (Engine): The image analyzer engine. - cameras (List[ReolinkCamera]): The list of cameras to get the visual streams from. + cameras (List[ReolinkCamera]): List of cameras to capture streams from. """ def __init__(self, engine: Engine, cameras: List[ReolinkCamera]) -> None: """ - Initializes the SystemController with an engine and a list of cameras. + Initializes the SystemController. Args: engine (Engine): The image analyzer engine. - cameras (List[ReolinkCamera]): The list of cameras to get the visual streams from. + cameras (List[ReolinkCamera]): List of cameras to capture streams from. """ self.engine = engine self.cameras = cameras self.day_time = True - def capture_images(self) -> MPQueue: + async def capture_images(self, image_queue: asyncio.Queue) -> None: """ - Captures images from all cameras using multiprocessing. + Captures images from all cameras using asyncio. - Returns: - MPQueue: A queue containing the captured images and their camera IDs. + Args: + image_queue (asyncio.Queue): The queue to put the captured images. """ + tasks = [capture_camera_image(camera, image_queue) for camera in self.cameras] + await asyncio.gather(*tasks) - manager = Manager() - queue: MPQueue = cast(MPQueue, manager.Queue()) # Cast to MPQueue - - # Create a list of arguments to pass to capture_camera_image - args_list: List[Tuple[ReolinkCamera, MPQueue]] = [(camera, queue) for camera in self.cameras] - - # Use a pool of processes to capture images concurrently - with Pool(processes=len(self.cameras)) as pool: - pool.map(capture_camera_image, args_list) - - return queue - - def analyze_stream(self, img: Image.Image, cam_id: str) -> None: + async def analyze_stream(self, image_queue: asyncio.Queue) -> None: """ - Analyzes the image stream from a specific camera. + Analyzes the image stream from the queue. Args: - img (Image.Image): The image to analyze. - cam_id (str): The ID of the camera. + image_queue (asyncio.Queue): The queue with images to analyze. """ - # Run the prediction using the engine - self.engine.predict(img, cam_id) + while True: + item = await image_queue.get() + if item is None: + break + cam_id, frame = item + try: + self.engine.predict(frame, cam_id) + except Exception as e: + logging.error(f"Error running prediction: {e}") + finally: + image_queue.task_done() # Mark the task as done def check_day_time(self) -> None: + """ + Checks and updates the day_time attribute based on the current frame. + """ try: frame = self.cameras[0].capture() if frame is not None: @@ -156,43 +148,31 @@ def check_day_time(self) -> None: except Exception as e: logging.exception(f"Exception during initial day time check: {e}") - def run(self, period: int = 30) -> None: + async def run(self, period: int = 30) -> None: """ Captures and analyzes all camera streams, then processes alerts. Args: period (int): The time period between captures in seconds. """ - try: - # Set the signal alarm - signal.signal(signal.SIGALRM, handler) - signal.alarm(period) - - if not self.day_time: - self.check_day_time() + self.check_day_time() if self.day_time: - # Capture images - queue = None - try: - queue = self.capture_images() - except Exception as e: - logging.error(f"Error capturing images: {e}") - - # Analyze each captured frame - if queue: - while not queue.empty(): - cam_id, frame = queue.get() - try: - if frame is not None: - self.analyze_stream(frame, cam_id) - except Exception as e: - logging.error(f"Error running prediction: {e}") - - # Use the last frame to check if it's day_time - if frame is not None: - self.day_time = is_day_time(None, frame, "ir") + image_queue: asyncio.Queue[Any] = asyncio.Queue() + + # Start the image processor task + processor_task = asyncio.create_task(self.analyze_stream(image_queue)) + + # Capture images concurrently + await self.capture_images(image_queue) + + # Wait for the image processor to finish processing + await image_queue.join() # Ensure all tasks are marked as done + + # Signal the image processor to stop processing + await image_queue.put(None) + await processor_task # Ensure the processor task completes # Process alerts try: @@ -200,10 +180,24 @@ def run(self, period: int = 30) -> None: except Exception as e: logging.error(f"Error processing alerts: {e}") - # Disable the alarm - signal.alarm(0) - except Exception: - logging.warning("Analyze stream timeout") + except Exception as e: + logging.warning(f"Analyze stream error: {e}") + + async def main_loop(self, period: int) -> None: + """ + Main loop to capture and process images at regular intervals. + + Args: + period (int): The time period between captures in seconds. + """ + while True: + start_ts = time.time() + await self.run(period) + # Sleep only once all images are processed + loop_time = time.time() - start_ts + sleep_time = max(period - (loop_time), 0) + logging.info(f"Loop run under {loop_time:.2f} seconds, sleeping for {sleep_time:.2f}") + await asyncio.sleep(sleep_time) def __repr__(self) -> str: """ diff --git a/pyroengine/engine.py b/pyroengine/engine.py index aec5e371..6c71f0f8 100644 --- a/pyroengine/engine.py +++ b/pyroengine/engine.py @@ -15,7 +15,6 @@ from pathlib import Path from typing import Any, Dict, Optional, Tuple -import cv2 # type: ignore[import-untyped] import numpy as np from PIL import Image from pyroclient import client @@ -57,12 +56,12 @@ class Engine: >>> "cam_id_1": {'login':'log1', 'password':'pwd1'}, >>> "cam_id_2": {'login':'log2', 'password':'pwd2'}, >>> } - >>> pyroEngine = Engine("data/model.onnx", 0.25, 'https://api.pyronear.org', cam_creds, 48.88, 2.38) + >>> pyroEngine = Engine(None, 0.25, 'https://api.pyronear.org', cam_creds, 48.88, 2.38) """ def __init__( self, - model_path: Optional[str] = "data/model.onnx", + model_path: Optional[str] = None, conf_thresh: float = 0.25, api_url: Optional[str] = None, cam_creds: Optional[Dict[str, Dict[str, str]]] = None, @@ -83,7 +82,7 @@ def __init__( """Init engine""" # Engine Setup - self.model = Classifier(model_path) + self.model = Classifier(model_path=model_path) self.conf_thresh = conf_thresh # API Setup @@ -120,12 +119,12 @@ def __init__( "ongoing": False, } - self.occlusion_masks = {"-1": None} + self.occlusion_masks: Dict[str, Optional[np.ndarray]] = {"-1": None} if isinstance(cam_creds, dict): for cam_id in cam_creds: mask_file = cache_folder + "/occlusion_masks/" + cam_id + ".jpg" if os.path.isfile(mask_file): - self.occlusion_masks[cam_id] = cv2.imread(mask_file, 0) + self.occlusion_masks[cam_id] = np.array(Image.open(mask_file).convert(("L"))) else: self.occlusion_masks[cam_id] = None diff --git a/pyroengine/sensors.py b/pyroengine/sensors.py index bf4b65c2..62131006 100644 --- a/pyroengine/sensors.py +++ b/pyroengine/sensors.py @@ -55,6 +55,9 @@ def __init__( self.cam_poses = cam_poses if cam_poses is not None else [] self.protocol = protocol + if len(self.cam_poses): + self.move_camera("ToPos", idx=int(self.cam_poses[0]), speed=50) + def _build_url(self, command: str) -> str: """Constructs a URL for API commands to the camera.""" return ( @@ -198,3 +201,33 @@ def delete_ptz_preset(self, idx: int): response = requests.post(url, json=data, verify=False) # nosec: B501 # Utilizing the shared response handling method self._handle_response(response, f"Preset {idx} deleted successfully.") + + def reboot_camera(self): + url = self._build_url("Reboot") + data = [{"cmd": "Reboot"}] + response = requests.post(url, json=data, verify=False) + return self._handle_response(response, "Camera reboot initiated successfully.") + + def get_auto_focus(self): + url = self._build_url("GetAutoFocus") + data = [{"cmd": "GetAutoFocus", "action": 1, "param": {"channel": 0}}] + response = requests.post(url, json=data, verify=False) + return self._handle_response(response, "Fetched AutoFocus settings successfully.") + + def set_auto_focus(self, disable: bool): + url = self._build_url("SetAutoFocus") + data = [{"cmd": "SetAutoFocus", "action": 0, "param": {"AutoFocus": {"channel": 0, "disable": int(disable)}}}] + response = requests.post(url, json=data, verify=False) + return self._handle_response(response, "Set AutoFocus settings successfully.") + + def start_zoom_focus(self, position: int): + url = self._build_url("StartZoomFocus") + data = [ + { + "cmd": "StartZoomFocus", + "action": 0, + "param": {"ZoomFocus": {"channel": 0, "pos": position, "op": "ZoomPos"}}, + } + ] + response = requests.post(url, json=data, verify=False) + return self._handle_response(response, "Started ZoomFocus successfully.") diff --git a/pyroengine/utils.py b/pyroengine/utils.py index ba68d4a3..30296353 100644 --- a/pyroengine/utils.py +++ b/pyroengine/utils.py @@ -4,65 +4,10 @@ # See LICENSE or go to for full license details. -import cv2 # type: ignore[import-untyped] import numpy as np from tqdm import tqdm # type: ignore[import-untyped] -__all__ = ["nms", "xywh2xyxy", "DownloadProgressBar", "letterbox"] - - -def xywh2xyxy(x: np.ndarray): - y = np.copy(x) - y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x - y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y - y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x - y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y - return y - - -def letterbox( - im: np.ndarray, new_shape: tuple = (640, 640), color: tuple = (114, 114, 114), auto: bool = False, stride: int = 32 -): - """Letterbox image transform for yolo models - Args: - im (np.ndarray): Input image - new_shape (tuple, optional): Image size. Defaults to (640, 640). - color (tuple, optional): Pixel fill value for the area outside the transformed image. - Defaults to (114, 114, 114). - auto (bool, optional): auto padding. Defaults to False. - stride (int, optional): padding stride. Defaults to 32. - Returns: - np.ndarray: Output image - """ - # Resize and pad image while meeting stride-multiple constraints - im = np.array(im) - shape = im.shape[:2] # current shape [height, width] - if isinstance(new_shape, int): - new_shape = (new_shape, new_shape) - - # Scale ratio (new / old) - r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) - - # Compute padding - new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) - dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding - - if auto: # minimum rectangle - dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding - - dw /= 2 # divide padding into 2 sides - dh /= 2 - - if shape[::-1] != new_unpad: # resize - im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) - top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) - left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) - # add border - h, w = im.shape[:2] - im_b = np.zeros((h + top + bottom, w + left + right, 3)) + color - im_b[top : top + h, left : left + w, :] = im - - return im_b.astype("uint8"), (left, top) +__all__ = ["nms", "DownloadProgressBar"] def box_iou(box1: np.ndarray, box2: np.ndarray, eps: float = 1e-7): diff --git a/pyroengine/vision.py b/pyroengine/vision.py index 9bac5dc8..17e54457 100644 --- a/pyroengine/vision.py +++ b/pyroengine/vision.py @@ -4,23 +4,29 @@ # See LICENSE or go to for full license details. import json +import logging import os -from typing import Optional, Tuple +import platform +import shutil +from typing import Optional from urllib.request import urlretrieve import numpy as np -import onnxruntime from huggingface_hub import HfApi # type: ignore[import-untyped] from PIL import Image +from ultralytics import YOLO # type: ignore[import-untyped] -from .utils import DownloadProgressBar, letterbox, nms, xywh2xyxy +from .utils import DownloadProgressBar __all__ = ["Classifier"] -MODEL_URL = "https://huggingface.co/pyronear/yolov8s/resolve/main/model.onnx" +MODEL_URL_FOLDER = "https://huggingface.co/pyronear/yolov8s/resolve/main/" MODEL_ID = "pyronear/yolov8s" -MODEL_NAME = "model.onnx" -METADATA_PATH = "data/model_metadata.json" +MODEL_NAME = "yolov8s.pt" +METADATA_NAME = "model_metadata.json" + + +logging.basicConfig(format="%(asctime)s | %(levelname)s: %(message)s", level=logging.INFO, force=True) # Utility function to save metadata @@ -30,7 +36,7 @@ def save_metadata(metadata_path, metadata): class Classifier: - """Implements an image classification model using ONNX backend. + """Implements an image classification model using YOLO backend. Examples: >>> from pyroengine.vision import Classifier @@ -40,56 +46,78 @@ class Classifier: model_path: model path """ - def __init__(self, model_path: Optional[str] = "data/model.onnx", img_size: tuple = (640, 640)) -> None: + def __init__(self, model_folder="data", imgsz=1024, conf=0.15, iou=0.05, format="ncnn", model_path=None) -> None: if model_path is None: - model_path = "data/model.onnx" - - # Get the expected SHA256 from Hugging Face - api = HfApi() - model_info = api.model_info(MODEL_ID, files_metadata=True) - expected_sha256 = self.get_sha(model_info.siblings) - - if not expected_sha256: - raise ValueError("SHA256 hash for the model file not found in the Hugging Face model metadata.") - - # Check if the model file exists - if os.path.isfile(model_path): - # Load existing metadata - metadata = self.load_metadata(METADATA_PATH) - if metadata and metadata.get("sha256") == expected_sha256: - print("Model already exists and the SHA256 hash matches. No download needed.") + if format == "ncnn": + if self.is_arm_architecture(): + model = "yolov8s_ncnn_model.zip" + else: + logging.info("NCNN format is optimized for arm architecture only, switching to onnx") + model = "yolov8s.onnx" + elif format in ["onnx", "pt"]: + model = f"yolov8s.{format}" + + model_path = os.path.join(model_folder, model) + metadata_path = os.path.join(model_folder, METADATA_NAME) + model_url = MODEL_URL_FOLDER + model + + # Get the expected SHA256 from Hugging Face + api = HfApi() + model_info = api.model_info(MODEL_ID, files_metadata=True) + expected_sha256 = self.get_sha(model_info.siblings) + + if not expected_sha256: + raise ValueError("SHA256 hash for the model file not found in the Hugging Face model metadata.") + + # Check if the model file exists + if os.path.isfile(model_path): + # Load existing metadata + metadata = self.load_metadata(metadata_path) + if metadata and metadata.get("sha256") == expected_sha256: + logging.info("Model already exists and the SHA256 hash matches. No download needed.") + else: + logging.info("Model exists but the SHA256 hash does not match or the file doesn't exist.") + os.remove(model_path) + self.download_model(model_url, model_path, expected_sha256, metadata_path) else: - print("Model exists but the SHA256 hash does not match or the file doesn't exist.") - os.remove(model_path) - self.download_model(model_path, expected_sha256) - else: - self.download_model(model_path, expected_sha256) + self.download_model(model_url, model_path, expected_sha256, metadata_path) + + file_name, ext = os.path.splitext(model_path) + if ext == ".zip": + if not os.path.isdir(file_name): + shutil.unpack_archive(model_path, model_folder) + model_path = file_name + + self.model = YOLO(model_path, task="detect") + self.imgsz = imgsz + self.conf = conf + self.iou = iou - self.ort_session = onnxruntime.InferenceSession(model_path) - self.img_size = img_size + def is_arm_architecture(self): + # Check for ARM architecture + return platform.machine().startswith("arm") or platform.machine().startswith("aarch") def get_sha(self, siblings): # Extract the SHA256 hash from the model files metadata for file in siblings: if file.rfilename == os.path.basename(MODEL_NAME): - expected_sha256 = file.lfs.sha256 - break - return expected_sha256 + return file.lfs["sha256"] + return None - def download_model(self, model_path, expected_sha256): + def download_model(self, model_url, model_path, expected_sha256, metadata_path): # Ensure the directory exists os.makedirs(os.path.split(model_path)[0], exist_ok=True) # Download the model - print(f"Downloading model from {MODEL_URL} ...") + logging.info(f"Downloading model from {model_url} ...") with DownloadProgressBar(unit="B", unit_scale=True, miniters=1, desc=model_path) as t: - urlretrieve(MODEL_URL, model_path, reporthook=t.update_to) - print("Model downloaded!") + urlretrieve(model_url, model_path, reporthook=t.update_to) + logging.info("Model downloaded!") # Save the metadata metadata = {"sha256": expected_sha256} - save_metadata(METADATA_PATH, metadata) - print("Metadata saved!") + save_metadata(metadata_path, metadata) + logging.info("Metadata saved!") # Utility function to load metadata def load_metadata(self, metadata_path): @@ -98,51 +126,14 @@ def load_metadata(self, metadata_path): return json.load(f) return None - def preprocess_image(self, pil_img: Image.Image) -> Tuple[np.ndarray, Tuple[int, int]]: - """Preprocess an image for inference - - Args: - pil_img: A valid PIL image. - - Returns: - A tuple containing: - - The resized and normalized image of shape (1, C, H, W). - - Padding information as a tuple of integers (pad_height, pad_width). - """ - - np_img, pad = letterbox(np.array(pil_img), self.img_size) # Applies letterbox resize with padding - np_img = np.expand_dims(np_img.astype("float"), axis=0) # Add batch dimension - np_img = np.ascontiguousarray(np_img.transpose((0, 3, 1, 2))) # Convert from BHWC to BCHW format - np_img = np_img.astype("float32") / 255 # Normalize to [0, 1] + def __call__(self, pil_img: Image.Image, occlusion_mask: Optional[np.ndarray] = None) -> np.ndarray: - return np_img, pad + results = self.model(pil_img, imgsz=self.imgsz, conf=self.conf, iou=self.iou) + y = np.concatenate( + (results[0].boxes.xyxyn.cpu().numpy(), results[0].boxes.conf.cpu().numpy().reshape((-1, 1))), axis=1 + ) - def __call__(self, pil_img: Image.Image, occlusion_mask: Optional[np.ndarray] = None) -> np.ndarray: - np_img, pad = self.preprocess_image(pil_img) - - # ONNX inference - y = self.ort_session.run(["output0"], {"images": np_img})[0][0] - # Drop low conf for speed-up - y = y[:, y[-1, :] > 0.05] - # Post processing - y = np.transpose(y) - y = xywh2xyxy(y) - # Sort by confidence - y = y[y[:, 4].argsort()] - y = nms(y) - y = y[::-1] - - # Normalize preds - if len(y) > 0: - # Remove padding - left_pad, top_pad = pad - y[:, :4:2] -= left_pad - y[:, 1:4:2] -= top_pad - y[:, :4:2] /= self.img_size[1] - 2 * 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python_version >= "3.8" and python_version < "4.0" +ultralytics-thop==2.0.0 ; python_version >= "3.8" and python_version < "4.0" +ultralytics==8.2.50 ; python_version >= "3.8" and python_version < "4.0" urllib3==2.2.2 ; python_version >= "3.8" and python_version < "4" +zipp==3.19.2 ; python_version >= "3.8" and python_version < "3.10" diff --git a/src/run.py b/src/run.py index 64a1f121..f6951359 100644 --- a/src/run.py +++ b/src/run.py @@ -4,11 +4,10 @@ # See LICENSE or go to for full license details. import argparse +import asyncio import json import logging import os -import time -from pathlib import Path import urllib3 from dotenv import load_dotenv @@ -28,6 +27,7 @@ def main(args): # .env loading load_dotenv(".env") API_URL = os.environ.get("API_URL") + API_URL = "https://api.pyronear.org" LAT = float(os.environ.get("LAT")) LON = float(os.environ.get("LON")) assert isinstance(API_URL, str) and isinstance(LAT, float) and isinstance(LON, float) @@ -52,13 +52,8 @@ def main(args): cameras.append(ReolinkCamera(_ip, CAM_USER, CAM_PWD, cam_data["type"], cam_poses, args.protocol)) - # Check if model is available in cache - cache = Path(args.cache) - - model_path = cache.joinpath("model.onnx") if args.model_path is None else args.model_path - engine = Engine( - model_path, + args.model_path, args.thresh, API_URL, splitted_cam_creds, @@ -80,11 +75,7 @@ def main(args): cameras, ) - while True: - start_ts = time.time() - sys_controller.run(args.period) - # Sleep only once all images are processed - time.sleep(max(args.period - time.time() + start_ts, 0)) + asyncio.run(sys_controller.main_loop(args.period)) if __name__ == "__main__": @@ -92,7 +83,7 @@ def main(args): description="Raspberry Pi system controller", formatter_class=argparse.ArgumentDefaultsHelpFormatter ) # Model - parser.add_argument("--model_path", type=str, default="data/model.onnx", help="model path") + parser.add_argument("--model_path", type=str, default=None, help="model path") parser.add_argument("--thresh", type=float, default=0.15, help="Confidence threshold") # Camera & cache parser.add_argument("--creds", type=str, default="data/credentials.json", help="Camera credentials") diff --git a/tests/test_core.py b/tests/test_core.py index 20615db4..071fe8ab 100644 --- a/tests/test_core.py +++ b/tests/test_core.py @@ -1,13 +1,11 @@ -import time +import asyncio from datetime import datetime -from multiprocessing import Queue -from unittest.mock import MagicMock, patch +from unittest.mock import AsyncMock, MagicMock, patch -import numpy as np import pytest from PIL import Image -from pyroengine.core import SystemController, capture_camera_image, is_day_time +from pyroengine.core import SystemController, is_day_time @pytest.fixture @@ -47,11 +45,11 @@ def system_controller_ptz(mock_engine, mock_cameras_ptz): def test_is_day_time_ir_strategy(mock_wildfire_image): - # Use the mock_forest_stream image to simulate daylight image + # Use day image assert is_day_time(None, mock_wildfire_image, "ir") - # Create a black and white image to simulate night image - frame = Image.fromarray(np.zeros((100, 100, 3), dtype=np.uint8)) + # Create a grayscale image to simulate night image + frame = Image.new("RGB", (100, 100), (255, 255, 255)) assert not is_day_time(None, frame, "ir") @@ -73,144 +71,62 @@ def test_is_day_time_time_strategy(tmp_path): assert not is_day_time(cache, None, "time") -def test_capture_images(system_controller): - queue = Queue(maxsize=10) - for camera in system_controller.cameras: - capture_camera_image((camera, queue)) +@pytest.mark.asyncio +async def test_capture_images(system_controller): + queue = asyncio.Queue(maxsize=10) + await system_controller.capture_images(queue) assert queue.qsize() == 1 - cam_id, frame = queue.get(timeout=1) # Use timeout to wait for the item + cam_id, frame = await queue.get() # Use timeout to wait for the item assert cam_id == "192.168.1.1" assert isinstance(frame, Image.Image) -def test_capture_images_ptz(system_controller_ptz): - queue = Queue(maxsize=10) - for camera in system_controller_ptz.cameras: - capture_camera_image((camera, queue)) - - # Retry logic to account for potential timing issues - retries = 10 - while retries > 0 and queue.qsize() < 2: - time.sleep(0.1) - retries -= 1 +@pytest.mark.asyncio +async def test_capture_images_ptz(system_controller_ptz): + queue = asyncio.Queue(maxsize=10) + await system_controller_ptz.capture_images(queue) assert queue.qsize() == 2 - cam_id, frame = queue.get(timeout=1) # Use timeout to wait for the item + cam_id, frame = await queue.get() # Use timeout to wait for the item assert cam_id == "192.168.1.1_1" assert isinstance(frame, Image.Image) -def test_analyze_stream(system_controller): +@pytest.mark.asyncio +async def test_analyze_stream(system_controller): + queue = asyncio.Queue() mock_frame = Image.new("RGB", (100, 100)) - cam_id = "192.168.1.1" - system_controller.analyze_stream(mock_frame, cam_id) - system_controller.engine.predict.assert_called_once_with(mock_frame, cam_id) - - -def test_run(system_controller): - with patch.object(system_controller, "capture_images", return_value=Queue()), patch.object( - system_controller, "analyze_stream" - ), patch.object(system_controller.engine, "_process_alerts"), patch("signal.signal"), patch("signal.alarm"), patch( - "time.sleep", side_effect=InterruptedError - ): # Mock sleep to exit the loop - - try: - system_controller.run(period=2) - except InterruptedError: - pass - - -def test_run_no_images(system_controller): - with patch.object(system_controller, "capture_images", return_value=Queue()), patch.object( - system_controller, "analyze_stream" - ) as mock_analyze_stream, patch.object(system_controller.engine, "_process_alerts"), patch("signal.signal"), patch( - "signal.alarm" - ), patch( - "time.sleep", side_effect=InterruptedError - ): # Mock sleep to exit the loop - - try: - system_controller.run(period=2) - except InterruptedError: - pass - - mock_analyze_stream.assert_not_called() - - -def test_run_capture_exception(system_controller): - with patch.object(system_controller, "capture_images", side_effect=Exception("Capture error")), patch.object( - system_controller.engine, "_process_alerts" - ), patch("signal.signal"), patch("signal.alarm"), patch( - "time.sleep", side_effect=InterruptedError - ): # Mock sleep to exit the loop - - try: - system_controller.run(period=2) - except InterruptedError: - pass - - -def test_capture_camera_image_exception(): - queue = Queue(maxsize=10) - camera = MagicMock() - camera.cam_type = "static" - camera.ip_address = "192.168.1.1" - camera.capture.side_effect = Exception("Capture error") - - capture_camera_image((camera, queue)) - - assert queue.qsize() == 0 - - -def test_repr_method(system_controller): - repr_str = repr(system_controller) - # Check if the representation is a string - assert isinstance(repr_str, str) - - -def test_repr_method_no_cameras(mock_engine): - system_controller = SystemController(engine=mock_engine, cameras=[]) - repr_str = repr(system_controller) - # Check if the representation is a string - assert isinstance(repr_str, str) + await queue.put(("192.168.1.1", mock_frame)) + analyze_task = asyncio.create_task(system_controller.analyze_stream(queue)) + await queue.put(None) # Signal the end of the stream + await analyze_task -def test_capture_camera_image(): - queue = Queue(maxsize=10) - camera = MagicMock() - camera.cam_type = "static" - camera.ip_address = "192.168.1.1" - camera.capture.return_value = Image.new("RGB", (100, 100)) # Mock captured image + system_controller.engine.predict.assert_called_once_with(mock_frame, "192.168.1.1") - capture_camera_image((camera, queue)) - assert queue.qsize() == 1 - cam_id, frame = queue.get(timeout=1) # Use timeout to wait for the item - assert cam_id == "192.168.1.1" - assert isinstance(frame, Image.Image) +@pytest.mark.asyncio +async def test_capture_images_method(system_controller): + with patch("pyroengine.core.capture_camera_image", new_callable=AsyncMock) as mock_capture: + queue = asyncio.Queue() + await system_controller.capture_images(queue) + for camera in system_controller.cameras: + mock_capture.assert_any_call(camera, queue) + assert mock_capture.call_count == len(system_controller.cameras) -def test_capture_camera_image_ptz(): - queue = Queue(maxsize=10) - camera = MagicMock() - camera.cam_type = "ptz" - camera.cam_poses = [1, 2] - camera.ip_address = "192.168.1.1" - camera.capture.return_value = Image.new("RGB", (100, 100)) # Mock captured image - capture_camera_image((camera, queue)) +@pytest.mark.asyncio +async def test_analyze_stream_method(system_controller): + queue = asyncio.Queue() + mock_frame = Image.new("RGB", (100, 100)) + await queue.put(("192.168.1.1", mock_frame)) + await queue.put(None) # Signal the end of the stream - # Retry logic to account for potential timing issues - retries = 10 - while retries > 0 and queue.qsize() < 2: - time.sleep(0.1) - retries -= 1 + await system_controller.analyze_stream(queue) - assert queue.qsize() == 2 - cam_id, frame = queue.get(timeout=1) # Use timeout to wait for the item - assert cam_id == "192.168.1.1_1" - assert isinstance(frame, Image.Image) + system_controller.engine.predict.assert_called_once_with(mock_frame, "192.168.1.1") def test_check_day_time(system_controller): @@ -230,3 +146,16 @@ def test_check_day_time(system_controller): system_controller.check_day_time() mock_is_day_time.assert_called_once() mock_logging_exception.assert_called_once_with("Exception during initial day time check: Error in is_day_time") + + +def test_repr_method(system_controller): + repr_str = repr(system_controller) + # Check if the representation is a string + assert isinstance(repr_str, str) + + +def test_repr_method_no_cameras(mock_engine): + system_controller = SystemController(engine=mock_engine, cameras=[]) + repr_str = repr(system_controller) + # Check if the representation is a string + assert isinstance(repr_str, str) diff --git a/tests/test_engine.py b/tests/test_engine.py index 1f2a48ee..9df30146 100644 --- a/tests/test_engine.py +++ b/tests/test_engine.py @@ -92,7 +92,6 @@ def test_engine_online(tmpdir_factory, mock_wildfire_stream, mock_wildfire_image if isinstance(api_url, str): engine = Engine( - folder + "model.onnx", api_url=api_url, cam_creds=cam_creds, latitude=float(lat), diff --git a/tests/test_sensors.py b/tests/test_sensors.py index f29679ea..d3d6dda8 100644 --- a/tests/test_sensors.py +++ b/tests/test_sensors.py @@ -127,3 +127,58 @@ def test_delete_ptz_preset_failure(): camera.delete_ptz_preset(idx=1) # Assert that a failed operation logs an error message assert mock_response.json.call_count == 1 + + +def test_reboot_camera_success(): + # Mock the response of requests.post to return a successful response for reboot + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = [{"code": 0}] + + with patch("requests.post", return_value=mock_response): + camera = ReolinkCamera("192.168.1.1", "login", "pwd", "static") + response = camera.reboot_camera() + # Assert that the reboot_camera method was called successfully + assert mock_response.json.call_count == 1 + assert response == mock_response.json.return_value + + +def test_get_auto_focus_success(): + # Mock the response of requests.post to return a successful response with autofocus data + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = [{"code": 0, "value": {"AutoFocus": [{"channel": 0, "disable": 0}]}}] + + with patch("requests.post", return_value=mock_response): + camera = ReolinkCamera("192.168.1.1", "login", "pwd", "static") + response = camera.get_auto_focus() + # Assert that the get_auto_focus method returns the correct data + assert response == mock_response.json.return_value + + +def test_set_auto_focus_success(): + # Mock the response of requests.post to return a successful response for setting autofocus + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = [{"code": 0}] + + with patch("requests.post", return_value=mock_response): + camera = ReolinkCamera("192.168.1.1", "login", "pwd", "static") + response = camera.set_auto_focus(disable=True) + # Assert that the set_auto_focus method was called successfully + assert mock_response.json.call_count == 1 + assert response == mock_response.json.return_value + + +def test_start_zoom_focus_success(): + # Mock the response of requests.post to return a successful response for starting zoom focus + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = [{"code": 0}] + + with patch("requests.post", return_value=mock_response): + camera = ReolinkCamera("192.168.1.1", "login", "pwd", "ptz") + response = camera.start_zoom_focus(position=100) + # Assert that the start_zoom_focus method was called successfully + assert mock_response.json.call_count == 1 + assert response == mock_response.json.return_value diff --git a/tests/test_vision.py b/tests/test_vision.py index 98a378ff..7e788ced 100644 --- a/tests/test_vision.py +++ b/tests/test_vision.py @@ -1,108 +1,92 @@ +import datetime import os -from unittest.mock import MagicMock, patch +from unittest.mock import patch import numpy as np -import pytest from pyroengine.vision import Classifier -METADATA_PATH = "data/model_metadata.json" -model_path = "data/model.onnx" -sha = "9f1b1c2654d98bbed91e514ce20ea73a0a5fbd1111880f230d516ed40ea2dc58" +def get_creation_date(file_path): + if os.path.exists(file_path): -def custom_isfile_false(path): - if path == model_path: - return False # or True based on your test case - return True # Default behavior for other paths + # For Unix-like systems + stat = os.stat(file_path) + try: + creation_time = stat.st_birthtime + except AttributeError: + # On Unix, use the last modification time as a fallback + creation_time = stat.st_mtime + creation_date = datetime.datetime.fromtimestamp(creation_time) + return creation_date + else: + return None -def custom_isfile_true(path): - if path == model_path: - return True # or True based on your test case - return True # Default behavior for other paths - -# Test for the case : the model doesn't exist -def test_classifier(mock_wildfire_image): +def test_classifier(tmpdir_factory, mock_wildfire_image): print("test_classifier") - with patch("os.path.isfile", side_effect=custom_isfile_false): - # Instantiate the ONNX model - model = Classifier() - # Check preprocessing - out, pad = model.preprocess_image(mock_wildfire_image) - assert isinstance(out, np.ndarray) and out.dtype == np.float32 - assert out.shape == (1, 3, 640, 640) - assert isinstance(pad, tuple) - # Check inference - out = model(mock_wildfire_image) - assert out.shape == (1, 5) - conf = np.max(out[:, 4]) - assert conf >= 0 and conf <= 1 - - # Test mask - mask = np.ones((384, 640)) - out = model(mock_wildfire_image, mask) - print(out) - assert out.shape == (1, 5) - - mask = np.zeros((384, 640)) - out = model(mock_wildfire_image, mask) - print(out) - assert out.shape == (0, 5) - os.remove(model_path) - os.remove(METADATA_PATH) - - -# Test that the model is not loaded -def test_no_download(): - print("test_no_download") - data = {"sha256": sha} - with patch("os.path.isfile", side_effect=custom_isfile_true): - with patch("pyroengine.vision.Classifier.load_metadata", return_value=data): - with patch("onnxruntime.InferenceSession", return_value=None): - Classifier() - assert os.path.isfile(model_path) is False - - -# Test if sha are not the same -@patch("pyroengine.vision.urlretrieve") -@patch("pyroengine.vision.DownloadProgressBar") -def test_sha_inequality(mock_download_progress, mock_urlretrieve): - print("test_sha_inequality") - data = {"sha256": "falsesha"} - - # Mock urlretrieve to create a fake file - def fake_urlretrieve(url, filename, reporthook=None): - with open(filename, "w") as f: - f.write("fake model content") - - mock_urlretrieve.side_effect = fake_urlretrieve - # Mock the DownloadProgressBar context manager - mock_progress_bar_instance = MagicMock() - mock_download_progress.return_value.__enter__.return_value = mock_progress_bar_instance - - with patch("os.path.isfile", side_effect=custom_isfile_true): - with patch("pyroengine.vision.Classifier.load_metadata", return_value=data): - with patch( - "pyroengine.vision.Classifier.get_sha", - return_value=sha, - ): - with patch("onnxruntime.InferenceSession", return_value=None): - with patch("os.remove", return_value=True): - model = Classifier() - - assert os.path.isfile(model_path) is True - assert model.load_metadata("non_existent_metadata.json") is None - os.remove(model_path) - os.remove(METADATA_PATH) + folder = str(tmpdir_factory.mktemp("engine_cache")) + + # Instantiate the ONNX model + model = Classifier(model_folder=folder) + # Check inference + out = model(mock_wildfire_image) + assert out.shape[1] == 5 + conf = np.max(out[:, 4]) + assert 0 <= conf <= 1 + + # Test onnx model + model = Classifier(model_folder=folder, format="onnx") + model_path = os.path.join(folder, "yolov8s.onnx") + assert os.path.isfile(model_path) + + # Test mask + mask = np.ones((384, 640)) + out = model(mock_wildfire_image, mask) + assert out.shape == (1, 5) + + mask = np.zeros((384, 640)) + out = model(mock_wildfire_image, mask) + assert out.shape == (0, 5) + + # Test dl pt model + _ = Classifier(model_folder=folder, format="pt") + model_path = os.path.join(folder, "yolov8s.pt") + assert os.path.isfile(model_path) + + # Test dl ncnn model + with patch.object(Classifier, "is_arm_architecture", return_value=True): + _ = Classifier(model_folder=folder) + model_path = os.path.join(folder, "yolov8s_ncnn_model") + assert os.path.isdir(model_path) + + +def test_download(tmpdir_factory): + print("test_classifier") + folder = str(tmpdir_factory.mktemp("engine_cache")) + # Instantiate the ONNX model + _ = Classifier(model_folder=folder) -# Test for raising ValueError if expected_sha256 is not found -def test_raise_value_error_if_no_sha256(): - print("test_raise_value_error_if_no_sha256") - with patch("pyroengine.vision.Classifier.get_sha", return_value=""): - with pytest.raises( - ValueError, match="SHA256 hash for the model file not found in the Hugging Face model metadata." - ): - Classifier(model_path="non_existent_model.onnx") + model_path = os.path.join(folder, "yolov8s.onnx") + model_creation_date = get_creation_date(model_path) + + # No download if exist + _ = Classifier(model_folder=folder) + model_creation_date2 = get_creation_date(model_path) + assert model_creation_date == model_creation_date2 + + # Download if does not exist + os.remove(model_path) + _ = Classifier(model_folder=folder) + model_creation_date3 = get_creation_date(model_path) + print(model_creation_date, model_creation_date3) + assert model_creation_date != model_creation_date3 + + # Download if sha is not the same + with patch.object(Classifier, "get_sha", return_value="sha12"): + _ = Classifier(model_folder=folder) + model_creation_date4 = get_creation_date(model_path) + print(model_creation_date, model_creation_date3) + assert model_creation_date4 != model_creation_date3