merge

.github/workflows/builds.yml

@@ -13,7 +13,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest]
-        python: ['3.10']
+        python: ['3.9']
 
     steps:
       - uses: actions/checkout@v4

.github/workflows/doc-status.yml

@@ -9,7 +9,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.10
+          python-version: 3.9
           architecture: x64
       - name: check status
         run: |

.github/workflows/docs.yml

@@ -9,7 +9,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python: ['3.10']
+        python: ['3.9']
     steps:
       - uses: actions/checkout@v4
         with:

.github/workflows/gh-page.yml

@@ -6,10 +6,10 @@ jobs:
   see-page-build-payload:
     runs-on: ubuntu-latest
     steps:
-      - name: Set up Python 3.10
+      - name: Set up Python 3.9
         uses: actions/setup-python@v4
         with:
-          python-version: 3.10
+          python-version: 3.9
           architecture: x64
       - name: check status
         run: |

.github/workflows/release.yml

@@ -13,7 +13,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.10
+          python-version: 3.9
           architecture: x64
       - name: Cache python modules
         uses: actions/cache@v4
@@ -47,7 +47,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.10
+          python-version: 3.9
           architecture: x64
       - name: Install package
         run: |
@@ -64,7 +64,7 @@ jobs:
         uses: conda-incubator/setup-miniconda@v4
         with:
           auto-update-conda: true
-          python-version: 3.10
+          python-version: 3.9
       - name: Install dependencies
         run: |
           conda install -y conda-build conda-verify anaconda-client
@@ -90,7 +90,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python: ['3.10']
+        python: ['3.9']
     steps:
       - uses: actions/checkout@v4
       - name: Miniconda setup

.github/workflows/style.yml

@@ -12,7 +12,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python: ['3.10']
+        python: ['3.9']
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python
@@ -31,7 +31,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python: ['3.10']
+        python: ['3.9']
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python
@@ -51,7 +51,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python: ['3.10']
+        python: ['3.9']
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python
@@ -79,7 +79,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python: ['3.10']
+        python: ['3.9']
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python
@@ -98,7 +98,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python: ['3.10']
+        python: ['3.9']
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python

.github/workflows/tests.yml

@@ -12,7 +12,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python: ['3.10']
+        python: ['3.9']
     steps:
       - uses: actions/checkout@v4
         with:
@@ -65,7 +65,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python: ['3.10']
+        python: ['3.9']
     steps:
       - uses: actions/checkout@v4
         with:

.gitignore

@@ -122,4 +122,9 @@ pyroengine/version.py
 conda-dist/
 
 docker-compose.yml.bak
-docker-compose.override.yml
+docker-compose.override.yml
+
+
+captured_images/
+captured_positions/
+*.jpg

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.10.16-slim
+FROM python:3.9.16-slim
 
 # set environment variables
 ENV PATH="/usr/local/bin:$PATH"

README.md

@@ -52,7 +52,7 @@ prediction = engine.predict(im)
 
 ## Setup
 
-Python 3.10 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install PyroEngine.
+Python 3.9 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install PyroEngine.
 
 ### Developer installation
 

docs/source/conf.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2022-2024, Pyronear.
+# Copyright (C) 2022-2025, Pyronear.
 
 # This program is licensed under the Apache License 2.0.
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.

pyproject.toml

@@ -20,6 +20,7 @@ classifiers = [
     "Natural Language :: English",
     "Operating System :: OS Independent",
     "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.9",
     "Topic :: Scientific/Engineering",
     "Topic :: Scientific/Engineering :: Mathematics",
     "Topic :: Scientific/Engineering :: Artificial Intelligence",

pyroengine/core.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2022-2024, Pyronear.
+# Copyright (C) 2022-2025, Pyronear.
 
 # This program is licensed under the Apache License 2.0.
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.

pyroengine/engine.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2022-2024, Pyronear.
+# Copyright (C) 2022-2025, Pyronear.
 
 # This program is licensed under the Apache License 2.0.
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
@@ -81,6 +81,7 @@ def __init__(
         model_path: Optional[str] = None,
         conf_thresh: float = 0.15,
         api_host: Optional[str] = None,
+        max_bbox_size: float = 0.4,
         cam_creds: Optional[Dict[str, Dict[str, str]]] = None,
         nb_consecutive_frames: int = 4,
         frame_size: Optional[Tuple[int, int]] = None,
@@ -96,7 +97,8 @@ def __init__(
     ) -> None:
         """Init engine"""
         # Engine Setup
-        self.model = Classifier(model_path=model_path, conf=0.05)
+
+        self.model = Classifier(model_path=model_path, conf=0.05, max_bbox_size=max_bbox_size)
         self.conf_thresh = conf_thresh
 
         # API Setup

pyroengine/sensors.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2022-2024, Pyronear.
+# Copyright (C) 2022-2025, Pyronear.
 
 # This program is licensed under the Apache License 2.0.
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
@@ -69,6 +69,7 @@ def _build_url(self, command: str) -> str:
 
     def _handle_response(self, response, success_message: str):
         """Handles HTTP responses, logging success or errors based on response data."""
+        logging.info(f"{response.status_code}")
         if response.status_code == 200:
             response_data = response.json()
             if response_data[0]["code"] == 0:
@@ -141,7 +142,7 @@ def move_in_seconds(self, s: float, operation: str = "Right", speed: int = 20, s
         self.move_camera("Stop")
         time.sleep(1)
         im = self.capture()
-        if im is not None:
+        if im is not None and save_path is not None:
             im.save(save_path)
 
     def get_ptz_preset(self):

pyroengine/utils.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2022-2024, Pyronear.
+# Copyright (C) 2022-2025, Pyronear.
 
 # This program is licensed under the Apache License 2.0.
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.

pyroengine/vision.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2023-2024, Pyronear.
+# Copyright (C) 2022-2025, Pyronear.
 
 # This program is licensed under the Apache License 2.0.
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
@@ -34,14 +34,18 @@ def save_metadata(metadata_path: str, metadata: dict) -> None:
 
 
 class Classifier:
+    """Implements an image classification model using YOLO backend.
+
+    Examples:
+        >>> from pyroengine.vision import Classifier
+        >>> model = Classifier()
+
+    Args:
+        model_path: model path
+    """
+
     def __init__(
-        self,
-        model_folder: str = "data",
-        imgsz: int = 1024,
-        conf: float = 0.15,
-        iou: float = 0,
-        format: str = "ncnn",
-        model_path: Optional[str] = None,
+        self, model_folder="data", imgsz=1024, conf=0.15, iou=0, format="ncnn", model_path=None, max_bbox_size=0.4
     ) -> None:
         if model_path is None:
             if format == "ncnn":
@@ -93,6 +97,7 @@ def __init__(
         self.imgsz = imgsz
         self.conf = conf
         self.iou = iou
+        self.max_bbox_size = max_bbox_size
 
     def is_arm_architecture(self) -> bool:
         return platform.machine().startswith("arm") or platform.machine().startswith("aarch")
@@ -120,41 +125,70 @@ def load_metadata(self, metadata_path: str) -> Optional[dict]:
                 return json.load(f)
         return None
 
-    def prep_process(self, pil_img: Image.Image) -> Tuple[Union[np.ndarray, ncnn.Mat], Tuple[int, int]]:
-        np_img, pad = letterbox(np.array(pil_img), (self.imgsz, self.imgsz))
+    def prep_process(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.imgsz)  # Applies letterbox resize with padding
+
         if self.format == "ncnn":
             np_img = ncnn.Mat.from_pixels(np_img, ncnn.Mat.PixelType.PIXEL_BGR, np_img.shape[1], np_img.shape[0])
             mean = [0, 0, 0]
             std = [1 / 255, 1 / 255, 1 / 255]
             np_img.substract_mean_normalize(mean=mean, norm=std)
         else:
-            np_img = np.expand_dims(np_img.astype("float32"), axis=0)
-            np_img = np.ascontiguousarray(np_img.transpose((0, 3, 1, 2)))
-            np_img /= 255.0
+            np_img = np.expand_dims(np_img.astype("float32"), axis=0)  # Add batch dimension
+            np_img = np.ascontiguousarray(np_img.transpose((0, 3, 1, 2)))  # Convert from BHWC to BCHW format
+            np_img /= 255.0  # Normalize to [0, 1]
 
         return np_img, pad
 
     def post_process(self, pred: np.ndarray, pad: Tuple[int, int]) -> np.ndarray:
-        pred = pred[:, pred[-1, :] > self.conf]
+        """Post-process model predictions.
+
+        Args:
+            pred: Raw predictions from the model.
+            pad: Padding information as (left_pad, top_pad).
+
+        Returns:
+            Processed predictions as a numpy array.
+        """
+        pred = pred[:, pred[-1, :] > self.conf]  # Drop low-confidence predictions
         pred = np.transpose(pred)
         pred = xywh2xyxy(pred)
-        pred = pred[pred[:, 4].argsort()]
+        pred = pred[pred[:, 4].argsort()]  # Sort by confidence
         pred = nms(pred)
-        pred = pred[::-1]
+        pred = pred[::-1]  # Reverse for highest confidence first
 
         if len(pred) > 0:
-            left_pad, top_pad = pad
+            left_pad, top_pad = pad  # Unpack the tuple
             pred[:, :4:2] -= left_pad
             pred[:, 1:4:2] -= top_pad
             pred[:, :4:2] /= max(1, self.imgsz - 2 * left_pad)
             pred[:, 1:4:2] /= max(1, self.imgsz - 2 * top_pad)
             pred = np.clip(pred, 0, 1)
         else:
-            pred = np.zeros((0, 5))
+            pred = np.zeros((0, 5))  # Return empty prediction array
 
         return pred
 
     def __call__(self, pil_img: Image.Image, occlusion_mask: Optional[np.ndarray] = None) -> np.ndarray:
+        """Run the classifier on an input image.
+
+        Args:
+            pil_img: The input PIL image.
+            occlusion_mask: Optional occlusion mask to exclude certain areas.
+
+        Returns:
+            Processed predictions.
+        """
         np_img, pad = self.prep_process(pil_img)
 
         if self.format == "ncnn":
@@ -167,7 +201,16 @@ def __call__(self, pil_img: Image.Image, occlusion_mask: Optional[np.ndarray] =
         else:
             pred = self.ort_session.run(["output0"], {"images": np_img})[0][0]
 
-        pred = self.post_process(pred, pad)
+        # Convert pad to a tuple if required
+        if isinstance(pad, list):
+            pad = tuple(pad)
+
+        pred = self.post_process(pred, pad)  # Ensure pad is passed as a tuple
+
+        # drop big detections
+        pred = np.clip(pred, 0, 1)
+        pred = pred[(pred[:, 2] - pred[:, 0]) < self.max_bbox_size, :]
+        pred = np.reshape(pred, (-1, 5))
 
         if occlusion_mask is not None:
             hm, wm = occlusion_mask.shape

setup.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2022-2024, Pyronear.
+# Copyright (C) 2022-2025, Pyronear.
 
 # This program is licensed under the Apache License 2.0.
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.