New model

pyproject.toml

@@ -36,6 +36,7 @@ dependencies = [
     "pyroclient @ git+https://github.com/pyronear/pyro-api.git@main#egg=pkg&subdirectory=client",
     "requests>=2.20.0,<3.0.0",
     "opencv-python==4.5.5.64",
+    "tqdm>=4.62.0", 
 ]
 
 [project.optional-dependencies]

pyroengine/utils.py

@@ -6,8 +6,9 @@
 
 import cv2  # type: ignore[import-untyped]
 import numpy as np
+from tqdm import tqdm
 
-__all__ = ["letterbox", "nms", "xywh2xyxy"]
+__all__ = ["letterbox", "nms", "xywh2xyxy", "DownloadProgressBar"]
 
 
 def xywh2xyxy(x: np.ndarray):
@@ -20,14 +21,14 @@ def xywh2xyxy(x: np.ndarray):
 
 
 def letterbox(
-    im: np.ndarray, new_shape: tuple = (640, 640), color: tuple = (114, 114, 114), auto: bool = False, stride: int = 32
+    im: np.ndarray, new_shape: tuple = (640, 640), color: tuple = (0, 0, 0), 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).
+        Defaults to (0, 0, 0).
         auto (bool, optional): auto padding. Defaults to True.
         stride (int, optional): padding stride. Defaults to 32.
     Returns:
@@ -109,3 +110,10 @@ def nms(boxes: np.ndarray, overlapThresh: int = 0):
             indices = indices[indices != i]
 
     return boxes[indices]
+
+
+class DownloadProgressBar(tqdm):
+    def update_to(self, b=1, bsize=1, tsize=None):
+        if tsize is not None:
+            self.total = tsize
+        self.update(b * bsize - self.n)

pyroengine/vision.py

@@ -11,11 +11,11 @@
 import onnxruntime
 from PIL import Image
 
-from .utils import letterbox, nms, xywh2xyxy
+from .utils import DownloadProgressBar, letterbox, nms, xywh2xyxy
 
 __all__ = ["Classifier"]
 
-MODEL_URL = "https://github.com/pyronear/pyro-vision/releases/download/v0.2.0/yolov8s_v001.onnx"
+MODEL_URL = "https://huggingface.co/pyronear/yolov8s/resolve/main/yolov8s.onnx"
 
 
 class Classifier:
@@ -29,14 +29,16 @@
         model_path: model path
     """
 
-    def __init__(self, model_path: Optional[str] = "data/model.onnx", img_size: tuple = (384, 640)) -> None:
+    def __init__(self, model_path: Optional[str] = "data/model.onnx", img_size: tuple = (1024, 1024)) -> None:
         if model_path is None:
             model_path = "data/model.onnx"
 
         if not os.path.isfile(model_path):
             os.makedirs(os.path.split(model_path)[0], exist_ok=True)
             print(f"Downloading model from {MODEL_URL} ...")
-            urlretrieve(MODEL_URL, model_path)
+            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!")
 
         self.ort_session = onnxruntime.InferenceSession(model_path)
         self.img_size = img_size

tests/test_vision.py

@@ -9,7 +9,7 @@ def test_classifier(mock_wildfire_image):
     # 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, 384, 640)
+    assert out.shape == (1, 3, 1024, 1024)
     assert isinstance(pad, tuple)
     # Check inference
     out = model(mock_wildfire_image)
@@ -18,10 +18,10 @@ def test_classifier(mock_wildfire_image):
     assert conf >= 0 and conf <= 1
 
     # Test mask
-    mask = np.ones((384, 640))
+    mask = np.ones((1024, 640))
     out = model(mock_wildfire_image, mask)
     assert out.shape == (1, 5)
 
-    mask = np.zeros((384, 640))
+    mask = np.zeros((1024, 1024))
     out = model(mock_wildfire_image, mask)
     assert out.shape == (0, 5)