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README.md

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+---
+license: mit
+---

chute_config.yml

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+Image:
+  from_base: parachutes/python:3.12
+  run_command:
+    - pip install --upgrade setuptools wheel
+    - pip install 'numpy>=1.23' 'onnxruntime-gpu[cuda,cudnn]>=1.16' 'opencv-python>=4.7' 'pillow>=9.5' 'huggingface_hub>=0.19.4' 'pydantic>=2.0' 'pyyaml>=6.0' 'aiohttp>=3.9'
+    - pip install torch torchvision
+  set_workdir: /app
+
+NodeSelector:
+  gpu_count: 1
+  min_vram_gb_per_gpu: 16
+  max_hourly_price_per_gpu: 1.0
+  exclude:
+    - "5090"
+    - b200
+    - h200
+    - h100
+    - mi300x 
+
+Chute:
+  timeout_seconds: 900
+  concurrency: 4
+  max_instances: 5
+  scaling_threshold: 0.5
+  shutdown_after_seconds: 288000

miner.py

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+from pathlib import Path
+import math
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+from numpy import ndarray
+from pydantic import BaseModel
+
+
+class BoundingBox(BaseModel):
+    x1: int
+    y1: int
+    x2: int
+    y2: int
+    cls_id: int
+    conf: float
+
+
+class TVFrameResult(BaseModel):
+    frame_id: int
+    boxes: list[BoundingBox]
+    keypoints: list[tuple[int, int]]
+
+
+class Miner:
+    """
+    ONNX-backed version of the petrol-tracking miner.
+
+    This class is responsible for:
+    - Loading the ONNX model via onnxruntime.
+    - Running predictions on images.
+    - Parsing ONNX outputs into structured results (TVFrameResult).
+
+    It must have the following to be compatible with the chute:
+        - be named `Miner`
+        - have a `predict_batch` function with the inputs and outputs specified
+        - be stored in a file called `miner.py` which lives in the root of the
+          HFHub repo (rename/copy this file to `miner.py` before deploying)
+    """
+
+    def __init__(self, path_hf_repo: Path) -> None:
+        model_path = path_hf_repo / "weights.onnx"
+
+        # Class order as exported from the training pt: must match model.names
+        self.class_names = ["petrol hose", "petrol pump", "price board", "roof canopy"]
+
+        print("ORT version:", ort.__version__)
+
+        try:
+            ort.preload_dlls()
+            print("✅ onnxruntime.preload_dlls() success")
+        except Exception as e:
+            print(f"⚠️ preload_dlls failed: {e}")
+
+        print("ORT available providers BEFORE session:", ort.get_available_providers())
+
+        sess_options = ort.SessionOptions()
+        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+
+        try:
+            self.session = ort.InferenceSession(
+                str(model_path),
+                sess_options=sess_options,
+                providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+            )
+            print("✅ Created ORT session with preferred CUDA provider list")
+        except Exception as e:
+            print(f"⚠️ CUDA session creation failed, falling back to CPU: {e}")
+            self.session = ort.InferenceSession(
+                str(model_path),
+                sess_options=sess_options,
+                providers=["CPUExecutionProvider"],
+            )
+
+        print("ORT session providers:", self.session.get_providers())
+
+        for inp in self.session.get_inputs():
+            print("INPUT:", inp.name, inp.shape, inp.type)
+
+        for out in self.session.get_outputs():
+            print("OUTPUT:", out.name, out.shape, out.type)
+
+        self.input_name = self.session.get_inputs()[0].name
+        self.output_names = [output.name for output in self.session.get_outputs()]
+        self.input_shape = self.session.get_inputs()[0].shape
+
+        self.input_height = self._safe_dim(self.input_shape[2], default=640)
+        self.input_width = self._safe_dim(self.input_shape[3], default=640)
+
+        # Thresholds
+        self.conf_thres = 0.4
+        self.iou_thres = 0.50
+        self.max_det = 300
+
+        print(f"✅ Petrol ONNX model loaded from: {model_path}")
+        print(f"✅ ONNX providers: {self.session.get_providers()}")
+        print(f"✅ ONNX input: name={self.input_name}, shape={self.input_shape}")
+
+    def __repr__(self) -> str:
+        return (
+            f"Petrol ONNXRuntime(session={type(self.session).__name__}, "
+            f"providers={self.session.get_providers()})"
+        )
+
+    @staticmethod
+    def _safe_dim(value, default: int) -> int:
+        return value if isinstance(value, int) and value > 0 else default
+
+    def _letterbox(
+        self,
+        image: ndarray,
+        new_shape: tuple[int, int],
+        color=(114, 114, 114),
+    ) -> tuple[ndarray, float, tuple[float, float]]:
+        h, w = image.shape[:2]
+        new_w, new_h = new_shape
+
+        ratio = min(new_w / w, new_h / h)
+        resized_w = int(round(w * ratio))
+        resized_h = int(round(h * ratio))
+
+        if (resized_w, resized_h) != (w, h):
+            interp = cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR
+            image = cv2.resize(image, (resized_w, resized_h), interpolation=interp)
+
+        dw = new_w - resized_w
+        dh = new_h - resized_h
+        dw /= 2.0
+        dh /= 2.0
+
+        left = int(round(dw - 0.1))
+        right = int(round(dw + 0.1))
+        top = int(round(dh - 0.1))
+        bottom = int(round(dh + 0.1))
+
+        padded = cv2.copyMakeBorder(
+            image,
+            top,
+            bottom,
+            left,
+            right,
+            borderType=cv2.BORDER_CONSTANT,
+            value=color,
+        )
+        return padded, ratio, (dw, dh)
+
+    def _preprocess(
+        self, image: ndarray
+    ) -> tuple[np.ndarray, float, tuple[float, float], tuple[int, int]]:
+        orig_h, orig_w = image.shape[:2]
+
+        img, ratio, pad = self._letterbox(
+            image, (self.input_width, self.input_height)
+        )
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = img.astype(np.float32) / 255.0
+        img = np.transpose(img, (2, 0, 1))[None, ...]
+        img = np.ascontiguousarray(img, dtype=np.float32)
+
+        return img, ratio, pad, (orig_w, orig_h)
+
+    @staticmethod
+    def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
+        w, h = image_size
+        boxes[:, 0] = np.clip(boxes[:, 0], 0, w - 1)
+        boxes[:, 1] = np.clip(boxes[:, 1], 0, h - 1)
+        boxes[:, 2] = np.clip(boxes[:, 2], 0, w - 1)
+        boxes[:, 3] = np.clip(boxes[:, 3], 0, h - 1)
+        return boxes
+
+    @staticmethod
+    def _xywh_to_xyxy(boxes: np.ndarray) -> np.ndarray:
+        out = np.empty_like(boxes)
+        out[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
+        out[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
+        out[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
+        out[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
+        return out
+
+    @staticmethod
+    def _hard_nms(
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        iou_thresh: float,
+    ) -> np.ndarray:
+        if len(boxes) == 0:
+            return np.array([], dtype=np.intp)
+
+        boxes = np.asarray(boxes, dtype=np.float32)
+        scores = np.asarray(scores, dtype=np.float32)
+        order = np.argsort(scores)[::-1]
+        keep = []
+
+        while len(order) > 0:
+            i = order[0]
+            keep.append(i)
+            if len(order) == 1:
+                break
+
+            rest = order[1:]
+
+            xx1 = np.maximum(boxes[i, 0], boxes[rest, 0])
+            yy1 = np.maximum(boxes[i, 1], boxes[rest, 1])
+            xx2 = np.minimum(boxes[i, 2], boxes[rest, 2])
+            yy2 = np.minimum(boxes[i, 3], boxes[rest, 3])
+
+            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+
+            area_i = max(0.0, (boxes[i, 2] - boxes[i, 0])) * max(0.0, (boxes[i, 3] - boxes[i, 1]))
+            area_r = np.maximum(0.0, boxes[rest, 2] - boxes[rest, 0]) * np.maximum(0.0, boxes[rest, 3] - boxes[rest, 1])
+
+            iou = inter / (area_i + area_r - inter + 1e-7)
+            order = rest[iou <= iou_thresh]
+
+        return np.array(keep, dtype=np.intp)
+
+    @classmethod
+    def _nms_per_class(
+        cls,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        iou_thresh: float,
+        max_det: int,
+    ) -> np.ndarray:
+        if len(boxes) == 0:
+            return np.array([], dtype=np.intp)
+        keep_all: list[int] = []
+        for c in np.unique(cls_ids):
+            idxs = np.nonzero(cls_ids == c)[0]
+            if len(idxs) == 0:
+                continue
+            local_keep = cls._hard_nms(boxes[idxs], scores[idxs], iou_thresh)
+            keep_all.extend(idxs[local_keep].tolist())
+        keep_all_arr = np.array(keep_all, dtype=np.intp)
+        order = np.argsort(scores[keep_all_arr])[::-1]
+        return keep_all_arr[order[:max_det]]
+
+    def _decode_yolov8(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+    ) -> list[BoundingBox]:
+        """
+        Decode a raw YOLOv8-style ONNX detection output.
+
+        Expected shape: [1, 4 + nc, num_boxes] (no objectness channel).
+        Some exporters emit [1, num_boxes, 4 + nc]; both are handled.
+        """
+        if preds.ndim != 3 or preds.shape[0] != 1:
+            raise ValueError(f"Unexpected ONNX output shape: {preds.shape}")
+
+        preds = preds[0]
+
+        # Normalize to [N, C] where C = 4 + nc
+        nc = len(self.class_names)
+        expected_c = 4 + nc
+        if preds.shape[0] == expected_c:
+            preds = preds.T
+        elif preds.shape[1] != expected_c:
+            # Fall back: treat smaller dim as channels
+            if preds.shape[0] < preds.shape[1]:
+                preds = preds.T
+
+        if preds.ndim != 2 or preds.shape[1] < 5:
+            raise ValueError(f"Unexpected normalized output shape: {preds.shape}")
+
+        boxes_xywh = preds[:, :4].astype(np.float32)
+        class_probs = preds[:, 4:].astype(np.float32)
+
+        cls_ids = np.argmax(class_probs, axis=1).astype(np.int32)
+        scores = class_probs[np.arange(len(class_probs)), cls_ids]
+
+        keep = scores >= self.conf_thres
+        boxes_xywh = boxes_xywh[keep]
+        scores = scores[keep]
+        cls_ids = cls_ids[keep]
+
+        if len(boxes_xywh) == 0:
+            return []
+
+        boxes = self._xywh_to_xyxy(boxes_xywh)
+
+        pad_w, pad_h = pad
+        orig_w, orig_h = orig_size
+
+        boxes[:, [0, 2]] -= pad_w
+        boxes[:, [1, 3]] -= pad_h
+        boxes /= ratio
+        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
+
+        keep_idx = self._nms_per_class(
+            boxes, scores, cls_ids, self.iou_thres, self.max_det
+        )
+
+        boxes = boxes[keep_idx]
+        scores = scores[keep_idx]
+        cls_ids = cls_ids[keep_idx]
+
+        return [
+            BoundingBox(
+                x1=int(math.floor(box[0])),
+                y1=int(math.floor(box[1])),
+                x2=int(math.ceil(box[2])),
+                y2=int(math.ceil(box[3])),
+                cls_id=int(cls_id),
+                conf=float(conf),
+            )
+            for box, conf, cls_id in zip(boxes, scores, cls_ids)
+            if box[2] > box[0] and box[3] > box[1]
+        ]
+
+    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
+        if image is None:
+            raise ValueError("Input image is None")
+        if not isinstance(image, np.ndarray):
+            raise TypeError(f"Input is not numpy array: {type(image)}")
+        if image.ndim != 3:
+            raise ValueError(f"Expected HWC image, got shape={image.shape}")
+        if image.shape[0] <= 0 or image.shape[1] <= 0:
+            raise ValueError(f"Invalid image shape={image.shape}")
+        if image.shape[2] != 3:
+            raise ValueError(f"Expected 3 channels, got shape={image.shape}")
+
+        if image.dtype != np.uint8:
+            image = image.astype(np.uint8)
+
+        input_tensor, ratio, pad, orig_size = self._preprocess(image)
+
+        expected_shape = (1, 3, self.input_height, self.input_width)
+        if input_tensor.shape != expected_shape:
+            raise ValueError(
+                f"Bad input tensor shape={input_tensor.shape}, expected={expected_shape}"
+            )
+
+        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
+        det_output = outputs[0]
+        return self._decode_yolov8(det_output, ratio, pad, orig_size)
+
+    def predict_batch(
+        self,
+        batch_images: list[ndarray],
+        offset: int,
+        n_keypoints: int,
+    ) -> list[TVFrameResult]:
+        """
+        Miner prediction for a batch of images using ONNX Runtime.
+
+        The petrol detector is a plain object-detection model (no pose),
+        so keypoints are returned as `n_keypoints` padding entries of (0, 0)
+        to keep the TVFrameResult schema stable across challenge types.
+        """
+        results: list[TVFrameResult] = []
+        n_kp = max(0, int(n_keypoints))
+
+        for frame_number_in_batch, image in enumerate(batch_images):
+            frame_idx = offset + frame_number_in_batch
+            try:
+                boxes = self._predict_single(image)
+            except Exception as e:
+                print(f"⚠️ Inference failed for frame {frame_idx}: {e}")
+                boxes = []
+
+            results.append(
+                TVFrameResult(
+                    frame_id=frame_idx,
+                    boxes=boxes,
+                    keypoints=[(0, 0) for _ in range(n_kp)],
+                )
+            )
+
+        print("✅ Petrol ONNX predictions complete")
+        return results
+
+
+def main() -> None:
+    """
+    Example runner for the ONNX Miner class.
+
+    Loads `weights.onnx` from the current directory and runs `predict_batch`
+    on one or more image files.
+
+    Usage:
+        python miner_onnx.py                         # dummy blank image
+        python miner_onnx.py image1.jpg              # single image
+        python miner_onnx.py image1.jpg image2.jpg   # batch of images
+    """
+    import sys
+
+    import numpy as np
+
+    repo_path = Path(__file__).parent
+    print(f"Loading miner from: {repo_path}")
+    miner = Miner(path_hf_repo=repo_path)
+    print(repr(miner))
+
+    batch_images: list[np.ndarray] = []
+
+    if len(sys.argv) > 1:
+        for image_path in sys.argv[1:]:
+            image = cv2.imread(image_path)
+            if image is None:
+                raise ValueError(f"Cannot read image: {image_path}")
+            batch_images.append(image)
+        print(f"Loaded {len(batch_images)} image(s)")
+    else:
+        batch_images = [np.zeros((640, 640, 3), dtype=np.uint8)]
+        print("No image provided — running on a single blank dummy frame")
+
+    results = miner.predict_batch(
+        batch_images=batch_images,
+        offset=0,
+        n_keypoints=32,
+    )
+
+    output_dir = repo_path / "predictions"
+    output_dir.mkdir(exist_ok=True)
+
+    class_names = {i: n for i, n in enumerate(miner.class_names)}
+
+    def color_for_class(cls_id: int) -> tuple[int, int, int]:
+        hue = (cls_id * 47) % 180
+        hsv = np.uint8([[[hue, 220, 255]]])
+        bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)[0, 0]
+        return int(bgr[0]), int(bgr[1]), int(bgr[2])
+
+    for image, r in zip(batch_images, results):
+        print(
+            f"frame={r.frame_id} "
+            f"boxes={len(r.boxes)} "
+            f"keypoints={len(r.keypoints)}"
+        )
+
+        vis = image.copy()
+        for box in r.boxes:
+            name = class_names.get(box.cls_id, str(box.cls_id))
+            color = color_for_class(box.cls_id)
+            print(
+                f"  box cls={box.cls_id}({name}) conf={box.conf:.2f} "
+                f"[{box.x1},{box.y1},{box.x2},{box.y2}]"
+            )
+            cv2.rectangle(vis, (box.x1, box.y1), (box.x2, box.y2), color, 2)
+            label = f"{name} {box.conf:.2f}"
+            (tw, th), baseline = cv2.getTextSize(
+                label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1
+            )
+            top = max(box.y1 - th - baseline, 0)
+            cv2.rectangle(
+                vis, (box.x1, top), (box.x1 + tw, top + th + baseline), color, -1
+            )
+            cv2.putText(
+                vis, label, (box.x1, top + th),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA,
+            )
+
+        for x, y in r.keypoints:
+            if x == 0 and y == 0:
+                continue
+            cv2.circle(vis, (x, y), 3, (0, 0, 255), -1)
+
+        out_path = output_dir / f"frame_{r.frame_id:04d}.jpg"
+        cv2.imwrite(str(out_path), vis)
+        print(f"  saved: {out_path}")
+
+
+if __name__ == "__main__":
+    main()

weights.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a6c8059cd1f69fd0ed8860fec08b24656d6ed177c6adf76dee85c14cd571cf7a
+size 22664875