scorevision: push artifact

miner.py

@@ -1,17 +1,19 @@
+"""Plate-detection miner — v1 "sparse conditional tile-aug".
+
+Base weights: alfred8995/arabic002 (YOLO26s @ 1280, fp16, ~19 MB).
+
+Inference pipeline:
+  1) Full-image primary pass with arabic002's strict tuning
+     (conf=0.27, iou=0.444, sigma=0.5, soft-NMS + hflip TTA, max_det=18).
+  2) If the primary returned fewer than SPARSE_THRESHOLD (5) boxes,
+     run a 2x2 overlapping tile pass with higher conf (tile_conf=0.40)
+     and novelty-merge: keep a tile box only when it does not overlap
+     any primary box at IoU >= 0.10. Tile augmentation is skipped
+     entirely on challenges where the primary already has enough
+     detections, so the FP score stays intact on dense scenes.
+"""
 from pathlib import Path
 import math
-import os
-import glob
-import site
-import ctypes
-
-# Preload pip-installed NVIDIA cuDNN so onnxruntime can use CUDAExecutionProvider
-for sp in site.getsitepackages():
-    for d in glob.glob(os.path.join(sp, 'nvidia', '*', 'lib')):
-        os.environ['LD_LIBRARY_PATH'] = d + ':' + os.environ.get('LD_LIBRARY_PATH', '')
-        _cudnn = os.path.join(d, 'libcudnn.so.9')
-        if os.path.exists(_cudnn):
-            ctypes.CDLL(_cudnn, mode=ctypes.RTLD_GLOBAL)
 
 import cv2
 import numpy as np
@@ -35,153 +37,372 @@ class TVFrameResult(BaseModel):
     keypoints: list[tuple[int, int]]
 
 
-class Miner:
-    """
-    Auto-generated by subnet_bridge from a Manako element repo.
-    This miner is intentionally self-contained for chute import restrictions.
-    """
+SIZE = 1280
+
 
+class Miner:
     def __init__(self, path_hf_repo: Path) -> None:
-        self.path_hf_repo = path_hf_repo
-        self.class_names = ['numberplate']
-        self.session = ort.InferenceSession(
-            str(path_hf_repo / "weights.onnx"),
-            providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
-        )
-        self.input_name = self.session.get_inputs()[0].name
-        input_shape = self.session.get_inputs()[0].shape
-        # expected [N, C, H, W]
-        self.input_h = int(input_shape[2])
-        self.input_w = int(input_shape[3])
-        self.conf_threshold = 0.15
-        self.iou_threshold = 0.3
-        self.use_tta = True
+        model_path = path_hf_repo / "weights.onnx"
+        cn_path = model_path.with_name("class_names.txt")
+        if cn_path.is_file():
+            lines = cn_path.read_text(encoding="utf-8").splitlines()
+            self.class_names = [
+                ln.strip()
+                for ln in lines
+                if ln.strip() and not ln.strip().startswith("#")
+            ]
+        else:
+            self.class_names = ["numberplate"]
+        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"],
+            )
 
-    def __repr__(self) -> str:
-        return f"ONNX Miner session={type(self.session).__name__} classes={len(self.class_names)}"
-
-    def _preprocess(self, image_bgr: ndarray) -> tuple[np.ndarray, tuple[int, int]]:
-        h, w = image_bgr.shape[:2]
-        rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
-        resized = cv2.resize(rgb, (self.input_w, self.input_h))
-        x = resized.astype(np.float32) / 255.0
-        x = np.transpose(x, (2, 0, 1))[None, ...]
-        return x, (h, w)
-
-    def _normalize_predictions(self, raw: np.ndarray) -> np.ndarray:
-        pred = raw[0]
-        if pred.ndim != 2:
-            raise ValueError(f"Unexpected prediction shape: {raw.shape}")
-        if pred.shape[0] < pred.shape[1]:
-            pred = pred.transpose(1, 0)
-        return pred
-
-    def _nms(self, dets: list[tuple[float, float, float, float, float, int]]) -> list[tuple[float, float, float, float, float, int]]:
-        if not dets:
-            return []
+        print("ORT session providers:", self.session.get_providers())
 
-        boxes = np.array([[d[0], d[1], d[2], d[3]] for d in dets], dtype=np.float32)
-        scores = np.array([d[4] for d in dets], dtype=np.float32)
-        order = scores.argsort()[::-1]
-        keep = []
+        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)
 
-        while order.size > 0:
-            i = order[0]
-            keep.append(i)
+        self.input_name = self.session.get_inputs()[0].name
+        self.output_names = [o.name for o in self.session.get_outputs()]
+        self.input_shape = self.session.get_inputs()[0].shape
 
-            xx1 = np.maximum(boxes[i, 0], boxes[order[1:], 0])
-            yy1 = np.maximum(boxes[i, 1], boxes[order[1:], 1])
-            xx2 = np.minimum(boxes[i, 2], boxes[order[1:], 2])
-            yy2 = np.minimum(boxes[i, 3], boxes[order[1:], 3])
+        self.input_height = self._safe_dim(self.input_shape[2], default=SIZE)
+        self.input_width = self._safe_dim(self.input_shape[3], default=SIZE)
 
-            w = np.maximum(0.0, xx2 - xx1)
-            h = np.maximum(0.0, yy2 - yy1)
-            inter = w * h
+        # Primary pass: arabic002 strict tuning
+        self.conf_thres = 0.27
+        self.iou_thres = 0.444
+        self.sigma = 0.5
+        self.max_det = 18
 
-            area_i = (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
-            area_rest = (boxes[order[1:], 2] - boxes[order[1:], 0]) * (boxes[order[1:], 3] - boxes[order[1:], 1])
-            union = np.maximum(area_i + area_rest - inter, 1e-6)
-            iou = inter / union
+        # Conditional tile-pass
+        self.sparse_threshold = 5       # fire tiles only if primary returns < this
+        self.tile_conf = 0.40
+        self.tile_overlap = 0.20
+        self.novelty_iou = 0.10
+        self.final_max_det = 22
 
-            remaining = np.where(iou <= self.iou_threshold)[0]
-            order = order[remaining + 1]
+        self.use_tta = True
 
-        return [dets[idx] for idx in keep]
+        print(f"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 _decode(self, image_bgr: ndarray) -> list[tuple[float, float, float, float, float, int]]:
-        """Run model and return raw detections before NMS."""
-        inp, (orig_h, orig_w) = self._preprocess(image_bgr)
-        out = self.session.run(None, {self.input_name: inp})[0]
-        pred = self._normalize_predictions(out)
+    def __repr__(self) -> str:
+        return (
+            f"ONNXRuntime(session={type(self.session).__name__}, "
+            f"providers={self.session.get_providers()})"
+        )
 
-        if pred.shape[1] < 5:
-            return []
+    @staticmethod
+    def _safe_dim(value, default: int) -> int:
+        return value if isinstance(value, int) and value > 0 else default
 
-        boxes = pred[:, :4]
-        cls_scores = pred[:, 4:]
+    # ---------- image preprocessing ----------
+    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) / 2.0
+        dh = (new_h - resized_h) / 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):
+        img, ratio, pad = self._letterbox(image, (self.input_width, self.input_height))
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
+        img = np.transpose(img, (2, 0, 1))[None, ...]
+        return np.ascontiguousarray(img, dtype=np.float32), ratio, pad
+
+    @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
+
+    # ---------- NMS primitives ----------
+    @staticmethod
+    def _hard_nms(boxes: np.ndarray, scores: np.ndarray, iou_thresh: float) -> np.ndarray:
+        N = len(boxes)
+        if N == 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)
+        keep: list[int] = []
+        while len(order):
+            i = int(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 = (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
+            area_r = (boxes[rest, 2] - boxes[rest, 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)
 
-        if cls_scores.shape[1] == 0:
+    def _soft_nms(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        sigma: float,
+        score_thresh: float = 0.01,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        N = len(boxes)
+        if N == 0:
+            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
+        boxes = boxes.astype(np.float32, copy=True)
+        scores = scores.astype(np.float32, copy=True)
+        order = np.arange(N)
+        for i in range(N):
+            max_pos = i + int(np.argmax(scores[i:]))
+            boxes[[i, max_pos]] = boxes[[max_pos, i]]
+            scores[[i, max_pos]] = scores[[max_pos, i]]
+            order[[i, max_pos]] = order[[max_pos, i]]
+            if i + 1 >= N:
+                break
+            xx1 = np.maximum(boxes[i, 0], boxes[i + 1:, 0])
+            yy1 = np.maximum(boxes[i, 1], boxes[i + 1:, 1])
+            xx2 = np.minimum(boxes[i, 2], boxes[i + 1:, 2])
+            yy2 = np.minimum(boxes[i, 3], boxes[i + 1:, 3])
+            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+            area_i = float(
+                (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
+            )
+            areas_j = (
+                np.maximum(0.0, boxes[i + 1:, 2] - boxes[i + 1:, 0])
+                * np.maximum(0.0, boxes[i + 1:, 3] - boxes[i + 1:, 1])
+            )
+            iou = inter / (area_i + areas_j - inter + 1e-7)
+            scores[i + 1:] *= np.exp(-(iou ** 2) / sigma)
+        mask = scores > score_thresh
+        return order[mask], scores[mask]
+
+    @staticmethod
+    def _box_iou_one_to_many(box: np.ndarray, boxes: np.ndarray) -> np.ndarray:
+        if len(boxes) == 0:
+            return np.zeros(0, dtype=np.float32)
+        xx1 = np.maximum(box[0], boxes[:, 0])
+        yy1 = np.maximum(box[1], boxes[:, 1])
+        xx2 = np.minimum(box[2], boxes[:, 2])
+        yy2 = np.minimum(box[3], boxes[:, 3])
+        inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+        area_a = max(0.0, (box[2] - box[0]) * (box[3] - box[1]))
+        area_b = np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) * np.maximum(0.0, boxes[:, 3] - boxes[:, 1])
+        return inter / (area_a + area_b - inter + 1e-7)
+
+    # ---------- raw-dets helper ----------
+    def _raw_dets(self, image: ndarray, conf: float) -> np.ndarray:
+        """Run a single forward pass and return [N, 5] dets in ORIGINAL image coords."""
+        x, ratio, (dw, dh) = self._preprocess(image)
+        out = self.session.run(self.output_names, {self.input_name: x})[0]
+        if out.ndim == 3:
+            out = out[0]
+        if out.shape[1] < 5:
+            return np.zeros((0, 5), dtype=np.float32)
+        boxes = out[:, :4].astype(np.float32)
+        scores = out[:, 4].astype(np.float32)
+        keep = scores >= conf
+        boxes, scores = boxes[keep], scores[keep]
+        if len(boxes) == 0:
+            return np.zeros((0, 5), dtype=np.float32)
+        boxes[:, [0, 2]] -= dw
+        boxes[:, [1, 3]] -= dh
+        boxes /= ratio
+        oh, ow = image.shape[:2]
+        boxes = self._clip_boxes(boxes, (ow, oh))
+        return np.concatenate([boxes, scores[:, None]], axis=1)
+
+    # ---------- primary pass: soft-NMS + hflip TTA ----------
+    def _primary(self, image: ndarray) -> np.ndarray:
+        d1 = self._raw_dets(image, self.conf_thres)
+        flipped = cv2.flip(image, 1)
+        d2 = self._raw_dets(flipped, self.conf_thres)
+        if len(d2):
+            w = image.shape[1]
+            x1 = w - d2[:, 2]
+            x2 = w - d2[:, 0]
+            d2 = np.stack([x1, d2[:, 1], x2, d2[:, 3], d2[:, 4]], axis=1)
+        all_d = np.concatenate([d1, d2], axis=0) if len(d2) else d1
+        if len(all_d) == 0:
+            return np.zeros((0, 5), dtype=np.float32)
+        # soft-NMS, then hard-NMS
+        keep_idx, scores = self._soft_nms(all_d[:, :4].copy(), all_d[:, 4].copy(), sigma=self.sigma)
+        if len(keep_idx) == 0:
+            return np.zeros((0, 5), dtype=np.float32)
+        merged = np.concatenate([all_d[keep_idx, :4], scores[:, None]], axis=1)
+        keep = self._hard_nms(merged[:, :4], merged[:, 4], self.iou_thres)
+        merged = merged[keep]
+        if len(merged) > self.max_det:
+            merged = merged[np.argsort(-merged[:, 4])[: self.max_det]]
+        return merged
+
+    # ---------- conditional tile pass ----------
+    def _tile_augment(self, image: ndarray, primary: np.ndarray) -> np.ndarray:
+        """Run 2x2 overlapping tiles + hflip, novelty-merge into primary."""
+        oh, ow = image.shape[:2]
+        tw, th = ow // 2, oh // 2
+        ox, oy = int(tw * self.tile_overlap), int(th * self.tile_overlap)
+        tiles = [
+            (0, 0, min(ow, tw + ox), min(oh, th + oy)),
+            (max(0, tw - ox), 0, ow, min(oh, th + oy)),
+            (0, max(0, th - oy), min(ow, tw + ox), oh),
+            (max(0, tw - ox), max(0, th - oy), ow, oh),
+        ]
+        collected: list[np.ndarray] = []
+        for x1, y1, x2, y2 in tiles:
+            crop = image[y1:y2, x1:x2]
+            if crop.size == 0:
+                continue
+            d = self._raw_dets(crop, self.tile_conf)
+            if len(d):
+                d[:, 0] += x1
+                d[:, 1] += y1
+                d[:, 2] += x1
+                d[:, 3] += y1
+                collected.append(d)
+
+        # hflip tile pass
+        flipped = cv2.flip(image, 1)
+        for x1, y1, x2, y2 in tiles:
+            fx1 = ow - x2
+            fx2 = ow - x1
+            if fx2 <= fx1:
+                continue
+            crop = flipped[y1:y2, fx1:fx2]
+            if crop.size == 0:
+                continue
+            d = self._raw_dets(crop, self.tile_conf)
+            if len(d):
+                d_un = d.copy()
+                d_un[:, 0] = (ow - (d[:, 2] + fx1))
+                d_un[:, 2] = (ow - (d[:, 0] + fx1))
+                d_un[:, 1] = d[:, 1] + y1
+                d_un[:, 3] = d[:, 3] + y1
+                collected.append(d_un)
+
+        if not collected:
+            return primary
+
+        tile_dets = np.concatenate(collected, axis=0)
+        keep = self._hard_nms(tile_dets[:, :4], tile_dets[:, 4], 0.5)
+        tile_dets = tile_dets[keep]
+
+        # Novelty: drop tile boxes that overlap any primary box at IoU >= novelty_iou
+        if len(primary) > 0 and len(tile_dets) > 0:
+            mask = np.ones(len(tile_dets), dtype=bool)
+            for i in range(len(tile_dets)):
+                ious = self._box_iou_one_to_many(tile_dets[i, :4], primary[:, :4])
+                if len(ious) and np.max(ious) >= self.novelty_iou:
+                    mask[i] = False
+            tile_dets = tile_dets[mask]
+
+        if len(tile_dets) == 0:
+            return primary
+
+        # Sanity filter: min/max size, aspect ratio
+        w = tile_dets[:, 2] - tile_dets[:, 0]
+        h = tile_dets[:, 3] - tile_dets[:, 1]
+        area = w * h
+        ar = np.maximum(w / np.maximum(h, 1e-6), h / np.maximum(w, 1e-6))
+        img_area = float(ow * oh)
+        ok = (w >= 6) & (h >= 6) & (area >= 36) & (area <= 0.5 * img_area) & (ar <= 10.0)
+        tile_dets = tile_dets[ok]
+        if len(tile_dets) == 0:
+            return primary
+
+        merged = np.concatenate([primary, tile_dets], axis=0)
+        keep = self._hard_nms(merged[:, :4], merged[:, 4], self.iou_thres)
+        merged = merged[keep]
+        if len(merged) > self.final_max_det:
+            merged = merged[np.argsort(-merged[:, 4])[: self.final_max_det]]
+        return merged
+
+    # ---------- single-image predict ----------
+    def _predict_single(self, image: ndarray) -> list[BoundingBox]:
+        if image is None or not isinstance(image, np.ndarray) or image.ndim != 3:
             return []
-
-        cls_ids = np.argmax(cls_scores, axis=1)
-        confs = np.max(cls_scores, axis=1)
-        keep = confs >= self.conf_threshold
-
-        boxes = boxes[keep]
-        confs = confs[keep]
-        cls_ids = cls_ids[keep]
-
-        if boxes.shape[0] == 0:
+        if image.shape[0] <= 0 or image.shape[1] <= 0 or image.shape[2] != 3:
             return []
-
-        sx = orig_w / float(self.input_w)
-        sy = orig_h / float(self.input_h)
-
-        dets: list[tuple[float, float, float, float, float, int]] = []
-        for i in range(boxes.shape[0]):
-            cx, cy, bw, bh = boxes[i].tolist()
-            x1 = (cx - bw / 2.0) * sx
-            y1 = (cy - bh / 2.0) * sy
-            x2 = (cx + bw / 2.0) * sx
-            y2 = (cy + bh / 2.0) * sy
-            dets.append((x1, y1, x2, y2, float(confs[i]), int(cls_ids[i])))
-
-        return dets
-
-    def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]:
-        orig_h, orig_w = image_bgr.shape[:2]
-
-        # Original pass
-        all_dets = self._decode(image_bgr)
-
-        # TTA: horizontal flip pass
-        if self.use_tta:
-            flipped = cv2.flip(image_bgr, 1)
-            flip_dets = self._decode(flipped)
-            for x1, y1, x2, y2, conf, cls_id in flip_dets:
-                all_dets.append((orig_w - x2, y1, orig_w - x1, y2, conf, cls_id))
-
-        # NMS
-        all_dets = self._nms(all_dets)
-
-        out_boxes: list[BoundingBox] = []
-        for x1, y1, x2, y2, conf, cls_id in all_dets:
-            ix1 = max(0, min(orig_w, math.floor(x1)))
-            iy1 = max(0, min(orig_h, math.floor(y1)))
-            ix2 = max(0, min(orig_w, math.ceil(x2)))
-            iy2 = max(0, min(orig_h, math.ceil(y2)))
-            out_boxes.append(
+        if image.dtype != np.uint8:
+            image = image.astype(np.uint8)
+
+        primary = self._primary(image)
+        if len(primary) < self.sparse_threshold:
+            dets = self._tile_augment(image, primary)
+        else:
+            dets = primary
+
+        results: list[BoundingBox] = []
+        for row in dets:
+            x1, y1, x2, y2, conf = row.tolist()
+            if x2 <= x1 or y2 <= y1:
+                continue
+            results.append(
                 BoundingBox(
-                    x1=ix1,
-                    y1=iy1,
-                    x2=ix2,
-                    y2=iy2,
-                    cls_id=cls_id,
-                    conf=max(0.0, min(1.0, conf)),
+                    x1=int(math.floor(x1)),
+                    y1=int(math.floor(y1)),
+                    x2=int(math.ceil(x2)),
+                    y2=int(math.ceil(y2)),
+                    cls_id=0,
+                    conf=float(conf),
                 )
             )
-        return out_boxes
+        return results
 
+    # ---------- chute entrypoint ----------
     def predict_batch(
         self,
         batch_images: list[ndarray],
@@ -189,14 +410,17 @@ class Miner:
         n_keypoints: int,
     ) -> list[TVFrameResult]:
         results: list[TVFrameResult] = []
-        for idx, image in enumerate(batch_images):
-            boxes = self._infer_single(image)
-            keypoints = [(0, 0) for _ in range(max(0, int(n_keypoints)))]
+        for frame_number_in_batch, image in enumerate(batch_images):
+            try:
+                boxes = self._predict_single(image)
+            except Exception as e:
+                print(f"Inference failed for frame {offset + frame_number_in_batch}: {e}")
+                boxes = []
             results.append(
                 TVFrameResult(
-                    frame_id=offset + idx,
+                    frame_id=offset + frame_number_in_batch,
                     boxes=boxes,
-                    keypoints=keypoints,
+                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
                 )
             )
         return results