scorevision: push artifact

miner.py

@@ -1,11 +1,8 @@
 from pathlib import Path
-import math
 
-import cv2
-import numpy as np
-import onnxruntime as ort
 from numpy import ndarray
 from pydantic import BaseModel
+from ultralytics import YOLO
 
 
 class BoundingBox(BaseModel):
@@ -17,585 +14,40 @@ class BoundingBox(BaseModel):
     conf: float
 
 
-class TVFrameResult(BaseModel):
-    frame_id: int
-    boxes: list[BoundingBox]
-    keypoints: list[tuple[int, int]]
+class Polygon(BaseModel):
+    cls_id: int
+    conf: float
+    points: list[tuple[int, int]]
 
 
-class Miner:
-    def __init__(self, path_hf_repo: Path) -> None:
-        model_path = path_hf_repo / "weights.onnx"
-        self.class_names = ["person"]
-        print("ORT version:", ort.__version__)
+class TVFrameResult(BaseModel):
+    frame_id: int
+    boxes: list[BoundingBox] | None = None
+    polygons: list[Polygon] | None = None
+    keypoints: list[tuple[int, int]] | None = None
 
-        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())
+# Element: manak0/Detect-fire — objects order: 0=fire, 1=smoke, 2=fire extinguisher.
+# Model trained with identical class order; ids pass through unmapped.
+# Tuned 2026-07-31 by replicating the element's own scorer
+# (0.6*mAP50 + 0.4*(1-FP_per_image/10)) across a threshold sweep on held-out
+# data: 0.15 maximised composite (0.809) — lower thresholds add false
+# positives faster than they add recall, higher ones lose mAP.
+CONF_THRESHOLD = 0.15
+IMGSZ = 1280  # matches element preproc resize_long
 
-        sess_options = ort.SessionOptions()
-        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
 
+class Miner:
+    def __init__(self, path_hf_repo: Path) -> None:
+        self.model = YOLO(str(path_hf_repo / "fire-detection.pt"))
         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=1280)
-        self.input_width = self._safe_dim(self.input_shape[3], default=1280)
-
-        # --- Scoring-aware adaptive confidence ---
-        # total_score = mAP50 * 0.65 + FP_score * 0.35
-        # FP_score = max(0, 1 - n_FP / n_images), typically n_images ≈ 10
-        #
-        # mAP50 weight is higher for person detection → favor recall slightly more
-        # Crossover at ~1.9 GT/image: below → recall wins, above → precision wins
-        self.conf_thres = 0.22       # Base threshold for candidate generation (wide net)
-        self.iou_thres = 0.5         # NMS threshold
-        self.max_det = 150
-
-        # TTA consensus thresholds
-        self.conf_high = 0.35        # Boxes above this survive without TTA confirmation
-        self.tta_match_iou = 0.45    # TTA cross-view match IoU
-
-        # Adaptive conf curve: lerp between low/high based on raw detection count
-        self.conf_adapt_low = 0.3   # Few objects: favor recall, each TP ≈ 0.065+ of total
-        self.conf_adapt_high = 0.6  # Many objects: favor precision, FP costs 0.035 each
-        self.count_low = 6           # Raw count below this → use conf_adapt_low
-        self.count_high = 70         # Raw count above this → use conf_adapt_high
-
-        self.use_tta = True
-
-        # Box sanity filters
-        self.min_box_area = 14 * 14
-        self.min_w = 8
-        self.min_h = 8
-        self.max_aspect_ratio = 6.5
-        self.max_box_area_ratio = 0.8
-
-        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}")
+            self.half = self.model.device is not None and "cuda" in str(self.model.device)
+        except Exception:
+            self.half = False
+        print("✅ Fire/Smoke/Extinguisher model loaded")
 
     def __repr__(self) -> str:
-        return (
-            f"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 = np.maximum(0.0, (boxes[i, 2] - boxes[i, 0])) * np.maximum(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)
-
-    @staticmethod
-    def _box_iou_one_to_many(box: np.ndarray, boxes: np.ndarray) -> np.ndarray:
-        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)
-
-    def _filter_sane_boxes(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-        orig_size: tuple[int, int],
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        if len(boxes) == 0:
-            return boxes, scores, cls_ids
-
-        orig_w, orig_h = orig_size
-        image_area = float(orig_w * orig_h)
-
-        keep = []
-        for i, box in enumerate(boxes):
-            x1, y1, x2, y2 = box.tolist()
-            bw = x2 - x1
-            bh = y2 - y1
-
-            if bw <= 0 or bh <= 0:
-                continue
-            if bw < self.min_w or bh < self.min_h:
-                continue
-
-            area = bw * bh
-            if area < self.min_box_area:
-                continue
-            if area > self.max_box_area_ratio * image_area:
-                continue
-
-            ar = max(bw / max(bh, 1e-6), bh / max(bw, 1e-6))
-            if ar > self.max_aspect_ratio:
-                continue
-
-            keep.append(i)
-
-        if not keep:
-            return (
-                np.empty((0, 4), dtype=np.float32),
-                np.empty((0,), dtype=np.float32),
-                np.empty((0,), dtype=np.int32),
-            )
-
-        keep = np.array(keep, dtype=np.intp)
-        return boxes[keep], scores[keep], cls_ids[keep]
-
-    def _decode_final_dets(
-        self,
-        preds: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        if preds.ndim == 3 and preds.shape[0] == 1:
-            preds = preds[0]
-
-        if preds.ndim != 2 or preds.shape[1] < 6:
-            raise ValueError(f"Unexpected ONNX final-det output shape: {preds.shape}")
-
-        boxes = preds[:, :4].astype(np.float32)
-        scores = preds[:, 4].astype(np.float32)
-        cls_ids = preds[:, 5].astype(np.int32)
-
-        # person only
-        keep = cls_ids == 0
-        boxes = boxes[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
-
-        # candidate threshold
-        keep = scores >= self.conf_thres
-        boxes = boxes[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
-
-        if len(boxes) == 0:
-            return []
-
-        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))
-
-        boxes, scores, cls_ids = self._filter_sane_boxes(boxes, scores, cls_ids, orig_size)
-        if len(boxes) == 0:
-            return []
-
-        keep_idx = self._hard_nms(boxes, scores, self.iou_thres)
-        keep_idx = keep_idx[: 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 _decode_raw_yolo(
-        self,
-        preds: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        if preds.ndim != 3:
-            raise ValueError(f"Unexpected raw ONNX output shape: {preds.shape}")
-        if preds.shape[0] != 1:
-            raise ValueError(f"Unexpected batch dimension in raw output: {preds.shape}")
-
-        preds = preds[0]
-
-        # Normalize to [N, C]
-        if preds.shape[0] <= 16 and preds.shape[1] > preds.shape[0]:
-            preds = preds.T
-
-        if preds.ndim != 2 or preds.shape[1] < 5:
-            raise ValueError(f"Unexpected normalized raw output shape: {preds.shape}")
-
-        boxes_xywh = preds[:, :4].astype(np.float32)
-        tail = preds[:, 4:].astype(np.float32)
-
-        # Supports:
-        # [x,y,w,h,score]                 single-class
-        # [x,y,w,h,obj,cls]               YOLO standard single-class
-        # [x,y,w,h,obj,cls1,cls2,...]     multi-class
-        if tail.shape[1] == 1:
-            scores = tail[:, 0]
-            cls_ids = np.zeros(len(scores), dtype=np.int32)
-        elif tail.shape[1] == 2:
-            obj = tail[:, 0]
-            cls_prob = tail[:, 1]
-            scores = obj * cls_prob
-            cls_ids = np.zeros(len(scores), dtype=np.int32)
-        else:
-            obj = tail[:, 0]
-            class_probs = tail[:, 1:]
-            cls_ids = np.argmax(class_probs, axis=1).astype(np.int32)
-            cls_scores = class_probs[np.arange(len(class_probs)), cls_ids]
-            scores = obj * cls_scores
-
-        keep = cls_ids == 0
-        boxes_xywh = boxes_xywh[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
-
-        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))
-
-        boxes, scores, cls_ids = self._filter_sane_boxes(boxes, scores, cls_ids, orig_size)
-        if len(boxes) == 0:
-            return []
-
-        keep_idx = self._hard_nms(boxes, scores, self.iou_thres)
-        keep_idx = keep_idx[: 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 _postprocess(
-        self,
-        output: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        if output.ndim == 2 and output.shape[1] >= 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-
-        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] >= 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-
-        return self._decode_raw_yolo(output, ratio, pad, orig_size)
-
-    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._postprocess(det_output, ratio, pad, orig_size)
-
-    def _merge_tta_consensus(
-        self,
-        boxes_orig: list[BoundingBox],
-        boxes_flip: list[BoundingBox],
-    ) -> list[BoundingBox]:
-        """
-        Keep:
-        - any box with conf >= conf_high
-        - low/medium-conf boxes only if confirmed across TTA views
-        Then run final hard NMS.
-        """
-        if not boxes_orig and not boxes_flip:
-            return []
-
-        coords_o = np.array([[b.x1, b.y1, b.x2, b.y2] for b in boxes_orig], dtype=np.float32) if boxes_orig else np.empty((0, 4), dtype=np.float32)
-        scores_o = np.array([b.conf for b in boxes_orig], dtype=np.float32) if boxes_orig else np.empty((0,), dtype=np.float32)
-
-        coords_f = np.array([[b.x1, b.y1, b.x2, b.y2] for b in boxes_flip], dtype=np.float32) if boxes_flip else np.empty((0, 4), dtype=np.float32)
-        scores_f = np.array([b.conf for b in boxes_flip], dtype=np.float32) if boxes_flip else np.empty((0,), dtype=np.float32)
-
-        accepted_boxes = []
-        accepted_scores = []
-
-        # Original view candidates
-        for i in range(len(coords_o)):
-            score = scores_o[i]
-            if score >= self.conf_high:
-                accepted_boxes.append(coords_o[i])
-                accepted_scores.append(score)
-            elif len(coords_f) > 0:
-                ious = self._box_iou_one_to_many(coords_o[i], coords_f)
-                j = int(np.argmax(ious))
-                if ious[j] >= self.tta_match_iou:
-                    fused_score = max(score, scores_f[j])
-                    accepted_boxes.append(coords_o[i])
-                    accepted_scores.append(fused_score)
-
-        # Flipped-view high-confidence boxes that original missed
-        for i in range(len(coords_f)):
-            score = scores_f[i]
-            if score < self.conf_high:
-                continue
-
-            if len(coords_o) == 0:
-                accepted_boxes.append(coords_f[i])
-                accepted_scores.append(score)
-                continue
-
-            ious = self._box_iou_one_to_many(coords_f[i], coords_o)
-            if np.max(ious) < self.tta_match_iou:
-                accepted_boxes.append(coords_f[i])
-                accepted_scores.append(score)
-
-        if not accepted_boxes:
-            return []
-
-        boxes = np.array(accepted_boxes, dtype=np.float32)
-        scores = np.array(accepted_scores, dtype=np.float32)
-
-        keep = self._hard_nms(boxes, scores, self.iou_thres)
-        keep = keep[: self.max_det]
-
-        out = []
-        for idx in keep:
-            x1, y1, x2, y2 = boxes[idx].tolist()
-            out.append(
-                BoundingBox(
-                    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(scores[idx]),
-                )
-            )
-        return out
-
-    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
-        boxes_orig = self._predict_single(image)
-
-        flipped = cv2.flip(image, 1)
-        boxes_flip_raw = self._predict_single(flipped)
-
-        w = image.shape[1]
-        boxes_flip = [
-            BoundingBox(
-                x1=w - b.x2,
-                y1=b.y1,
-                x2=w - b.x1,
-                y2=b.y2,
-                cls_id=b.cls_id,
-                conf=b.conf,
-            )
-            for b in boxes_flip_raw
-        ]
-
-        return self._merge_tta_consensus(boxes_orig, boxes_flip)
-
-    def _adaptive_conf_threshold(self, n_raw: int) -> float:
-        """
-        Dynamic confidence threshold based on raw detection count.
-
-        total_score = mAP50 * 0.65 + FP_score * 0.35
-        - Few objects → each TP worth ~0.065/n for mAP50 → keep low conf (maximize recall)
-        - Many objects → each TP worth little, FPs dominate → raise conf (minimize FP)
-        """
-        if n_raw <= self.count_low:
-            return self.conf_adapt_low
-        if n_raw >= self.count_high:
-            return self.conf_adapt_high
-        t = (n_raw - self.count_low) / (self.count_high - self.count_low)
-        return self.conf_adapt_low + t * (self.conf_adapt_high - self.conf_adapt_low)
-
-    def _apply_adaptive_filter(self, boxes: list[BoundingBox]) -> list[BoundingBox]:
-        if not boxes:
-            return boxes
-        n_raw = len(boxes)
-        thresh = self._adaptive_conf_threshold(n_raw)
-        return [b for b in boxes if b.conf >= thresh]
+        return f"Detect-fire miner: {type(self.model).__name__} @ imgsz={IMGSZ}, conf={CONF_THRESHOLD}"
 
     def predict_batch(
         self,
@@ -603,25 +55,28 @@ class Miner:
         offset: int,
         n_keypoints: int,
     ) -> list[TVFrameResult]:
-        results: list[TVFrameResult] = []
-
-        for frame_number_in_batch, image in enumerate(batch_images):
-            try:
-                if self.use_tta:
-                    boxes = self._predict_tta(image)
-                else:
-                    boxes = self._predict_single(image)
-                boxes = self._apply_adaptive_filter(boxes)
-            except Exception as e:
-                print(f"⚠️ Inference failed for frame {offset + frame_number_in_batch}: {e}")
-                boxes = []
-
-            results.append(
-                TVFrameResult(
-                    frame_id=offset + frame_number_in_batch,
-                    boxes=boxes,
-                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
-                )
-            )
-
-        return results
+        results = self.model.predict(
+            batch_images,
+            imgsz=IMGSZ,
+            conf=CONF_THRESHOLD,
+            verbose=False,
+            half=True,
+        )
+        out: list[TVFrameResult] = []
+        for i, det in enumerate(results):
+            boxes: list[BoundingBox] = []
+            if det.boxes is not None:
+                for box in det.boxes.data:
+                    x1, y1, x2, y2, conf, cls_id = box.tolist()
+                    boxes.append(
+                        BoundingBox(
+                            x1=int(x1),
+                            y1=int(y1),
+                            x2=int(x2),
+                            y2=int(y2),
+                            cls_id=int(cls_id),
+                            conf=float(conf),
+                        )
+                    )
+            out.append(TVFrameResult(frame_id=offset + i, boxes=boxes))
+        return out