scorevision: push artifact

miner.py

@@ -22,11 +22,41 @@ class TVFrameResult(BaseModel):
     boxes: list[BoundingBox]
     keypoints: list[tuple[int, int]]
 
+SIZE = 1280
+TARGET_CLASS_NAMES = ["petrol hose", "petrol pump", "price board", "roof canopy"]
+
 
 class Miner:
     def __init__(self, path_hf_repo: Path) -> None:
         model_path = path_hf_repo / "weights.onnx"
-        self.class_names = ["person"]
+        cn_path = model_path.with_name("class_names.txt")
+        self.class_names = TARGET_CLASS_NAMES.copy()
+        if cn_path.is_file():
+            lines = cn_path.read_text(encoding="utf-8").splitlines()
+            model_class_order = [
+                ln.strip()
+                for ln in lines
+                if ln.strip() and not ln.strip().startswith("#")
+            ]
+            if len(model_class_order) == len(self.class_names) and set(model_class_order) == set(self.class_names):
+                self.cls_remap = np.array(
+                    [self.class_names.index(n) for n in model_class_order], dtype=np.int32
+                )
+            else:
+                # If class_names.txt is missing/invalid for this target order, keep identity mapping.
+                self.cls_remap = np.arange(len(self.class_names), dtype=np.int32)
+        else:
+            # Fallback when no class_names.txt is present: assume ONNX class order == target order.
+            self.cls_remap = np.arange(len(self.class_names), dtype=np.int32)
+        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
@@ -37,71 +67,120 @@ class Miner:
                 sess_options=sess_options,
                 providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
             )
-        except Exception:
+            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 = [o.name for o in self.session.get_outputs()]
+        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], 1280)
-        self.input_width = self._safe_dim(self.input_shape[3], 1280)
-
-        # Tuned for MAP50 (65%) + FALSE_POSITIVE (35%) scoring
-        # Lower conf = more recall = higher MAP50, but more FP
-        # Balance: slightly aggressive recall since MAP50 weight > FP weight
-        self.conf_thres = 0.40
-        self.conf_high = 0.55
-        self.iou_thres = 0.50
-        self.tta_match_iou = 0.45
-        self.max_det = 200
-        self.use_tta = True
 
-        # Box sanity filters
-        self.min_box_area = 12 * 12
-        self.min_w = 6
-        self.min_h = 6
-        self.max_aspect_ratio = 7.0
-        self.max_box_area_ratio = 0.85
+        self.input_height = self._safe_dim(self.input_shape[2], default=SIZE)
+        self.input_width = self._safe_dim(self.input_shape[3], default=SIZE)
 
-        print(f"Model loaded: {model_path}, providers={self.session.get_providers()}")
+        self.conf_thres = 0.5
+        self.iou_thres = 0.45
+        self.max_det = 30
+        self.use_tta = True
+
+        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 __repr__(self) -> str:
-        return f"ONNXRuntime(providers={self.session.get_providers()})"
+        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]]:
+    def _letterbox(
+        self,
+        image: ndarray,
+        new_shape: tuple[int, int],
+        color=(114, 114, 114),
+    ) -> tuple[ndarray, float, tuple[float, float]]:
+        """
+        Resize with unchanged aspect ratio and pad to target shape.
+        Returns:
+            padded_image,
+            ratio,
+            (pad_w, pad_h)  # half-padding
+        """
         h, w = image.shape[:2]
         new_w, new_h = new_shape
+
         ratio = min(new_w / w, new_h / h)
-        rw, rh = int(round(w * ratio)), int(round(h * ratio))
-        if (rw, rh) != (w, 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, (rw, rh), interpolation=interp)
-        dw, dh = (new_w - rw) / 2.0, (new_h - rh) / 2.0
+            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, int(round(dh - 0.1)), int(round(dh + 0.1)),
-            int(round(dw - 0.1)), int(round(dw + 0.1)),
-            borderType=cv2.BORDER_CONSTANT, value=color)
+            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]]:
+    def _preprocess(
+        self, image: ndarray
+    ) -> tuple[np.ndarray, float, tuple[float, float], tuple[int, int]]:
+        """
+        Preprocess for fixed-size ONNX export:
+        - enhance image quality (CLAHE, denoise, sharpen)
+        - letterbox to model input size
+        - BGR -> RGB
+        - normalize to [0,1]
+        - HWC -> NCHW float32
+        """
         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).astype(np.float32) / 255.0
-        img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))[None, ...], dtype=np.float32)
+
+        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, size: tuple[int, int]) -> np.ndarray:
-        w, h = size
+    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)
@@ -109,206 +188,457 @@ class Miner:
         return boxes
 
     @staticmethod
-    def _xywh_to_xyxy(b: np.ndarray) -> np.ndarray:
-        o = np.empty_like(b)
-        o[:, 0] = b[:, 0] - b[:, 2] / 2
-        o[:, 1] = b[:, 1] - b[:, 3] / 2
-        o[:, 2] = b[:, 0] + b[:, 2] / 2
-        o[:, 3] = b[:, 1] + b[:, 3] / 2
-        return o
+    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
+
+    def _soft_nms(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        sigma: float = 0.5,
+        score_thresh: float = 0.01,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """
+        Soft-NMS: Gaussian decay of overlapping scores instead of hard removal.
+        Returns (kept_original_indices, updated_scores).
+        """
+        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 = max(0.0, 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 _hard_nms(boxes: np.ndarray, scores: np.ndarray, iou_thresh: float) -> np.ndarray:
-        if len(boxes) == 0:
+    def _hard_nms(
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        iou_thresh: float,
+    ) -> np.ndarray:
+        """
+        Standard NMS: keep one box per overlapping cluster (the one with highest score).
+        Returns indices of kept boxes (into the boxes/scores arrays).
+        """
+        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)[::-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, xx2 - xx1) * np.maximum(0, yy2 - yy1)
-            area_i = max(0, (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1]))
-            area_r = np.maximum(0, boxes[rest, 2] - boxes[rest, 0]) * np.maximum(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)
+        keep: list[int] = []
+        suppressed = np.zeros(N, dtype=bool)
+        for i in range(N):
+            idx = order[i]
+            if suppressed[idx]:
+                continue
+            keep.append(idx)
+            bi = boxes[idx]
+            for k in range(i + 1, N):
+                jdx = order[k]
+                if suppressed[jdx]:
+                    continue
+                bj = boxes[jdx]
+                xx1 = max(bi[0], bj[0])
+                yy1 = max(bi[1], bj[1])
+                xx2 = min(bi[2], bj[2])
+                yy2 = min(bi[3], bj[3])
+                inter = max(0.0, xx2 - xx1) * max(0.0, yy2 - yy1)
+                area_i = (bi[2] - bi[0]) * (bi[3] - bi[1])
+                area_j = (bj[2] - bj[0]) * (bj[3] - bj[1])
+                iou = inter / (area_i + area_j - inter + 1e-7)
+                if iou > iou_thresh:
+                    suppressed[jdx] = True
+        return np.array(keep)
 
     @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, xx2 - xx1) * np.maximum(0, yy2 - yy1)
-        a = max(0, (box[2] - box[0]) * (box[3] - box[1]))
-        b = np.maximum(0, boxes[:, 2] - boxes[:, 0]) * np.maximum(0, boxes[:, 3] - boxes[:, 1])
-        return inter / (a + b - inter + 1e-7)
-
-    def _filter_sane(self, boxes, scores, cls_ids, orig_size):
+    def _max_score_per_cluster(
+        coords: np.ndarray,
+        scores: np.ndarray,
+        keep_indices: np.ndarray,
+        iou_thresh: float,
+    ) -> np.ndarray:
+        """
+        For each kept box, return the max original score among itself and any
+        box that overlaps it with IOU >= iou_thresh (so TTA cluster keeps best conf).
+        """
+        n_keep = len(keep_indices)
+        if n_keep == 0:
+            return np.array([], dtype=np.float32)
+        out = np.empty(n_keep, dtype=np.float32)
+        coords = np.asarray(coords, dtype=np.float32)
+        scores = np.asarray(scores, dtype=np.float32)
+        for i in range(n_keep):
+            idx = keep_indices[i]
+            bi = coords[idx]
+            xx1 = np.maximum(bi[0], coords[:, 0])
+            yy1 = np.maximum(bi[1], coords[:, 1])
+            xx2 = np.minimum(bi[2], coords[:, 2])
+            yy2 = np.minimum(bi[3], coords[:, 3])
+            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+            area_i = (bi[2] - bi[0]) * (bi[3] - bi[1])
+            areas_j = (coords[:, 2] - coords[:, 0]) * (coords[:, 3] - coords[:, 1])
+            iou = inter / (area_i + areas_j - inter + 1e-7)
+            in_cluster = iou >= iou_thresh
+            out[i] = float(np.max(scores[in_cluster]))
+        return out
+
+    def _decode_final_dets(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+        apply_optional_dedup: bool = False,
+    ) -> list[BoundingBox]:
+        """
+        Primary path:
+        expected output rows like [x1, y1, x2, y2, conf, cls_id]
+        in letterboxed input coordinates.
+        """
+        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)
+        cls_ids = self.cls_remap[np.clip(cls_ids, 0, len(self.cls_remap) - 1)]
+
+        keep = scores >= self.conf_thres
+        boxes = boxes[keep]
+        scores = scores[keep]
+        cls_ids = cls_ids[keep]
+
         if len(boxes) == 0:
-            return boxes, scores, cls_ids
-        ow, oh = orig_size
-        area_img = float(ow * oh)
-        keep = []
-        for i, box in enumerate(boxes):
-            bw, bh = box[2] - box[0], box[3] - box[1]
-            if bw <= 0 or bh <= 0 or bw < self.min_w or bh < self.min_h:
-                continue
-            area = bw * bh
-            if area < self.min_box_area or area > self.max_box_area_ratio * area_img:
-                continue
-            if max(bw / max(bh, 1e-6), bh / max(bw, 1e-6)) > self.max_aspect_ratio:
+            return []
+
+        pad_w, pad_h = pad
+        orig_w, orig_h = orig_size
+
+        # reverse letterbox
+        boxes[:, [0, 2]] -= pad_w
+        boxes[:, [1, 3]] -= pad_h
+        boxes /= ratio
+        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
+
+        if apply_optional_dedup and len(boxes) > 1:
+            keep_idx, scores = self._soft_nms(boxes, scores)
+            boxes = boxes[keep_idx]
+            cls_ids = cls_ids[keep_idx]
+
+        results: list[BoundingBox] = []
+        for box, conf, cls_id in zip(boxes, scores, cls_ids):
+            x1, y1, x2, y2 = box.tolist()
+
+            if x2 <= x1 or y2 <= y1:
                 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)
-        k = np.array(keep, dtype=np.intp)
-        return boxes[k], scores[k], cls_ids[k]
 
-    def _decode_raw_yolo(self, preds, ratio, pad, orig_size):
-        if preds.ndim == 3 and preds.shape[0] == 1:
-            preds = preds[0]
+            results.append(
+                BoundingBox(
+                    x1=int(math.floor(x1)),
+                    y1=int(math.floor(y1)),
+                    x2=int(math.ceil(x2)),
+                    y2=int(math.ceil(y2)),
+                    cls_id=int(cls_id),
+                    conf=float(conf),
+                )
+            )
+
+        return results
+
+    def _decode_raw_yolo(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+    ) -> list[BoundingBox]:
+        """
+        Fallback path for raw YOLO predictions.
+        Supports common layouts:
+        - [1, C, N]
+        - [1, N, C]
+        """
+        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:]
+        cls_part = preds[:, 4:].astype(np.float32)
 
-        if tail.shape[1] == 1:
-            scores = tail[:, 0]
+        if cls_part.shape[1] == 1:
+            scores = cls_part[:, 0]
             cls_ids = np.zeros(len(scores), dtype=np.int32)
         else:
-            cls_ids = np.argmax(tail, axis=1).astype(np.int32)
-            scores = tail[np.arange(len(tail)), cls_ids]
+            cls_ids = np.argmax(cls_part, axis=1).astype(np.int32)
+            scores = cls_part[np.arange(len(cls_part)), cls_ids]
+        cls_ids = self.cls_remap[np.clip(cls_ids, 0, len(self.cls_remap) - 1)]
+
+        keep = scores >= self.conf_thres
+        boxes_xywh = boxes_xywh[keep]
+        scores = scores[keep]
+        cls_ids = cls_ids[keep]
 
-        # person only (class 0)
-        mask = (cls_ids == 0) & (scores >= self.conf_thres)
-        boxes_xywh, scores, cls_ids = boxes_xywh[mask], scores[mask], cls_ids[mask]
         if len(boxes_xywh) == 0:
             return []
 
         boxes = self._xywh_to_xyxy(boxes_xywh)
-        boxes[:, [0, 2]] -= pad[0]
-        boxes[:, [1, 3]] -= pad[1]
-        boxes /= ratio
-        boxes = self._clip_boxes(boxes, orig_size)
-        boxes, scores, cls_ids = self._filter_sane(boxes, scores, cls_ids, orig_size)
-        if len(boxes) == 0:
-            return []
+        keep_idx, scores = self._soft_nms(boxes, scores)
+        keep_idx = keep_idx[: self.max_det]
+        scores = scores[: self.max_det]
 
-        keep = self._hard_nms(boxes, scores, self.iou_thres)[:self.max_det]
-        return [BoundingBox(
-            x1=int(math.floor(boxes[i, 0])), y1=int(math.floor(boxes[i, 1])),
-            x2=int(math.ceil(boxes[i, 2])), y2=int(math.ceil(boxes[i, 3])),
-            cls_id=0, conf=float(scores[i]))
-            for i in keep if boxes[i, 2] > boxes[i, 0] and boxes[i, 3] > boxes[i, 1]]
-
-    def _decode_final_dets(self, preds, ratio, pad, orig_size):
-        if preds.ndim == 3 and preds.shape[0] == 1:
-            preds = preds[0]
-        boxes = preds[:, :4].astype(np.float32)
-        scores = preds[:, 4].astype(np.float32)
-        cls_ids = preds[:, 5].astype(np.int32)
+        boxes = boxes[keep_idx]
+        cls_ids = cls_ids[keep_idx]
 
-        mask = (cls_ids == 0) & (scores >= self.conf_thres)
-        boxes, scores, cls_ids = boxes[mask], scores[mask], cls_ids[mask]
-        if len(boxes) == 0:
-            return []
+        pad_w, pad_h = pad
+        orig_w, orig_h = orig_size
 
-        boxes[:, [0, 2]] -= pad[0]
-        boxes[:, [1, 3]] -= pad[1]
+        boxes[:, [0, 2]] -= pad_w
+        boxes[:, [1, 3]] -= pad_h
         boxes /= ratio
-        boxes = self._clip_boxes(boxes, orig_size)
-        boxes, scores, cls_ids = self._filter_sane(boxes, scores, cls_ids, orig_size)
-        if len(boxes) == 0:
-            return []
+        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
+
+        results: list[BoundingBox] = []
+        for box, conf, cls_id in zip(boxes, scores, cls_ids):
+            x1, y1, x2, y2 = box.tolist()
+
+            if x2 <= x1 or y2 <= y1:
+                continue
+
+            results.append(
+                BoundingBox(
+                    x1=int(math.floor(x1)),
+                    y1=int(math.floor(y1)),
+                    x2=int(math.ceil(x2)),
+                    y2=int(math.ceil(y2)),
+                    cls_id=int(cls_id),
+                    conf=float(conf),
+                )
+            )
 
-        keep = self._hard_nms(boxes, scores, self.iou_thres)[:self.max_det]
-        return [BoundingBox(
-            x1=int(math.floor(boxes[i, 0])), y1=int(math.floor(boxes[i, 1])),
-            x2=int(math.ceil(boxes[i, 2])), y2=int(math.ceil(boxes[i, 3])),
-            cls_id=0, conf=float(scores[i]))
-            for i in keep if boxes[i, 2] > boxes[i, 0] and boxes[i, 3] > boxes[i, 1]]
+        return results
 
-    def _postprocess(self, output, ratio, pad, orig_size):
+    def _postprocess(
+        self,
+        output: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+    ) -> list[BoundingBox]:
+        """
+        Prefer final detections first.
+        Fallback to raw decode only if needed.
+        """
+        # final detections: [N,6]
         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:
+
+        # final detections: [1,N,6]
+        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] == 6:
             return self._decode_final_dets(output, ratio, pad, orig_size)
+
+        # fallback raw decode
         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)
-        tensor, ratio, pad, orig_size = self._preprocess(image)
-        outputs = self.session.run(self.output_names, {self.input_name: tensor})
-        return self._postprocess(outputs[0], ratio, pad, orig_size)
 
-    def _merge_tta(self, boxes_orig, boxes_flip):
-        if not boxes_orig and not boxes_flip:
-            return []
+        input_tensor, ratio, pad, orig_size = self._preprocess(image)
 
-        co = 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)
-        so = np.array([b.conf for b in boxes_orig], dtype=np.float32) if boxes_orig else np.empty(0, dtype=np.float32)
-        cf = 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)
-        sf = np.array([b.conf for b in boxes_flip], dtype=np.float32) if boxes_flip else np.empty(0, dtype=np.float32)
+        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}"
+            )
 
-        acc_b, acc_s = [], []
+        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)
 
-        for i in range(len(co)):
-            if so[i] >= self.conf_high:
-                acc_b.append(co[i]); acc_s.append(so[i])
-            elif len(cf) > 0:
-                ious = self._box_iou_one_to_many(co[i], cf)
-                j = int(np.argmax(ious))
-                if ious[j] >= self.tta_match_iou:
-                    acc_b.append(co[i]); acc_s.append(max(so[i], sf[j]))
+    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
+        """Horizontal-flip TTA: merge original + flipped via hard NMS."""
+        boxes_orig = self._predict_single(image)
 
-        for i in range(len(cf)):
-            if sf[i] < self.conf_high:
-                continue
-            if len(co) == 0:
-                acc_b.append(cf[i]); acc_s.append(sf[i]); continue
-            if np.max(self._box_iou_one_to_many(cf[i], co)) < self.tta_match_iou:
-                acc_b.append(cf[i]); acc_s.append(sf[i])
+        flipped = cv2.flip(image, 1)
+        boxes_flip = self._predict_single(flipped)
 
-        if not acc_b:
-            return []
+        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
+        ]
 
-        boxes = np.array(acc_b, dtype=np.float32)
-        scores = np.array(acc_s, dtype=np.float32)
-        keep = self._hard_nms(boxes, scores, self.iou_thres)[:self.max_det]
+        all_boxes = boxes_orig + boxes_flip
+        if len(all_boxes) == 0:
+            return []
 
-        return [BoundingBox(
-            x1=int(math.floor(boxes[i, 0])), y1=int(math.floor(boxes[i, 1])),
-            x2=int(math.ceil(boxes[i, 2])), y2=int(math.ceil(boxes[i, 3])),
-            cls_id=0, conf=float(scores[i])) for i in keep]
+        coords = np.array(
+            [[b.x1, b.y1, b.x2, b.y2] for b in all_boxes], dtype=np.float32
+        )
+        scores = np.array([b.conf for b in all_boxes], dtype=np.float32)
 
-    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(boxes_orig, boxes_flip)
+        hard_keep = self._hard_nms(coords, scores, self.iou_thres)
+        if len(hard_keep) == 0:
+            return []
 
-    def predict_batch(self, batch_images: list[ndarray], offset: int, n_keypoints: int) -> list[TVFrameResult]:
-        results = []
-        for i, image in enumerate(batch_images):
+        # _hard_nms already orders kept indices by descending score.
+        hard_keep = hard_keep[: self.max_det]
+
+        return [
+            BoundingBox(
+                x1=all_boxes[i].x1,
+                y1=all_boxes[i].y1,
+                x2=all_boxes[i].x2,
+                y2=all_boxes[i].y2,
+                cls_id=all_boxes[i].cls_id,
+                conf=float(scores[i]),
+            )
+            for i in hard_keep
+        ]
+
+    def predict_batch(
+        self,
+        batch_images: list[ndarray],
+        offset: int,
+        n_keypoints: int,
+    ) -> list[TVFrameResult]:
+        results: list[TVFrameResult] = []
+
+        for frame_number_in_batch, image in enumerate(batch_images):
             try:
-                boxes = self._predict_tta(image) if self.use_tta else self._predict_single(image)
+                if self.use_tta:
+                    boxes = self._predict_tta(image)
+                else:
+                    boxes = self._predict_single(image)
             except Exception as e:
-                print(f"Inference failed frame {offset + i}: {e}")
+                print(f"⚠️ Inference failed for frame {offset + frame_number_in_batch}: {e}")
                 boxes = []
-            results.append(TVFrameResult(
-                frame_id=offset + i, boxes=boxes,
-                keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))]))
+            # for box in boxes:
+            #     if box.cls_id == 2:
+            #         box.cls_id = 3
+            #     elif box.cls_id == 3:
+            #         box.cls_id = 2
+                    
+            
+            
+            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
+    
+
+if __name__ == "__main__":
+    # Simple manual test: load weights.onnx, run on 1.png, and draw bboxes
+    repo_dir = Path(__file__).parent
+    miner = Miner(repo_dir)
+
+    image_path = repo_dir / "car1.png"
+    if not image_path.exists():
+        raise FileNotFoundError(f"Test image not found: {image_path}")
+
+    image = cv2.imread(str(image_path), cv2.IMREAD_COLOR)
+    if image is None:
+        raise RuntimeError(f"Failed to read image: {image_path}")
+
+    results = miner.predict_batch([image], offset=0, n_keypoints=0)
+    # Draw bounding boxes on a copy of the image
+    vis = image.copy()
+    colors = [(0, 255, 0), (0, 0, 255), (255, 0, 0)]
+    for frame in results:
+        print(f"Frame {frame.frame_id}:")
+        for i, box in enumerate(frame.boxes):
+            color = colors[i % len(colors)]
+            cv2.rectangle(
+                vis,
+                (box.x1, box.y1),
+                (box.x2, box.y2),
+                color,
+                2,
+            )
+            label = f"{box.cls_id }_{miner.class_names[box.cls_id] if box.cls_id < len(miner.class_names) else box.cls_id}:{box.conf:.2f}"
+            cv2.putText(
+                vis,
+                label,
+                (box.x1, max(0, box.y1 - 5)),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                box.conf,
+                color,
+                1,
+                cv2.LINE_AA,
+            )
+            print(
+                f"  cls={box.cls_id} conf={box.conf:.3f} "
+                f"box=({box.x1},{box.y1},{box.x2},{box.y2})"
+            )
+        print(len(frame.boxes))
+
+    out_path = repo_dir / f"1_out_iou{miner.iou_thres:.2f}.png"
+    cv2.imwrite(str(out_path), vis)
+    print(f"Saved visualization to: {out_path}")