scorevision: push artifact

miner.py

@@ -1,3 +1,13 @@
+"""Plate-detection miner — v2.0 "hermestech + tile s<3".
+
+Base weights: hermestech00/numberplate0 (YOLO26s retrained, fp16, ~19 MB).
+
+Inference pipeline:
+  1) Full-image primary pass with alfred001 tuning
+     (conf=0.22, iou=0.41, sigma=0.685, soft-NMS + hflip TTA).
+  2) If the primary returned fewer than 3 boxes, run a 2x2
+     overlapping tile pass (tile_conf=0.40) with novelty-merge.
+"""
 from pathlib import Path
 import math
 
@@ -22,6 +32,7 @@ class TVFrameResult(BaseModel):
     boxes: list[BoundingBox]
     keypoints: list[tuple[int, int]]
 
+
 SIZE = 1280
 
 
@@ -37,17 +48,27 @@ class Miner:
                 if ln.strip() and not ln.strip().startswith("#")
             ]
         else:
-            self.class_names = ["person"]
+            self.class_names = ["numberplate"]
         print("ORT version:", ort.__version__)
 
         try:
             ort.preload_dlls()
-            print("✅ onnxruntime.preload_dlls() success")
+            print("onnxruntime.preload_dlls() success")
         except Exception as e:
-            print(f"⚠️ preload_dlls failed: {e}")
+            print(f"preload_dlls failed: {e}")
 
         print("ORT available providers BEFORE session:", ort.get_available_providers())
 
+        try:
+            import torch
+            if torch.cuda.is_available():
+                print(f"GPU: {torch.cuda.get_device_name(0)}")
+                print(f"GPU memory: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB")
+            else:
+                print("GPU: CUDA not available via torch")
+        except Exception as e:
+            print(f"GPU detection failed: {e}")
+
         sess_options = ort.SessionOptions()
         sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
 
@@ -57,9 +78,9 @@ class Miner:
                 sess_options=sess_options,
                 providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
             )
-            print("✅ Created ORT session with preferred CUDA provider list")
+            print("Created ORT session with preferred CUDA provider list")
         except Exception as e:
-            print(f"⚠️ CUDA session creation failed, falling back to CPU: {e}")
+            print(f"CUDA session creation failed, falling back to CPU: {e}")
             self.session = ort.InferenceSession(
                 str(model_path),
                 sess_options=sess_options,
@@ -70,25 +91,35 @@ class Miner:
 
         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.output_names = [o.name for o 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=SIZE)
         self.input_width = self._safe_dim(self.input_shape[3], default=SIZE)
 
-        self.conf_thres = 0.45
-        self.iou_thres = 0.5
-        self.max_det = 30
+        # Primary pass: alfred001 tuning (optimized for hermestech weights)
+        self.conf_thres = 0.22
+        self.iou_thres = 0.41
+        self.sigma = 0.685
+        self.max_det = 300
+
+        # Conditional tile-pass (trimmed for latency: no hflip, tighter sparse)
+        self.sparse_threshold = 3       # 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
+        self.tile_use_hflip = False     # skip hflip tile pass to save ~4 forwards
+
         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}")
+        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 (
@@ -100,73 +131,38 @@ class Miner:
     def _safe_dim(value, default: int) -> int:
         return value if isinstance(value, int) and value > 0 else default
 
+    # ---------- image preprocessing ----------
     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)
         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
-
+        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,
+            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]]:
-        """
-        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)
-        img = img.astype(np.float32) / 255.0
+    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, ...]
-        img = np.ascontiguousarray(img, dtype=np.float32)
-
-        return img, ratio, pad, (orig_w, orig_h)
+        return np.ascontiguousarray(img, dtype=np.float32), ratio, pad
 
     @staticmethod
     def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
@@ -177,375 +173,244 @@ class Miner:
         boxes[:, 3] = np.clip(boxes[:, 3], 0, h - 1)
         return boxes
 
+    # ---------- NMS primitives ----------
     @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
+    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)
 
     def _soft_nms(
         self,
         boxes: np.ndarray,
         scores: np.ndarray,
-        sigma: float = 0.5,
+        sigma: float,
         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(
+            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 _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: 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 _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)
-
-        keep = scores >= self.conf_thres
-        boxes = boxes[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
-
+    def _box_iou_one_to_many(box: np.ndarray, boxes: np.ndarray) -> np.ndarray:
         if len(boxes) == 0:
-            return []
-
-        pad_w, pad_h = pad
-        orig_w, orig_h = orig_size
-
-        # reverse letterbox
-        boxes[:, [0, 2]] -= pad_w
-        boxes[:, [1, 3]] -= pad_h
+            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
-        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()
+        oh, ow = image.shape[:2]
+        boxes = self._clip_boxes(boxes, (ow, oh))
+        return np.concatenate([boxes, scores[:, None]], axis=1)
 
-            if x2 <= x1 or y2 <= y1:
+    # ---------- 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
-
-            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)
-        cls_part = preds[:, 4:].astype(np.float32)
-
-        if cls_part.shape[1] == 1:
-            scores = cls_part[:, 0]
-            cls_ids = np.zeros(len(scores), dtype=np.int32)
-        else:
-            cls_ids = np.argmax(cls_part, axis=1).astype(np.int32)
-            scores = cls_part[np.arange(len(cls_part)), 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:
+            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 (skipped when tile_use_hflip=False — saves 4 ONNX forwards)
+        if self.tile_use_hflip:
+            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 []
+        if image.shape[0] <= 0 or image.shape[1] <= 0 or image.shape[2] != 3:
+            return []
+        if image.dtype != np.uint8:
+            image = image.astype(np.uint8)
 
-        boxes = self._xywh_to_xyxy(boxes_xywh)
-        keep_idx, scores = self._soft_nms(boxes, scores)
-        keep_idx = keep_idx[: self.max_det]
-        scores = scores[: self.max_det]
-
-        boxes = boxes[keep_idx]
-        cls_ids = cls_ids[keep_idx]
-
-        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))
+        primary = self._primary(image)
+        if len(primary) < self.sparse_threshold:
+            dets = self._tile_augment(image, primary)
+        else:
+            dets = primary
 
         results: list[BoundingBox] = []
-        for box, conf, cls_id in zip(boxes, scores, cls_ids):
-            x1, y1, x2, y2 = box.tolist()
-
+        for row in dets:
+            x1, y1, x2, y2, conf = row.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),
+                    cls_id=0,
                     conf=float(conf),
                 )
             )
-
         return results
 
-    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)
-
-        # 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)
-
-        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 _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
-        """Horizontal-flip TTA: merge original + flipped via hard NMS."""
-        boxes_orig = self._predict_single(image)
-
-        flipped = cv2.flip(image, 1)
-        boxes_flip = 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
-        ]
-
-        all_boxes = boxes_orig + boxes_flip
-        if len(all_boxes) == 0:
-            return []
-
-        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)
-
-        hard_keep = self._hard_nms(coords, scores, self.iou_thres)
-        if len(hard_keep) == 0:
-            return []
-
-        # _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
-        ]
-
+    # ---------- chute entrypoint ----------
     def predict_batch(
         self,
         batch_images: list[ndarray],
@@ -553,24 +418,12 @@ class Miner:
         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._predict_single(image)
             except Exception as e:
-                print(f"⚠️ Inference failed for frame {offset + frame_number_in_batch}: {e}")
+                print(f"Inference failed for frame {offset + frame_number_in_batch}: {e}")
                 boxes = []
-            # 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,
@@ -578,55 +431,4 @@ class Miner:
                     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}")