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11306a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | # -*- coding: utf-8 -*-
"""Shelf detection and grouping utilities."""
from __future__ import annotations
from dataclasses import dataclass
from typing import List, Tuple, Dict, Any
import numpy as np
from PIL import Image, ImageDraw
@dataclass
class ShelfMetadata:
shelf_id: int
num_items: int
y_range: Tuple[int, int]
confidence: float
status: str
class ShelfInventoryProcessor:
def __init__(
self,
model,
overlap_threshold: float = 0.5,
min_box_height: int = 20,
min_items_per_shelf: int = 8,
merge_overlap_threshold: float = 0.3,
) -> None:
self.model = model
self.overlap_threshold = overlap_threshold
self.min_box_height = min_box_height
self.min_items_per_shelf = min_items_per_shelf
self.merge_overlap_threshold = merge_overlap_threshold
# ---------- Geometry Utilities ----------
@staticmethod
def vertical_overlap(range1: Tuple[float, float], range2: Tuple[float, float]) -> float:
inter = min(range1[1], range2[1]) - max(range1[0], range2[0])
if inter <= 0:
return 0.0
h1 = range1[1] - range1[0]
return inter / h1 if h1 > 0 else 0.0
# ---------- Inference ----------
def run_inference(self, image: Image.Image) -> Tuple[np.ndarray | None, Image.Image, ImageDraw.ImageDraw]:
results = self.model.predict(image, verbose=False)[0]
img = image.convert("RGB")
draw = ImageDraw.Draw(img)
if not results.boxes:
return None, img, draw
boxes = results.boxes.xyxy.cpu().numpy()
boxes = boxes[np.argsort(boxes[:, 1])] # top → bottom
return boxes, img, draw
# ---------- Initial Shelf Grouping ----------
def group_boxes_into_shelves(self, boxes: np.ndarray) -> List[List[np.ndarray]]:
shelves: List[List[np.ndarray]] = []
for box in boxes:
x1, y1, x2, y2 = box
box_h = y2 - y1
if box_h < self.min_box_height:
continue
matched = False
for shelf in shelves:
s_y1 = np.median([b[1] for b in shelf])
s_y2 = np.median([b[3] for b in shelf])
inter = min(y2, s_y2) - max(y1, s_y1)
overlap_ratio = inter / box_h if box_h > 0 else 0
if overlap_ratio > self.overlap_threshold:
shelf.append(box)
matched = True
break
if not matched:
shelves.append([box])
return shelves
# ---------- Shelf Object Builder ----------
def build_shelf_objects(self, shelves: List[List[np.ndarray]]) -> List[Dict[str, Any]]:
shelf_objs: List[Dict[str, Any]] = []
for shelf in shelves:
ys = [b[1] for b in shelf] + [b[3] for b in shelf]
shelf_objs.append({
"boxes": shelf,
"y_range": (min(ys), max(ys)),
})
return shelf_objs
# ---------- Post-processing Merge ----------
def merge_weak_shelves(self, shelf_objs: List[Dict[str, Any]]) -> List[List[np.ndarray]]:
merged: List[List[np.ndarray]] = []
used = [False] * len(shelf_objs)
for i in range(len(shelf_objs)):
if used[i]:
continue
cur_boxes = shelf_objs[i]["boxes"]
cur_range = shelf_objs[i]["y_range"]
for j in range(i + 1, len(shelf_objs)):
if used[j]:
continue
overlap = self.vertical_overlap(cur_range, shelf_objs[j]["y_range"])
if (
overlap > self.merge_overlap_threshold
and (
len(cur_boxes) < self.min_items_per_shelf
or len(shelf_objs[j]["boxes"]) < self.min_items_per_shelf
)
):
cur_boxes.extend(shelf_objs[j]["boxes"])
used[j] = True
merged.append(cur_boxes)
used[i] = True
return merged
# ---------- Annotation & Metadata ----------
def annotate_and_build_metadata(
self,
shelves: List[List[np.ndarray]],
draw: ImageDraw.ImageDraw,
) -> Tuple[List[np.ndarray], List[ShelfMetadata], List[Dict[str, Any]]]:
final_boxes: List[np.ndarray] = []
shelf_metadata: List[ShelfMetadata] = []
object_metadata: List[Dict[str, Any]] = []
avg_items = float(np.mean([len(s) for s in shelves])) if shelves else 1.0
box_counter = 0
for shelf_id, shelf in enumerate(shelves, start=1):
ys = [b[1] for b in shelf] + [b[3] for b in shelf]
min_y, max_y = min(ys), max(ys)
num_items = len(shelf)
confidence = round(num_items / avg_items, 2)
shelf_metadata.append(
ShelfMetadata(
shelf_id=shelf_id,
num_items=num_items,
y_range=(int(min_y), int(max_y)),
confidence=confidence,
status="stable" if confidence >= 0.5 else "unstable",
)
)
for b in shelf:
draw.rectangle([b[0], b[1], b[2], b[3]], outline="red", width=2)
draw.text((b[0], b[1] - 10), f"S{shelf_id}", fill="red")
final_boxes.append(b)
object_metadata.append(
{
"box_id": box_counter,
"shelf_id": shelf_id,
"box": [int(v) for v in b],
}
)
box_counter += 1
return final_boxes, shelf_metadata, object_metadata
# ---------- Crop Utilities ----------
def crop_annotated_image_by_object(
self,
annotated_img: Image.Image,
boxes: List[np.ndarray],
box_id: int | None = None,
padding: int = 5,
) -> Image.Image | Dict[int, Image.Image]:
width, height = annotated_img.size
def _safe_crop(x1, y1, x2, y2):
x1 = max(0, int(x1 - padding))
y1 = max(0, int(y1 - padding))
x2 = min(width, int(x2 + padding))
y2 = min(height, int(y2 + padding))
return annotated_img.crop((x1, y1, x2, y2))
if box_id is not None:
if box_id < 0 or box_id >= len(boxes):
raise IndexError(f"Box ID {box_id} out of range")
x1, y1, x2, y2 = boxes[box_id]
return _safe_crop(x1, y1, x2, y2)
cropped: Dict[int, Image.Image] = {}
for i, (x1, y1, x2, y2) in enumerate(boxes):
cropped[i] = _safe_crop(x1, y1, x2, y2)
return cropped
# ---------- Run Full Pipeline ----------
def run(self, image: Image.Image):
boxes, img, draw = self.run_inference(image)
if boxes is None:
return [], [], [], 0, img
shelves = self.group_boxes_into_shelves(boxes)
shelf_objs = self.build_shelf_objects(shelves)
merged_shelves = self.merge_weak_shelves(shelf_objs)
final_boxes, shelf_metadata, object_metadata = self.annotate_and_build_metadata(
merged_shelves, draw
)
return final_boxes, shelf_metadata, object_metadata, len(merged_shelves), img
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