src/processing/bounding_box/bounding_box.py

# ----------------------------------------------------------------------
# IMPORTS
# ----------------------------------------------------------------------
import torch
import numpy as np
import logging
import time
import spaces
import sys
from PIL import Image, ImageDraw
from collections import defaultdict
from typing import Dict, List, Optional, Tuple, Any, Union
from src.utils import ProcessingContext, create_pipeline_step, LOG_LEVEL_MAP, EMOJI_MAP
from src.processing.bounding_box.yolos_fashionpedia_model import (
    detect_with_yolos_fashionpedia,
    detect_yolos_in_roi,
    process_yolos_results
)
from src.processing.bounding_box.rtdetr_model import (
    detect_rtdetr_in_roi,
    detect_rtdetr_artifacts_in_roi,
    update_fallback_detection,
    get_rtdetr_clothing_labels,
    get_rtdetr_person_and_product_labels,
    get_rtdetr_artifact_labels,
    process_rtdetr_results
)
from src.processing.bounding_box.head_model import detect_head_in_roi

# ----------------------------------------------------------------------
# CONSTANTS
# ----------------------------------------------------------------------

Apply_Draw = False
DETECT_ARTIFACTS = False

PRODUCT_TYPE_BOX_FILTER_THRESHOLD = 0.4
HEAD_BOX_FILTER_THRESHOLD = 0.5
SHOES_BOX_FILTER_THRESHOLD = 0.5
CLOTHING_FEATURES_BOX_FILTER_THRESHOLD = 0.4
ARTIFACTS_BOX_FILTER_THRESHOLD = 0.7

BOX_OVERLAP = 50

LARGEST_BOX_EXTENSION_RATIO = 0.02
LARGEST_BOX_THRESHOLD = 5

CREATE_UPPER_BLUE_LOWER_GREEN_RATIO = 0.02
CREATE_LOWER_BLUE_LOWER_VIOLET_RATIO = 0.5
LEFT_RIGHT_BLUE_OFFSET = 0.5
LOWER_UPPER_BLUE_OFFSET = 0.02
BLUE_BOX_FALLBACK_THRESHOLD = 33

UPPER_HEAD_FILTER = 33
LOWER_SHOE_FILTER = 33
NECKLINE_COLLAR_HEAD = 50

BLUE_BOX_PRODUCT_TYPE = (0, 0, 255, 200)
GREEN_BOX_HEAD = (0, 255, 0, 200)
VIOLET_BOX_SHOES = (238, 130, 238, 200)
ORANGE_BOX_CLOTHING_FEATURES = (255, 165, 0, 200)
RED_BOX_ARTIFACTS = (255, 0, 0, 200)
BLACK_BOX_PERSON = (0, 0, 0, 200)

# ----------------------------------------------------------------------
# DYNAMIC LIST EXTRACTION
# ----------------------------------------------------------------------
def extract_category_lists():
    from .yolos_fashionpedia_model import MODEL_LABEL_CONFIG as YOLOS_CONFIG
    from .rtdetr_model import MODEL_LABEL_CONFIG as RTDETR_CONFIG
    from .head_model import MODEL_LABEL_CONFIG as HEAD_CONFIG
    
    MODEL_LABEL_CONFIG = {**YOLOS_CONFIG, **RTDETR_CONFIG, **HEAD_CONFIG}
    
    lists = {
        "PERSON_LIST": set(),
        "PRODUCT_TYPE_LIST": set(),
        "HEAD_LIST": set(),
        "SHOES_LIST": set(),
        "CLOTHING_FEATURES_LIST": set(),
        "ARTIFACTS_LIST": set()
    }
    
    for model_name, model_config in MODEL_LABEL_CONFIG.items():
        for list_type, keywords in model_config.items():
            list_key = list_type.upper()
            if list_key in lists:
                lists[list_key].update(keywords.keys())
    
    return {k: list(v) for k, v in lists.items()}

extracted_lists = extract_category_lists()
PERSON_LIST = extracted_lists["PERSON_LIST"]
PRODUCT_TYPE_LIST = extracted_lists["PRODUCT_TYPE_LIST"]
HEAD_LIST = extracted_lists["HEAD_LIST"]
SHOES_LIST = extracted_lists["SHOES_LIST"]
CLOTHING_FEATURES_LIST = extracted_lists["CLOTHING_FEATURES_LIST"]
ARTIFACTS_LIST = extracted_lists["ARTIFACTS_LIST"]

# ----------------------------------------------------------------------
# SHARED UTILITY FUNCTIONS
# ----------------------------------------------------------------------
def get_label_name_from_model(model, label_id):
    if hasattr(model, 'config') and hasattr(model.config, 'id2label'):
        return model.config.id2label.get(label_id, f"unknown_{label_id}").lower()
    if hasattr(model, 'model_labels') and isinstance(model.model_labels, dict):
        return model.model_labels.get(label_id, f"unknown_{label_id}").lower()
    return f"unknown_{label_id}"

def clamp_box_to_region(box: List[int], region: List[int]) -> List[int]:
    x1, y1, x2, y2 = box
    rx1, ry1, rx2, ry2 = region
    xx1 = max(rx1, min(x1, rx2))
    yy1 = max(ry1, min(y1, ry2))
    xx2 = max(rx1, min(x2, rx2))
    yy2 = max(ry1, min(y2, ry2))
    return [xx1, yy1, xx2, yy2]

def box_iou(b1, b2):
    xx1 = max(b1[0], b2[0])
    yy1 = max(b1[1], b2[1])
    xx2 = min(b1[2], b2[2])
    yy2 = min(b1[3], b2[3])
    
    iw = max(0, xx2 - xx1)
    ih = max(0, yy2 - yy1)
    inter_area = iw * ih
    
    area1 = abs(b1[2] - b1[0]) * abs(b1[3] - b1[1])
    area2 = abs(b2[2] - b2[0]) * abs(b2[3] - b2[1])
    union = area1 + area2 - inter_area
    
    if union <= 0:
        return 0
    return inter_area / union

def build_keywords(product_type: str) -> List[str]:
    pt = product_type.lower().strip()
    if not pt or pt == "unknown":
        return []
    
    kw = [pt]
    if pt in ["jeans", "shorts", "skirt"]:
        kw += ["shoes", "closure", "pocket"]
    elif pt in ["jacket", "vest", "shirt"]:
        kw += ["head", "closure", "pocket", "collar", "sleeve"]
    elif pt in ["overall", "dress"]:
        kw += ["head", "shoes", "closure", "pocket", "neckline", "sleeve"]

    if DETECT_ARTIFACTS:
        kw += ["bag", "cup"]
    return kw

def apply_box_overlap_filter(
    boxes: List[List[int]],
    labels: List[int],
    scores: List[float],
    keywords: List[str],
    raw_labels: List[str],
    models: List[str]
):
    grouped = defaultdict(list)
    for i in range(len(boxes)):
        kwd = keywords[i]
        grouped[kwd].append({
            "box": boxes[i],
            "label": labels[i],
            "score": scores[i],
            "raw_label": raw_labels[i],
            "model": models[i]
        })

    new_boxes = []
    new_labels = []
    new_scores = []
    new_kws = []
    new_raws = []
    new_models = []
    overlap_thresh = BOX_OVERLAP / 100.0

    for kwd, items in grouped.items():
        sorted_items = sorted(items, key=lambda x: x["score"], reverse=True)
        
        if sorted_items:
            best_item = sorted_items[0]
            new_boxes.append(best_item["box"])
            new_labels.append(best_item["label"])
            new_scores.append(round(best_item["score"], 2))
            new_kws.append(kwd)
            new_raws.append(best_item["raw_label"])
            new_models.append(best_item["model"])

    return new_boxes, new_labels, new_scores, new_kws, new_raws, new_models

def get_threshold_for_keyword(kw: str) -> float:
    if kw in PRODUCT_TYPE_LIST:
        return PRODUCT_TYPE_BOX_FILTER_THRESHOLD
    elif kw in HEAD_LIST:
        return HEAD_BOX_FILTER_THRESHOLD
    elif kw in SHOES_LIST:
        return SHOES_BOX_FILTER_THRESHOLD
    elif kw in CLOTHING_FEATURES_LIST:
        return CLOTHING_FEATURES_BOX_FILTER_THRESHOLD
    elif kw in ARTIFACTS_LIST:
        return ARTIFACTS_BOX_FILTER_THRESHOLD
    return 0.0

def map_label_to_keyword(label_name: str, valid_kws: List[str], model_name: str) -> Optional[str]:
    ln = label_name.strip().lower()
    
    if ln in valid_kws:
        return ln
    
    if model_name == "yolos_fashionpedia_model":
        from .yolos_fashionpedia_model import MODEL_LABEL_CONFIG
    elif model_name == "rtdetr_model":
        from .rtdetr_model import MODEL_LABEL_CONFIG
    elif model_name == "head_model":
        from .head_model import MODEL_LABEL_CONFIG
    else:
        return None
    
    model_config = MODEL_LABEL_CONFIG.get(model_name, {})
    
    for list_type in ["person_list", "product_type_list", "head_list", 
                      "shoes_list", "clothing_features_list", "artifacts_list"]:
        category_config = model_config.get(list_type, {})
        
        for keyword, labels in category_config.items():
            if keyword in valid_kws:
                for label in labels:
                    if ln == label.lower() or ln in label.lower():
                        return keyword
    
    return None

# ----------------------------------------------------------------------
# MODEL VALIDATION
# ----------------------------------------------------------------------
def validate_model_configurations():
    from .yolos_fashionpedia_model import MODEL_LABEL_CONFIG as YOLOS_CONFIG
    from .rtdetr_model import MODEL_LABEL_CONFIG as RTDETR_CONFIG
    from .head_model import MODEL_LABEL_CONFIG as HEAD_CONFIG
    
    MODEL_LABEL_CONFIG = {**YOLOS_CONFIG, **RTDETR_CONFIG, **HEAD_CONFIG}
    
    logging.info("=" * 70)
    logging.info("✅ MODEL CONFIGURATIONS VALIDATION")
    logging.info("=" * 70)
    
    for model_name, model_config in MODEL_LABEL_CONFIG.items():
        logging.info(f"\n📌 {model_name.upper()}")
        logging.info("-" * 50)
        
        total_prompts = 0
        for category, category_config in model_config.items():
            category_prompts = 0
            for keyword, labels in category_config.items():
                category_prompts += len(labels)
                total_prompts += len(labels)
            
            if category_config:
                logging.info(f"  📂 {category}: {len(category_config)} keywords, {category_prompts} prompts")
        
        logging.info(f"  🎯 Total: {total_prompts} prompts")
        
        if model_name == "yolos_fashionpedia_model":
            logging.info("  ✨ YOLOS Fashionpedia specializes in fashion detection!")
    
    logging.info("\n" + "=" * 70)
    logging.info("🚀 FEATURE CAPABILITIES:")
    logging.info("-" * 50)
    logging.info(f"  • YOLOS Fashionpedia Detection: ENABLED")
    logging.info(f"  • Artifact Detection: {'ENABLED' if DETECT_ARTIFACTS else 'DISABLED'}")
    logging.info(f"  • Apply Draw (Visualization): {'ENABLED' if Apply_Draw else 'DISABLED'}")
    logging.info("=" * 70)

# ----------------------------------------------------------------------
# DETECTION PIPELINE FUNCTIONS
# ----------------------------------------------------------------------
def define_largest_box_batch(contexts, batch_logs, RTDETR_PROCESSOR, RTDETR_MODEL, RTDETR_FULL_PRECISION, DEVICE, MODELS_LOADED, LOAD_ERROR):
    from .rtdetr_model import RTDETR_CONF
    
    function_name = "define_largest_box_batch"
    start_time = time.perf_counter()
    logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Starting {function_name} for {len(contexts)} items")
    
    items_for_batch = []
    valid_ctx_indices = []
    batch_items = []
    image_sizes = []

    for i, ctx in enumerate(contexts):
        item_ = {
            "image_url": ctx.url, 
            "data": {"largest_box": None},
            "function": function_name
        }

        if ctx.skip_run or ctx.skip_processing:
            item_["status"] = "skipped"
            batch_logs.append(item_)
            continue

        if not MODELS_LOADED:
            import sys
            import traceback
            
            error_msg = LOAD_ERROR or "Models not loaded"
            error_trace = traceback.format_exc()
            
            logging.error(f"CRITICAL: Model not loaded in {function_name}: {error_msg}")
            logging.error(f"Traceback:\n{error_trace}")
            
            item_["status"] = "critical_error"
            item_["exception"] = error_msg
            item_["traceback"] = error_trace
            ctx.skip_run = True
            ctx.error = error_msg
            ctx.error_traceback = error_trace
            batch_logs.append(item_)
            
            logging.critical("Terminating due to model loading failure")
            sys.exit(1)

        if "original" not in ctx.pil_img:
            item_["status"] = "error"
            item_["exception"] = "No RBC 'original' found"
            ctx.skip_run = True
            batch_logs.append(item_)
            continue

        pi_rgba, _, _ = ctx.pil_img["original"]
        rgb_img = pi_rgba.convert("RGB")
        items_for_batch.append(rgb_img)
        image_sizes.append([rgb_img.height, rgb_img.width])
        valid_ctx_indices.append(i)
        batch_items.append(item_)

    if not items_for_batch:
        processing_time = time.perf_counter() - start_time
        logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} {function_name}: No valid items for batch processing in {processing_time:.3f}s")
        return batch_logs

    try:
        results = process_rtdetr_batch(
            items_for_batch, image_sizes, RTDETR_PROCESSOR, RTDETR_MODEL, DEVICE
        )

        rtdetr_clothing_labels = get_rtdetr_clothing_labels()
        rtdetr_person_product_labels = get_rtdetr_person_and_product_labels()

        for b_idx, i_ in enumerate(valid_ctx_indices):
            ctx = contexts[i_]
            item_ = batch_items[b_idx]
            pi_rgb = items_for_batch[b_idx]
            W, H = pi_rgb.size

            detection_log = {
                "model": "RT-DETR",
                "largest_area": 0,
                "total_area": W * H,
                "extension_ratio": LARGEST_BOX_EXTENSION_RATIO,
                "threshold": LARGEST_BOX_THRESHOLD,
                "raw_detections": []
            }

            if isinstance(results, list):
                if b_idx < len(results):
                    result = results[b_idx]
                else:
                    result = results[0]
            else:
                result = results
            
            largest_area = 0
            main_box = None

            for score, label, box in zip(result["scores"], result["labels"], result["boxes"]):
                label_id = label.item()
                score_val = score.item()
                x1, y1, x2, y2 = [int(val) for val in box.tolist()]
                label_name = get_label_name_from_model(RTDETR_MODEL, label_id)
                
                if label_name in rtdetr_person_product_labels:
                    logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} RT-DETR detected: {label_name} at score {score_val:.3f} | box=[{x1},{y1},{x2},{y2}]")
                    detection_log["raw_detections"].append({
                        "box": [x1, y1, x2, y2],
                        "score": round(score_val, 3),
                        "label": label_name,
                        "is_clothing": label_name in rtdetr_clothing_labels
                    })
                
                if label_name in rtdetr_clothing_labels:
                    area_ = (x2 - x1) * (y2 - y1)

                    if area_ > largest_area:
                        largest_area = area_
                        main_box = [x1, y1, x2, y2]

            if main_box is not None:
                ratio_off = int(min(W, H) * LARGEST_BOX_EXTENSION_RATIO)
                x1, y1, x2, y2 = main_box
                x1e = max(0, x1 - ratio_off)
                y1e = max(0, y1 - ratio_off)
                x2e = min(W, x2 + ratio_off)
                y2e = min(H, y2 + ratio_off)
                main_box = [x1e, y1e, x2e, y2e]

                total_area = W * H
                if largest_area < total_area * (LARGEST_BOX_THRESHOLD / 100.0):
                    main_box = [0, 0, W, H]
                    logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Fallback to full image: box=[0,0,{W},{H}] | {ctx.url}")
                else:
                    logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Largest box detected: box=[{x1e},{y1e},{x2e},{y2e}] | area={largest_area} | {ctx.url}")
            else:
                main_box = [0, 0, W, H]
                logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} No clothing detected, using full image: box=[0,0,{W},{H}] | {ctx.url}")

            ctx.define_result["largest_box"] = main_box
            item_["data"]["largest_box"] = main_box
            
            detection_log["largest_area"] = largest_area
            item_["data"]["largest_box_detection"] = detection_log
            item_["status"] = "ok"

    except Exception as e:
        import sys
        import traceback
        
        error_msg = f"{function_name} error: {str(e)}"
        error_trace = traceback.format_exc()
        
        logging.error(f"CRITICAL: {error_msg}")
        logging.error(f"Traceback:\n{error_trace}")
        
        for b_idx, i_ in enumerate(valid_ctx_indices):
            ctx = contexts[i_]
            item_ = batch_items[b_idx]
            item_["status"] = "critical_error"
            item_["exception"] = error_msg
            item_["traceback"] = error_trace
            ctx.skip_run = True
            ctx.error = str(e)
            ctx.error_traceback = error_trace
            
        logging.critical("Terminating due to bounding box processing failure")
        sys.exit(1)

    for item_ in batch_items:
        batch_logs.append(item_)

    processing_time = time.perf_counter() - start_time
    logging.log(LOG_LEVEL_MAP["SUCCESS"], f"{EMOJI_MAP['SUCCESS']} Completed {function_name} for {len(batch_items)} items in {processing_time:.3f}s")
    return batch_logs
     
def process_rtdetr_batch(images, image_sizes, RTDETR_PROCESSOR, RTDETR_MODEL, DEVICE):
    from .rtdetr_model import RTDETR_CONF
    
    try:
        rtdetr_inputs = RTDETR_PROCESSOR(images=images, return_tensors="pt").to(DEVICE)
        
        with torch.no_grad():
            rtdetr_outputs = RTDETR_MODEL(**rtdetr_inputs)
        
        rtdetr_results = RTDETR_PROCESSOR.post_process_object_detection(
            rtdetr_outputs,
            target_sizes=torch.tensor(image_sizes).to(DEVICE),
            threshold=RTDETR_CONF
        )
        
        return rtdetr_results
        
    except Exception as e:
        logging.error(f"RT-DETR batch processing error: {str(e)}")
        return []

def process_single_image_detection(roi_rgb, keywords, rx1, ry1, rW, rH, detect_artifacts, detect_head,
                                 YOLOS_PROCESSOR, YOLOS_MODEL,
                                 RTDETR_PROCESSOR, RTDETR_MODEL,
                                 HEAD_PROCESSOR, HEAD_MODEL, HEAD_DETECTION_FULL_PRECISION,
                                 DEVICE):
    log_item = {"warnings": []}
    
    yolos_boxes, yolos_labels, yolos_scores, yolos_raws = detect_yolos_in_roi(
        roi_rgb, keywords, YOLOS_PROCESSOR, YOLOS_MODEL, DEVICE, log_item
    )
    
    rtdetr_boxes, rtdetr_labels, rtdetr_scores, rtdetr_raws = detect_rtdetr_in_roi(
        roi_rgb, RTDETR_PROCESSOR, RTDETR_MODEL, DEVICE, log_item
    )
    
    if detect_artifacts:
        artifact_boxes, artifact_labels, artifact_scores, artifact_raws = detect_rtdetr_artifacts_in_roi(
            roi_rgb, keywords, RTDETR_PROCESSOR, RTDETR_MODEL, DEVICE, log_item
        )
    else:
        artifact_boxes, artifact_labels, artifact_scores, artifact_raws = [], [], [], []
    
    if detect_head:
        head_boxes, head_labels, head_scores, head_raws = detect_head_in_roi(
            roi_rgb, rx1, ry1, rW, rH, HEAD_PROCESSOR, HEAD_MODEL, 
            HEAD_DETECTION_FULL_PRECISION, DEVICE, log_item
        )
    else:
        head_boxes, head_labels, head_scores, head_raws = [], [], [], []
    
    return {
        'yolos': (yolos_boxes, yolos_labels, yolos_scores, yolos_raws),
        'rtdetr': (rtdetr_boxes, rtdetr_labels, rtdetr_scores, rtdetr_raws),
        'artifacts': (artifact_boxes, artifact_labels, artifact_scores, artifact_raws),
        'head': (head_boxes, head_labels, head_scores, head_raws),
        'warnings': log_item.get("warnings", [])
    }

def detect_batch(contexts, batch_logs,
                HEAD_PROCESSOR, HEAD_MODEL, HEAD_DETECTION_FULL_PRECISION,
                RTDETR_PROCESSOR, RTDETR_MODEL, RTDETR_FULL_PRECISION, 
                YOLOS_PROCESSOR, YOLOS_MODEL,
                DEVICE, MODELS_LOADED, LOAD_ERROR):
    
    function_name = "detect_batch"
    start_time = time.perf_counter()
    logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Starting {function_name} for {len(contexts)} items")
    
    for ctx in contexts:
        log_item = {
            "image_url": ctx.url, 
            "data": {
                "detection_result_log": {},
                "product_type": ctx.product_type,
                "keywords": ctx.keywords
            },
            "function": function_name
        }
        
        logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Processing image: {ctx.url} | Product Type: {ctx.product_type} | Keywords: {ctx.keywords}")
        
        if ctx.skip_run or ctx.skip_processing:
            log_item["status"] = "skipped"
            batch_logs.append(log_item)
            continue
        
        if "original" not in ctx.pil_img:
            log_item["status"] = "error"
            log_item["exception"] = "No RBC 'original'"
            ctx.skip_run = True
            batch_logs.append(log_item)
            continue
        
        pi_rgba, _, _ = ctx.pil_img["original"]
        W, H = pi_rgba.size
        
        largest_box = ctx.define_result.get("largest_box")
        if (not largest_box or
            not isinstance(largest_box, list) or
            len(largest_box) != 4):
            log_item["status"] = "no_detection"
            log_item["data"]["detection_result_log"] = "no_main_box_detected"
            batch_logs.append(log_item)
            continue
        
        rx1, ry1, rx2, ry2 = largest_box
        rW = rx2 - rx1
        rH = ry2 - ry1
        
        logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} ROI box: [{rx1},{ry1},{rx2},{ry2}] | size: {rW}x{rH} | {ctx.url}")
        
        roi = pi_rgba.crop((rx1, ry1, rx2, ry2))
        roi_rgb = roi.convert("RGB")
        
        all_boxes = []
        all_labels = []
        all_scores = []
        all_raw = []
        all_models = []
        
        def box_to_global(x1, y1, x2, y2):
            gx1 = int(rx1 + x1)
            gy1 = int(ry1 + y1)
            gx2 = int(rx1 + x2)
            gy2 = int(ry1 + y2)
            return [gx1, gy1, gx2, gy2]
        
        detect_artifacts = DETECT_ARTIFACTS and any(kw in ctx.keywords for kw in ARTIFACTS_LIST)
        lower_body_types = ["jeans", "shorts", "skirt"]
        detect_head = ctx.product_type.lower() not in lower_body_types
        
        detection_start = time.perf_counter()
        
        detection_results = process_single_image_detection(
            roi_rgb, ctx.keywords, rx1, ry1, rW, rH, detect_artifacts, detect_head,
            YOLOS_PROCESSOR, YOLOS_MODEL,
            RTDETR_PROCESSOR, RTDETR_MODEL,
            HEAD_PROCESSOR, HEAD_MODEL, HEAD_DETECTION_FULL_PRECISION,
            DEVICE
        )
        
        yolos_boxes, yolos_labels, yolos_scores, yolos_raws = detection_results['yolos']
        rtdetr_boxes, rtdetr_labels, rtdetr_scores, rtdetr_raws = detection_results['rtdetr']
        artifact_boxes, artifact_labels, artifact_scores, artifact_raws = detection_results['artifacts']
        head_boxes, head_labels, head_scores, head_raws = detection_results['head']
        
        if detection_results['warnings']:
            log_item["warnings"] = log_item.get("warnings", []) + detection_results['warnings']
        
        detection_time = time.perf_counter() - detection_start
        logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Detection completed in {detection_time:.3f}s")
        
        for i in range(len(yolos_boxes)):
            x1, y1, x2, y2 = yolos_boxes[i]
            global_box = box_to_global(x1, y1, x2, y2)
            global_box = clamp_box_to_region(global_box, [rx1, ry1, rx2, ry2])
            
            all_boxes.append(global_box)
            all_labels.append(40000 + yolos_labels[i])
            all_scores.append(yolos_scores[i])
            all_raw.append(yolos_raws[i])
            all_models.append("yolos_fashionpedia_model")
        
        for i in range(len(rtdetr_boxes)):
            x1, y1, x2, y2 = rtdetr_boxes[i]
            global_box = box_to_global(x1, y1, x2, y2)
            global_box = clamp_box_to_region(global_box, [rx1, ry1, rx2, ry2])
            
            all_boxes.append(global_box)
            all_labels.append(10000 + rtdetr_labels[i])
            all_scores.append(rtdetr_scores[i])
            all_raw.append(rtdetr_raws[i])
            all_models.append("rtdetr_model")
        
        for i in range(len(artifact_boxes)):
            x1, y1, x2, y2 = artifact_boxes[i]
            global_box = box_to_global(x1, y1, x2, y2)
            global_box = clamp_box_to_region(global_box, [rx1, ry1, rx2, ry2])
            
            all_boxes.append(global_box)
            all_labels.append(20000 + artifact_labels[i])
            all_scores.append(artifact_scores[i])
            all_raw.append(artifact_raws[i])
            all_models.append("rtdetr_artifact")
        
        for i in range(len(head_boxes)):
            all_boxes.append(head_boxes[i])
            all_labels.append(9999)
            all_scores.append(head_scores[i])
            all_raw.append(head_raws[i])
            all_models.append("head_model")
        
        logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Total detections before filtering: {len(all_boxes)} - YOLOS: {sum(1 for m in all_models if m == 'yolos_fashionpedia_model')}, RTDETR: {sum(1 for m in all_models if m == 'rtdetr_model')}, Head: {sum(1 for m in all_models if m == 'head_model')}")
        
        final_keywords = [None] * len(all_boxes)
        
        for i_ in range(len(all_boxes)):
            lb_ = all_labels[i_]
            model_name = all_models[i_]
            
            if lb_ == 9999:
                if "head" in ctx.keywords:
                    final_keywords[i_] = "head"
            else:
                label_str = all_raw[i_]
                mapped = map_label_to_keyword(label_str, ctx.keywords, model_name)
                if mapped:
                    final_keywords[i_] = mapped
        
        keep_mask = [True] * len(all_boxes)
        for i_ in range(len(all_boxes)):
            cat_ = final_keywords[i_]
            sc_ = all_scores[i_]
            if not cat_:
                keep_mask[i_] = False
            else:
                threshold_needed = get_threshold_for_keyword(cat_)
                if sc_ < threshold_needed:
                    keep_mask[i_] = False
        
        fb, fl, fs, fk, fr, fm = [], [], [], [], [], []
        for i_ in range(len(all_boxes)):
            if keep_mask[i_]:
                fb.append(all_boxes[i_])
                fl.append(all_labels[i_])
                fs.append(all_scores[i_])
                fk.append(final_keywords[i_])
                fr.append(all_raw[i_])
                fm.append(all_models[i_])
        
        fb, fl, fs, fk, fr, fm = apply_box_overlap_filter(
            fb, fl, fs, fk, fr, fm
        )
        
        second_pass = []
        for i_ in range(len(fb)):
            cat_ = fk[i_]
            (bx1, by1, bx2, by2) = fb[i_]
            centerY = 0.5 * (by1 + by2)
            if cat_ == "head":
                if centerY <= (H * (UPPER_HEAD_FILTER / 100.0)):
                    second_pass.append(i_)
            elif cat_ == "shoes":
                if centerY >= (H * (1 - (LOWER_SHOE_FILTER / 100.0))):
                    second_pass.append(i_)
            else:
                second_pass.append(i_)
        
        collar_idx = [i_ for i_, kw_ in enumerate(fk) if kw_ in ["collar", "neckline"]]
        collar_boxes = [fb[x] for x in collar_idx]
        
        def intersect_area(a_, b_):
            xx1 = max(a_[0], b_[0])
            yy1 = max(a_[1], b_[1])
            xx2 = min(a_[2], b_[2])
            yy2 = min(a_[3], b_[3])
            iw = max(0, xx2 - xx1)
            ih = max(0, yy2 - yy1)
            return iw * ih
        
        final_keep = []
        for idx2 in second_pass:
            kw_ = fk[idx2]
            if kw_ == "head":
                hx1, hy1, hx2, hy2 = fb[idx2]
                hArea = (hx2 - hx1) * (hy2 - hy1)
                if hArea <= 0:
                    continue
                remove = False
                for cb_ in collar_boxes:
                    ia = intersect_area([hx1, hy1, hx2, hy2], cb_)
                    if ia / float(hArea) > (NECKLINE_COLLAR_HEAD / 100.0):
                        remove = True
                        break
                if not remove:
                    final_keep.append(idx2)
            else:
                final_keep.append(idx2)
        
        final_boxes = [fb[x] for x in final_keep]
        final_labels = [fl[x] for x in final_keep]
        final_scores = [round(fs[x], 2) for x in final_keep]
        final_kws = [fk[x] for x in final_keep]
        final_raws = [fr[x] for x in final_keep]
        final_mods = [fm[x] for x in final_keep]
        
        logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Final detections after filtering: {len(final_boxes)} boxes")
        for i in range(len(final_boxes)):
            box = final_boxes[i]
            logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Final detection: {final_kws[i]} ({final_raws[i]}) at score {final_scores[i]} | box=[{box[0]},{box[1]},{box[2]},{box[3]}]")
        
        dd_log = defaultdict(list)
        for i in range(len(final_boxes)):
            cat_ = final_kws[i]
            if cat_:
                dd_log[cat_].append({
                    "box": final_boxes[i],
                    "score": final_scores[i],
                    "raw_label": final_raws[i],
                    "model": final_mods[i]
                })
        
        if ctx.product_type not in dd_log:
            from .rtdetr_model import RTDETR_CONF
            fallback_box = ctx.define_result.get("largest_box", None)
            final_boxes, final_labels, final_scores, final_kws, final_raws, final_mods, dd_log = update_fallback_detection(
                ctx, pi_rgba, fallback_box, RTDETR_PROCESSOR, RTDETR_MODEL, 
                DEVICE, RTDETR_CONF, final_boxes, final_labels, final_scores, 
                final_kws, final_raws, final_mods, dd_log
            )
        
        detection_result = {
            "status": "ok",
            "boxes": final_boxes,
            "labels": final_labels,
            "scores": final_scores,
            "final_keywords": final_kws,
            "raw_labels": final_raws,
            "models": final_mods
        }
        
        BOTTOM_CLOTHING_TYPES = ["jeans", "shorts", "skirt"]
        
        is_bottom_clothing = ctx.product_type.lower() in BOTTOM_CLOTHING_TYPES
        
        detection_log_schema = {
            "yolos_fashionpedia_model": {
                "person_list": {},
                "product_type_list": {},
                "head_list": {},
                "shoes_list": {},
                "clothing_features_list": {},
                "artifacts_list": {}
            },
            "rtdetr_model": {
                "person_list": {},
                "product_type_list": {},
                "head_list": {},
                "shoes_list": {},
                "clothing_features_list": {},
                "artifacts_list": {}
            }
        }
        
        if not is_bottom_clothing:
            detection_log_schema["head_model"] = {
                "head_list": {}
            }
        
        for i in range(len(final_boxes)):
            keyword = final_kws[i]
            raw_label = final_raws[i]
            score = final_scores[i]
            model = final_mods[i]
            
            if is_bottom_clothing and model == "head_model":
                continue
                
            if model not in detection_log_schema:
                continue
                
            model_dict = detection_log_schema[model]
            
            if keyword in PERSON_LIST and "person_list" in model_dict:
                if keyword not in model_dict["person_list"]:
                    model_dict["person_list"][keyword] = {}
                model_dict["person_list"][keyword][raw_label] = score
            elif keyword in PRODUCT_TYPE_LIST and "product_type_list" in model_dict:
                if keyword not in model_dict["product_type_list"]:
                    model_dict["product_type_list"][keyword] = {}
                model_dict["product_type_list"][keyword][raw_label] = score
            elif keyword in HEAD_LIST and "head_list" in model_dict:
                if keyword not in model_dict["head_list"]:
                    model_dict["head_list"][keyword] = {}
                model_dict["head_list"][keyword][raw_label] = score
            elif keyword in SHOES_LIST and "shoes_list" in model_dict:
                if keyword not in model_dict["shoes_list"]:
                    model_dict["shoes_list"][keyword] = {}
                model_dict["shoes_list"][keyword][raw_label] = score
            elif keyword in CLOTHING_FEATURES_LIST and "clothing_features_list" in model_dict:
                if keyword not in model_dict["clothing_features_list"]:
                    model_dict["clothing_features_list"][keyword] = {}
                model_dict["clothing_features_list"][keyword][raw_label] = score
            elif keyword in ARTIFACTS_LIST and "artifacts_list" in model_dict:
                if keyword not in model_dict["artifacts_list"]:
                    model_dict["artifacts_list"][keyword] = {}
                model_dict["artifacts_list"][keyword][raw_label] = score
        
        for model in list(detection_log_schema.keys()):
            model_dict = detection_log_schema[model]
            for cat in list(model_dict.keys()):
                if not model_dict[cat]:
                    del model_dict[cat]
            if not model_dict:
                del detection_log_schema[model]
        
        log_item["status"] = "ok"
        log_item["data"]["detection_result_log"] = detection_log_schema
        ctx.detection_result = detection_result
        batch_logs.append(log_item)
    
    processing_time = time.perf_counter() - start_time
    logging.log(LOG_LEVEL_MAP["SUCCESS"], f"{EMOJI_MAP['SUCCESS']} Completed {function_name} for {len(contexts)} items in {processing_time:.3f}s")
    return batch_logs
       
def choose_color_for_feature_batch(contexts, batch_logs):
    function_name = "choose_color_for_feature_batch"
    start_time = time.perf_counter()
    logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Starting {function_name} for {len(contexts)} items")
    
    processed_count = 0
    skipped_count = 0
    error_count = 0
    
    for ctx in contexts:
        it_ = {
            "image_url": ctx.url,
            "function": function_name
        }
        
        if ctx.skip_run or ctx.skip_processing:
            it_["status"] = "skipped"
            batch_logs.append(it_)
            skipped_count += 1
            continue

        dr = ctx.detection_result
        if dr.get("status") != "ok":
            it_["status"] = "no_detection"
            batch_logs.append(it_)
            error_count += 1
            continue

        bxs = dr["boxes"]
        kws = dr["final_keywords"]
        pt = ctx.product_type
        colors = []
        
        for i, (bx1, by1, bx2, by2) in enumerate(bxs):
            kw = kws[i]
            if kw == pt:
                colors.append(BLUE_BOX_PRODUCT_TYPE)
            elif kw in HEAD_LIST:
                colors.append(GREEN_BOX_HEAD)
            elif kw in SHOES_LIST:
                colors.append(VIOLET_BOX_SHOES)
            elif kw in CLOTHING_FEATURES_LIST:
                colors.append(ORANGE_BOX_CLOTHING_FEATURES)
            elif kw in ARTIFACTS_LIST:
                colors.append(RED_BOX_ARTIFACTS)
            else:
                colors.append(BLUE_BOX_PRODUCT_TYPE)
        
        ctx.box_colors = colors
        it_["status"] = "ok"
        it_["data"] = {
            "color_assignments": {
                "total_boxes": len(bxs),
                "color_counts": {
                    "product_type": colors.count(BLUE_BOX_PRODUCT_TYPE),
                    "head": colors.count(GREEN_BOX_HEAD),
                    "shoes": colors.count(VIOLET_BOX_SHOES),
                    "clothing_features": colors.count(ORANGE_BOX_CLOTHING_FEATURES),
                    "artifacts": colors.count(RED_BOX_ARTIFACTS)
                }
            }
        }
        batch_logs.append(it_)
        processed_count += 1
    
    processing_time = time.perf_counter() - start_time
    logging.log(LOG_LEVEL_MAP["SUCCESS"], f"{EMOJI_MAP['SUCCESS']} Completed {function_name}: processed={processed_count}, skipped={skipped_count}, errors={error_count} in {processing_time:.3f}s")
    return batch_logs

def adjust_blue_box_batch(contexts, batch_logs):
    function_name = "adjust_blue_box_batch"
    start_time = time.perf_counter()
    logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Starting {function_name} for {len(contexts)} items")
    
    processed_count = 0
    skipped_count = 0
    error_count = 0
    
    for ctx in contexts:
        it_ = {
            "image_url": ctx.url,
            "function": function_name
        }
        
        if ctx.skip_run or ctx.skip_processing:
            it_["status"] = "skipped"
            batch_logs.append(it_)
            skipped_count += 1
            continue

        dr = ctx.detection_result
        if dr.get("status") != "ok":
            it_["status"] = "no_detection"
            batch_logs.append(it_)
            error_count += 1
            continue

        if "original" not in ctx.pil_img:
            it_["status"] = "error"
            it_["exception"] = "No original image found"
            batch_logs.append(it_)
            error_count += 1
            continue

        pi_rgba, _, _ = ctx.pil_img["original"]
        largest_box = ctx.define_result.get("largest_box")
        if not largest_box:
            it_["status"] = "error"
            it_["exception"] = "No main box detected"
            batch_logs.append(it_)
            error_count += 1
            continue

        final_boxes = dr["boxes"]
        final_kws = dr["final_keywords"]
        final_cols = ctx.box_colors

        bx1, by1, bx2, by2 = map(int, largest_box)
        largest_green = None
        largest_violet = None
        largest_blue = None
        areaG = 0
        areaV = 0
        areaB = 0

        for i, (xx1, yy1, xx2, yy2) in enumerate(final_boxes):
            area_ = (xx2 - xx1) * (yy2 - yy1)
            col_ = final_cols[i]
            kw_ = final_kws[i]
            if kw_ in HEAD_LIST and area_ > areaG:
                areaG = area_
                largest_green = (xx1, yy1, xx2, yy2)
            elif kw_ in SHOES_LIST and area_ > areaV:
                areaV = area_
                largest_violet = (xx1, yy1, xx2, yy2)
            if col_ == BLUE_BOX_PRODUCT_TYPE and area_ > areaB:
                areaB = area_
                largest_blue = (xx1, yy1, xx2, yy2)

        adjusted_box = largest_blue
        lines_update = {}
        
        adjustment_log = {
            "original_blue_box": largest_blue,
            "head_box": largest_green,
            "shoes_box": largest_violet,
            "adjustments": {}
        }

        if adjusted_box and areaB > 0:
            x1b, y1b, x2b, y2b = adjusted_box
            m = min(pi_rgba.width, pi_rgba.height)
            horiz_off = int(m * LEFT_RIGHT_BLUE_OFFSET)
            up_ofs = int(m * LOWER_UPPER_BLUE_OFFSET)
            dn_ofs = int(m * LOWER_UPPER_BLUE_OFFSET)

            x1_ext = max(x1b - horiz_off, bx1)
            x2_ext = min(x2b + horiz_off, bx2)
            y1_ext = y1b
            y2_ext = y2b
            
            adjustment_log["adjustments"]["horizontal_offset"] = horiz_off
            adjustment_log["adjustments"]["vertical_offset"] = {"up": up_ofs, "down": dn_ofs}

            if largest_green:
                gx1, gy1, gx2, gy2 = largest_green
                top_off = int(m * CREATE_UPPER_BLUE_LOWER_GREEN_RATIO)
                new_top = gy2 - top_off
                y1_ext = max(by1, new_top)
                adjustment_log["adjustments"]["head_top_offset"] = top_off
            else:
                y1_ext = max(y1_ext - up_ofs, by1)
                adjustment_log["adjustments"]["default_top_offset"] = up_ofs

            if largest_violet:
                vx1, vy1, vx2, vy2 = largest_violet
                bot_off = int(m * CREATE_LOWER_BLUE_LOWER_VIOLET_RATIO)
                new_bot = vy2 + bot_off
                y2_ext = min(by2, max(y2_ext, new_bot))
                adjustment_log["adjustments"]["shoes_bottom_offset"] = bot_off
            else:
                y2_ext = min(y2_ext + dn_ofs, by2)
                adjustment_log["adjustments"]["default_bottom_offset"] = dn_ofs

            x1_ext = max(bx1, min(x1_ext, bx2))
            x2_ext = max(bx1, min(x2_ext, bx2))
            y1_ext = max(by1, min(y1_ext, by2))
            y2_ext = max(by1, min(y2_ext, by2))
            adjusted_box = (x1_ext, y1_ext, x2_ext, y2_ext)

            lines_update = {
                "left":  f"{x1b}->{x1_ext}",
                "right": f"{x2b}->{x2_ext}",
                "upper": f"{y1b}->{y1_ext}",
                "lower": f"{y2b}->{y2_ext}"
            }
            
            adjustment_log["adjustments"]["before"] = [x1b, y1b, x2b, y2b]
            adjustment_log["adjustments"]["after"] = [x1_ext, y1_ext, x2_ext, y2_ext]

            largest_area = (bx2 - bx1) * (by2 - by1)
            adjusted_area = (x2_ext - x1_ext) * (y2_ext - y1_ext)
            
            adjustment_log["areas"] = {
                "largest_box_area": largest_area,
                "adjusted_area": adjusted_area
            }
            
            if largest_area > 0:
                ratio = (adjusted_area / float(largest_area)) * 100
                adjustment_log["areas"]["ratio_percent"] = round(ratio, 2)
                
                if ratio < BLUE_BOX_FALLBACK_THRESHOLD:
                    adjusted_box = (bx1, by1, bx2, by2)
                    lines_update["fallback"] = (
                        f"Adjusted area {round(ratio,1)}% < {BLUE_BOX_FALLBACK_THRESHOLD}%, "
                        "fallback to entire largest box."
                    )
                    adjustment_log["fallback_used"] = True
                    adjustment_log["fallback_reason"] = f"Area ratio {round(ratio,2)}% below threshold {BLUE_BOX_FALLBACK_THRESHOLD}%"
                else:
                    adjustment_log["fallback_used"] = False

        ctx.adjusted_blue_box = adjusted_box
        it_["status"] = "ok"
        it_["lines_update"] = lines_update
        it_["data"] = {"adjustment_log": adjustment_log}
        batch_logs.append(it_)
        processed_count += 1
    
    processing_time = time.perf_counter() - start_time
    logging.log(LOG_LEVEL_MAP["SUCCESS"], f"{EMOJI_MAP['SUCCESS']} Completed {function_name}: processed={processed_count}, skipped={skipped_count}, errors={error_count} in {processing_time:.3f}s")
    return batch_logs

def draw_batch(contexts, batch_logs):
    function_name = "draw_batch"
    start_time = time.perf_counter()
    logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Starting {function_name} for {len(contexts)} items")
    
    processed_count = 0
    skipped_count = 0
    error_count = 0
    
    for ctx in contexts:
        it_ = {
            "image_url": ctx.url,
            "function": function_name
        }
        
        if ctx.skip_run or ctx.skip_processing:
            it_["status"] = "skipped"
            batch_logs.append(it_)
            skipped_count += 1
            continue

        dr = ctx.detection_result
        if dr.get("status") != "ok":
            it_["status"] = "no_detection"
            batch_logs.append(it_)
            error_count += 1
            continue

        if "original" not in ctx.pil_img:
            it_["status"] = "error"
            it_["exception"] = "No original image found"
            batch_logs.append(it_)
            error_count += 1
            continue

        pi_rgba, orig_filename, _ = ctx.pil_img["original"]
        final_boxes = dr["boxes"]
        final_kws = dr["final_keywords"]
        final_cols = ctx.box_colors
        largest_box = ctx.define_result.get("largest_box")

        box_drawn = 0
        color_log = {
            "BLACK_BOX_PERSON": {"count": 0, "boxes": []},
            "BLUE_BOX_PRODUCT_TYPE": {"count": 0, "boxes": []},
            "GREEN_BOX_HEAD": {"count": 0, "boxes": []},
            "VIOLET_BOX_SHOES": {"count": 0, "boxes": []},
            "ORANGE_BOX_CLOTHING_FEATURES": {"count": 0, "boxes": []},
            "RED_BOX_ARTIFACTS": {"count": 0, "boxes": []},
        }

        apply_draw = Apply_Draw
        if apply_draw:
            d_ = ImageDraw.Draw(pi_rgba, mode="RGBA")

            for i, (bx1, by1, bx2, by2) in enumerate(final_boxes):
                c_ = final_cols[i]
                if c_ != BLUE_BOX_PRODUCT_TYPE:
                    s_ = f"({bx1},{by1},{bx2},{by2})"
                    d_.rectangle([bx1, by1, bx2, by2], outline=c_, width=2)
                    box_drawn += 1
                    if c_ == GREEN_BOX_HEAD:
                        color_log["GREEN_BOX_HEAD"]["count"] += 1
                        color_log["GREEN_BOX_HEAD"]["boxes"].append(s_)
                    elif c_ == VIOLET_BOX_SHOES:
                        color_log["VIOLET_BOX_SHOES"]["count"] += 1
                        color_log["VIOLET_BOX_SHOES"]["boxes"].append(s_)
                    elif c_ == ORANGE_BOX_CLOTHING_FEATURES:
                        color_log["ORANGE_BOX_CLOTHING_FEATURES"]["count"] += 1
                        color_log["ORANGE_BOX_CLOTHING_FEATURES"]["boxes"].append(s_)
                    elif c_ == RED_BOX_ARTIFACTS:
                        color_log["RED_BOX_ARTIFACTS"]["count"] += 1
                        color_log["RED_BOX_ARTIFACTS"]["boxes"].append(s_)

            if ctx.adjusted_blue_box:
                abx1, aby1, abx2, aby2 = ctx.adjusted_blue_box
                s_ = f"({abx1},{aby1},{abx2},{aby2})"
                d_.rectangle([abx1, aby1, abx2, aby2], outline=BLUE_BOX_PRODUCT_TYPE, width=2)
                color_log["BLUE_BOX_PRODUCT_TYPE"]["count"] += 1
                color_log["BLUE_BOX_PRODUCT_TYPE"]["boxes"].append(s_)
                box_drawn += 1

            if largest_box:
                lx1, ly1, lx2, ly2 = largest_box
                s_ = f"({lx1},{ly1},{lx2},{ly2})"
                d_.rectangle([lx1, ly1, lx2, ly2], outline=BLACK_BOX_PERSON, width=4)
                color_log["BLACK_BOX_PERSON"]["count"] += 1
                color_log["BLACK_BOX_PERSON"]["boxes"].append(s_)
                box_drawn += 1

        ctx.pil_img["original"] = [pi_rgba, orig_filename, None]
        it_["status"] = "ok" if apply_draw else "no_drawing"
        it_["boxes_drawn"] = box_drawn
        it_["data"] = {
            "colors": color_log,
            "draw_enabled": apply_draw,
            "boxes_count": {
                "total": len(final_boxes),
                "drawn": box_drawn
            }
        }
        batch_logs.append(it_)
        processed_count += 1
    
    processing_time = time.perf_counter() - start_time
    logging.log(LOG_LEVEL_MAP["SUCCESS"], f"{EMOJI_MAP['SUCCESS']} Completed {function_name}: processed={processed_count}, skipped={skipped_count}, errors={error_count} in {processing_time:.3f}s")
    return batch_logs

# ----------------------------------------------------------------------
# MAIN PIPELINE FUNCTION
# ----------------------------------------------------------------------
def ensure_models_loaded():
    import app
    app.ensure_models_loaded()

pipeline_step = create_pipeline_step(ensure_models_loaded)

@pipeline_step
def bounding_box(contexts: List[ProcessingContext], batch_logs: List[dict] = None):
    if batch_logs is None:
        batch_logs = []
    
    start_time = time.perf_counter()
    logging.log(LOG_LEVEL_MAP["INFO"], f"{EMOJI_MAP['INFO']} Starting bounding_box pipeline for {len(contexts)} items")
    
    from src.models import model_loader
    
    RTDETR_PROCESSOR = model_loader.RTDETR_PROCESSOR
    RTDETR_MODEL = model_loader.RTDETR_MODEL
    RTDETR_FULL_PRECISION = model_loader.RTDETR_FULL_PRECISION
    HEAD_PROCESSOR = model_loader.HEAD_PROCESSOR
    HEAD_MODEL = model_loader.HEAD_MODEL
    HEAD_DETECTION_FULL_PRECISION = model_loader.HEAD_DETECTION_FULL_PRECISION
    YOLOS_PROCESSOR = model_loader.YOLOS_PROCESSOR
    YOLOS_MODEL = model_loader.YOLOS_MODEL
    DEVICE = model_loader.DEVICE
    MODELS_LOADED = model_loader.MODELS_LOADED
    LOAD_ERROR = model_loader.LOAD_ERROR
    
    define_largest_box_batch(
        contexts, batch_logs, RTDETR_PROCESSOR, RTDETR_MODEL, 
        RTDETR_FULL_PRECISION, DEVICE, MODELS_LOADED, LOAD_ERROR
    )
    
    detect_batch(
        contexts, batch_logs,
        HEAD_PROCESSOR, HEAD_MODEL, HEAD_DETECTION_FULL_PRECISION, 
        RTDETR_PROCESSOR, RTDETR_MODEL, RTDETR_FULL_PRECISION,
        YOLOS_PROCESSOR, YOLOS_MODEL,
        DEVICE, MODELS_LOADED, LOAD_ERROR
    )
    
    choose_color_for_feature_batch(contexts, batch_logs)
    
    adjust_blue_box_batch(contexts, batch_logs)
    
    draw_batch(contexts, batch_logs)
    
    processing_time = time.perf_counter() - start_time
    logging.log(LOG_LEVEL_MAP["SUCCESS"], f"{EMOJI_MAP['SUCCESS']} Completed bounding_box pipeline for {len(contexts)} items in {processing_time:.3f}s")
    
    return batch_logs