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# ----------------------------------------------------------------------
# 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
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