Fix RT-DETR module dependency issue

- Integrate RT-DETR functions directly into app.py
- Remove dependency on deleted test_visual_validation module
- Add load_rtdetr_model(), detect_with_rtdetr()
- Add apply_universal_filter() and helper functions
- Fixes ModuleNotFoundError on Hugging Face

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

app.py

@@ -22,14 +22,202 @@ from io import BytesIO
 from inference_sdk import InferenceHTTPClient
 import tempfile
 
-# test_visual_validation에서 가져오기 (지연 import로 변경)
-# from test_visual_validation import (
-#     load_rtdetr_model,
-#     detect_with_rtdetr,
-#     apply_universal_filter,
-#     calculate_morphological_features,
-#     calculate_visual_features
-# )
+# ============================================================
+# RT-DETR 및 필터링 함수들 (이전 test_visual_validation에서 통합)
+# ============================================================
+import cv2
+
+def load_rtdetr_model():
+    """RT-DETR 모델 로드"""
+    print("🔄 RT-DETR 모델 로딩 중...")
+    processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r50vd_coco_o365")
+    model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd_coco_o365")
+    model.eval()
+    print("✅ RT-DETR 로딩 완료")
+    return processor, model
+
+def detect_with_rtdetr(image, processor, model, confidence=0.3):
+    """RT-DETR로 객체 검출"""
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    target_sizes = torch.tensor([image.size[::-1]])
+    results = processor.post_process_object_detection(
+        outputs,
+        target_sizes=target_sizes,
+        threshold=confidence
+    )[0]
+
+    detections = []
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        x1, y1, x2, y2 = box.tolist()
+        detections.append({
+            'bbox': [x1, y1, x2, y2],
+            'confidence': score.item(),
+            'label': label.item()
+        })
+
+    return detections
+
+def calculate_morphological_features(bbox, image_size):
+    """형태학적 특징 계산"""
+    x1, y1, x2, y2 = bbox
+    width = x2 - x1
+    height = y2 - y1
+
+    # Aspect ratio (긴 쪽 / 짧은 쪽)
+    aspect_ratio = max(width, height) / max(min(width, height), 1)
+
+    # Area ratio (이미지 대비 면적)
+    img_w, img_h = image_size
+    area_ratio = (width * height) / (img_w * img_h)
+
+    # Compactness (4π * Area / Perimeter²)
+    perimeter = 2 * (width + height)
+    compactness = (4 * np.pi * width * height) / max(perimeter ** 2, 1)
+
+    return {
+        'aspect_ratio': aspect_ratio,
+        'area_ratio': area_ratio,
+        'compactness': compactness,
+        'width': width,
+        'height': height
+    }
+
+def calculate_visual_features(image_pil, bbox):
+    """시각적 특징 계산 (색상, 텍스처)"""
+    # PIL → OpenCV
+    image_cv = cv2.cvtColor(np.array(image_pil), cv2.COLOR_RGB2BGR)
+    x1, y1, x2, y2 = [int(v) for v in bbox]
+
+    # 바운딩 박스 영역 추출
+    roi = image_cv[y1:y2, x1:x2]
+    if roi.size == 0:
+        return {'hue': 100, 'saturation': 255, 'color_std': 255}
+
+    # HSV 변환
+    hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
+
+    # 색상 (Hue)
+    hue_mean = np.mean(hsv[:, :, 0])
+
+    # 채도 (Saturation)
+    saturation = np.mean(hsv[:, :, 1])
+
+    # 색상 일관성 (표준편차)
+    color_std = np.std(hsv[:, :, 0])
+
+    return {
+        'hue': hue_mean,
+        'saturation': saturation,
+        'color_std': color_std
+    }
+
+def apply_universal_filter(detections, image, threshold=90):
+    """범용 새우 필터 적용"""
+    img_size = image.size
+    filtered = []
+
+    for det in detections:
+        bbox = det['bbox']
+
+        # 1. 형태학적 특징
+        morph = calculate_morphological_features(bbox, img_size)
+
+        # 2. 시각적 특징
+        visual = calculate_visual_features(image, bbox)
+
+        # 3. 점수 계산
+        score = 0
+        reasons = []
+
+        # Aspect ratio (4:1 ~ 9:1)
+        if 4.0 <= morph['aspect_ratio'] <= 9.0:
+            score += 25
+            reasons.append(f"✓ 종횡비 {morph['aspect_ratio']:.1f}")
+        elif 3.0 <= morph['aspect_ratio'] < 4.0 or 9.0 < morph['aspect_ratio'] <= 10.0:
+            score += 12
+            reasons.append(f"△ 종횡비 {morph['aspect_ratio']:.1f}")
+        else:
+            score -= 5
+            reasons.append(f"✗ 종횡비 {morph['aspect_ratio']:.1f}")
+
+        # Compactness (< 0.50, 긴 형태)
+        if morph['compactness'] < 0.40:
+            score += 30
+            reasons.append(f"✓ 세장도 {morph['compactness']:.2f}")
+        elif 0.40 <= morph['compactness'] < 0.50:
+            score += 15
+            reasons.append(f"△ 세장도 {morph['compactness']:.2f}")
+        else:
+            reasons.append(f"✗ 세장도 {morph['compactness']:.2f}")
+            score -= 20
+
+        # Area
+        abs_area = morph['width'] * morph['height']
+        if 50000 <= abs_area <= 500000:
+            score += 35
+            reasons.append(f"✓ 면적 {abs_area/1000:.0f}K")
+        elif 500000 < abs_area <= 800000:
+            score -= 10
+            reasons.append(f"△ 면적 {abs_area/1000:.0f}K")
+        elif abs_area > 800000:
+            score -= 30
+            reasons.append(f"✗ 면적 {abs_area/1000:.0f}K (너무큼)")
+        else:
+            score -= 10
+            reasons.append(f"✗ 면적 {abs_area/1000:.0f}K (너무작음)")
+
+        # Hue (색상)
+        hue = visual['hue']
+        if hue < 40 or hue > 130:
+            score += 10
+            reasons.append(f"✓ 색상 {hue:.0f}")
+        elif 90 <= hue <= 130:
+            score -= 5
+            reasons.append(f"✗ 색상 {hue:.0f} (배경)")
+        else:
+            reasons.append(f"△ 색상 {hue:.0f}")
+
+        # Saturation
+        if visual['saturation'] < 85:
+            score += 20
+            reasons.append(f"✓ 채도 {visual['saturation']:.0f}")
+        elif 85 <= visual['saturation'] < 120:
+            score += 5
+            reasons.append(f"△ 채도 {visual['saturation']:.0f}")
+        else:
+            score -= 15
+            reasons.append(f"✗ 채도 {visual['saturation']:.0f} (높음)")
+
+        # Color consistency
+        if visual['color_std'] < 50:
+            score += 15
+            reasons.append(f"✓ 색상일관성 {visual['color_std']:.1f}")
+        elif 50 <= visual['color_std'] < 80:
+            score += 5
+            reasons.append(f"△ 색상일관성 {visual['color_std']:.1f}")
+        else:
+            score -= 10
+            reasons.append(f"✗ 색상일관성 {visual['color_std']:.1f} (불일치)")
+
+        # RT-DETR confidence
+        if 'confidence' in det:
+            if det['confidence'] >= 0.3:
+                score += 15
+                reasons.append(f"✓ 신뢰도 {det['confidence']:.0%}")
+            elif det['confidence'] >= 0.1:
+                score += 8
+                reasons.append(f"△ 신뢰도 {det['confidence']:.0%}")
+            else:
+                reasons.append(f"✗ 신뢰도 {det['confidence']:.0%}")
+
+        det['filter_score'] = score
+        det['filter_reasons'] = reasons
+        filtered.append(det)
+
+    return filtered
 
 # YOLOv8 import
 # ============================================================
@@ -234,10 +422,7 @@ def load_rtdetr_on_demand():
     """RT-DETR 모델을 필요시에만 로딩"""
     global processor, model
     if processor is None or model is None:
-        print("🔄 RT-DETR 모델 로딩 중...")
-        from test_visual_validation import load_rtdetr_model
         processor, model = load_rtdetr_model()
-        print("✅ RT-DETR 로딩 완료")
         return "✅ RT-DETR 모델 로딩 완료"
     else:
         return "ℹ️ RT-DETR 모델이 이미 로딩되어 있습니다"
@@ -270,7 +455,6 @@ def detect_with_selected_model(image, confidence, model_type):
     if model_type == "RT-DETR":
         if processor is None or model is None:
             raise ValueError("⚠️ RT-DETR 모델이 로딩되지 않았습니다. '🔄 RT-DETR 로드' 버튼을 먼저 클릭하세요.")
-        from test_visual_validation import detect_with_rtdetr
         return detect_with_rtdetr(image, processor, model, confidence)
     elif model_type == "VIDraft/Shrimp":
         return detect_with_roboflow(image, confidence)
@@ -310,7 +494,6 @@ def interactive_detect(image, confidence, filter_threshold, show_all, model_type
                 all_detections_scored = all_detections
             else:
                 # RT-DETR: Universal Filter 사용
-                from test_visual_validation import apply_universal_filter
                 all_detections_scored = apply_universal_filter(all_detections, image, threshold=0)
 
             # 필터 임계값 적용