Refactor/cleanup

identify.py

@@ -1,70 +1,59 @@
+import argparse
 import os
 import sys
+
 import cv2
-import torch
+
+from src.data.renderer import generate_high_fidelity_spinda
 from src.models.inference import SpindaInference
-from src.utils.resolver import SpindaResolver
 from src.registry.database import SpindaRegistry
-from src.data.high_fidelity_generator import generate_high_fidelity_spinda
-from src.utils.detector import SpindaDetector # Import the detector
+from src.utils.detector import SpindaDetector
+from src.utils.resolver import SpindaResolver
+
 
 def identify_spinda(
     image_path: str,
-    model_path: str = "models/best_spinda_model.pth",
-    backbone: str = "resnet18",
-):
+    model_path: str = "models/best_resnet34_model.pth",
+    backbone: str = "resnet34",
+) -> None:
     if not os.path.exists(image_path):
         print(f"Error: File {image_path} not found.")
         return
 
     print(f"--- Identifying Spinda in {image_path} ---")
-    
-    # 1. Detect and Crop Spinda
+
+    # 1. Detect and crop
     detector = SpindaDetector()
     cropped_img = detector.detect_and_crop(image_path)
-
     if cropped_img is None:
         print("Error: Could not detect Spinda in the image.")
         return
-    
-    # Save cropped image for debug/visual check
+
     cv2.imwrite("detected_spinda_crop.png", cropped_img)
     print("Detected Spinda saved to detected_spinda_crop.png")
 
-    # We need to save the cropped image to a temporary file for the inference model to read
-    temp_cropped_path = "temp_cropped_spinda.png"
-    cv2.imwrite(temp_cropped_path, cropped_img)
-
-    # 2. Inference (Model Prediction) using the cropped image
-    try:
-        inf = SpindaInference(model_path=model_path, backbone=backbone)
-        coords, fingerprint = inf.predict(temp_cropped_path)
-    except Exception as e:
-        print(f"Error during inference: {e}")
-        os.remove(temp_cropped_path) # Clean up temp file
-        return
-    finally:
-        os.remove(temp_cropped_path) # Clean up temp file
+    # 2. Inference — pass the BGR array directly, no temp file needed
+    inf = SpindaInference(model_path=model_path, backbone=backbone)
+    coords, fingerprint = inf.predict(cropped_img)
 
     print(f"Visual Fingerprint: {fingerprint}")
     print(f"Predicted Grid Coordinates: {coords}")
 
-    # 3. Resolution (Mathematical PIDs)
+    # 3. Resolve to PIDs
     resolved = SpindaResolver.resolve_fingerprint(fingerprint)
     print("\nPossible PIDs:")
     print(f"  Standard (Gen 3-8, HOME): 0x{resolved['standard']}")
     print(f"  BDSP (Big-Endian Flip):   0x{resolved['bdsp']}")
 
-    # 4. Visual Verification
+    # 4. Visual verification
     print("\nGenerating visual verification image...")
     verify_img = generate_high_fidelity_spinda(int(resolved['standard'], 16))
     cv2.imwrite("prediction_verify.png", verify_img)
     print("Verification image saved to: prediction_verify.png")
 
-    # 5. Registry Lookup
+    # 5. Registry lookup
     reg = SpindaRegistry()
     matches = reg.lookup_by_fingerprint(fingerprint)
-    
     if matches:
         print("\nMatches found in Global Registry:")
         for pid in matches:
@@ -72,13 +61,12 @@ def identify_spinda(
     else:
         print("\nNo matching entries in Global Registry.")
 
-    print("\nNote: Accuracy depends on model training progress.")
 
 if __name__ == "__main__":
-    import argparse
     parser = argparse.ArgumentParser()
     parser.add_argument("image_path")
-    parser.add_argument("--backbone",   type=str, default="resnet18", choices=["resnet18", "resnet34", "convnext_tiny"])
-    parser.add_argument("--model_path", type=str, default="models/best_spinda_model.pth")
+    parser.add_argument("--backbone",   type=str, default="resnet34",
+                        choices=["resnet18", "resnet34", "convnext_tiny"])
+    parser.add_argument("--model_path", type=str, default="models/best_resnet34_model.pth")
     args = parser.parse_args()
     identify_spinda(args.image_path, model_path=args.model_path, backbone=args.backbone)