First commit

identify.py

@@ -0,0 +1,77 @@
+import os
+import sys
+import cv2
+import torch
+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
+
+def identify_spinda(image_path: str):
+    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
+    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="models/best_spinda_model.pth")
+        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
+
+    print(f"Visual Fingerprint: {fingerprint}")
+    print(f"Predicted Grid Coordinates: {coords}")
+
+    # 3. Resolution (Mathematical 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
+    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
+    reg = SpindaRegistry()
+    matches = reg.lookup_by_fingerprint(fingerprint)
+    
+    if matches:
+        print("\nMatches found in Global Registry:")
+        for pid in matches:
+            print(f"  - Registered PID: 0x{pid}")
+    else:
+        print("\nNo matching entries in Global Registry.")
+
+    print("\nNote: Accuracy depends on model training progress.")
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print("Usage: python identify.py <image_path>")
+    else:
+        identify_spinda(sys.argv[1])