feat/tracking and save in redis finished

test/test.py

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+from ultralytics import YOLO
+import cv2
+import os
+
+# Load the model
+# model = YOLO('../models/webcam-local-01/4/bangchak_poc/yolo11n.pt')
+model = YOLO('yolo11m.pt')
+
+def test_image(image_path):
+    """Test a single image with YOLO model"""
+    if not os.path.exists(image_path):
+        print(f"Image not found: {image_path}")
+        return
+    
+    # Run inference - filter for car class only (class 2 in COCO)
+    results = model(image_path, classes=[2, 5, 7])  # 2, 5, 7 = car, bus, truck in COCO dataset
+    
+    # Display results
+    for r in results:
+        im_array = r.plot()  # plot a BGR numpy array of predictions
+        
+        # Resize image for display (max width/height 800px)
+        height, width = im_array.shape[:2]
+        max_dimension = 800
+        if width > max_dimension or height > max_dimension:
+            if width > height:
+                new_width = max_dimension
+                new_height = int(height * (max_dimension / width))
+            else:
+                new_height = max_dimension
+                new_width = int(width * (max_dimension / height))
+            im_array = cv2.resize(im_array, (new_width, new_height))
+        
+        # Show image with predictions
+        cv2.imshow('YOLO Test - Car Detection Only', im_array)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+        
+        # Print detection info
+        print(f"\nDetections for {image_path}:")
+        if r.boxes is not None and len(r.boxes) > 0:
+            for i, box in enumerate(r.boxes):
+                cls = int(box.cls[0])
+                conf = float(box.conf[0])
+                original_class = model.names[cls]  # Original class name (car/bus/truck)
+                # Get bounding box coordinates
+                x1, y1, x2, y2 = box.xyxy[0].tolist()
+                # Rename all vehicle types to "car"
+                print(f"Detection {i+1}: car (was: {original_class}) - Confidence: {conf:.3f} - BBox: ({x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f})")
+            print(f"Total cars detected: {len(r.boxes)}")
+        else:
+            print("No cars detected in the image")
+
+if __name__ == "__main__":
+    # Test with an image file
+    image_path = input("Enter image path (or press Enter for default test): ")
+    if not image_path:
+        image_path = "sample.png"  # Default test image
+    
+    test_image(image_path)