Delete script.py

script.py

@@ -1,110 +0,0 @@
-import torch
-from PIL import Image
-import matplotlib.pyplot as plt
-import cv2
-import numpy as np
-import os
-import pandas as pd
-import ultralytics
-from torchvision import transforms
-
-def run_inference(model, image_path, conf_threshold, save_path):
-
-    test_images = os.listdir(image_path)
-    test_images.sort()
-    
-    bboxes = []
-    category_ids = []
-    test_images_names = []
-    
-    print(f"Starting inference on {len(test_images)} images...")
-    
-    for image in test_images:
-        
-        # Skip non-image files if any
-        if not (image.endswith('.jpg') or image.endswith('.png')):
-            continue
-            
-        test_images_names.append(image)
-        bbox = []
-        category_id = []
-        
-        # Run inference
-        results = model(os.path.join(image_path, image))
-        
-        # Parse detections
-        for pred in results.pred[0]:
-            xmin, ymin, xmax, ymax, conf, class_id = pred.tolist()
-            
-            if conf >= conf_threshold:
-                
-                # --- CALCULATE WIDTH & HEIGHT ---
-                width = xmax - xmin
-                height = ymax - ymin
-
-                # --- FORMATTING FIXES FOR LEADERBOARD ---
-                # Round coordinates to avoid messy floats (e.g. 100.0 instead of 100.43242)
-                final_box = [round(xmin, 1), round(ymin, 1), round(width, 1), round(height, 1)]
-                
-                # --- CRITICAL FIX: SHIFT CLASS ID ---
-                # YOLO predicts 0, 1, 2.
-                # Your JSON categories are 1 (Needle), 2 (Grasper), 3 (Scissors).
-                # We simply add +1 to align them.
-                final_id = int(class_id) + 1 
-
-                bbox.append(final_box)
-                category_id.append(final_id)
-            
-        bboxes.append(bbox)
-        category_ids.append(category_id)
-    
-    # Create Submission DataFrame
-    df_predictions = pd.DataFrame(columns=["file_name", "bbox", "category_id"])
-    
-    for i in range(len(test_images_names)):
-        file_name = test_images_names[i]
-        
-        # Format the list exactly how standard submission files look
-        # e.g. "[[10, 10, 50, 50]]" instead of just list objects
-        str_bbox = str(bboxes[i])
-        str_cat_id = str(category_ids[i])
-        
-        new_row = pd.DataFrame({
-            "file_name": file_name,
-            "bbox": str_bbox,
-            "category_id": str_cat_id
-        }, index=[0])
-        
-        df_predictions = pd.concat([df_predictions, new_row], ignore_index=True)
-        
-    df_predictions.to_csv(save_path, index=False)
-    print(f"Submission saved to: {save_path}")
-
-
-if __name__ == "__main__":
-
-    current_directory = os.path.dirname(os.path.abspath(__file__))
-    
-    # Default paths (These usually don't change on the server)
-    TEST_IMAGE_PATH = "/tmp/data/test_images"
-    SUBMISSION_SAVE_PATH = os.path.join(current_directory, "submission.csv")
-    
-    # --- MODEL LOADING CONFIG ---
-    # We look for 'best.pt' in the SAME directory as this script
-    MODEL_WEIGHTS_PATH = os.path.join(current_directory, "best.pt")
-    
-    # Increase threshold slightly to avoid garbage predictions
-    CONF_THRESHOLD = 0.30
-    
-    print(f"Loading model from: {MODEL_WEIGHTS_PATH}")
-    
-    # Load YOLOv5 from the local folder to ensure it works offline
-    # 'source=local' means it looks for the 'yolov5' folder in this directory
-    model = torch.hub.load(
-        os.path.join(current_directory, 'yolov5'), 
-        'custom', 
-        path=MODEL_WEIGHTS_PATH, 
-        source='local'
-    )
-    
-    run_inference(model, TEST_IMAGE_PATH, CONF_THRESHOLD, SUBMISSION_SAVE_PATH)