Upload parkseg-to-fo.py

parkseg-to-fo.py

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+"""
+Simple script to create a FiftyOne dataset from the parkseg12k dataset with NDVI calculation.
+"""
+
+# Import necessary libraries
+import fiftyone as fo
+import os
+import numpy as np
+from PIL import Image
+from datasets import load_dataset
+
+def main():
+    # Create a new FiftyOne dataset
+    name = "parkseg12k_train"
+    dataset = fo.Dataset(name, overwrite=True, persistent=True)
+
+    # Load the Hugging Face dataset
+    try:
+        hf_dataset = load_dataset("file://" + os.path.join(os.getcwd(), "parkseg12k_dataset"))
+        print("Loaded dataset from local storage")
+    except:
+        print("Loading from HuggingFace")
+        hf_dataset = load_dataset("UTEL-UIUC/parkseg12k")
+
+    # Create directories for storing images
+    images_dir = os.path.join(os.getcwd(), "parkseg12k_images_train")
+    ndvi_dir = os.path.join(os.getcwd(), "parkseg12k_ndvi")
+    os.makedirs(images_dir, exist_ok=True)
+    os.makedirs(ndvi_dir, exist_ok=True)
+
+    # Only process the train split
+    split = "train"
+    print(f"Processing {split} split...")
+    samples = []
+
+    # Process each sample
+    for i, sample in enumerate(hf_dataset[split]):
+        if i % 100 == 0:
+            print(f"Processing sample {i}/{len(hf_dataset[split])}")
+        
+        # Create paths for saving images
+        rgb_path = os.path.join(images_dir, f"{i}_rgb.png")
+        mask_path = os.path.join(images_dir, f"{i}_mask.png")
+        nir_path = os.path.join(images_dir, f"{i}_nir.png")
+        ndvi_path = os.path.join(ndvi_dir, f"{i}_ndvi.npy")
+        
+        # Save images to disk
+        sample['rgb'].save(rgb_path)
+        sample['mask'].save(mask_path)
+        sample['nir'].save(nir_path)
+        
+        # Calculate NDVI
+        rgb_array = np.array(sample['rgb'])
+        nir_array = np.array(sample['nir'])
+        
+        # Extract red channel and normalize
+        red = rgb_array[:, :, 0].astype(np.float32) / 255.0
+        nir = nir_array.astype(np.float32) / 255.0
+        
+        # Calculate NDVI: (NIR - Red) / (NIR + Red)
+        numerator = nir - red
+        denominator = nir + red
+        
+        # Avoid division by zero
+        ndvi = np.where(denominator != 0, 
+                         numerator / denominator,
+                         0)
+        
+        # Clip to valid NDVI range
+        ndvi = np.clip(ndvi, -1, 1)
+        
+        # Save NDVI array
+        np.save(ndvi_path, ndvi)
+        
+        # Create FiftyOne sample
+        fo_sample = fo.Sample(filepath=rgb_path)
+        
+        # Add mask as Segmentation using mask_path
+        fo_sample["segmentation"] = fo.Segmentation(mask_path=mask_path)
+        
+        # Add NIR as Heatmap using map_path with range [0,1]
+        fo_sample["nir"] = fo.Heatmap(map_path=nir_path, range=[0, 1])
+        
+        # Add NDVI as Heatmap with the array directly
+        fo_sample["ndvi"] = fo.Heatmap(map=ndvi, range=[-1, 1])
+        
+        # Optional: Add NDVI statistics as metadata
+        fo_sample["ndvi_mean"] = float(np.mean(ndvi))
+        fo_sample["ndvi_std"] = float(np.std(ndvi))
+        fo_sample["ndvi_min"] = float(np.min(ndvi))
+        fo_sample["ndvi_max"] = float(np.max(ndvi))
+        
+        # Add to samples list
+        samples.append(fo_sample)
+
+    # Add all samples at once
+    print(f"Adding {len(samples)} samples to dataset...")
+    dataset.add_samples(samples)
+
+    # Compute metadata and add dynamic fields
+    dataset.compute_metadata()
+    dataset.add_dynamic_sample_fields()
+
+    return dataset
+
+if __name__ == "__main__":
+    dataset = main()