Update scripts/download_dataset.py

scripts/download_dataset.py

@@ -1,41 +1,53 @@
+from datasets import load_dataset
 import argparse
-import requests
 from pathlib import Path
+import json
 
-classlist = ["Blends", "Ceramic", "Concrete", "Fabric", "Leather", "Marble", "Metal", "Misc", "Plaster", "Plastic", "Stone", "Terracotta", "Wood"]
 
-def download_stream(url, dest_file):
-    with requests.get(url, stream=True) as r:
-        r.raise_for_status()
-        with open(dest_file, "wb") as f:
-            for chunk in r.iter_content(chunk_size=8192):
-                f.write(chunk)
+def process_batch(examples):
+    return examples
 
-def download_dataset(base_dir, class_names=None):
-    dset_url = "https://huggingface.co/datasets/gvecchio/MatSynth/resolve/main/maps"
 
-    classes = class_names.split(",") if class_names else classlist
-    if classes:
-        for split in ["train", "test"]:
-            dest_dir = base_dir/split
-            dest_dir.mkdir(parents=True, exist_ok=True)
-
-            for class_name in classes:
-                req = f"{dset_url}/{split}/{class_name}.zip"
-                download_stream(req, dest_dir/(class_name + ".zip"))
-
-                if class_name in ["Ground", "Wood"] and split == "train":
-                    req = f"{dset_url}/{split}/{class_name}.z01"
-                    download_stream(req, dest_dir/(class_name + ".z01"))
-    
 if __name__ == "__main__":
     # Create argument parser
-    parser = argparse.ArgumentParser(description="Download dataset categories.")
-    parser.add_argument("--base_dir", required=True, help="Base directory to save the downloaded files.")
-    parser.add_argument("--class_names", help="Specify the class name to download a specific category.")
+    parser = argparse.ArgumentParser(description="Download dataset.")
+    parser.add_argument("--base_dir", required=True, help="Directory to save the downloaded files.")
     args = parser.parse_args()
 
-    # Call the download_dataset function with command-line arguments
-    download_dataset(Path(args.base_dir), args.class_names)
-
-# python script.py --base_dir /path/to/save --class_name Leather
+    base_dir = Path(args.base_dir)
+    base_dir.mkdir(exist_ok=True, parents=True)
+
+    # Load dataset
+    ds = load_dataset(
+        "gvecchio/MatSynth",
+        streaming=True,
+    )
+
+    # Setup dummy processing
+    ds = ds.map(process_batch, batched=False, batch_size=1)
+
+    for split in ds:
+        for item in ds[split]:
+            name = item["name"]
+            dest_dir = base_dir / split / item["metadata"]["category"] / name
+            dest_dir.mkdir(exist_ok=True, parents=True)
+
+            # Save metadata
+            with open(dest_dir / "metadata.json", "w") as f:
+                item["metadata"]["physical_size"] = str(
+                    item["metadata"]["physical_size"]
+                )
+                json.dump(item["metadata"], f, indent=4)
+
+            # Save images
+            item["basecolor"].save(dest_dir / "basecolor.png")
+            item["diffuse"].save(dest_dir / "diffuse.png")
+            item["displacement"].save(dest_dir / "displacement.png")
+            item["specular"].save(dest_dir / "specular.png")
+            item["height"].save(dest_dir / "height.png")
+            item["metallic"].save(dest_dir / "metallic.png")
+            item["normal"].save(dest_dir / "normal.png")
+            item["opacity"].save(dest_dir / "opacity.png")
+            item["roughness"].save(dest_dir / "roughness.png")
+            if item["blend_mask"] is not None:
+                item["blend_mask"].save(dest_dir / "blend_mask.png")