Duplicate from ariG23498/coco2017

Co-authored-by: Aritra Roy Gosthipaty <ariG23498@users.noreply.huggingface.co>

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: objects
+    struct:
+    - name: bbox
+      sequence:
+        sequence: float64
+    - name: segmentation
+      sequence:
+        sequence:
+          sequence: float64
+    - name: categories
+      sequence: int64
+  splits:
+  - name: train
+    num_bytes: 17598458856.47
+    num_examples: 117266
+  - name: validation
+    num_bytes: 795110726.04
+    num_examples: 4952
+  download_size: 20170024873
+  dataset_size: 18393569582.510002
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+task_categories:
+- object-detection
+---
+
+# MS-COCO2017
+
+## Use the dataset
+
+```py
+from datasets import load_dataset
+ds = load_dataset("ariG23498/coco2017", streaming=True, split="validation")
+
+sample = next(iter(ds))
+
+from PIL import Image, ImageDraw
+
+def draw_bboxes_on_image(
+        image: Image.Image,
+        objects: dict,
+        category_names: dict = None,
+        box_color: str = "red",
+        text_color: str = "white"
+    ):
+    draw = ImageDraw.Draw(image)
+    bboxes = objects.get("bbox", [])
+    categories = objects.get("categories", [])
+
+    for i, bbox in enumerate(bboxes):
+        x, y, width, height = bbox
+        # PIL expects (x_min, y_min, x_max, y_max) for rectangle
+        x_min, y_min, x_max, y_max = x, y, x + width, y + height
+
+        # Draw the rectangle
+        draw.rectangle([x_min, y_min, x_max, y_max], outline=box_color, width=2)
+
+        # Get category label
+        category_id = categories[i]
+        label = str(category_id)
+        if category_names and category_id in category_names:
+            label = category_names[category_id]
+
+        # Draw the category label
+        text_bbox = draw.textbbox((x_min, y_min), label) # Use textbbox to get text size
+        text_width = text_bbox[2] - text_bbox[0]
+        text_height = text_bbox[3] - text_bbox[1]
+
+        # Draw a filled rectangle behind the text for better readability
+        draw.rectangle([x_min, y_min - text_height - 5, x_min + text_width + 5, y_min], fill=box_color)
+        draw.text((x_min + 2, y_min - text_height - 2), label, fill=text_color)
+
+    return image
+
+draw_bboxes_on_image(
+    image=sample["image"],
+    objects=sample["objects"],
+)
+```
+
+## Get the categories
+
+```py
+import json
+
+with open("/content/annotations/instances_train2017.json") as f:
+    instances = json.load(f)
+
+instances["categories"]
+```
+
+## Build the dataset and upload to Hub
+
+```py
+!wget http://images.cocodataset.org/zips/train2017.zip
+!wget http://images.cocodataset.org/zips/val2017.zip
+!wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
+
+!unzip train2017.zip
+!unzip val2017.zip
+!unzip annotations_trainval2017.zip
+
+import json
+from pathlib import Path
+from tqdm import tqdm
+from huggingface_hub import upload_folder
+from datasets import Dataset, DatasetDict, Features, Value, Sequence, Array2D
+import shutil
+
+# === Paths ===
+base_dir = Path("/content")
+splits = {
+    "train": {
+        "image_dir": base_dir / "train2017",
+        "annotation_file": base_dir / "annotations" / "instances_train2017.json",
+    },
+    "val": {
+        "image_dir": base_dir / "val2017",
+        "annotation_file": base_dir / "annotations" / "instances_val2017.json",
+    }
+}
+output_dir = base_dir / "coco_imagefolder"
+
+# Clean existing directory
+if output_dir.exists():
+    shutil.rmtree(output_dir)
+output_dir.mkdir(parents=True)
+
+def convert_coco_to_jsonl(image_dir, annotation_path, output_metadata_path):
+    with open(annotation_path) as f:
+        data = json.load(f)
+
+    id_to_filename = {img['id']: img['file_name'] for img in data['images']}
+    annotations_by_image = {}
+
+    for ann in data['annotations']:
+        img_id = ann['image_id']
+        bbox = ann['bbox']  # [x, y, width, height]
+        category = ann['category_id']
+
+        if img_id not in annotations_by_image:
+            annotations_by_image[img_id] = {
+                "file_name": id_to_filename[img_id],
+                "objects": {
+                    "bbox": [],
+                    "categories": []
+                }
+            }
+
+        annotations_by_image[img_id]["objects"]["bbox"].append(bbox)
+        annotations_by_image[img_id]["objects"]["categories"].append(category)
+
+    with open(output_metadata_path, "w") as f:
+        for img_id, metadata in annotations_by_image.items():
+            json.dump(metadata, f)
+            f.write("\n")
+
+# Convert and copy files to imagefolder-style structure
+for split, info in splits.items():
+    split_dir = output_dir / split
+    split_dir.mkdir(parents=True)
+
+    # Copy images
+    for img_path in tqdm(info["image_dir"].glob("*.jpg"), desc=f"Copying {split} images"):
+        shutil.copy(img_path, split_dir / img_path.name)
+
+    # Convert annotations
+    metadata_path = split_dir / "metadata.jsonl"
+    convert_coco_to_jsonl(split_dir, info["annotation_file"], metadata_path)
+
+# HF Dataset
+from datasets import load_dataset
+
+dataset = load_dataset("imagefolder", data_dir="/content/coco_imagefolder")
+dataset.push_to_hub("ariG23498/coco2017")
+```

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