| --- |
| dataset_info: |
| features: |
| - name: original_image |
| dtype: image |
| - name: prompt |
| dtype: string |
| - name: transformed_image |
| dtype: image |
| splits: |
| - name: train |
| num_bytes: 604990210.0 |
| num_examples: 994 |
| download_size: 604849707 |
| dataset_size: 604990210.0 |
| --- |
| # Canny DiffusionDB |
|
|
| This dataset is the [DiffusionDB dataset](https://huggingface.co/datasets/poloclub/diffusiondb) that is transformed using Canny transformation. |
|
|
| You can see samples below 👇 |
|
|
| **Sample:** |
|
|
| Original Image: |
|  |
| Transformed Image: |
|  |
| Caption: |
| "a small wheat field beside a forest, studio lighting, golden ratio, details, masterpiece, fine art, intricate, decadent, ornate, highly detailed, digital painting, octane render, ray tracing reflections, 8 k, featured, by claude monet and vincent van gogh " |
|
|
| Below you can find a small script used to create this dataset: |
| ```python |
| |
| def canny_convert(image): |
| image_array = np.array(image) |
| gray_image = cv2.cvtColor(image_array, cv2.COLOR_BGR2GRAY) |
| edges = cv2.Canny(gray_image, 100, 200) |
| edge_image = Image.fromarray(edges) |
| return edge_image |
| |
| dataset = load_dataset("poloclub/diffusiondb", split = "train") |
| |
| dataset_list = [] |
| for data in dataset: |
| |
| image_path = data["image"] |
| prompt = data["prompt"] |
| transformed_image_path = canny_convert(image_path) |
| |
| new_data = { |
| "original_image": image, |
| "prompt": prompt, |
| "transformed_image": transformed_image, |
| } |
| dataset_list.append(new_data) |
| |
| ``` |
