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---
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)
``` |