# 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:
![image](https://datasets-server.huggingface.co/assets/merve/canny_diffusiondb/--/merve--canny_diffusiondb/train/0/original_image/image.jpg)
Transformed Image:
![image](https://datasets-server.huggingface.co/assets/merve/canny_diffusiondb/--/merve--canny_diffusiondb/train/0/transformed_image/image.jpg)
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)

```
region:us
{"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}}
TAGS
#region-us 

# Canny DiffusionDB 

This dataset is the DiffusionDB dataset that is transformed using Canny transformation.

You can see samples below  

Sample:

Original Image:
!image
Transformed Image:
!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:

# Canny DiffusionDB 

This dataset is the DiffusionDB dataset that is transformed using Canny transformation.

You can see samples below  

Sample:

Original Image:
!image
Transformed Image:
!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:
TAGS
#region-us 
 # Canny DiffusionDB 

This dataset is the DiffusionDB dataset that is transformed using Canny transformation.

You can see samples below  

Sample:

Original Image:
!image
Transformed Image:
!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:
passage: TAGS
#region-us 
# Canny DiffusionDB 

This dataset is the DiffusionDB dataset that is transformed using Canny transformation.

You can see samples below  

Sample:

Original Image:
!image
Transformed Image:
!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: