Update README.md

README.md

@@ -14,6 +14,45 @@ dataset_info:
   download_size: 604849707
   dataset_size: 604990210.0
 ---
-# Dataset Card for "canny_diffusiondb"
+# Canny DiffusionDB 
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+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)
+
+```