Update README.md

README.md

@@ -21,13 +21,13 @@ dataset_info:
     dtype: string
   splits:
   - name: validation
-    num_bytes: 64018244.0
+    num_bytes: 64018244
     num_examples: 101
   - name: test
-    num_bytes: 125460818.0
+    num_bytes: 125460818
     num_examples: 199
   download_size: 189448472
-  dataset_size: 189479062.0
+  dataset_size: 189479062
 configs:
 - config_name: default
   data_files:
@@ -35,4 +35,48 @@ configs:
     path: data/validation-*
   - split: test
     path: data/test-*
+license: mit
+task_categories:
+- image-to-text
+- object-detection
+size_categories:
+- n<1K
 ---
+
+# DM codes dataset
+
+The dataset contains photos of Data Matrix (DM) codes and their annotations. The photos were taken on an iPhone and annotated manually by a human.
+The annotations contain **text**, which is encoded in the DM code and the pixel coordinates of the DM code vertices.
+The vertices are: **tl** = top left, **tr** = top right, **br** = bottom right, **bl** = bottom left.
+Attribute **is_clean** specifies whether the DM code on the image is expected to be easily readable. For every DM code, there is exactly one image
+with `is_clean=true` and several images with `is_clean=false`.
+
+If you want to crop the DM codes from the images, use the following code:
+
+```python
+import numpy as np
+import datasets
+from PIL import Image
+from skimage import transform
+
+def crop_dm_code(example: dict, square_side: int = 200, square_padding: int = 25) -> dict:
+    vertices = np.asarray((example["tl"], example["tr"], example["br"], example["bl"]))
+    unit_square = np.asarray([
+        [square_padding, square_padding],
+        [square_side + square_padding, square_padding],
+        [square_side + square_padding, square_side + square_padding],
+        [square_padding, square_side + square_padding]
+    ])
+    transf = transform.ProjectiveTransform()
+    if not transf.estimate(unit_square, vertices): raise Exception("estimate failed")
+    cropped_np_image = transform.warp(
+        np.array(example["image"]),
+        transf,
+        output_shape=(square_side + square_padding * 2, square_side + square_padding * 2)
+    )
+    cropped_image = Image.fromarray((cropped_np_image * 255).astype(np.uint8))
+    return {"cropped_image": cropped_image}
+
+dataset = datasets.load_dataset("shortery/dm-codes")
+dataset = dataset.map(crop_dm_code)
+```