Update README.md

README.md

@@ -362,6 +362,57 @@ dataset_info:
   download_size: 62877590
   dataset_size: 64950879.933217384
 ---
-# Dataset Card for "quickdraw-bins-1pct-sample"
+# Quick!Draw! 1pct Sample (per-row bin format)
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+This is a sample 1-percent of the entire 50M-row [QuickDraw! dataset](https://github.com/googlecreativelab/quickdraw-dataset).  The row for each drawing contains a byte-encoded packed representation of the drawing and data, which you can unpack using the following snippet:
+
+```
+def unpack_drawing(file_handle):
+    key_id, = unpack('Q', file_handle.read(8))
+    country_code, = unpack('2s', file_handle.read(2))
+    recognized, = unpack('b', file_handle.read(1))
+    timestamp, = unpack('I', file_handle.read(4))
+    n_strokes, = unpack('H', file_handle.read(2))
+    image = []
+    n_bytes = 17
+    for i in range(n_strokes):
+        n_points, = unpack('H', file_handle.read(2))
+        fmt = str(n_points) + 'B'
+        x = unpack(fmt, file_handle.read(n_points))
+        y = unpack(fmt, file_handle.read(n_points))
+        image.append((x, y))
+        n_bytes += 2 + 2*n_points
+    result = {
+        'key_id': key_id,
+        'country_code': country_code,
+        'recognized': recognized,
+        'timestamp': timestamp,
+        'image': image,
+    }
+    return result
+```
+
+The `image` in the above is still in line vector format.  To convert render this to a raster image (I recommend you do this on-the-fly in a pre-processor):
+
+```
+# packed bin -> RGB PIL
+def binToPIL(packed_drawing):
+    padding = 8
+    radius = 7
+    scale = (224.0-(2*padding)) / 256
+    
+    unpacked = unpack_drawing(io.BytesIO(packed_drawing))
+    unpacked_image = unpacked['image']
+    image = np.full((224,224), 255, np.uint8)
+    for stroke in unpacked['image']:
+        prevX = round(stroke[0][0]*scale)
+        prevY = round(stroke[1][0]*scale)
+        for i in range(1, len(stroke[0])):
+            x = round(stroke[0][i]*scale)
+            y = round(stroke[1][i]*scale)
+            cv2.line(image, (padding+prevX, padding+prevY), (padding+x, padding+y), 0, radius, -1)
+            prevX = x
+            prevY = y
+    pilImage = Image.fromarray(image).convert("RGB")     
+    return pilImage
+```