Update README.md

README.md

@@ -17,4 +17,60 @@ dataset_info:
 # Dataset Card for "hagrid-mediapipe-hands"
 
 This dataset is designed to train a ControlNet with human hands. It includes hand landmarks detected by MediaPipe(for more information refer to: https://developers.google.com/mediapipe/solutions/vision/hand_landmarker).
-The source image data is from [HaGRID dataset](https://github.com/hukenovs/hagrid) and we use a modified version from Kaggle(https://www.kaggle.com/datasets/innominate817/hagrid-classification-512p) to build this dataset. There are 507050 data samples in total and the image resolution is 512x512.
+The source image data is from [HaGRID dataset](https://github.com/hukenovs/hagrid) and we use a modified version from Kaggle(https://www.kaggle.com/datasets/innominate817/hagrid-classification-512p) to build this dataset. There are 507050 data samples in total and the image resolution is 512x512.
+
+### Generate Mediapipe annotation
+We use the script below to generate hand landmarks and you should download `hand_landmarker.task` file first. For more information please refer to [this](https://developers.google.com/mediapipe/solutions/vision/hand_landmarker).
+```
+import mediapipe as mp
+from mediapipe import solutions
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.tasks import python
+from mediapipe.tasks.python import vision
+from PIL import Image
+import cv2
+import numpy as np
+
+def draw_landmarks_on_image(rgb_image, detection_result):
+  hand_landmarks_list = detection_result.hand_landmarks
+  handedness_list = detection_result.handedness
+  annotated_image = np.zeros_like(rgb_image)
+
+  # Loop through the detected hands to visualize.
+  for idx in range(len(hand_landmarks_list)):
+    hand_landmarks = hand_landmarks_list[idx]
+    handedness = handedness_list[idx]
+
+    # Draw the hand landmarks.
+    hand_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
+    hand_landmarks_proto.landmark.extend([
+      landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in hand_landmarks
+    ])
+    solutions.drawing_utils.draw_landmarks(
+      annotated_image,
+      hand_landmarks_proto,
+      solutions.hands.HAND_CONNECTIONS,
+      solutions.drawing_styles.get_default_hand_landmarks_style(),
+      solutions.drawing_styles.get_default_hand_connections_style())
+
+  return annotated_image
+
+# Create an HandLandmarker object.
+base_options = python.BaseOptions(model_asset_path='hand_landmarker.task')
+options = vision.HandLandmarkerOptions(base_options=base_options,
+                                       num_hands=2)
+detector = vision.HandLandmarker.create_from_options(options)
+
+# Load the input image.
+image = np.asarray(Image.open("./test.png"))
+image = mp.Image(
+    image_format=mp.ImageFormat.SRGB, data=image
+)
+
+# Detect hand landmarks from the input image.
+detection_result = detector.detect(image)
+
+# Process the classification result and save it.
+annotated_image = draw_landmarks_on_image(image.numpy_view(), detection_result)
+cv2.imwrite("ann.png", cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR))
+```