--- dataset_info: features: - name: image dtype: image - name: conditioning_image dtype: image - name: text dtype: string splits: - name: train num_bytes: 111989279184.95 num_examples: 507050 download_size: 112032639870 dataset_size: 111989279184.95 --- # 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. ### 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)) ```