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---
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))
``` |