InterHand26M（handposeX-json格式）数据集-release＞＞DataBall

DataBall 助力快速掌握数据集的信息和使用方式，会员享有 百种数据集，持续增加中。 

需要更多数据资源和技术解决方案，知识星球： “DataBall - X 数据球(free)”

贵在坚持！

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注意：

1)为了方便使用，按照 handposeX json 自定义格式存储

2)使用常见依赖库进行调用,降低数据集使用难度。

3)部分数据集获取请加入：DataBall-X数据球(free)

4)完整数据集获取请加入：DataBall-X数据球(vip)

InterHand26M 数据集官方项目地址：InterHand2.6M dataset | InterHand2.6M

handposeX-json 格式 项目地址：  github /XIAN-HHappy/handpose_x_plus

1、 handposeX json 格式

cx,cy,fx,fy为相机内参。

label:左右手标签

joint3d:三维手部21关键点

vertex3d:三维手mesh网格点

{ "author": "XIAN", "img_name:": "", "cx": 112.0, "cy": 112.0, "fx": 388.9018310596544, "fy": 388.71231836584275, "hands": [ { "label": "right", "joint3d": [ [ 29.402047395706177, -27.920207008719444, 587.0807766914368 ], ······ ], "vertex3d": [ [ 10.056010007858276, 29.915300235152245, -626.9440693855286 ], ······ ] } ] }

2、脚本运行

# 下载 handposeX json 格式数据集放在根目录 cd script/InterHand26M python read_InterHand26M.py

read_InterHand26M.py 具体实现如下：

#-*-coding:utf-8-*- # date:2025-03-02 # Author: XIAN # function: handposeX json 格式读取数据标签 import sys sys.path.append("./") import os import cv2 import json import numpy as np import random from pathlib import Path import re ''' function: 绘制二维关键点连线 ''' def draw_joints(img_,hand_,x,y): thick = 2 colors = [(0,215,255),(255,115,55),(5,255,55),(25,15,255),(225,15,55)] # cv2.line(img_, (int(hand_['0']['x']+x), int(hand_['0']['y']+y)),(int(hand_['1']['x']+x), int(hand_['1']['y']+y)), colors[0], thick) cv2.line(img_, (int(hand_['1']['x']+x), int(hand_['1']['y']+y)),(int(hand_['2']['x']+x), int(hand_['2']['y']+y)), colors[0], thick) cv2.line(img_, (int(hand_['2']['x']+x), int(hand_['2']['y']+y)),(int(hand_['3']['x']+x), int(hand_['3']['y']+y)), colors[0], thick) cv2.line(img_, (int(hand_['3']['x']+x), int(hand_['3']['y']+y)),(int(hand_['4']['x']+x), int(hand_['4']['y']+y)), colors[0], thick) cv2.line(img_, (int(hand_['0']['x']+x), int(hand_['0']['y']+y)),(int(hand_['5']['x']+x), int(hand_['5']['y']+y)), colors[1], thick) cv2.line(img_, (int(hand_['5']['x']+x), int(hand_['5']['y']+y)),(int(hand_['6']['x']+x), int(hand_['6']['y']+y)), colors[1], thick) cv2.line(img_, (int(hand_['6']['x']+x), int(hand_['6']['y']+y)),(int(hand_['7']['x']+x), int(hand_['7']['y']+y)), colors[1], thick) cv2.line(img_, (int(hand_['7']['x']+x), int(hand_['7']['y']+y)),(int(hand_['8']['x']+x), int(hand_['8']['y']+y)), colors[1], thick) cv2.line(img_, (int(hand_['0']['x']+x), int(hand_['0']['y']+y)),(int(hand_['9']['x']+x), int(hand_['9']['y']+y)), colors[2], thick) cv2.line(img_, (int(hand_['9']['x']+x), int(hand_['9']['y']+y)),(int(hand_['10']['x']+x), int(hand_['10']['y']+y)), colors[2], thick) cv2.line(img_, (int(hand_['10']['x']+x), int(hand_['10']['y']+y)),(int(hand_['11']['x']+x), int(hand_['11']['y']+y)), colors[2], thick) cv2.line(img_, (int(hand_['11']['x']+x), int(hand_['11']['y']+y)),(int(hand_['12']['x']+x), int(hand_['12']['y']+y)), colors[2], thick) cv2.line(img_, (int(hand_['0']['x']+x), int(hand_['0']['y']+y)),(int(hand_['13']['x']+x), int(hand_['13']['y']+y)), colors[3], thick) cv2.line(img_, (int(hand_['13']['x']+x), int(hand_['13']['y']+y)),(int(hand_['14']['x']+x), int(hand_['14']['y']+y)), colors[3], thick) cv2.line(img_, (int(hand_['14']['x']+x), int(hand_['14']['y']+y)),(int(hand_['15']['x']+x), int(hand_['15']['y']+y)), colors[3], thick) cv2.line(img_, (int(hand_['15']['x']+x), int(hand_['15']['y']+y)),(int(hand_['16']['x']+x), int(hand_['16']['y']+y)), colors[3], thick) cv2.line(img_, (int(hand_['0']['x']+x), int(hand_['0']['y']+y)),(int(hand_['17']['x']+x), int(hand_['17']['y']+y)), colors[4], thick) cv2.line(img_, (int(hand_['17']['x']+x), int(hand_['17']['y']+y)),(int(hand_['18']['x']+x), int(hand_['18']['y']+y)), colors[4], thick) cv2.line(img_, (int(hand_['18']['x']+x), int(hand_['18']['y']+y)),(int(hand_['19']['x']+x), int(hand_['19']['y']+y)), colors[4], thick) cv2.line(img_, (int(hand_['19']['x']+x), int(hand_['19']['y']+y)),(int(hand_['20']['x']+x), int(hand_['20']['y']+y)), colors[4], thick) ''' function: 21个三维关键点转为二维点，并进行绘制 ''' def Draw_KeyPoints3D(img,Joints_,fx,fy,cx,cy): #----------------------------------- 计算 3D到 2D相机的投影 X = Joints_[:,0] Y = Joints_[:,1] Z = Joints_[:,2] x_p = X / Z y_p = Y / Z #三维点转为二维点 x_2d = fx* x_p + cx y_2d = fy* y_p + cy pts2d_list = {} pts2d_ss = [] for ii in range(x_2d.shape[0]): x_,y_ = x_2d[ii],y_2d[ii] pts2d_list[str(ii)]={"x":x_,"y":y_} cv2.circle(img, (int(x_),int(y_)), 4, (25,155,255), -1) cv2.circle(img, (int(x_),int(y_)), 2, (255,0,55), -1) pts2d_ss.append((x_,y_)) draw_joints(img,pts2d_list,0,0) pts2d_ss = np.array(pts2d_ss) return pts2d_ss def letterbox(img, height=416, augment=False, color=(266,255,255)): # Resize a rectangular image to a padded square shape = img.shape[:2] # shape = [height, width] ratio = float(height) / max(shape) # ratio = old / new new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) dw = (height - new_shape[0]) / 2 # width padding dh = (height - new_shape[1]) / 2 # height padding top, bottom = round(dh - 0.1), round(dh + 0.1) left, right = round(dw - 0.1), round(dw + 0.1) # resize img if augment: interpolation = np.random.choice([None, cv2.INTER_NEAREST, cv2.INTER_LINEAR, None, cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_AREA, cv2.INTER_CUBIC, cv2.INTER_LANCZOS4]) if interpolation is None: img = cv2.resize(img, new_shape) else: img = cv2.resize(img, new_shape, interpolation=interpolation) else: img = cv2.resize(img, new_shape, interpolation=cv2.INTER_NEAREST) # print("resize time:",time.time()-s1) img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # padded square return img def Draw_Vertex_KeyPoints(img,img_mesh,Vertex,fx_d,fy_d,cx_d,cy_d,triangles_index,RGB_ = (245, 125, 35)): #----------------------------------- 计算 Mano 到 相机的投影 Xdc = -Vertex[:,0].reshape(-1) Ydc = -Vertex[:,1].reshape(-1) Zdc = -Vertex[:,2].reshape(-1) x_mano_p = Xdc / Zdc y_mano_p = Ydc / Zdc #点云转为二维图 x_mano = fx_d* x_mano_p + cx_d y_mano = fy_d* y_mano_p + cy_d manopts_list = [] mesh_list = [] color_rr = (random.randint(100,230),random.randint(120,250),random.randint(100,240)) for ii in range(triangles_index.shape[0]): a,b,c = triangles_index[ii] x1_,y1_ = x_mano[a].astype(np.int32),y_mano[a].astype(np.int32) x2_,y2_ = x_mano[b].astype(np.int32),y_mano[b].astype(np.int32) x3_,y3_ = x_mano[c].astype(np.int32),y_mano[c].astype(np.int32) area_ = np.array([[int(x1_), int(y1_)], [int(x2_), int(y2_)], [int(x3_),int(y3_)]]) color_ = (255, 0, 0) # cv2.fillPoly(mask_v, [area_], (255)) cv2.fillPoly(img_mesh, [area_], color_rr) mesh_list.append(area_) cv2.fillPoly(img, mesh_list, RGB_) def find_jpg_files(root_folder): root_path = Path(root_folder) jpg_files = list(root_path.rglob('*.jpg')) # rglob递归查找 return jpg_files if __name__ == '__main__': root_folder = '../../InterHand2.6M_5fps_batch1-20250302-min/' jpg_files = find_jpg_files(root_folder) path_data = [] for path in jpg_files: path_str = str(path) # print(path) # print(type(path)) path_img = path_str.replace("\\","/") path_json = re.sub(r"(cam\d+)", r"\1_label", path_img) path_json = path_json.replace(".jpg",".json") if not os.access(path_json,os.F_OK): continue # print(path_img) # print(path_label) path_data.append((path_img,path_json)) triangles_index = np.load("../../config/triangles_index.npy").reshape(-1,3) out_cnt = 0 hand_cnt = 0 for (path_img,path_json) in path_data: if not os.access(path_json,os.F_OK): continue try: with open(path_json, 'r', encoding='utf-8') as file: data_json = json.load(file) except: print("error ") os.remove(path_img) os.remove(path_json) continue hands_json = data_json["hands"] cx,cy,fx,fy = data_json["cx"],data_json["cy"],data_json["fx"],data_json["fy"] img_ = cv2.imread(path_img) img_joint = img_.copy() img_mesh = img_.copy() img_mask = np.zeros(img_.shape).astype(np.uint8) img_mask[:,:,:]=255 for msg_ in hands_json: RGB_ = (245, 55, 133) if msg_["label"] == "left": RGB_ = (25, 255, 133) Joints3D = np.array(msg_["joint3d"]) Vertex3D = np.array(msg_["vertex3d"]) pts2d_ss = Draw_KeyPoints3D(img_joint,Joints3D,fx,fy,cx,cy) Draw_Vertex_KeyPoints(img_mesh,img_mask,Vertex3D,fx,fy,cx,cy,triangles_index,RGB_ = RGB_) hand_cnt += 1 stk_1 = np.hstack((img_,img_joint)) stk_2 = np.hstack((img_mesh,img_mask)) stk_ = np.vstack((stk_1,stk_2)) cv2.namedWindow("img_stk",0) cv2.imshow("img_stk",stk_) # cv2.imwrite("example.jpg",stk_) out_cnt += 1 print("---->>> InterHand26M [{}] , hand_num: [{}]".format(out_cnt,hand_cnt)) key_id = cv2.waitKey(1) if key_id == 27: break

 

助力快速掌握数据集的信息和使用方式。

数据可以如此美好！