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

article 2025/2/25 14:33:20

FreiHAND （handposeX-json 格式）数据集-release

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)

FreiHAND 数据集官方项目地址：https://github.com/lmb-freiburg/freihand

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

---------------

一、handposeX json 格式示例

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

],

······

]

}

]

}

---------------

二、脚本运行

cd script/FreiHAND

python read_handposex_json.py

read_handposex_json.py具体实现如下，简洁明了：

#-*-coding:utf-8-*-
# date:2024-02-23
# 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
'''
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 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_)
if __name__ == '__main__':
    path_root = "datasets/"
    path_s_image = "{}/image/".format(path_root)
    path_s_label = "{}/label/".format(path_root)

    triangles_index = np.load("../../config/triangles_index.npy").reshape(-1,3)

    out_cnt = 0
    hand_cnt = 0
    for f_ in os.listdir(path_s_image):
        path_img = path_s_image + f_
        path_json = path_s_label + f_.replace(".jpg",".json")

        with open(path_json, 'r', encoding='utf-8') as file:
            data_json = json.load(file)
        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_)
        out_cnt += 1
        print("--------->>>imgs_num : [{}] , hands_num: [{}]".format(out_cnt,hand_cnt))
        key_id = cv2.waitKey(0)
        cv2.imwrite("f1.jpg",stk_)
        if key_id == 27:
            break