因为之前正好看了CMU在CVPR2017上的论文《Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields》，而且他们提供了训练好的模型。所以就直接用CMU训练的模型在AI challenge的数据集上做了测试。最后没有使用AI challenge训练集训练的模型在AI challenge上的得分是0.1667，可以看作是一个baseline。

以下是预处理的说明以及加入预处理程序的源代码。openpose的源代码使用#openpose ##openpose标注出来了，剩下的就是AI challenge的预处理程序。

在Google Cloud 上使用1片NVIDIA Tesla K80 跑完AI challenge的测试集大约需要24小时，4秒左右处理一副图。

AI challenge测试要求的关键点顺序是：1右肩，2右肘，3右腕，4左肩，5左肘，6左腕，7右髋，8右膝，9右踝，10左髋，11左膝，12左踝，13头顶，14脖子

openpose源码中subset输出的关键点顺序是：1鼻子，2脖子，3右肩，4右肘，5右腕，6左肩，7左肘，8左腕，9右髋，10右膝，11右踝，12左髋，13左膝，14左踝，15左眼，16右眼，17左耳，18右耳，19 pt19

函数 subset2AIsubset, all_peaks2all_peaks_1d, listMultiKeypoints 负责把openpose的关键点转换成AI challenge 的关键点。

当然还得按照官网上的要求输出特定格式的JSON文件,如下所示：

[

    {

        "image_id": "a0f6bdc065a602b7b84a67fb8d14ce403d902e0d",

        "keypoint_annotations": {

        "human1": [261, 294, 1, 281, 328, 1, 0, 0, 0, 213, 295, 1, 208, 346, 1, 192, 335, 1, 245, 375, 1, 255, 432, 1, 244, 494, 1, 221, 379, 1, 219, 442, 1, 226, 491, 1, 226, 256, 1, 231, 284, 1],

        "human2": [313, 301, 1, 305, 337, 1, 321, 345, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 313, 359, 1, 320, 409, 1, 311, 454, 1, 0, 0, 0, 330, 409, 1, 324, 446, 1, 337, 284, 1, 327, 302, 1],

        "human3": [373, 304, 1, 346, 286, 1, 332, 263, 1, 0, 0, 0, 0, 0, 0, 345, 313, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 363, 386, 1, 361, 424, 1, 361, 475, 1, 365, 273, 1, 369, 297, 1],

        ...

        }

    }

    ...

]
 #import numpy as np

 import json

 import os

 #openpose

 import keras

 from keras.models import Sequential

 from keras.models import Model

 from keras.layers import Input, Dense, Activation

 from keras.layers.convolutional import Conv2D

 from keras.layers.pooling import MaxPooling2D

 from keras.layers.normalization import BatchNormalization

 from keras.layers.merge import Concatenate

 from config_reader import config_reader

 import scipy

 import cv2

 import numpy as np

 np.seterr(divide='ignore', invalid='ignore')

 import util

 import math

 from numpy import ma

 from scipy.ndimage.filters import gaussian_filter

 ##openpose

 #openpose

 def relu(x):

     return Activation('relu')(x)

 def conv(x, nf, ks, name):

     x1 = Conv2D(nf, (ks, ks), padding='same', name=name)(x)

     return x1

 def pooling(x, ks, st, name):

     x = MaxPooling2D((ks, ks), strides=(st, st), name=name)(x)

     return x

 def vgg_block(x):

     # Block 1

     x = conv(x, 64, 3, "conv1_1")

     x = relu(x)

     x = conv(x, 64, 3, "conv1_2")

     x = relu(x)

     x = pooling(x, 2, 2, "pool1_1")

     # Block 2

     x = conv(x, 128, 3, "conv2_1")

     x = relu(x)

     x = conv(x, 128, 3, "conv2_2")

     x = relu(x)

     x = pooling(x, 2, 2, "pool2_1")

     # Block 3

     x = conv(x, 256, 3, "conv3_1")

     x = relu(x)

     x = conv(x, 256, 3, "conv3_2")

     x = relu(x)

     x = conv(x, 256, 3, "conv3_3")

     x = relu(x)

     x = conv(x, 256, 3, "conv3_4")

     x = relu(x)

     x = pooling(x, 2, 2, "pool3_1")

     # Block 4

     x = conv(x, 512, 3, "conv4_1")

     x = relu(x)

     x = conv(x, 512, 3, "conv4_2")

     x = relu(x)    

     # Additional non vgg layers

     x = conv(x, 256, 3, "conv4_3_CPM")

     x = relu(x)

     x = conv(x, 128, 3, "conv4_4_CPM")

     x = relu(x)

     return x

 def stage1_block(x, num_p, branch):

     # Block 1

     x = conv(x, 128, 3, "conv5_1_CPM_L%d" % branch)

     x = relu(x)

     x = conv(x, 128, 3, "conv5_2_CPM_L%d" % branch)

     x = relu(x)

     x = conv(x, 128, 3, "conv5_3_CPM_L%d" % branch)

     x = relu(x)

     x = conv(x, 512, 1, "conv5_4_CPM_L%d" % branch)

     x = relu(x)

     x = conv(x, num_p, 1, "conv5_5_CPM_L%d" % branch)

     return x

 def stageT_block(x, num_p, stage, branch):

     # Block 1

     x = conv(x, 128, 7, "Mconv1_stage%d_L%d" % (stage, branch))

     x = relu(x)

     x = conv(x, 128, 7, "Mconv2_stage%d_L%d" % (stage, branch))

     x = relu(x)

     x = conv(x, 128, 7, "Mconv3_stage%d_L%d" % (stage, branch))

     x = relu(x)

     x = conv(x, 128, 7, "Mconv4_stage%d_L%d" % (stage, branch))

     x = relu(x)

     x = conv(x, 128, 7, "Mconv5_stage%d_L%d" % (stage, branch))

     x = relu(x)

     x = conv(x, 128, 1, "Mconv6_stage%d_L%d" % (stage, branch))

     x = relu(x)

     x = conv(x, num_p, 1, "Mconv7_stage%d_L%d" % (stage, branch))

     return x

 ##openpose

 def subset2AIsubset(t, numPersons):

     AIsubset=[]

     for j in xrange(numPersons):

         tempsubset=[]

         for i in xrange(12):

             #

             #print(i+2)

             tempsubset.append(t[j][i+2])

         tempsubset.append(t[j][0])

         tempsubset.append(t[j][1])

         #print(AIsubset)

         AIsubset.append(tempsubset)

     return AIsubset

 def all_peaks2all_peaks_1d(all_peaks):

     all_peaks_1d=[]

     for item in all_peaks:

         for item1 in item:

             all_peaks_1d.append(item1)

     return all_peaks_1d

 def listMultiKeypoints(all_peaks_1d, numPersons):

     multi_keypoints=[]

     for i in xrange(numPersons):

         sp_keypoints=[]

         for j in xrange(14):

             if(AIsubset[i][j]== -1.):

                 sp_keypoints.append(0)

                 sp_keypoints.append(0)

                 sp_keypoints.append(0)

             else:

                 sp_keypoints.append(all_peaks_1d[int(AIsubset[i][j])][0])

                 sp_keypoints.append(all_peaks_1d[int(AIsubset[i][j])][1])

                 sp_keypoints.append(1)

         #print(sp_keypoints)

         multi_keypoints.append(sp_keypoints)

     return multi_keypoints

 def nPersons(t):

     return len(t)

 def listHuman(nPersons):

     list_human=[]

     for i in xrange(numPersons):

         list_human.append('human'+str(i+1))

     return list_human

 #openpose

 weights_path = "model/keras/model.h5"

 input_shape = (None,None,3)

 img_input = Input(shape=input_shape)

 stages = 6

 np_branch1 = 38

 np_branch2 = 19

 # VGG

 stage0_out = vgg_block(img_input)

 # stage 1

 stage1_branch1_out = stage1_block(stage0_out, np_branch1, 1)

 stage1_branch2_out = stage1_block(stage0_out, np_branch2, 2)

 x = Concatenate()([stage1_branch1_out, stage1_branch2_out, stage0_out])

 # stage t >= 2

 for sn in range(2, stages + 1):

     stageT_branch1_out = stageT_block(x, np_branch1, sn, 1)

     stageT_branch2_out = stageT_block(x, np_branch2, sn, 2)

     if (sn < stages):

         x = Concatenate()([stageT_branch1_out, stageT_branch2_out, stage0_out])

 model = Model(img_input, [stageT_branch1_out, stageT_branch2_out])

 model.load_weights(weights_path)

 ##openpose

 #openpose

 # find connection in the specified sequence, center 29 is in the position 15

 limbSeq = [[2,3], [2,6], [3,4], [4,5], [6,7], [7,8], [2,9], [9,10], \

            [10,11], [2,12], [12,13], [13,14], [2,1], [1,15], [15,17], \

            [1,16], [16,18], [3,17], [6,18]]

 # the middle joints heatmap correpondence

 mapIdx = [[31,32], [39,40], [33,34], [35,36], [41,42], [43,44], [19,20], [21,22], \

           [23,24], [25,26], [27,28], [29,30], [47,48], [49,50], [53,54], [51,52], \

           [55,56], [37,38], [45,46]]

 ##openpose

 path = "./test0"

 files = os.listdir(path)

 list_image_names=[]

 final_results=[]

 num_processed_images=0.

 total_images=30000.

 for file in files:

     num_processed_images+=1

     print('file:',file)

     print('number of image:',num_processed_images)

     print('%.2f%%'%(num_processed_images/total_images*100))

     list_image_names.append(str(file)[:-4])

     #openpose

     test_image = './test0/'+file

     #test_image = 'sample_images/000a902c8674739c97f188157c63d709b45b7595.jpg'

     oriImg = cv2.imread(test_image)

     param, model_params = config_reader()

     multiplier = [x * model_params['boxsize'] / oriImg.shape[0] for x in param['scale_search']]

     heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))

     paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))

     for m in range(len(multiplier)):

         scale = multiplier[m]

         imageToTest = cv2.resize(oriImg, (0,0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)

         imageToTest_padded, pad = util.padRightDownCorner(imageToTest, model_params['stride'], model_params['padValue'])        

         input_img = np.transpose(np.float32(imageToTest_padded[:,:,:,np.newaxis]), (3,0,1,2))/256 - 0.5; # required shape (1, width, height, channels)

         print("Input shape: " + str(input_img.shape))  

         output_blobs = model.predict(input_img)

         print("Output shape (heatmap): " + str(output_blobs[1].shape))

         # extract outputs, resize, and remove padding

         heatmap = np.squeeze(output_blobs[1]) # output 1 is heatmaps

         heatmap = cv2.resize(heatmap, (0,0), fx=model_params['stride'], fy=model_params['stride'], interpolation=cv2.INTER_CUBIC)

         heatmap = heatmap[:imageToTest_padded.shape[0]-pad[2], :imageToTest_padded.shape[1]-pad[3], :]

         heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)

         paf = np.squeeze(output_blobs[0]) # output 0 is PAFs

         paf = cv2.resize(paf, (0,0), fx=model_params['stride'], fy=model_params['stride'], interpolation=cv2.INTER_CUBIC)

         paf = paf[:imageToTest_padded.shape[0]-pad[2], :imageToTest_padded.shape[1]-pad[3], :]

         paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)

         heatmap_avg = heatmap_avg + heatmap / len(multiplier)

         paf_avg = paf_avg + paf / len(multiplier)

     ##openpose

     #openpose

     U = paf_avg[:,:,16] * -1

     V = paf_avg[:,:,17]

     X, Y = np.meshgrid(np.arange(U.shape[1]), np.arange(U.shape[0]))

     M = np.zeros(U.shape, dtype='bool')

     M[U**2 + V**2 < 0.5 * 0.5] = True

     U = ma.masked_array(U, mask=M)

     V = ma.masked_array(V, mask=M)

     all_peaks = []

     peak_counter = 0

     for part in range(19-1):

         map_ori = heatmap_avg[:,:,part]

         map = gaussian_filter(map_ori, sigma=3)

         map_left = np.zeros(map.shape)

         map_left[1:,:] = map[:-1,:]

         map_right = np.zeros(map.shape)

         map_right[:-1,:] = map[1:,:]

         map_up = np.zeros(map.shape)

         map_up[:,1:] = map[:,:-1]

         map_down = np.zeros(map.shape)

         map_down[:,:-1] = map[:,1:]

         peaks_binary = np.logical_and.reduce((map>=map_left, map>=map_right, map>=map_up, map>=map_down, map > param['thre1']))

         peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse

         peaks_with_score = [x + (map_ori[x[1],x[0]],) for x in peaks]

         id = range(peak_counter, peak_counter + len(peaks))

         peaks_with_score_and_id = [peaks_with_score[i] + (id[i],) for i in range(len(id))]

         all_peaks.append(peaks_with_score_and_id)

         peak_counter += len(peaks)

     ##openpose

     #openpose

     connection_all = []

     special_k = []

     mid_num = 10

     for k in range(len(mapIdx)):

         score_mid = paf_avg[:,:,[x-19 for x in mapIdx[k]]]

         candA = all_peaks[limbSeq[k][0]-1]

         candB = all_peaks[limbSeq[k][1]-1]

         nA = len(candA)

         nB = len(candB)

         indexA, indexB = limbSeq[k]

         if(nA != 0 and nB != 0):

             connection_candidate = []

             for i in range(nA):

                 for j in range(nB):

                     vec = np.subtract(candB[j][:2], candA[i][:2])

                     #

                     #print('vec0:',vec[0],'vec1:',vec[1])

                     # #

                     norm = math.sqrt(vec[0]*vec[0] + vec[1]*vec[1]+0.1)

                     vec = np.divide(vec, norm)

                     startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \

                                 np.linspace(candA[i][1], candB[j][1], num=mid_num)))

                     vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \

                                     for I in range(len(startend))])

                     vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \

                                     for I in range(len(startend))])

                     score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])

                     #

                     #print('norm',norm)

                     # #

                     score_with_dist_prior = sum(score_midpts)/len(score_midpts) + min(0.5*oriImg.shape[0]/norm-1, 0)

                     criterion1 = len(np.nonzero(score_midpts > param['thre2'])[0]) > 0.8 * len(score_midpts)

                     criterion2 = score_with_dist_prior > 0

                     if criterion1 and criterion2:

                         connection_candidate.append([i, j, score_with_dist_prior, score_with_dist_prior+candA[i][2]+candB[j][2]])

             connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)

             connection = np.zeros((0,5))

             for c in range(len(connection_candidate)):

                 i,j,s = connection_candidate[c][0:3]

                 if(i not in connection[:,3] and j not in connection[:,4]):

                     connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])

                     if(len(connection) >= min(nA, nB)):

                         break

             connection_all.append(connection)

         else:

             special_k.append(k)

             connection_all.append([])

     ##openpose

     #openpose

     # last number in each row is the total parts number of that person

     # the second last number in each row is the score of the overall configuration

     subset = -1 * np.ones((0, 20))

     candidate = np.array([item for sublist in all_peaks for item in sublist])

     for k in range(len(mapIdx)):

         if k not in special_k:

             partAs = connection_all[k][:,0]

             partBs = connection_all[k][:,1]

             indexA, indexB = np.array(limbSeq[k]) - 1

             for i in range(len(connection_all[k])): #= 1:size(temp,1)

                 found = 0

                 subset_idx = [-1, -1]

                 for j in range(len(subset)): #1:size(subset,1):

                     if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:

                         subset_idx[found] = j

                         found += 1

                 if found == 1:

                     j = subset_idx[0]

                     if(subset[j][indexB] != partBs[i]):

                         subset[j][indexB] = partBs[i]

                         subset[j][-1] += 1

                         subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]

                 elif found == 2: # if found 2 and disjoint, merge them

                     j1, j2 = subset_idx

                     print ("found = 2")

                     membership = ((subset[j1]>=0).astype(int) + (subset[j2]>=0).astype(int))[:-2]

                     if len(np.nonzero(membership == 2)[0]) == 0: #merge

                         subset[j1][:-2] += (subset[j2][:-2] + 1)

                         subset[j1][-2:] += subset[j2][-2:]

                         subset[j1][-2] += connection_all[k][i][2]

                         subset = np.delete(subset, j2, 0)

                     else: # as like found == 1

                         subset[j1][indexB] = partBs[i]

                         subset[j1][-1] += 1

                         subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]

                 # if find no partA in the subset, create a new subset

                 elif not found and k < 17:

                     row = -1 * np.ones(20)

                     row[indexA] = partAs[i]

                     row[indexB] = partBs[i]

                     row[-1] = 2

                     row[-2] = sum(candidate[connection_all[k][i,:2].astype(int), 2]) + connection_all[k][i][2]

                     subset = np.vstack([subset, row])

     # delete some rows of subset which has few parts occur

     deleteIdx = [];

     for i in range(len(subset)):

         if subset[i][-1] < 4 or subset[i][-2]/subset[i][-1] < 0.4:

             deleteIdx.append(i)

     subset = np.delete(subset, deleteIdx, axis=0)

     ##openpose

     numPersons= nPersons(subset)

     #print(subset2AIsubset(subset, numPersons))

     AIsubset = subset2AIsubset(subset,numPersons)

     #print(all_peaks[i][numPersons][3]==[int(AIsubset[0][0])])

     #all_peaks->all_peaks_1d

     all_peaks_1d=all_peaks2all_peaks_1d(all_peaks)

     #print('numPersons:',numPersons)

     #print('multi_keypoints:',listMultiKeypoints(all_peaks_1d, numPersons))

     keys=['image_id','keypoint_annotations']

     values=[]

     image_id=str(file)[:-4]

     keypoint_annotations = dict(zip(listHuman(numPersons), listMultiKeypoints(all_peaks_1d, numPersons)))

     values.append(image_id)

     values.append(keypoint_annotations)

     d = dict(zip(keys, values))

     final_results.append(d)

 print(final_results)

 with open('data.json', 'w') as f:

     json.dump(final_results, f)

 print(list_image_names)

[
{
"image_id": "a0f6bdc065a602b7b84a67fb8d14ce403d902e0d",
"keypoint_annotations": {
"human1": [261, 294, 1, 281, 328, 1, 0, 0, 0, 213, 295, 1, 208, 346, 1, 192, 335, 1, 245, 375, 1, 255, 432, 1, 244, 494, 1, 221, 379, 1, 219, 442, 1, 226, 491, 1, 226, 256, 1, 231, 284, 1],
"human2": [313, 301, 1, 305, 337, 1, 321, 345, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 313, 359, 1, 320, 409, 1, 311, 454, 1, 0, 0, 0, 330, 409, 1, 324, 446, 1, 337, 284, 1, 327, 302, 1],
"human3": [373, 304, 1, 346, 286, 1, 332, 263, 1, 0, 0, 0, 0, 0, 0, 345, 313, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 363, 386, 1, 361, 424, 1, 361, 475, 1, 365, 273, 1, 369, 297, 1],
...
}
}
...
]

openpose模型在AI challenge人体骨骼关键点检测的表现的更多相关文章

  1. dlib人脸关键点检测的模型分析与压缩

    本文系原创,转载请注明出处~ 小喵的博客:https://www.miaoerduo.com 博客原文(排版更精美):https://www.miaoerduo.com/c/dlib人脸关键点检测的模 ...

  2. 用华为HMS ML kit人体骨骼识别技术，Android快速实现人体姿势动作抓拍&nbsp;

      你有没有过这种体验,拍照时对着镜头,脑子一片空白.表情僵硬.手和脚无处安放,最后拍出来的照片很是奇怪.拍照软件中的固定姿势抓拍功能可以帮助你:选择一个你想要的姿势模板,当你摆出同款姿势时,软件会进 ...

  3. 『关键点检测』CPN：Cascaded Pyramid Network for Multi-Person Pose Estimation

    论文连接 网络简介 face++2017年coco keypoint benchmark 数据集冠军的文章,发表于CVPR201 1  提出了一种金字塔型的串接模型,即CPN(cascaded pyr ...

  4. 『计算机视觉』Mask-RCNN_从服装关键点检测看KeyPoints分支

    下图Github地址:Mask_RCNN       Mask_RCNN_KeyPoints『计算机视觉』Mask-RCNN_论文学习『计算机视觉』Mask-RCNN_项目文档翻译『计算机视觉』Mas ...

  5. 用keras实现人脸关键点检测(2)

    上一个代码只能实现小数据的读取与训练,在大数据训练的情况下.会造内存紧张,于是我根据keras的官方文档,对上一个代码进行了改进. 用keras实现人脸关键点检测 数据集:https://pan.ba ...

  6. keras实现简单CNN人脸关键点检测

    用keras实现人脸关键点检测 改良版:http://www.cnblogs.com/ansang/p/8583122.html 第一步:准备好需要的库 tensorflow  1.4.0 h5py ...

  7. OpenCV实战：人脸关键点检测（FaceMark）

    Summary:利用OpenCV中的LBF算法进行人脸关键点检测(Facial Landmark Detection) Author:    Amusi Date:       2018-03-20 ...

  8. PCL—低层次视觉—关键点检测（iss&Trajkovic）

    关键点检测往往需要和特征提取联合在一起,关键点检测的一个重要性质就是旋转不变性,也就是说,物体旋转后还能够检测出对应的关键点.不过说实话我觉的这个要求对机器人视觉来说是比较鸡肋的.因为机器人采集到的三 ...

  9. PCL—关键点检测（iss&Trajkovic）低层次点云处理

    博客转载自:http://www.cnblogs.com/ironstark/p/5069311.html 关键点检测往往需要和特征提取联合在一起,关键点检测的一个重要性质就是旋转不变性,也就是说,物 ...

随机推荐

  1. wmic命令

    WMIC扩展WMI(Windows Management Instrumentation,Windows管理工具) ,提供了从命令行接口和批命令脚本执行系统管理的支持. 一.如何使用帮助文档: 1.w ...

  2. Quartz.NET实现作业调度

    一.Quartz.NET介绍 Quartz.NET是一个强大.开源.轻量的作业调度框架,是 OpenSymphony 的 Quartz API 的.NET移植,用C#改写,可用于winform和asp ...

  3. Spring Boot Starter 介绍

    http://www.baeldung.com/spring-boot-starters 作者:baeldung 译者:http://oopsguy.com 1.概述 依赖管理是任何复杂项目的关键部分 ...

  4. java web Servlet学习笔记-2 请求重定向和请求转发的区别

    请求转发与请求重定向的区别 请求重定向和转发 1.请求重定向:浏览器的行为(通过响应对象HttpServletResponse来执行) 特点:可以重新定向访问其他Web应用下的资源 浏览器发出了2次请 ...

  5. WPF---Effect效果

    在 WPF 中,可以使用 BitmapEffect 对象为每一个 Visual 对象生成各种各样的效果,一个 Visual 对象可以设置一种或多种 BitmapEffect 效果,WPF 内置了几种效 ...

  6. DevOps之网络

    唠叨话 关于德语噢屁事的知识点,仅提供专业性的精华汇总,具体知识点细节,参考教程网址,如需帮助,请留言. <网络(Network)> 关于网络的网络架构和网络模型:知识与技能的层次(知道. ...

  7. win10 uwp 获得元素绝对坐标

    有时候需要获得一个元素,相对窗口的坐标,在修改他的位置可以使用. 那么 UWP 如何获得元素坐标? 我提供了一个方法,可以获得元素的坐标. 首先需要获得元素,如果没有获得元素,那么如何得到他的坐标? ...

  8. win10 uwp 异步进度条

    本文主要讲我设计的几个进度条,还有如何使用异步控制进度条,如何使用动画做进度. 进度条可以参见:http://edi.wang/post/2016/2/25/windows-10-uwp-modal- ...

  9. hibernate5使用注解遇到的问题

    问题描述 出现MappingException:Unknown entity,看到这个我以为在cfg配置文件中没有配置,实际上我是配置了的,那么问题出在那里呢,既然找不到实体,那么会不会是注解类出现了 ...

  10. http服务详解（2）——httpd2.2的配置文件常见设置

    摘要:一个服务的配置文件非常重要,弄懂配置文件是熟练掌握服务的必要前提. 一.httpd-2.2常见文件介绍 (1)配置文件: 主配置文件尽量别改,改自己的子配置文件 /etc/httpd/conf/ ...