text: from java import jclass
  import numpy as np
  import math
  import copy
  from scipy.ndimage.filters import gaussian_filter
  import matplotlib.pyplot as plt
  import matplotlib
  import cv2
  import sys
  import util
  import torch
  from torchvision import transforms
  import time
  
  
  def image(data):
      global img;
      img=data.reshape((640,480,3))
      img=img*255;
      # scale_search = [0.5, 1.0, 1.5, 2.0]
      scale_search = [0.5]
      boxsize = 368
      stride = 8
      padValue = 128
      multiplier = [x * boxsize / img.shape[0] for x in scale_search]
      for m in range(len(multiplier)):
          scale = multiplier[m]
          imageToTest = cv2.resize(img, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
          #print("imageToTest",imageToTest.shape)#(184,123,3)
          imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
          #print("imageToTest",imageToTest.shape)#(184,123,3)
          im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
          im = np.ascontiguousarray(im)
          #print("(im",im.shape)#(1,3,184,128)
          imputarry = im.flatten()
          #print("(source",ins.shape)
  
  
      return imputarry
  
  def openpose_imge(Mconv7_stage6_L1,Mconv7_stage6_L2):
  
      # img=np.transpose(img,(1,0,2))
      scale_search = [0.5]
      boxsize = 368
      stride = 8
      padValue = 128
      thre1 = 0.1
      thre2 = 0.05
      multiplier = [x * boxsize / img.shape[0] for x in scale_search]
      heatmap_avg = np.zeros((img.shape[0], img.shape[1], 19))
      paf_avg = np.zeros((img.shape[0], img.shape[1], 38))
      # Mconv7_stage6_L1=Mconv7_stage6_L1.reshape((1,38,23,16))
      # Mconv7_stage6_L2=Mconv7_stage6_L2.reshape((1,19,23,16))
      Mconv7_stage6_L1=Mconv7_stage6_L1.reshape((1,38,23,18))
      Mconv7_stage6_L2=Mconv7_stage6_L2.reshape((1,19,23,18))
      for m in range(len(multiplier)):
          scale = multiplier[m]
          imageToTest = cv2.resize(img, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
          #print("imageToTest",imageToTest.shape)#(184,123,3)
          imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
          #print("imageToTest",imageToTest.shape)#(184,123,3)
  
          heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0))  # output 1 is heatmaps
  
          heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
  
          heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
          heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_CUBIC)
  
          # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0))  # output 0 is PAFs
          paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0))  # output 0 is PAFs
          paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
          paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
          paf = cv2.resize(paf, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_CUBIC)
  
          heatmap_avg += heatmap_avg + heatmap / len(multiplier)
          #print("heatmap_avg",heatmap_avg.shape)#(500,333,19)
          paf_avg += + paf / len(multiplier)
          #print("paf_avg",paf_avg.shape)#(500,333,38)
  
      all_peaks = []
      peak_counter = 0
  
      for part in range(18):
          map_ori = heatmap_avg[:, :, part]
          one_heatmap = gaussian_filter(map_ori, sigma=3)
  
          map_left = np.zeros(one_heatmap.shape)
          map_left[1:, :] = one_heatmap[:-1, :]
          map_right = np.zeros(one_heatmap.shape)
          map_right[:-1, :] = one_heatmap[1:, :]
          map_up = np.zeros(one_heatmap.shape)
          map_up[:, 1:] = one_heatmap[:, :-1]
          map_down = np.zeros(one_heatmap.shape)
          map_down[:, :-1] = one_heatmap[:, 1:]
  
          peaks_binary = np.logical_and.reduce(
              (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > 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]
          peak_id = range(peak_counter, peak_counter + len(peaks))
          peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))]
  
          all_peaks.append(peaks_with_score_and_id)
          peak_counter += len(peaks)
  
      # 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]]
  
      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])
                      norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
                      np.seterr(divide='ignore',invalid='ignore')
                      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])
                      score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
                          0.5 * img.shape[0] / norm - 1, 0)
                      criterion1 = len(np.nonzero(score_midpts > 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([])
  
      # 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
                      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)
      canvas = copy.deepcopy(img)
      #andidate[:][0]=480-candidate[:][0]
      canvas = util.draw_bodypose(canvas, candidate, subset)
  
      # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
      # candidate: x, y, score, id
  
      # plt.imsave("/storage/emulated/0/Download/222.jpg", canvas[:, :, [2, 1, 0]])
      canvas=canvas/255
      out=canvas.flatten()
      # plt.imsave(path, canvas[:, :, [2, 1, 0]])
      # plt.imsave("/storage/emulated/0/Download/test.jpg", canvas[:, :, [2, 1, 0]])
  
      return out
  
  
      #return candidate, subset