text: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
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  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  # Copyright 2021 Huawei Technologies Co., Ltd
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Functions to visualize human poses"""
  from npu_bridge.npu_init import *
  
  import matplotlib.pyplot as plt
  import data_utils
  import numpy as np
  import h5py
  import os
  from mpl_toolkits.mplot3d import Axes3D
  
  def show3Dpose(channels, ax, lcolor="#3498db", rcolor="#e74c3c", add_labels=False): # blue, orange
    """
    Visualize the ground truth 3d skeleton
  
    Args
      channels: 96x1 vector. The pose to plot.
      ax: matplotlib 3d axis to draw on
      lcolor: color for left part of the body
      rcolor: color for right part of the body
      add_labels: whether to add coordinate labels
    Returns
      Nothing. Draws on ax.
    """
  
    assert channels.size == len(data_utils.H36M_NAMES)*3, "channels should have 96 entries, it has %d instead" % channels.size
    vals = np.reshape( channels, (len(data_utils.H36M_NAMES), -1) )
  
    I   = np.array([1,2,3,1,7,8,1, 13,14,15,14,18,19,14,26,27])-1 # start points
    J   = np.array([2,3,4,7,8,9,13,14,15,16,18,19,20,26,27,28])-1 # end points
    LR  = np.array([1,1,1,0,0,0,0, 0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)
  
    # Make connection matrix
    for i in np.arange( len(I) ):
      x, y, z = [np.array( [vals[I[i], j], vals[J[i], j]] ) for j in range(3)]
      # ax.plot(x, y, z, lw=3, marker = 'o', markersize = 5, c=lcolor if LR[i] else rcolor, markeredgecolor = lcolor)
      ax.plot(x, y, z, lw=2, c=lcolor if LR[i] else rcolor)
  
    RADIUS = 750 # space around the subject
    xroot, yroot, zroot = vals[0,0], vals[0,1], vals[0,2]
    ax.set_xlim3d([-RADIUS+xroot, RADIUS+xroot])
    ax.set_zlim3d([-RADIUS+zroot, RADIUS+zroot])
    ax.set_ylim3d([-RADIUS+yroot, RADIUS+yroot])
  
    # ax.set_xlim3d([np.min(vals[:, 0]), np.max(vals[:, 0])])
    # ax.set_zlim3d([np.min(vals[:, 2]), np.max(vals[:, 2])])
    # ax.set_ylim3d([np.min(vals[:, 1]), np.max(vals[:, 1])])
  
    if add_labels:
      ax.set_xlabel("x")
      ax.set_ylabel("y")
      ax.set_zlabel("z")
  
    # Get rid of the ticks and tick labels
  
    ax.set_xticks([])
    ax.set_yticks([])
    ax.set_zticks([])
  
    ax.get_xaxis().set_ticklabels([])
    ax.get_yaxis().set_ticklabels([])
    ax.set_zticklabels([])
    ax.set_aspect('equal')
  
    # Get rid of the panes (actually, make them white)
    white = (1.0, 1.0, 1.0, 0.0)
    ax.w_xaxis.set_pane_color(white)
    # ax.w_zaxis.set_pane_color(white)
    ax.w_yaxis.set_pane_color(white)
    # # Keep z pane
    #
    # # Get rid of the lines in 3d
    ax.w_xaxis.line.set_color(white)
    ax.w_yaxis.line.set_color(white)
    ax.w_zaxis.line.set_color(white)
  
  
    ax.view_init(azim=129, elev=10)
  
  
  
  def show2Dpose(channels, ax, lcolor="#3498db", rcolor="#e74c3c", add_labels=False):
    """
    Visualize a 2d skeleton with 32 joints
  
    Args
      channels: 64x1 vector. The pose to plot.
      ax: matplotlib axis to draw on
      lcolor: color for left part of the body
      rcolor: color for right part of the body
      add_labels: whether to add coordinate labels
    Returns
      Nothing. Draws on ax.
    """
  
    assert channels.size == len(data_utils.H36M_NAMES)*2, "channels should have 64 entries, it has %d instead" % channels.size
    vals = np.reshape( channels, (len(data_utils.H36M_NAMES), -1) )
  
    I  = np.array([1,2,3,1,7,8,1, 13,14,14,18,19,14,26,27])-1 # start points
    J  = np.array([2,3,4,7,8,9,13,14,16,18,19,20,26,27,28])-1 # end points
    LR = np.array([1,1,1,0,0,0,0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)
  
    # Make connection matrix
    for i in np.arange( len(I) ):
      x, y = [np.array( [vals[I[i], j], vals[J[i], j]] ) for j in range(2)]
      ax.plot(x, y, lw=2, c=lcolor if LR[i] else rcolor)
  
    # Get rid of the ticks
    ax.set_xticks([])
    ax.set_yticks([])
  
    # Get rid of tick labels
    ax.get_xaxis().set_ticklabels([])
    ax.get_yaxis().set_ticklabels([])
  
    RADIUS = 300 # space around the subject
    xroot, yroot = vals[0,0], vals[0,1]
    ax.set_xlim([-RADIUS+xroot, RADIUS+xroot])
    ax.set_ylim([-RADIUS+yroot, RADIUS+yroot])
    if add_labels:
      ax.set_xlabel("x")
      ax.set_ylabel("z")
  
    ax.set_aspect('equal')
  
  
  
  
  def show2Dpose_mdm(channels, ax, lcolor="#3498db", rcolor="#e74c3c", add_labels=False):
    """
    Visualize 2d reprojections of all 3d pose hypotheses in one fig in order to show the similarity between them
  
    Args
      channels: 64 * 5, 2d reprojections of all 3d pose hypotheses
      ax: matplotlib axis to draw on
      lcolor: color for left part of the body
      rcolor: color for right part of the body. Note that we do not really use lcolor and rcolor in this function. 
  	        In stead, we define a color for each hypotheses to show the overlap between them.
      add_labels: whether to add coordinate labels
    Returns
      Nothing. Draws on ax.
    """
  
  
  
  
    I  = np.array([1,2,3,1,7,8,1, 13,14,14,18,19,14,26,27])-1 # start points
    J  = np.array([2,3,4,7,8,9,13,14,16,18,19,20,26,27,28])-1 # end points
    LR = np.array([1,1,1,0,0,0,0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)
    colors = ['#FF8000', '#4169E1', '#308014', '#000080', '#FF83FA'] # color used for 2d reprejection from each 3d pose hypotheses 
    for m in range(channels.shape[-1]):
      vals = np.reshape(channels[:,m], [len(data_utils.H36M_NAMES), -1])
      for i in np.arange( len(I) ):
        x, y = [np.array( [vals[I[i], j], vals[J[i], j]] ) for j in range(2)]
        ax.plot(x, y, lw=2, c=colors[m])
  
    # Get rid of the ticks
    ax.set_xticks([])
    ax.set_yticks([])
  
    # Get rid of tick labels
    ax.get_xaxis().set_ticklabels([])
    ax.get_yaxis().set_ticklabels([])
  
    RADIUS = 300 # space around the subject
    xroot, yroot = vals[0,0], vals[0,1]
    ax.set_xlim([-RADIUS+xroot, RADIUS+xroot])
    ax.set_ylim([-RADIUS+yroot, RADIUS+yroot])
    if add_labels:
      ax.set_xlabel("x")
      ax.set_ylabel("z")
  
    ax.set_aspect('equal')
  
  