text: 
  
  import numpy as np
  import matplotlib.pyplot as plt
  import cv2
  import math
  
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import confusion_matrix
  from sklearn.utils.multiclass import unique_labels
  
  
  def plot_confusion_matrix(y_true, y_pred, classes,
                            normalize=False,
                            title=None,
                            cmap=plt.cm.Blues,
                            size=None):
      """ (Copied from sklearn website)
      This function prints and plots the confusion matrix.
      Normalization can be applied by setting `normalize=True`.
      """
      if not title:
          if normalize:
              title = 'Normalized confusion matrix'
          else:
              title = 'Confusion matrix, without normalization'
  
      # Compute confusion matrix
      cm = confusion_matrix(y_true, y_pred)
      # Only use the labels that appear in the data
      classes = classes[unique_labels(y_true, y_pred)]
      if normalize:
          cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
          print("Display normalized confusion matrix ...")
      else:
          print('Display confusion matrix without normalization ...')
  
      # print(cm)
  
      fig, ax = plt.subplots()
      if size is None:
          size = (12, 8)
      fig.set_size_inches(size[0], size[1])
  
      im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
      ax.figure.colorbar(im, ax=ax)
      # We want to show all ticks...
      ax.set(xticks=np.arange(cm.shape[1]),
             yticks=np.arange(cm.shape[0]),
             # ... and label them with the respective list entries
             xticklabels=classes, yticklabels=classes,
             title=title,
             ylabel='True label',
             xlabel='Predicted label')
      ax.set_ylim([-0.5, len(classes)-0.5])
  
      # Rotate the tick labels and set their alignment.
      plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
               rotation_mode="anchor")
  
      # Loop over data dimensions and create text annotations.
      fmt = '.2f' if normalize else 'd'
      thresh = cm.max() / 2.
      for i in range(cm.shape[0]):
          for j in range(cm.shape[1]):
              ax.text(j, i, format(cm[i, j], fmt),
                      ha="center", va="center",
                      color="white" if cm[i, j] > thresh else "black")
      fig.tight_layout()
      return ax, cm
  
  
  # Drawings ==============================================================
  
  def draw_action_result(img_display, id, skeleton, str_action_label):
      font = cv2.FONT_HERSHEY_SIMPLEX
  
      minx = 999
      miny = 999
      maxx = -999
      maxy = -999
      i = 0
      NaN = 0
  
      while i < len(skeleton):
          if not(skeleton[i] == NaN or skeleton[i+1] == NaN):
              minx = min(minx, skeleton[i])
              maxx = max(maxx, skeleton[i])
              miny = min(miny, skeleton[i+1])
              maxy = max(maxy, skeleton[i+1])
          i += 2
  
      minx = int(minx * img_display.shape[1])
      miny = int(miny * img_display.shape[0])
      maxx = int(maxx * img_display.shape[1])
      maxy = int(maxy * img_display.shape[0])
  
      # Draw bounding box
      # drawBoxToImage(img_display, [minx, miny], [maxx, maxy])
      img_display = cv2.rectangle(
          img_display, (minx, miny), (maxx, maxy), (0, 255, 0), 4)
  
      # Draw text at left corner
      box_scale = max(
          0.5, min(2.0, (1.0*(maxx - minx)/img_display.shape[1] / (0.3))**(0.5)))
      fontsize = 1.4 * box_scale
      linewidth = int(math.ceil(3 * box_scale))
  
      TEST_COL = int(minx + 5 * box_scale)
      TEST_ROW = int(miny - 10 * box_scale)
      # TEST_ROW = int( miny)
      # TEST_ROW = int( skeleton[3] * img_display.shape[0])
  
      img_display = cv2.putText(
          img_display, "P"+str(id % 10)+": "+str_action_label, (TEST_COL, TEST_ROW), font, fontsize, (0, 0, 255), linewidth, cv2.LINE_AA)
  
  
  def add_white_region_to_left_of_image(img_disp):
      r, c, d = img_disp.shape
      blank = 255 + np.zeros((r, int(c/4), d), np.uint8)
      img_disp = np.hstack((blank, img_disp))
      return img_disp
  