text: # coding: utf-8
  # copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
  #
  # 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.
  
  from __future__ import division
  
  import os
  import cv2
  import numpy as np
  from PIL import Image, ImageDraw
  import math
  
  
  def visualize_box_mask(im, results, labels, threshold=0.5):
      """
      Args:
          im (str/np.ndarray): path of image/np.ndarray read by cv2
          results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
                          matix element:[class, score, x_min, y_min, x_max, y_max]
                          MaskRCNN's results include 'masks': np.ndarray:
                          shape:[N, im_h, im_w]
          labels (list): labels:['class1', ..., 'classn']
          threshold (float): Threshold of score.
      Returns:
          im (PIL.Image.Image): visualized image
      """
      if isinstance(im, str):
          im = Image.open(im).convert('RGB')
      else:
          im = Image.fromarray(im)
      if 'masks' in results and 'boxes' in results and len(results['boxes']) > 0:
          im = draw_mask(
              im, results['boxes'], results['masks'], labels, threshold=threshold)
      if 'boxes' in results and len(results['boxes']) > 0:
          im = draw_box(im, results['boxes'], labels, threshold=threshold)
      if 'segm' in results:
          im = draw_segm(
              im,
              results['segm'],
              results['label'],
              results['score'],
              labels,
              threshold=threshold)
      return im
  
  
  def get_color_map_list(num_classes):
      """
      Args:
          num_classes (int): number of class
      Returns:
          color_map (list): RGB color list
      """
      color_map = num_classes * [0, 0, 0]
      for i in range(0, num_classes):
          j = 0
          lab = i
          while lab:
              color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
              color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
              color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
              j += 1
              lab >>= 3
      color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
      return color_map
  
  
  def draw_mask(im, np_boxes, np_masks, labels, threshold=0.5):
      """
      Args:
          im (PIL.Image.Image): PIL image
          np_boxes (np.ndarray): shape:[N,6], N: number of box,
              matix element:[class, score, x_min, y_min, x_max, y_max]
          np_masks (np.ndarray): shape:[N, im_h, im_w]
          labels (list): labels:['class1', ..., 'classn']
          threshold (float): threshold of mask
      Returns:
          im (PIL.Image.Image): visualized image
      """
      color_list = get_color_map_list(len(labels))
      w_ratio = 0.4
      alpha = 0.7
      im = np.array(im).astype('float32')
      clsid2color = {}
      expect_boxes = (np_boxes[:, 1] > threshold) & (np_boxes[:, 0] > -1)
      np_boxes = np_boxes[expect_boxes, :]
      np_masks = np_masks[expect_boxes, :, :]
      for i in range(len(np_masks)):
          clsid, score = int(np_boxes[i][0]), np_boxes[i][1]
          mask = np_masks[i]
          if clsid not in clsid2color:
              clsid2color[clsid] = color_list[clsid]
          color_mask = clsid2color[clsid]
          for c in range(3):
              color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
          idx = np.nonzero(mask)
          color_mask = np.array(color_mask)
          im[idx[0], idx[1], :] *= 1.0 - alpha
          im[idx[0], idx[1], :] += alpha * color_mask
      return Image.fromarray(im.astype('uint8'))
  
  
  def draw_box(im, np_boxes, labels, threshold=0.5):
      """
      Args:
          im (PIL.Image.Image): PIL image
          np_boxes (np.ndarray): shape:[N,6], N: number of box,
                                 matix element:[class, score, x_min, y_min, x_max, y_max]
          labels (list): labels:['class1', ..., 'classn']
          threshold (float): threshold of box
      Returns:
          im (PIL.Image.Image): visualized image
      """
      draw_thickness = min(im.size) // 320
      draw = ImageDraw.Draw(im)
      clsid2color = {}
      color_list = get_color_map_list(len(labels))
      expect_boxes = (np_boxes[:, 1] > threshold) & (np_boxes[:, 0] > -1)
      np_boxes = np_boxes[expect_boxes, :]
  
      for dt in np_boxes:
          clsid, bbox, score = int(dt[0]), dt[2:], dt[1]
          if clsid not in clsid2color:
              clsid2color[clsid] = color_list[clsid]
          color = tuple(clsid2color[clsid])
  
          if len(bbox) == 4:
              xmin, ymin, xmax, ymax = bbox
              print('class_id:{:d}, confidence:{:.4f}, left_top:[{:.2f},{:.2f}],'
                    'right_bottom:[{:.2f},{:.2f}]'.format(
                        int(clsid), score, xmin, ymin, xmax, ymax))
              # draw bbox
              draw.line(
                  [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin),
                   (xmin, ymin)],
                  width=draw_thickness,
                  fill=color)
          elif len(bbox) == 8:
              x1, y1, x2, y2, x3, y3, x4, y4 = bbox
              draw.line(
                  [(x1, y1), (x2, y2), (x3, y3), (x4, y4), (x1, y1)],
                  width=2,
                  fill=color)
              xmin = min(x1, x2, x3, x4)
              ymin = min(y1, y2, y3, y4)
  
          # draw label
          text = "{} {:.4f}".format(labels[clsid], score)
          tw, th = draw.textsize(text)
          draw.rectangle(
              [(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color)
          draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255))
      return im
  
  
  def draw_segm(im,
                np_segms,
                np_label,
                np_score,
                labels,
                threshold=0.5,
                alpha=0.7):
      """
      Draw segmentation on image
      """
      mask_color_id = 0
      w_ratio = .4
      color_list = get_color_map_list(len(labels))
      im = np.array(im).astype('float32')
      clsid2color = {}
      np_segms = np_segms.astype(np.uint8)
      for i in range(np_segms.shape[0]):
          mask, score, clsid = np_segms[i], np_score[i], np_label[i]
          if score < threshold:
              continue
  
          if clsid not in clsid2color:
              clsid2color[clsid] = color_list[clsid]
          color_mask = clsid2color[clsid]
          for c in range(3):
              color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
          idx = np.nonzero(mask)
          color_mask = np.array(color_mask)
          idx0 = np.minimum(idx[0], im.shape[0] - 1)
          idx1 = np.minimum(idx[1], im.shape[1] - 1)
          im[idx0, idx1, :] *= 1.0 - alpha
          im[idx0, idx1, :] += alpha * color_mask
          sum_x = np.sum(mask, axis=0)
          x = np.where(sum_x > 0.5)[0]
          sum_y = np.sum(mask, axis=1)
          y = np.where(sum_y > 0.5)[0]
          x0, x1, y0, y1 = x[0], x[-1], y[0], y[-1]
          cv2.rectangle(im, (x0, y0), (x1, y1),
                        tuple(color_mask.astype('int32').tolist()), 1)
          bbox_text = '%s %.2f' % (labels[clsid], score)
          t_size = cv2.getTextSize(bbox_text, 0, 0.3, thickness=1)[0]
          cv2.rectangle(im, (x0, y0), (x0 + t_size[0], y0 - t_size[1] - 3),
                        tuple(color_mask.astype('int32').tolist()), -1)
          cv2.putText(
              im,
              bbox_text, (x0, y0 - 2),
              cv2.FONT_HERSHEY_SIMPLEX,
              0.3, (0, 0, 0),
              1,
              lineType=cv2.LINE_AA)
      return Image.fromarray(im.astype('uint8'))
  
  
  def get_color(idx):
      idx = idx * 3
      color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255)
      return color
  
  
  def draw_pose(imgfile,
                results,
                visual_thread=0.6,
                save_name='pose.jpg',
                save_dir='output',
                returnimg=False,
                ids=None):
      try:
          import matplotlib.pyplot as plt
          import matplotlib
          plt.switch_backend('agg')
      except Exception as e:
          logger.error('Matplotlib not found, please install matplotlib.'
                       'for example: `pip install matplotlib`.')
          raise e
  
      skeletons, scores = results['keypoint']
      kpt_nums = 17
      if len(skeletons) > 0:
          kpt_nums = skeletons.shape[1]
      if kpt_nums == 17:  #plot coco keypoint
          EDGES = [(0, 1), (0, 2), (1, 3), (2, 4), (3, 5), (4, 6), (5, 7), (6, 8),
                   (7, 9), (8, 10), (5, 11), (6, 12), (11, 13), (12, 14),
                   (13, 15), (14, 16), (11, 12)]
      else:  #plot mpii keypoint
          EDGES = [(0, 1), (1, 2), (3, 4), (4, 5), (2, 6), (3, 6), (6, 7), (7, 8),
                   (8, 9), (10, 11), (11, 12), (13, 14), (14, 15), (8, 12),
                   (8, 13)]
      NUM_EDGES = len(EDGES)
  
      colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
              [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
      cmap = matplotlib.cm.get_cmap('hsv')
      plt.figure()
  
      img = cv2.imread(imgfile) if type(imgfile) == str else imgfile
  
      color_set = results['colors'] if 'colors' in results else None
  
      if 'bbox' in results and ids is None:
          bboxs = results['bbox']
          for j, rect in enumerate(bboxs):
              xmin, ymin, xmax, ymax = rect
              color = colors[0] if color_set is None else colors[color_set[j] %
                                                                 len(colors)]
              cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 1)
  
      canvas = img.copy()
      for i in range(kpt_nums):
          for j in range(len(skeletons)):
              if skeletons[j][i, 2] < visual_thread:
                  continue
              if ids is None:
                  color = colors[i] if color_set is None else colors[color_set[j]
                                                                     %
                                                                     len(colors)]
              else:
                  color = get_color(ids[j])
  
              cv2.circle(
                  canvas,
                  tuple(skeletons[j][i, 0:2].astype('int32')),
                  2,
                  color,
                  thickness=-1)
  
      to_plot = cv2.addWeighted(img, 0.3, canvas, 0.7, 0)
      fig = matplotlib.pyplot.gcf()
  
      stickwidth = 2
  
      for i in range(NUM_EDGES):
          for j in range(len(skeletons)):
              edge = EDGES[i]
              if skeletons[j][edge[0], 2] < visual_thread or skeletons[j][edge[
                      1], 2] < visual_thread:
                  continue
  
              cur_canvas = canvas.copy()
              X = [skeletons[j][edge[0], 1], skeletons[j][edge[1], 1]]
              Y = [skeletons[j][edge[0], 0], skeletons[j][edge[1], 0]]
              mX = np.mean(X)
              mY = np.mean(Y)
              length = ((X[0] - X[1])**2 + (Y[0] - Y[1])**2)**0.5
              angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
              polygon = cv2.ellipse2Poly((int(mY), int(mX)),
                                         (int(length / 2), stickwidth),
                                         int(angle), 0, 360, 1)
              if ids is None:
                  color = colors[i] if color_set is None else colors[color_set[j]
                                                                     %
                                                                     len(colors)]
              else:
                  color = get_color(ids[j])
              cv2.fillConvexPoly(cur_canvas, polygon, color)
              canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
      if returnimg:
          return canvas
      save_name = os.path.join(
          save_dir, os.path.splitext(os.path.basename(imgfile))[0] + '_vis.jpg')
      plt.imsave(save_name, canvas[:, :, ::-1])
      print("keypoint visualize image saved to: " + save_name)
      plt.close()
  