text: # Copyright (c) 2019 PaddlePaddle 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.
  # 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 absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  import numpy as np
  from PIL import Image, ImageDraw,ImageFont
  import cv2
  import math
  
  from .colormap import colormap
  from ppdet.utils.logger import setup_logger
  logger = setup_logger(__name__)
  FONT=ImageFont.truetype(font='./SimHei.ttf',encoding='utf-8',size=100)
  
  __all__ = ['visualize_results']
  
  
  def visualize_results(image,
                        bbox_res,
                        mask_res,
                        segm_res,
                        keypoint_res,
                        im_id,
                        catid2name,
                        threshold=0.5):
      """
      Visualize bbox and mask results
      """
      if bbox_res is not None:
          image = draw_bbox(image, im_id, catid2name, bbox_res, threshold)
      if mask_res is not None:
          image = draw_mask(image, im_id, mask_res, threshold)
      if segm_res is not None:
          image = draw_segm(image, im_id, catid2name, segm_res, threshold)
      if keypoint_res is not None:
          image = draw_pose(image, keypoint_res, threshold)
      return image
  
  
  def draw_mask(image, im_id, segms, threshold, alpha=0.7):
      """
      Draw mask on image
      """
      mask_color_id = 0
      w_ratio = .4
      color_list = colormap(rgb=True)
      img_array = np.array(image).astype('float32')
      for dt in np.array(segms):
          if im_id != dt['image_id']:
              continue
          segm, score = dt['segmentation'], dt['score']
          if score < threshold:
              continue
          import pycocotools.mask as mask_util
          mask = mask_util.decode(segm) * 255
          color_mask = color_list[mask_color_id % len(color_list), 0:3]
          mask_color_id += 1
          for c in range(3):
              color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
          idx = np.nonzero(mask)
          img_array[idx[0], idx[1], :] *= 1.0 - alpha
          img_array[idx[0], idx[1], :] += alpha * color_mask
      return Image.fromarray(img_array.astype('uint8'))
  
  
  def draw_bbox(image, im_id, catid2name, bboxes, threshold):
      """
      Draw bbox on image
      """
      draw = ImageDraw.Draw(image)
  
      catid2color = {}
      color_list = colormap(rgb=True)[:40]
      for dt in np.array(bboxes):
          if im_id != dt['image_id']:
              continue
          catid, bbox, score = dt['category_id'], dt['bbox'], dt['score']
          if score < threshold:
              continue
  
          if catid not in catid2color:
              idx = np.random.randint(len(color_list))
              catid2color[catid] = color_list[idx]
          color = tuple(catid2color[catid])
  
          # draw bbox
          if len(bbox) == 4:
              # draw bbox
              xmin, ymin, w, h = bbox
              xmax = xmin + w
              ymax = ymin + h
              draw.line(
                  [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin),
                   (xmin, ymin)],
                  width=2,
                  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)
          else:
              logger.error('the shape of bbox must be [M, 4] or [M, 8]!')
  
          # draw label
          text = "{} {:.2f}".format(catid2name[catid], score)
          tw, th = draw.textsize(text,font=FONT)
          draw.rectangle(
              [(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color)
          draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255),font=FONT)
  
      return image
  
  
  def save_result(save_path, results, catid2name, threshold):
      """
      save result as txt
      """
      img_id = int(results["im_id"])
      with open(save_path, 'w') as f:
          if "bbox_res" in results:
              for dt in results["bbox_res"]:
                  catid, bbox, score = dt['category_id'], dt['bbox'], dt['score']
                  if score < threshold:
                      continue
                  # each bbox result as a line
                  # for rbox: classname score x1 y1 x2 y2 x3 y3 x4 y4
                  # for bbox: classname score x1 y1 w h
                  bbox_pred = '{} {} '.format(catid2name[catid],
                                              score) + ' '.join(
                                                  [str(e) for e in bbox])
                  f.write(bbox_pred + '\n')
          elif "keypoint_res" in results:
              for dt in results["keypoint_res"]:
                  kpts = dt['keypoints']
                  scores = dt['score']
                  keypoint_pred = [img_id, scores, kpts]
                  print(keypoint_pred, file=f)
          else:
              print("No valid results found, skip txt save")
  
  
  def draw_segm(image,
                im_id,
                catid2name,
                segms,
                threshold,
                alpha=0.7,
                draw_box=True):
      """
      Draw segmentation on image
      """
      mask_color_id = 0
      w_ratio = .4
      color_list = colormap(rgb=True)
      img_array = np.array(image).astype('float32')
      for dt in np.array(segms):
          if im_id != dt['image_id']:
              continue
          segm, score, catid = dt['segmentation'], dt['score'], dt['category_id']
          if score < threshold:
              continue
          import pycocotools.mask as mask_util
          mask = mask_util.decode(segm) * 255
          color_mask = color_list[mask_color_id % len(color_list), 0:3]
          mask_color_id += 1
          for c in range(3):
              color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
          idx = np.nonzero(mask)
          img_array[idx[0], idx[1], :] *= 1.0 - alpha
          img_array[idx[0], idx[1], :] += alpha * color_mask
  
          if not draw_box:
              center_y, center_x = ndimage.measurements.center_of_mass(mask)
              label_text = "{}".format(catid2name[catid])
              vis_pos = (max(int(center_x) - 10, 0), int(center_y))
              cv2.putText(img_array, label_text, vis_pos,
                          cv2.FONT_HERSHEY_COMPLEX, 0.3, (255, 255, 255))
          else:
              mask = mask_util.decode(segm) * 255
              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(img_array, (x0, y0), (x1, y1),
                            tuple(color_mask.astype('int32').tolist()), 1)
              bbox_text = '%s %.2f' % (catid2name[catid], score)
              t_size = cv2.getTextSize(bbox_text, 0, 0.3, thickness=1)[0]
              cv2.rectangle(img_array, (x0, y0), (x0 + t_size[0],
                                                  y0 - t_size[1] - 3),
                            tuple(color_mask.astype('int32').tolist()), -1)
              cv2.putText(
                  img_array,
                  bbox_text, (x0, y0 - 2),
                  cv2.FONT_HERSHEY_SIMPLEX,
                  0.3, (0, 0, 0),
                  1,
                  lineType=cv2.LINE_AA)
  
      return Image.fromarray(img_array.astype('uint8'))
  
  
  def draw_pose(image,
                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 = np.array([item['keypoints'] for item in results])
      kpt_nums = 17
      if len(skeletons) > 0:
          kpt_nums = int(skeletons.shape[1] / 3)
      skeletons = skeletons.reshape(-1, kpt_nums, 3)
      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 = np.array(image).astype('float32')
  
      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)
      image = Image.fromarray(canvas.astype('uint8'))
      plt.close()
      return image
  