def evaluate(self, pred_dir, gt_dir):
img_cnt = 0
for filename in os.listdir(pred_dir):
pred_path = os.path.join(pred_dir, filename)
gt_path = os.path.join(gt_dir, filename)
predmap = ImageHelper.img2np(ImageHelper.read_image(pred_path, tool='pil', mode='P'))
gtmap = ImageHelper.img2np(ImageHelper.read_image(gt_path, tool='pil', mode='P'))
predmap = self.relabel(predmap)
gtmap = self.relabel(gtmap)
self.seg_running_score.update(predmap[np.newaxis, :, :], gtmap[np.newaxis, :, :])
img_cnt += 1
Log.info('Evaluate {} images'.format(img_cnt))
Log.info('mIOU: {}'.format(self.seg_running_score.get_mean_iou()))
Log.info('Pixel ACC: {}'.format(self.seg_running_score.get_pixel_acc()))
def vis_bboxes(self,
image_in,
bboxes_list,
name='default',
sub_dir='bbox'):
"""
Show the diff bbox of individuals.
"""
base_dir = os.path.join(self.configer.get('project_dir'), DET_DIR,
sub_dir)
if isinstance(image_in, Image.Image):
image = ImageHelper.rgb2bgr(ImageHelper.img2np(image_in))
else:
image = image_in.copy()
if not os.path.exists(base_dir):
log.error('Dir:{} not exists!'.format(base_dir))
os.makedirs(base_dir)
img_path = os.path.join(base_dir, '{}.jpg'.format(name))
for bbox in bboxes_list:
image = cv2.rectangle(image, (bbox[0], bbox[1]),
(bbox[2], bbox[3]), (0, 255, 0), -1)
image = cv2.resize(image,
tuple(self.configer.get('data', 'input_size')))
cv2.imwrite(img_path, image)
def __test_img(self, image_path, json_path, raw_path, vis_path):
Log.info('Image Path: {}'.format(image_path))
ori_img_rgb = ImageHelper.img2np(ImageHelper.pil_open_rgb(image_path))
cur_img_rgb = ImageHelper.resize(ori_img_rgb,
self.configer.get(
'data', 'input_size'),
interpolation=Image.CUBIC)
ori_img_bgr = ImageHelper.bgr2rgb(ori_img_rgb)
paf_avg, heatmap_avg = self.__get_paf_and_heatmap(cur_img_rgb)
all_peaks = self.__extract_heatmap_info(heatmap_avg)
special_k, connection_all = self.__extract_paf_info(
cur_img_rgb, paf_avg, all_peaks)
subset, candidate = self.__get_subsets(connection_all, special_k,
all_peaks)
json_dict = self.__get_info_tree(cur_img_rgb, subset, candidate)
for i in range(len(json_dict['objects'])):
for index in range(len(json_dict['objects'][i]['keypoints'])):
if json_dict['objects'][i]['keypoints'][index][2] == -1:
continue
json_dict['objects'][i]['keypoints'][index][0] *= (
ori_img_rgb.shape[1] / cur_img_rgb.shape[1])
json_dict['objects'][i]['keypoints'][index][1] *= (
ori_img_rgb.shape[0] / cur_img_rgb.shape[0])
image_canvas = self.pose_parser.draw_points(ori_img_bgr.copy(),
json_dict)
image_canvas = self.pose_parser.link_points(image_canvas, json_dict)
cv2.imwrite(vis_path, image_canvas)
cv2.imwrite(raw_path, ori_img_bgr)
Log.info('Json Save Path: {}'.format(json_path))
with open(json_path, 'w') as save_stream:
save_stream.write(json.dumps(json_dict))
def __test_img(self, image_path, json_path, raw_path, vis_path):
Log.info('Image Path: {}'.format(image_path))
ori_img_rgb = ImageHelper.img2np(ImageHelper.pil_open_rgb(image_path))
ori_img_bgr = ImageHelper.rgb2bgr(ori_img_rgb)
inputs = ImageHelper.resize(ori_img_rgb, tuple(self.configer.get('data', 'input_size')), Image.CUBIC)
inputs = ToTensor()(inputs)
inputs = Normalize(mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params', 'std'))(inputs)
with torch.no_grad():
inputs = inputs.unsqueeze(0).to(self.device)
bbox, cls = self.det_net(inputs)
bbox = bbox.cpu().data.squeeze(0)
cls = F.softmax(cls.cpu().squeeze(0), dim=-1).data
boxes, lbls, scores = self.__decode(bbox, cls)
json_dict = self.__get_info_tree(boxes, lbls, scores, ori_img_rgb)
image_canvas = self.det_parser.draw_bboxes(ori_img_bgr.copy(),
json_dict,
conf_threshold=self.configer.get('vis', 'conf_threshold'))
cv2.imwrite(vis_path, image_canvas)
cv2.imwrite(raw_path, ori_img_bgr)
Log.info('Json Path: {}'.format(json_path))
JsonHelper.save_file(json_dict, json_path)
return json_dict
def __test_img(self, image_path, json_path, raw_path, vis_path):
Log.info('Image Path: {}'.format(image_path))
ori_img_rgb = ImageHelper.img2np(ImageHelper.pil_open_rgb(image_path))
ori_img_bgr = ImageHelper.bgr2rgb(ori_img_rgb)
paf_avg, heatmap_avg = self.__get_paf_and_heatmap(ori_img_rgb)
all_peaks = self.__extract_heatmap_info(heatmap_avg)
special_k, connection_all = self.__extract_paf_info(
ori_img_rgb, paf_avg, all_peaks)
subset, candidate = self.__get_subsets(connection_all, special_k,
all_peaks)
json_dict = self.__get_info_tree(ori_img_rgb, subset, candidate)
image_canvas = self.pose_parser.draw_points(ori_img_bgr.copy(),
json_dict)
image_canvas = self.pose_parser.link_points(image_canvas, json_dict)
cv2.imwrite(vis_path, image_canvas)
cv2.imwrite(raw_path, ori_img_bgr)
Log.info('Json Save Path: {}'.format(json_path))
JsonHelper.save_file(json_dict, json_path)
