def visualize_from_png_path(refvg_split,
png_path,
out_path=None,
all_task_num=-1,
subset_task_num=0,
pred_plot=True,
gt_skip_exist=True,
verbose=True):
if out_path is None:
out_path = os.path.normpath(os.path.join(png_path, os.pardir))
if pred_plot:
plot_path = os.path.join(out_path, 'pred_plots')
png_path_dict = None
else:
plot_path = None
png_path_dict = {'prediction': png_path}
fnames = os.listdir(png_path)
if all_task_num < 0:
all_task_num = len(fnames)
visualizer = Visualizer(refvg_split=refvg_split,
png_path_dict=png_path_dict,
pred_plot_path=plot_path,
pred_skip_exist=True,
gt_skip_exist=gt_skip_exist,
all_task_num=all_task_num,
subset_task_num=subset_task_num)
random.shuffle(fnames)
for fname in fnames:
if not fname.endswith('.png'):
continue
task_id = fname.split('.')[0]
img_id = int(task_id.split('__')[0])
if visualizer.task_is_needed(img_id, task_id):
if not pred_plot:
visualizer.plot_single_task(img_id, task_id, verbose=verbose)
else:
pred_mask = png_to_pred_mask(os.path.join(png_path, fname))
visualizer.plot_single_task(img_id,
task_id, {'pred_mask': pred_mask},
pred_bin_tags=('pred_mask', ),
verbose=verbose)
if visualizer.is_enough_plots():
break
# generate html
html_path = os.path.join(out_path, 'htmls')
result_path = os.path.join(out_path, 'results.txt')
if not os.path.exists(result_path):
result_path = None
visualizer.generate_html(html_path,
enable_subsets=subset_task_num > 0,
result_txt_path=result_path)
return
def visualize_from_pred_dict(pred_eval_dict=None,
pred_eval_dict_path=None,
refvg_split=None,
out_path=None,
pred_bin_tags=None,
pred_score_tags=None,
pred_box_tags=None,
all_task_num=40,
subset_task_num=20,
gt_skip_exist=True,
pred_skip_exist=True,
verbose=True):
if pred_eval_dict is None:
predictions = np.load(pred_eval_dict_path, allow_pickle=True).item()
assert isinstance(predictions, dict)
else:
predictions = pred_eval_dict
# sample
if subset_task_num > 0:
subsets = subset_utils.subsets
else:
subsets = ['all']
subset_dict = dict()
for s in subsets:
subset_dict[s] = list()
for img_id, img_pred in predictions.items():
for task_id, pred in img_pred.items():
if 'subsets' in pred:
for subset in pred['subsets']:
if subset in subset_dict:
subset_dict[subset].append((img_id, task_id))
else:
pred['subsets'] = ['all']
subset_dict['all'].append((img_id, task_id))
to_plot = list()
for subset in subset_utils.subsets:
if subset not in subset_dict:
continue
img_task_ids = subset_dict[subset]
if subset == 'all':
sample_num = all_task_num
else:
sample_num = subset_task_num
if len(img_task_ids) > sample_num:
img_task_ids = random.sample(img_task_ids, sample_num)
to_plot += img_task_ids
# plot
visualizer = Visualizer(refvg_split=refvg_split,
pred_plot_path=os.path.join(
out_path, 'pred_plots'),
pred_skip_exist=pred_skip_exist,
gt_skip_exist=gt_skip_exist)
for img_id, task_id in to_plot:
visualizer.plot_single_task(img_id,
task_id,
predictions[img_id][task_id],
pred_bin_tags=pred_bin_tags,
pred_score_tags=pred_score_tags,
pred_box_tags=pred_box_tags,
verbose=verbose)
# generate html
html_path = os.path.join(out_path, 'htmls')
result_path = os.path.join(out_path, 'results.txt')
if not os.path.exists(result_path):
result_path = None
visualizer.generate_html(html_path,
enable_subsets=subset_task_num > 0,
result_txt_path=result_path)
return
