def _parse_reader(reader_cfg, dataset_cfg, metric, arch, image_shape):
preprocess_list = []
anno_file = dataset_cfg.get_anno()
with_background = reader_cfg['with_background']
clsid2catid, catid2name = get_categories(metric, anno_file, with_background)
label_list = [str(cat) for cat in catid2name.values()]
sample_transforms = reader_cfg['sample_transforms']
for st in sample_transforms[1:]:
for key, value in st.items():
p = {'type': key}
if key == 'ResizeOp':
if value.get('keep_ratio',
False) and image_shape[1] is not None:
max_size = max(image_shape[1:])
image_shape = [3, max_size, max_size]
value['target_size'] = image_shape[1:]
p.update(value)
preprocess_list.append(p)
batch_transforms = reader_cfg.get('batch_transforms', None)
if batch_transforms:
methods = [list(bt.keys())[0] for bt in batch_transforms]
for bt in batch_transforms:
for key, value in bt.items():
if key == 'PadBatchOp':
preprocess_list.append({'type': 'PadStride'})
preprocess_list[-1].update({
'stride': value['pad_to_stride']
})
break
return with_background, preprocess_list, label_list, image_shape
def _parse_reader(reader_cfg, dataset_cfg, metric, arch, image_shape):
preprocess_list = []
anno_file = dataset_cfg.get_anno()
clsid2catid, catid2name = get_categories(metric, anno_file)
label_list = [str(cat) for cat in catid2name.values()]
sample_transforms = reader_cfg['sample_transforms']
for st in sample_transforms[1:]:
for key, value in st.items():
p = {'type': key}
p.update(value)
preprocess_list.append(p)
batch_transforms = reader_cfg.get('batch_transforms', None)
if batch_transforms:
methods = [list(bt.keys())[0] for bt in batch_transforms]
for bt in batch_transforms:
for key, value in bt.items():
# for deploy/infer, use PadStride(stride) instead PadBatch(pad_to_stride, pad_gt)
if key == 'PadBatch':
preprocess_list.append({
'type': 'PadStride',
'stride': value['pad_to_stride']
})
break
return preprocess_list, label_list, image_shape
def predict(self, images, draw_threshold=0.5, output_dir='output'):
self.dataset.set_images(images)
loader = create('TestReader')(self.dataset, 0)
imid2path = self.dataset.get_imid2path()
anno_file = self.dataset.get_anno()
with_background = self.cfg.with_background
clsid2catid, catid2name = get_categories(self.cfg.metric, anno_file,
with_background)
# Run Infer
for step_id, data in enumerate(loader):
self.status['step_id'] = step_id
# forward
self.model.eval()
outs = self.model(data)
for key in ['im_shape', 'scale_factor', 'im_id']:
outs[key] = data[key]
for key, value in outs.items():
outs[key] = value.numpy()
# FIXME: for more elegent coding
if 'mask' in outs and 'bbox' in outs:
mask_resolution = self.model.mask_post_process.mask_resolution
from ppdet.py_op.post_process import mask_post_process
outs['mask'] = mask_post_process(outs, outs['im_shape'],
outs['scale_factor'],
mask_resolution)
batch_res = get_infer_results(outs, clsid2catid)
bbox_num = outs['bbox_num']
start = 0
for i, im_id in enumerate(outs['im_id']):
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
end = start + bbox_num[i]
bbox_res = batch_res['bbox'][start:end] \
if 'bbox' in batch_res else None
mask_res = batch_res['mask'][start:end] \
if 'mask' in batch_res else None
segm_res = batch_res['segm'][start:end] \
if 'segm' in batch_res else None
image = visualize_results(image, bbox_res, mask_res, segm_res,
int(outs['im_id']), catid2name,
draw_threshold)
# save image with detection
save_name = self._get_save_image_name(output_dir, image_path)
logger.info("Detection bbox results save in {}".format(
save_name))
image.save(save_name, quality=95)
start = end
