def insert_pre_processing(graph: Graph, input_node: Node, node_mean_scale_values: np.array,
preprocessing_name: str):
assert preprocessing_name in ['scale', 'mean']
if node_mean_scale_values.get(preprocessing_name) is None:
return
user_value = node_mean_scale_values[preprocessing_name]
value = 1 / user_value if preprocessing_name == 'scale' else user_value * (-1)
optimize_value = int(preprocessing_name == 'scale')
op = Mul if preprocessing_name == 'scale' else Add
if all([x == optimize_value for x in value]):
return
assert input_node.has_valid('shape')
features_dim_idx = get_features_dim(graph.graph['layout'], len(input_node.shape))
assert value.size == input_node.shape[features_dim_idx] or value.size == 1
shape = np.ones(len(input_node.shape), dtype=np.int64)
shape[features_dim_idx] = value.size
value = value.reshape(shape)
name = input_node.soft_get('name', input_node.id) + '/' + preprocessing_name
preprocessing = create_op_with_const_inputs(graph, op=op, port_value_dict={1: value}, op_attrs={'name': name})
for dst in input_node.out_port(0).get_destinations():
if dst.node.soft_get('type') != 'ShapeOf':
dst.get_connection().set_source(preprocessing.out_port(0))
input_node.out_port(0).connect(preprocessing.in_port(0))
def insert_pre_processing(graph: Graph, input_node: Node, node_mean_scale_values: np.array,
preprocessing_name: str):
assert preprocessing_name in ['scale', 'mean']
if node_mean_scale_values.get(preprocessing_name) is None:
return
user_value = node_mean_scale_values[preprocessing_name]
value = 1 / user_value if preprocessing_name == 'scale' else user_value * (-1)
optimize_value = int(preprocessing_name == 'scale')
op = Mul if preprocessing_name == 'scale' else Add
if all([x == optimize_value for x in value]):
return
assert input_node.has_valid('shape')
features_dim_idx = get_features_dim(graph.graph['layout'], len(input_node.shape))
assert value.size == input_node.shape[features_dim_idx] or value.size == 1
shape = np.ones(len(input_node.shape), dtype=np.int64)
shape[features_dim_idx] = value.size
value = value.reshape(shape)
name = input_node.soft_get('name', input_node.id) + '/' + preprocessing_name
preprocessing = create_op_with_const_inputs(graph, op=op, port_value_dict={1: value}, op_attrs={'name': name})
for dst in input_node.out_port(0).get_destinations():
if dst.node.soft_get('type') != 'ShapeOf':
# After the insertion of additional operations model optimizer
# should keep the link to the input layer. Parameter node in framework
# should map to parameter node in IR.
# For this reason 'fw_tensor_debug_info' should be kept in data node.
dst.get_connection().set_source(preprocessing.out_port(0), "source")
input_node.out_port(0).connect(preprocessing.in_port(0))
def insert_pre_processing(graph: Graph, input_node: Node,
node_mean_scale_values: np.array,
preprocessing_name: str):
assert preprocessing_name in ['scale', 'mean']
if node_mean_scale_values.get(preprocessing_name) is None:
return
user_value = node_mean_scale_values[preprocessing_name]
value = 1 / user_value if preprocessing_name == 'scale' else user_value * (
-1)
optimize_value = int(preprocessing_name == 'scale')
op = Mul if preprocessing_name == 'scale' else Add
if all([x == optimize_value for x in value]):
return
assert input_node.has_valid('shape')
features_dim_idx = get_features_dim(graph.graph['layout'],
len(input_node.shape))
assert compatible_dims(
value.size, input_node.shape[features_dim_idx]) or value.size == 1
shape = np.ones(len(input_node.shape), dtype=np.int64)
shape[features_dim_idx] = value.size
value = value.reshape(shape)
name = input_node.soft_get('name',
input_node.id) + '/' + preprocessing_name
preprocessing = create_op_with_const_inputs(graph,
op=op,
port_value_dict={1: value},
op_attrs={'name': name})
if input_node.is_out_port_connected(0) and len(
input_node.out_port(0).get_destinations()) == 1:
# There are models with pattern Parameter(uint8) -> Convert(float).
# Adding mean/scale leads to the following:
# Parameter(uint8) -> Mean/Scale -> Convert(float) which is incorrect.
# To fix this mean and scale preprocessing node is inserted after Convert(float) node.
out_node = input_node.out_port(0).get_destination().node
convert_type = out_node.soft_get('dst_type')
if out_node.soft_get('type') == "Convert" and (convert_type in [
np.float32, np.float16
]):
input_node = out_node
if convert_type != value.dtype:
new_value = value.astype(convert_type)
const_node = preprocessing.in_port(
1).get_connection().get_source().node
const_node['value'] = new_value
for dst in input_node.out_port(0).get_destinations():
if dst.node.soft_get('type') != 'ShapeOf':
# After the insertion of additional operations model optimizer
# should keep the link to the input layer. Parameter node in framework
# should map to parameter node in IR.
# For this reason 'fw_tensor_debug_info' should be kept in data node.
dst.get_connection().set_source(preprocessing.out_port(0),
"source")
input_node.out_port(0).connect(preprocessing.in_port(0))
def draw_boxes(image: np.array,
bboxes: list,
ids: list = None,
box_type: str = 'detector'):
'''
Draw the bounding boxes on the image, return image with boxes
:param image: `np.array` with shape (H, W, 3) in RGB format
:param bboxes: list of `np.array`s, each np.array contains coordinates of BB in tlbr format (see `convert_bbox`)
:param ids: if not None, put ID from 'ids' on top of each bounding box
:return image: image with the drawn boxes
'''
if box_type == 'detector':
ids = range(len(bboxes)) # just in bboxes order
bboxes_for_draw = bboxes
if box_type == 'tracker':
ids = [tr_id for bbox, tr_id in bboxes]
bboxes_for_draw = [bbox for bbox, tr_id in bboxes]
if box_type == 'vis_to_file':
ids = ids
bboxes_for_draw = bboxes
# print('bboxes=', bboxes)
# print('bboxes_for_draw=', bboxes_for_draw)
digit_height = 35
digit_width = 23
for ID, bbox in zip(ids, bboxes_for_draw):
# print('ID, bbox, box_type==', ID, bbox, box_type)
image = cv2.rectangle(
image,
(int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])),
(255, 255, 255),
2,
)
if box_type == 'detector':
note = 'det=' + str(ID)
note_coords = (int(bbox[0]), int(bbox[1]) + digit_height)
note_color = (255, 0, 0)
elif box_type == 'tracker' or 'vis_to_file':
note = str(ID) #'tr_id=' + str(ID)
note_coords = (int(bbox[0]), int(bbox[1]) + digit_height)
note_color = (0, 0, 0)
else:
raise ValueError
image = cv2.rectangle(image, (int(bbox[0]), int(bbox[1])),
(int(bbox[0]) + digit_width * len(str(note)),
int(bbox[1]) + digit_height), (255, 255, 255),
cv2.FILLED)
image = cv2.putText(
image,
text=note,
org=note_coords,
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1,
color=note_color,
thickness=2,
lineType=cv2.LINE_AA,
)
return image if type(image) == type(np.array([])) else image.get()
