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"""OTXMaskRCNNModel & OTXSSDModel of OTX Detection."""
# Copyright (C) 2022 Intel Corporation
#
# 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 typing import Dict
import cv2
import numpy as np
from openvino.model_api.models.instance_segmentation import MaskRCNNModel, _expand_box, _segm_postprocess
from openvino.model_api.models.ssd import SSD, find_layer_by_name
from openvino.model_api.models.utils import Detection
class OTXMaskRCNNModel(MaskRCNNModel):
"""OpenVINO model wrapper for OTX MaskRCNN model."""
__model__ = "OTX_MaskRCNN"
def __init__(self, model_adapter, configuration, preload=False):
super().__init__(model_adapter, configuration, preload)
self.resize_mask = True
def _check_io_number(self, number_of_inputs, number_of_outputs):
"""Checks whether the number of model inputs/outputs is supported.
Args:
number_of_inputs (int, Tuple(int)): number of inputs supported by wrapper.
Use -1 to omit the check
number_of_outputs (int, Tuple(int)): number of outputs supported by wrapper.
Use -1 to omit the check
Raises:
WrapperError: if the model has unsupported number of inputs/outputs
"""
super()._check_io_number(number_of_inputs, -1)
def _get_outputs(self):
output_match_dict = {}
output_names = ["boxes", "labels", "masks", "feature_vector", "saliency_map"]
for output_name in output_names:
for node_name, node_meta in self.outputs.items():
if output_name in node_meta.names:
output_match_dict[output_name] = node_name
break
return output_match_dict
def postprocess(self, outputs, meta):
"""Post process function for OTX MaskRCNN model."""
# pylint: disable-msg=too-many-locals
# FIXME: here, batch dim of IR must be 1
boxes = outputs[self.output_blob_name["boxes"]]
if boxes.shape[0] == 1:
boxes = boxes.squeeze(0)
assert boxes.ndim == 2
masks = outputs[self.output_blob_name["masks"]]
if masks.shape[0] == 1:
masks = masks.squeeze(0)
assert masks.ndim == 3
classes = outputs[self.output_blob_name["labels"]].astype(np.uint32)
if classes.shape[0] == 1:
classes = classes.squeeze(0)
assert classes.ndim == 1
if self.is_segmentoly:
scores = outputs[self.output_blob_name["scores"]]
else:
scores = boxes[:, 4]
boxes = boxes[:, :4]
classes += 1
# Filter out detections with low confidence.
detections_filter = scores > self.confidence_threshold # pylint: disable=no-member
scores = scores[detections_filter]
boxes = boxes[detections_filter]
masks = masks[detections_filter]
classes = classes[detections_filter]
inputImgWidth, inputImgHeight = (
meta["original_shape"][1],
meta["original_shape"][0],
)
invertedScaleX, invertedScaleY = (
inputImgWidth / self.orig_width,
inputImgHeight / self.orig_height,
)
padLeft, padTop = 0, 0
if "fit_to_window" == self.resize_type or "fit_to_window_letterbox" == self.resize_type:
invertedScaleX = invertedScaleY = max(invertedScaleX, invertedScaleY)
if "fit_to_window_letterbox" == self.resize_type:
padLeft = (self.orig_width - round(inputImgWidth / invertedScaleX)) // 2
padTop = (self.orig_height - round(inputImgHeight / invertedScaleY)) // 2
boxes -= (padLeft, padTop, padLeft, padTop)
boxes *= (invertedScaleX, invertedScaleY, invertedScaleX, invertedScaleY)
np.around(boxes, out=boxes)
np.clip(
boxes,
0.0,
[inputImgWidth, inputImgHeight, inputImgWidth, inputImgHeight],
out=boxes,
)
resized_masks = []
for box, cls, raw_mask in zip(boxes, classes, masks):
raw_cls_mask = raw_mask[cls, ...] if self.is_segmentoly else raw_mask
if self.resize_mask:
resized_masks.append(_segm_postprocess(box, raw_cls_mask, *meta["original_shape"][:-1]))
else:
resized_masks.append(raw_cls_mask)
return scores, classes, boxes, resized_masks
def get_saliency_map_from_prediction(self, outputs, meta, num_classes):
"""Post process function for saliency map of OTX MaskRCNN model."""
boxes = outputs[self.output_blob_name["boxes"]]
if boxes.shape[0] == 1:
boxes = boxes.squeeze(0)
scores = boxes[:, 4]
boxes = boxes[:, :4]
masks = outputs[self.output_blob_name["masks"]]
if masks.shape[0] == 1:
masks = masks.squeeze(0)
classes = outputs[self.output_blob_name["labels"]].astype(np.uint32)
if classes.shape[0] == 1:
classes = classes.squeeze(0)
scale_x = meta["resized_shape"][0] / meta["original_shape"][1]
scale_y = meta["resized_shape"][1] / meta["original_shape"][0]
boxes[:, 0::2] /= scale_x
boxes[:, 1::2] /= scale_y
saliency_maps = [None for _ in range(num_classes)]
for box, score, cls, raw_mask in zip(boxes, scores, classes, masks):
resized_mask = self._resize_mask(box, raw_mask * score, *meta["original_shape"][:-1])
if saliency_maps[cls] is None:
saliency_maps[cls] = [resized_mask]
else:
saliency_maps[cls].append(resized_mask)
saliency_maps = self._average_and_normalize(saliency_maps, num_classes)
return saliency_maps
def _resize_mask(self, box, raw_cls_mask, im_h, im_w):
# Add zero border to prevent upsampling artifacts on segment borders.
raw_cls_mask = np.pad(raw_cls_mask, ((1, 1), (1, 1)), "constant", constant_values=0)
extended_box = _expand_box(box, raw_cls_mask.shape[0] / (raw_cls_mask.shape[0] - 2.0)).astype(int)
w, h = np.maximum(extended_box[2:] - extended_box[:2] + 1, 1)
x0, y0 = np.clip(extended_box[:2], a_min=0, a_max=[im_w, im_h])
x1, y1 = np.clip(extended_box[2:] + 1, a_min=0, a_max=[im_w, im_h])
raw_cls_mask = cv2.resize(raw_cls_mask.astype(np.float32), (w, h))
# Put an object mask in an image mask.
im_mask = np.zeros((im_h, im_w), dtype=np.float32)
im_mask[y0:y1, x0:x1] = raw_cls_mask[
(y0 - extended_box[1]) : (y1 - extended_box[1]), (x0 - extended_box[0]) : (x1 - extended_box[0])
]
return im_mask
@staticmethod
def _average_and_normalize(saliency_maps, num_classes):
for i in range(num_classes):
if saliency_maps[i] is not None:
saliency_maps[i] = np.array(saliency_maps[i]).mean(0)
for i in range(num_classes):
per_class_map = saliency_maps[i]
if per_class_map is not None:
max_values = np.max(per_class_map)
per_class_map = 255 * (per_class_map) / (max_values + 1e-12)
per_class_map = per_class_map.astype(np.uint8)
saliency_maps[i] = per_class_map
return saliency_maps
def segm_postprocess(self, *args, **kwargs):
"""Post-process for segmentation masks."""
return _segm_postprocess(*args, **kwargs)
def disable_mask_resizing(self):
"""Disable mask resizing.
There is no need to resize mask in tile as it will be processed at the end.
"""
self.resize_mask = False
class OTXSSDModel(SSD):
"""OpenVINO model wrapper for OTX SSD model."""
__model__ = "OTX_SSD"
def __init__(self, model_adapter, configuration=None, preload=False):
# pylint: disable-next=bad-super-call
super(SSD, self).__init__(model_adapter, configuration, preload)
self.image_info_blob_name = self.image_info_blob_names[0] if len(self.image_info_blob_names) == 1 else None
self.output_parser = BatchBoxesLabelsParser(
self.outputs,
self.inputs[self.image_blob_name].shape[2:][::-1],
)
def _get_outputs(self) -> Dict:
"""Match the output names with graph node index."""
output_match_dict = {}
output_names = ["boxes", "labels", "feature_vector", "saliency_map"]
for output_name in output_names:
for node_name, node_meta in self.outputs.items():
if output_name in node_meta.names:
output_match_dict[output_name] = node_name
break
return output_match_dict
class BatchBoxesLabelsParser:
"""Batched output parser."""
def __init__(self, layers, input_size, labels_layer="labels", default_label=0):
try:
self.labels_layer = find_layer_by_name(labels_layer, layers)
except ValueError:
self.labels_layer = None
self.default_label = default_label
try:
self.bboxes_layer = self.find_layer_bboxes_output(layers)
except ValueError:
self.bboxes_layer = find_layer_by_name("boxes", layers)
self.input_size = input_size
@staticmethod
def find_layer_bboxes_output(layers):
"""find_layer_bboxes_output."""
filter_outputs = [name for name, data in layers.items() if len(data.shape) == 3 and data.shape[-1] == 5]
if not filter_outputs:
raise ValueError("Suitable output with bounding boxes is not found")
if len(filter_outputs) > 1:
raise ValueError("More than 1 candidate for output with bounding boxes.")
return filter_outputs[0]
def __call__(self, outputs):
"""Parse bboxes."""
# FIXME: here, batch dim of IR must be 1
bboxes = outputs[self.bboxes_layer]
if bboxes.shape[0] == 1:
bboxes = bboxes.squeeze(0)
assert bboxes.ndim == 2
scores = bboxes[:, 4]
bboxes = bboxes[:, :4]
bboxes[:, 0::2] /= self.input_size[0]
bboxes[:, 1::2] /= self.input_size[1]
if self.labels_layer:
labels = outputs[self.labels_layer]
else:
labels = np.full(len(bboxes), self.default_label, dtype=bboxes.dtype)
if labels.shape[0] == 1:
labels = labels.squeeze(0)
detections = [Detection(*bbox, score, label) for label, score, bbox in zip(labels, scores, bboxes)]
return detections
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