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# Copyright 2020-2021 The MediaPipe Authors.
#
# 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.
"""MediaPipe Objectron."""
import enum
from typing import List, Tuple, NamedTuple, Optional
import attr
import numpy as np
from mediapipe.calculators.core import constant_side_packet_calculator_pb2
# pylint: disable=unused-import
from mediapipe.calculators.core import gate_calculator_pb2
from mediapipe.calculators.core import split_vector_calculator_pb2
from mediapipe.calculators.tensor import image_to_tensor_calculator_pb2
from mediapipe.calculators.tensor import inference_calculator_pb2
from mediapipe.calculators.tensor import tensors_to_detections_calculator_pb2
from mediapipe.calculators.tensor import tensors_to_floats_calculator_pb2
from mediapipe.calculators.tensor import tensors_to_landmarks_calculator_pb2
from mediapipe.calculators.tflite import ssd_anchors_calculator_pb2
from mediapipe.calculators.util import association_calculator_pb2
from mediapipe.calculators.util import collection_has_min_size_calculator_pb2
from mediapipe.calculators.util import detection_label_id_to_text_calculator_pb2
from mediapipe.calculators.util import detections_to_rects_calculator_pb2
from mediapipe.calculators.util import landmark_projection_calculator_pb2
from mediapipe.calculators.util import local_file_contents_calculator_pb2
from mediapipe.calculators.util import non_max_suppression_calculator_pb2
from mediapipe.calculators.util import rect_transformation_calculator_pb2
from mediapipe.calculators.util import thresholding_calculator_pb2
from mediapipe.framework.formats import landmark_pb2
from mediapipe.modules.objectron.calculators import annotation_data_pb2
from mediapipe.modules.objectron.calculators import frame_annotation_to_rect_calculator_pb2
from mediapipe.modules.objectron.calculators import lift_2d_frame_annotation_to_3d_calculator_pb2
# pylint: enable=unused-import
from mediapipe.python.solution_base import SolutionBase
from mediapipe.python.solutions import download_utils
class BoxLandmark(enum.IntEnum):
"""The 9 3D box landmarks."""
#
# 3 + + + + + + + + 7
# +\ +\ UP
# + \ + \
# + \ + \ |
# + 4 + + + + + + + + 8 | y
# + + + + |
# + + + + |
# + + (0) + + .------- x
# + + + + \
# 1 + + + + + + + + 5 + \
# \ + \ + \ z
# \ + \ + \
# \+ \+
# 2 + + + + + + + + 6
CENTER = 0
BACK_BOTTOM_LEFT = 1
FRONT_BOTTOM_LEFT = 2
BACK_TOP_LEFT = 3
FRONT_TOP_LEFT = 4
BACK_BOTTOM_RIGHT = 5
FRONT_BOTTOM_RIGHT = 6
BACK_TOP_RIGHT = 7
FRONT_TOP_RIGHT = 8
BINARYPB_FILE_PATH = 'mediapipe/modules/objectron/objectron_cpu.binarypb'
BOX_CONNECTIONS = frozenset([
(BoxLandmark.BACK_BOTTOM_LEFT, BoxLandmark.FRONT_BOTTOM_LEFT),
(BoxLandmark.BACK_BOTTOM_LEFT, BoxLandmark.BACK_TOP_LEFT),
(BoxLandmark.BACK_BOTTOM_LEFT, BoxLandmark.BACK_BOTTOM_RIGHT),
(BoxLandmark.FRONT_BOTTOM_LEFT, BoxLandmark.FRONT_TOP_LEFT),
(BoxLandmark.FRONT_BOTTOM_LEFT, BoxLandmark.FRONT_BOTTOM_RIGHT),
(BoxLandmark.BACK_TOP_LEFT, BoxLandmark.FRONT_TOP_LEFT),
(BoxLandmark.BACK_TOP_LEFT, BoxLandmark.BACK_TOP_RIGHT),
(BoxLandmark.FRONT_TOP_LEFT, BoxLandmark.FRONT_TOP_RIGHT),
(BoxLandmark.BACK_BOTTOM_RIGHT, BoxLandmark.FRONT_BOTTOM_RIGHT),
(BoxLandmark.BACK_BOTTOM_RIGHT, BoxLandmark.BACK_TOP_RIGHT),
(BoxLandmark.FRONT_BOTTOM_RIGHT, BoxLandmark.FRONT_TOP_RIGHT),
(BoxLandmark.BACK_TOP_RIGHT, BoxLandmark.FRONT_TOP_RIGHT),
])
@attr.s(auto_attribs=True)
class ObjectronModel(object):
model_path: str
label_name: str
@attr.s(auto_attribs=True, frozen=True)
class ShoeModel(ObjectronModel):
model_path: str = ('mediapipe/modules/objectron/'
'object_detection_3d_sneakers.tflite')
label_name: str = 'Footwear'
@attr.s(auto_attribs=True, frozen=True)
class ChairModel(ObjectronModel):
model_path: str = ('mediapipe/modules/objectron/'
'object_detection_3d_chair.tflite')
label_name: str = 'Chair'
@attr.s(auto_attribs=True, frozen=True)
class CameraModel(ObjectronModel):
model_path: str = ('mediapipe/modules/objectron/'
'object_detection_3d_camera.tflite')
label_name: str = 'Camera'
@attr.s(auto_attribs=True, frozen=True)
class CupModel(ObjectronModel):
model_path: str = ('mediapipe/modules/objectron/'
'object_detection_3d_cup.tflite')
label_name: str = 'Coffee cup, Mug'
_MODEL_DICT = {
'Shoe': ShoeModel(),
'Chair': ChairModel(),
'Cup': CupModel(),
'Camera': CameraModel()
}
def _download_oss_objectron_models(objectron_model: str):
"""Downloads the objectron models from the MediaPipe Github repo if they don't exist in the package."""
download_utils.download_oss_model(
'mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite'
)
download_utils.download_oss_model(objectron_model)
def get_model_by_name(name: str) -> ObjectronModel:
if name not in _MODEL_DICT:
raise ValueError(f'{name} is not a valid model name for Objectron.')
_download_oss_objectron_models(_MODEL_DICT[name].model_path)
return _MODEL_DICT[name]
@attr.s(auto_attribs=True)
class ObjectronOutputs(object):
landmarks_2d: landmark_pb2.NormalizedLandmarkList
landmarks_3d: landmark_pb2.LandmarkList
rotation: np.ndarray
translation: np.ndarray
scale: np.ndarray
class Objectron(SolutionBase):
"""MediaPipe Objectron.
MediaPipe Objectron processes an RGB image and returns the 3D box landmarks
and 2D rectangular bounding box of each detected object.
"""
def __init__(self,
static_image_mode: bool = False,
max_num_objects: int = 5,
min_detection_confidence: float = 0.5,
min_tracking_confidence: float = 0.99,
model_name: str = 'Shoe',
focal_length: Tuple[float, float] = (1.0, 1.0),
principal_point: Tuple[float, float] = (0.0, 0.0),
image_size: Optional[Tuple[int, int]] = None,
):
"""Initializes a MediaPipe Objectron class.
Args:
static_image_mode: Whether to treat the input images as a batch of static
and possibly unrelated images, or a video stream.
max_num_objects: Maximum number of objects to detect.
min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for object
detection to be considered successful.
min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the
box landmarks to be considered tracked successfully.
model_name: Name of model to use for predicting box landmarks, currently
support {'Shoe', 'Chair', 'Cup', 'Camera'}.
focal_length: Camera focal length `(fx, fy)`, by default is defined in NDC
space. To use focal length (fx_pixel, fy_pixel) in pixel space, users
should provide image_size = (image_width, image_height) to enable
conversions inside the API.
principal_point: Camera principal point (px, py), by default is defined in
NDC space. To use principal point (px_pixel, py_pixel) in pixel space,
users should provide image_size = (image_width, image_height) to enable
conversions inside the API.
image_size (Optional): size (image_width, image_height) of the input image
, ONLY needed when use focal_length and principal_point in pixel space.
Raises:
ConnectionError: If the objectron open source model can't be downloaded
from the MediaPipe Github repo.
"""
# Get Camera parameters.
fx, fy = focal_length
px, py = principal_point
if image_size is not None:
half_width = image_size[0] / 2.0
half_height = image_size[1] / 2.0
fx = fx / half_width
fy = fy / half_height
px = - (px - half_width) / half_width
py = - (py - half_height) / half_height
# Create and init model.
model = get_model_by_name(model_name)
super().__init__(
binary_graph_path=BINARYPB_FILE_PATH,
side_inputs={
'box_landmark_model_path': model.model_path,
'allowed_labels': model.label_name,
'max_num_objects': max_num_objects,
},
calculator_params={
'ConstantSidePacketCalculator.packet': [
constant_side_packet_calculator_pb2
.ConstantSidePacketCalculatorOptions.ConstantSidePacket(
bool_value=not static_image_mode)
],
('objectdetectionoidv4subgraph'
'__TensorsToDetectionsCalculator.min_score_thresh'):
min_detection_confidence,
('boxlandmarksubgraph__ThresholdingCalculator'
'.threshold'):
min_tracking_confidence,
('Lift2DFrameAnnotationTo3DCalculator'
'.normalized_focal_x'): fx,
('Lift2DFrameAnnotationTo3DCalculator'
'.normalized_focal_y'): fy,
('Lift2DFrameAnnotationTo3DCalculator'
'.normalized_principal_point_x'): px,
('Lift2DFrameAnnotationTo3DCalculator'
'.normalized_principal_point_y'): py,
},
outputs=['detected_objects'])
def process(self, image: np.ndarray) -> NamedTuple:
"""Processes an RGB image and returns the box landmarks and rectangular bounding box of each detected object.
Args:
image: An RGB image represented as a numpy ndarray.
Raises:
RuntimeError: If the underlying graph throws any error.
ValueError: If the input image is not three channel RGB.
Returns:
A NamedTuple object with a "detected_objects" field that contains a list
of detected 3D bounding boxes. Each detected box is represented as an
"ObjectronOutputs" instance.
"""
results = super().process(input_data={'image': image})
if results.detected_objects:
results.detected_objects = self._convert_format(results.detected_objects)
else:
results.detected_objects = None
return results
def _convert_format(
self,
inputs: annotation_data_pb2.FrameAnnotation) -> List[ObjectronOutputs]:
new_outputs = list()
for annotation in inputs.annotations:
# Get 3d object pose.
rotation = np.reshape(np.array(annotation.rotation), (3, 3))
translation = np.array(annotation.translation)
scale = np.array(annotation.scale)
# Get 2d/3d landmakrs.
landmarks_2d = landmark_pb2.NormalizedLandmarkList()
landmarks_3d = landmark_pb2.LandmarkList()
for keypoint in annotation.keypoints:
point_2d = keypoint.point_2d
landmarks_2d.landmark.add(x=point_2d.x, y=point_2d.y)
point_3d = keypoint.point_3d
landmarks_3d.landmark.add(x=point_3d.x, y=point_3d.y, z=point_3d.z)
# Add to objectron outputs.
new_outputs.append(ObjectronOutputs(landmarks_2d, landmarks_3d,
rotation, translation, scale=scale))
return new_outputs