def get_face_parts(model='COCO'):
if model == 'COCO':
return [
HKP.Value('NOSE'),
HKP.Value('LEFT_EYE'),
HKP.Value('LEFT_EAR'),
HKP.Value('RIGHT_EYE'),
HKP.Value('RIGHT_EAR')
]
elif model == 'MPI':
# TODO
return []
else:
return []
def data_frame_to_object_annotations(annotations,
model,
has_z=False,
frame_id=0,
resolution=None):
is_valid_model(model)
if annotations.empty:
return ObjectAnnotations()
n_model_joints = int(model.strip('joints'))
joints_values = len(
annotations.drop(['sample_id', 'person_id'], axis=1).columns)
n_annotations_joints = int(joints_values / (4 if has_z else 3))
n_joints = min(n_model_joints, n_annotations_joints)
annotations_pb = ObjectAnnotations()
annotations_pb.frame_id = frame_id
if resolution is not None:
annotations_pb.resolution.width = resolution[0]
annotations_pb.resolution.height = resolution[1]
for _, annotation in annotations.iterrows():
skeleton = annotations_pb.objects.add()
skeleton.id = int(annotation['person_id'])
for joint_id in range(n_joints):
x = annotation['j{:d}x'.format(joint_id)]
y = annotation['j{:d}y'.format(joint_id)]
z = annotation['j{:d}z'.format(joint_id)] if has_z else 0.0
c = annotation['j{:d}c'.format(joint_id)]
human_keypoint = index_to_human_keypoint(joint_id, model)
# check for invalid joint
if (x == 0.0 and y == 0.0 and z == 0.0) or c < 0.0:
continue
if human_keypoint == HKP.Value('UNKNOWN_HUMAN_KEYPOINT'):
continue
keypoint = skeleton.keypoints.add()
keypoint.position.x = x
keypoint.position.y = y
keypoint.position.z = z
keypoint.score = c
keypoint.id = human_keypoint
return annotations_pb
def joints_to_object_annotation(joints, model, valid_joints=None):
ob = ObjectAnnotation()
if joints.shape[0] < 2 or joints.shape[0] > 3:
return ob
is_3d = joints.shape[0] == 3
model = model.lower()
if model == 'coco':
n_joints = 19
PB_PARTS = COCO_PB_PARTS
elif model == 'mpii':
n_joints = 15
PB_PARTS = MPII_PB_PARTS
else:
__raise_wrong_model__()
if joints.shape[1] != n_joints:
raise Exception('Invalid number of joints for this model.')
invalid_joints = np.zeros(n_joints, dtype=bool)
for c in range(n_joints):
if isinstance(valid_joints, np.ndarray) and not valid_joints[c]:
invalid_joints[c] = True
continue
human_keypoint = PB_PARTS[c]
if human_keypoint == HumanKeypoints.Value('UNKNOWN_HUMAN_KEYPOINT'):
invalid_joints[c] = True
continue
x, y, z = joints[0, c], joints[1, c], joints[2, c] if is_3d else 0.0
if all([x == 0.0, y == 0.0, z == 0.0]):
invalid_joints[c] = True
continue
keypoint = ob.keypoints.add()
keypoint.position.x = x
keypoint.position.y = y
if is_3d:
keypoint.position.z = z
keypoint.id = human_keypoint
return ob, invalid_joints
def get_links(model='COCO'):
if model == 'COCO':
return [(HKP.Value('NECK'), HKP.Value('LEFT_SHOULDER')),
(HKP.Value('LEFT_SHOULDER'), HKP.Value('LEFT_ELBOW')),
(HKP.Value('LEFT_ELBOW'), HKP.Value('LEFT_WRIST')),
(HKP.Value('NECK'), HKP.Value('LEFT_HIP')),
(HKP.Value('LEFT_HIP'), HKP.Value('LEFT_KNEE')),
(HKP.Value('LEFT_KNEE'), HKP.Value('LEFT_ANKLE')),
(HKP.Value('NECK'), HKP.Value('RIGHT_SHOULDER')),
(HKP.Value('RIGHT_SHOULDER'), HKP.Value('RIGHT_ELBOW')),
(HKP.Value('RIGHT_ELBOW'), HKP.Value('RIGHT_WRIST')),
(HKP.Value('NECK'), HKP.Value('RIGHT_HIP')),
(HKP.Value('RIGHT_HIP'), HKP.Value('RIGHT_KNEE')),
(HKP.Value('RIGHT_KNEE'), HKP.Value('RIGHT_ANKLE')),
(HKP.Value('NOSE'), HKP.Value('LEFT_EYE')),
(HKP.Value('LEFT_EYE'), HKP.Value('LEFT_EAR')),
(HKP.Value('NOSE'), HKP.Value('RIGHT_EYE')),
(HKP.Value('RIGHT_EYE'), HKP.Value('RIGHT_EAR'))]
elif model == 'MPI':
# TODO
return []
else:
return []
from video_loader import MultipleVideoLoader
from is_wire.core import Logger
from collections import defaultdict, OrderedDict
import matplotlib
import statistics
from is_msgs.image_pb2 import ObjectAnnotations
from is_msgs.image_pb2 import HumanKeypoints as HKP
from google.protobuf.json_format import ParseDict
from itertools import permutations
from analysis import SkeletonsCoord
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
colors = list(permutations([0, 255, 85, 170], 3))
links = [(HKP.Value('HEAD'), HKP.Value('NECK')),
(HKP.Value('NECK'), HKP.Value('CHEST')),
(HKP.Value('CHEST'), HKP.Value('RIGHT_HIP')),
(HKP.Value('CHEST'), HKP.Value('LEFT_HIP')),
(HKP.Value('NECK'), HKP.Value('LEFT_SHOULDER')),
(HKP.Value('LEFT_SHOULDER'), HKP.Value('LEFT_ELBOW')),
(HKP.Value('LEFT_ELBOW'), HKP.Value('LEFT_WRIST')),
(HKP.Value('NECK'), HKP.Value('LEFT_HIP')),
(HKP.Value('LEFT_HIP'), HKP.Value('LEFT_KNEE')),
(HKP.Value('LEFT_KNEE'), HKP.Value('LEFT_ANKLE')),
(HKP.Value('NECK'), HKP.Value('RIGHT_SHOULDER')),
(HKP.Value('RIGHT_SHOULDER'), HKP.Value('RIGHT_ELBOW')),
(HKP.Value('RIGHT_ELBOW'), HKP.Value('RIGHT_WRIST')),
(HKP.Value('NECK'), HKP.Value('RIGHT_HIP')),
(HKP.Value('RIGHT_HIP'), HKP.Value('RIGHT_KNEE')),
(HKP.Value('RIGHT_KNEE'), HKP.Value('RIGHT_ANKLE')),
from src.panoptic_dataset.utils import is_valid_model
from is_msgs.image_pb2 import HumanKeypoints as HKP
def _reverse_model_joints(joints_list):
reverse_list = [-1] * (max(joints_list) + 1)
for joint_index, human_keypoint in enumerate(joints_list):
reverse_list[human_keypoint] = joint_index
return reverse_list
MODEL_15_JOINTS = [
HKP.Value('NECK'), \
HKP.Value('HEAD'), \
HKP.Value('CHEST'), \
HKP.Value('LEFT_SHOULDER'), \
HKP.Value('LEFT_ELBOW'), \
HKP.Value('LEFT_WRIST'), \
HKP.Value('LEFT_HIP'), \
HKP.Value('LEFT_KNEE'), \
HKP.Value('LEFT_ANKLE'), \
HKP.Value('RIGHT_SHOULDER'), \
HKP.Value('RIGHT_ELBOW'), \
HKP.Value('RIGHT_WRIST'), \
HKP.Value('RIGHT_HIP'), \
HKP.Value('RIGHT_KNEE'), \
HKP.Value('RIGHT_ANKLE')
]
# HumanKeyPoint.Value('UNKNOWN_HUMAN_KEYPOINT') represents the Background
MODEL_19_JOINTS = [
import json
import numpy as np
from is_msgs.image_pb2 import ObjectAnnotation, HumanKeypoints
from is_msgs.camera_pb2 import CameraCalibration
from .numpy import to_tensor
REGEX_DETECTION_ID = re.compile(r'body3DScene_([0-9]+).json')
DETECTION_FILE = 'body3DScene_{:08d}.json'
'''
http://domedb.perception.cs.cmu.edu/tools.html
Neck, HeadTop, BodyCenter, lShoulder,lElbow, lWrist, lHip,
lKnee, lAnkle, rShoulder, rElbow, rWrist, rHip, rKnee, rAnkle
'''
MPII_PB_PARTS = [
HumanKeypoints.Value('NECK'), \
HumanKeypoints.Value('HEAD'), \
HumanKeypoints.Value('CHEST'), \
HumanKeypoints.Value('LEFT_SHOULDER'), \
HumanKeypoints.Value('LEFT_ELBOW'), \
HumanKeypoints.Value('LEFT_WRIST'), \
HumanKeypoints.Value('LEFT_HIP'), \
HumanKeypoints.Value('LEFT_KNEE'), \
HumanKeypoints.Value('LEFT_ANKLE'), \
HumanKeypoints.Value('RIGHT_SHOULDER'), \
HumanKeypoints.Value('RIGHT_ELBOW'), \
HumanKeypoints.Value('RIGHT_WRIST'), \
HumanKeypoints.Value('RIGHT_HIP'), \
HumanKeypoints.Value('RIGHT_KNEE'), \
HumanKeypoints.Value('RIGHT_ANKLE')
]
sid = 0
while True:
sks = reader.next(Skeletons())
if not sks:
log.info("[Done][{}] camera \'{}\' with {} sequences", dataset,
camera, sid + 1)
break
objs = ObjectAnnotations()
# panoptic dataset HD cameras resolution
objs.resolution.width = width
objs.resolution.height = height
for sk in sks.skeletons:
obj = objs.objects.add()
for part in sk.parts:
type_str = SkeletonPartType.Name(part.type)
if type_str == 'UNKNOWN' or type_str == 'BACKGROUND':
continue
keypoint = obj.keypoints.add()
keypoint.id = HumanKeypoints.Value(type_str)
keypoint.score = part.score
keypoint.position.x = part.x
keypoint.position.y = part.y
sequence_id = Int64Value()
sequence_id.value = sid
writer.insert(sequence_id)
writer.insert(objs)
sid += 1
def __init__(self, options):
if not isinstance(options, Options):
raise Exception(
'Invalid parameter on \'SkeletonsDetector\' constructor: not a Options type'
)
self.__op = options
model = '{}x{}'.format(self.__op.resize.width, self.__op.resize.height)
w, h = model_wh(model)
self.__resize_to_default = (w > 0 and h > 0)
model_name = Options.Model.Name(self.__op.model).lower()
graph_path = get_graph_path(model_name=model_name,
base_path=self.__op.models_folder)
self.__e = TfPoseEstimator(
graph_path,
target_size=(w, h),
gpu_mem_allow_growth=options.gpu_mem_allow_growth,
per_process_gpu_memory_fraction=options.
per_process_gpu_memory_fraction)
self.__to_sks_part = {
0: HumanKeypoints.Value('NOSE'),
1: HumanKeypoints.Value('NECK'),
2: HumanKeypoints.Value('RIGHT_SHOULDER'),
3: HumanKeypoints.Value('RIGHT_ELBOW'),
4: HumanKeypoints.Value('RIGHT_WRIST'),
5: HumanKeypoints.Value('LEFT_SHOULDER'),
6: HumanKeypoints.Value('LEFT_ELBOW'),
7: HumanKeypoints.Value('LEFT_WRIST'),
8: HumanKeypoints.Value('RIGHT_HIP'),
9: HumanKeypoints.Value('RIGHT_KNEE'),
10: HumanKeypoints.Value('RIGHT_ANKLE'),
11: HumanKeypoints.Value('LEFT_HIP'),
12: HumanKeypoints.Value('LEFT_KNEE'),
13: HumanKeypoints.Value('LEFT_ANKLE'),
14: HumanKeypoints.Value('RIGHT_EYE'),
15: HumanKeypoints.Value('LEFT_EYE'),
16: HumanKeypoints.Value('RIGHT_EAR'),
17: HumanKeypoints.Value('LEFT_EAR'),
}