code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def gating_distance(self,
mean,
covariance,
measurements,
only_position=False,
metric='maha'):
"""
Compute gating distance between state distribution and measurements.
A suitab... |
Compute gating distance between state distribution and measurements.
A suitable distance threshold can be obtained from `chi2inv95`. If
`only_position` is False, the chi-square distribution has 4 degrees of
freedom, otherwise 2.
Args:
mean (ndarray): Mean ve... | gating_distance | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/motion/kalman_filter.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/motion/kalman_filter.py | Apache-2.0 |
def sub_cluster(cid_tid_dict,
scene_cluster,
use_ff=True,
use_rerank=True,
use_camera=False,
use_st_filter=False):
'''
cid_tid_dict: all camera_id and track_id
scene_cluster: like [41, 42, 43, 44, 45, 46] in AIC21 MTMCT S06 test... |
cid_tid_dict: all camera_id and track_id
scene_cluster: like [41, 42, 43, 44, 45, 46] in AIC21 MTMCT S06 test videos
| sub_cluster | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/mtmct/postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/mtmct/postprocess.py | Apache-2.0 |
def getData(fpath, names=None, sep='\s+|\t+|,'):
""" Get the necessary track data from a file handle.
Args:
fpath (str) : Original path of file reading from.
names (list[str]): List of column names for the data.
sep (str): Allowed separators regular expression string.
Return:
... | Get the necessary track data from a file handle.
Args:
fpath (str) : Original path of file reading from.
names (list[str]): List of column names for the data.
sep (str): Allowed separators regular expression string.
Return:
df (pandas.DataFrame): Data frame containing the data l... | getData | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/mtmct/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/mtmct/utils.py | Apache-2.0 |
def init_count(num_classes):
"""
Initiate _count for all object classes
:param num_classes:
"""
for cls_id in range(num_classes):
BaseTrack._count_dict[cls_id] = 0 |
Initiate _count for all object classes
:param num_classes:
| init_count | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | Apache-2.0 |
def tlwh(self):
"""Get current position in bounding box format `(top left x, top left y,
width, height)`.
"""
if self.mean is None:
return self._tlwh.copy()
ret = self.mean[:4].copy()
ret[2] *= ret[3]
ret[:2] -= ret[2:] / 2
return ret | Get current position in bounding box format `(top left x, top left y,
width, height)`.
| tlwh | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | Apache-2.0 |
def tlbr(self):
"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
`(top left, bottom right)`.
"""
ret = self.tlwh.copy()
ret[2:] += ret[:2]
return ret | Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
`(top left, bottom right)`.
| tlbr | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | Apache-2.0 |
def tlwh_to_xyah(tlwh):
"""Convert bounding box to format `(center x, center y, aspect ratio,
height)`, where the aspect ratio is `width / height`.
"""
ret = np.asarray(tlwh).copy()
ret[:2] += ret[2:] / 2
ret[2] /= ret[3]
return ret | Convert bounding box to format `(center x, center y, aspect ratio,
height)`, where the aspect ratio is `width / height`.
| tlwh_to_xyah | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_jde_tracker.py | Apache-2.0 |
def to_tlwh(self):
"""Get position in format `(top left x, top left y, width, height)`."""
ret = self.mean[:4].copy()
ret[2] *= ret[3]
ret[:2] -= ret[2:] / 2
return ret | Get position in format `(top left x, top left y, width, height)`. | to_tlwh | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | Apache-2.0 |
def to_tlbr(self):
"""Get position in bounding box format `(min x, miny, max x, max y)`."""
ret = self.to_tlwh()
ret[2:] = ret[:2] + ret[2:]
return ret | Get position in bounding box format `(min x, miny, max x, max y)`. | to_tlbr | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | Apache-2.0 |
def predict(self, kalman_filter):
"""
Propagate the state distribution to the current time step using a Kalman
filter prediction step.
"""
self.mean, self.covariance = kalman_filter.predict(self.mean,
self.covariance)
... |
Propagate the state distribution to the current time step using a Kalman
filter prediction step.
| predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | Apache-2.0 |
def update(self, kalman_filter, detection):
"""
Perform Kalman filter measurement update step and update the associated
detection feature cache.
"""
self.mean, self.covariance = kalman_filter.update(self.mean,
self.covaria... |
Perform Kalman filter measurement update step and update the associated
detection feature cache.
| update | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | Apache-2.0 |
def mark_missed(self):
"""Mark this track as missed (no association at the current time step).
"""
if self.state == TrackState.Tentative:
self.state = TrackState.Deleted
elif self.time_since_update > self._max_age:
self.state = TrackState.Deleted | Mark this track as missed (no association at the current time step).
| mark_missed | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/base_sde_tracker.py | Apache-2.0 |
def update(self, pred_dets, pred_embs):
"""
Perform measurement update and track management.
Args:
pred_dets (np.array): Detection results of the image, the shape is
[N, 6], means 'cls_id, score, x0, y0, x1, y1'.
pred_embs (np.array): Embedding results of ... |
Perform measurement update and track management.
Args:
pred_dets (np.array): Detection results of the image, the shape is
[N, 6], means 'cls_id, score, x0, y0, x1, y1'.
pred_embs (np.array): Embedding results of the image, the shape is
[N, 128], u... | update | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/deepsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/deepsort_tracker.py | Apache-2.0 |
def update(self, pred_dets, pred_embs=None):
"""
Processes the image frame and finds bounding box(detections).
Associates the detection with corresponding tracklets and also handles
lost, removed, refound and active tracklets.
Args:
pred_dets (np.array): Detectio... |
Processes the image frame and finds bounding box(detections).
Associates the detection with corresponding tracklets and also handles
lost, removed, refound and active tracklets.
Args:
pred_dets (np.array): Detection results of the image, the shape is
[N,... | update | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/jde_tracker.py | Apache-2.0 |
def convert_bbox_to_z(bbox):
"""
Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
[x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
the aspect ratio
"""
w = bbox[2] - bbox[0]
h = bbox[3] - bbox[1]
x = bbox[0] + w / 2.
y = bbox[1... |
Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
[x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
the aspect ratio
| convert_bbox_to_z | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | Apache-2.0 |
def convert_x_to_bbox(x, score=None):
"""
Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
[x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
"""
w = np.sqrt(x[2] * x[3])
h = x[2] / w
if (score == None):
return np.array(
... |
Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
[x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
| convert_x_to_bbox | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | Apache-2.0 |
def update(self, bbox):
"""
Updates the state vector with observed bbox.
"""
if bbox is not None:
if self.last_observation.sum() >= 0: # no previous observation
previous_box = None
for i in range(self.delta_t):
dt = self.de... |
Updates the state vector with observed bbox.
| update | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | Apache-2.0 |
def predict(self):
"""
Advances the state vector and returns the predicted bounding box estimate.
"""
if ((self.kf.x[6] + self.kf.x[2]) <= 0):
self.kf.x[6] *= 0.0
self.kf.predict()
self.age += 1
if (self.time_since_update > 0):
self.hit_st... |
Advances the state vector and returns the predicted bounding box estimate.
| predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | Apache-2.0 |
def update(self, pred_dets, pred_embs=None):
"""
Args:
pred_dets (np.array): Detection results of the image, the shape is
[N, 6], means 'cls_id, score, x0, y0, x1, y1'.
pred_embs (np.array): Embedding results of the image, the shape is
[N, 128] or ... |
Args:
pred_dets (np.array): Detection results of the image, the shape is
[N, 6], means 'cls_id, score, x0, y0, x1, y1'.
pred_embs (np.array): Embedding results of the image, the shape is
[N, 128] or [N, 512], default as None.
Return:
... | update | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/pptracking/python/mot/tracker/ocsort_tracker.py | Apache-2.0 |
def __init__(self,
config,
model_info: dict={},
data_info: dict={},
perf_info: dict={},
resource_info: dict={},
**kwargs):
"""
Construct PaddleInferBenchmark Class to format logs.
args:
... |
Construct PaddleInferBenchmark Class to format logs.
args:
config(paddle.inference.Config): paddle inference config
model_info(dict): basic model info
{'model_name': 'resnet50'
'precision': 'fp32'}
data_info(dict): input data info
... | __init__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | Apache-2.0 |
def parse_config(self, config) -> dict:
"""
parse paddle predictor config
args:
config(paddle.inference.Config): paddle inference config
return:
config_status(dict): dict style config info
"""
if isinstance(config, paddle_infer.Config):
... |
parse paddle predictor config
args:
config(paddle.inference.Config): paddle inference config
return:
config_status(dict): dict style config info
| parse_config | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | Apache-2.0 |
def report(self, identifier=None):
"""
print log report
args:
identifier(string): identify log
"""
if identifier:
identifier = f"[{identifier}]"
else:
identifier = ""
self.logger.info("\n")
self.logger.info(
... |
print log report
args:
identifier(string): identify log
| report | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/benchmark_utils.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
... |
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's result include 'mask... | predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): 'segm': np.ndarray,shape:[N, im_h, im_w]
'cate_label': label of segm, shape:[N]
'cate_score': confidence sco... |
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): 'segm': np.ndarray,shape:[N, im_h, im_w]
'cate_label': label of segm, shape:[N]
'cate_score': confidence score of segm, shape:[N]
| predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
'''
... |
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
| predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def create_inputs(imgs, im_info):
"""generate input for different model type
Args:
imgs (list(numpy)): list of images (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
"""
inputs = {}
im_shape = []
scale_factor = []
if l... | generate input for different model type
Args:
imgs (list(numpy)): list of images (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
| create_inputs | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def check_model(self, yml_conf):
"""
Raises:
ValueError: loaded model not in supported model type
"""
for support_model in SUPPORT_MODELS:
if support_model in yml_conf['arch']:
return True
raise ValueError("Unsupported arch: {}, expect {}"... |
Raises:
ValueError: loaded model not in supported model type
| check_model | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def load_predictor(model_dir,
run_mode='paddle',
batch_size=1,
device='CPU',
min_subgraph_size=3,
use_dynamic_shape=False,
trt_min_shape=1,
trt_max_shape=1280,
trt_opt_... | set AnalysisConfig, generate AnalysisPredictor
Args:
model_dir (str): root path of __model__ and __params__
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(paddle/trt_fp32/trt_fp16/trt_int8)
use_dynamic_shape (bo... | load_predictor | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def get_test_images(infer_dir, infer_img):
"""
Get image path list in TEST mode
"""
assert infer_img is not None or infer_dir is not None, \
"--image_file or --image_dir should be set"
assert infer_img is None or os.path.isfile(infer_img), \
"{} is not a file".format(infer_img)
... |
Get image path list in TEST mode
| get_test_images | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/infer.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeat number for prediction
Returns:
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
... |
Args:
repeats (int): repeat number for prediction
Returns:
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's results include 'mas... | predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | Apache-2.0 |
def create_inputs(imgs, im_info):
"""generate input for different model type
Args:
imgs (list(numpy)): list of image (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
"""
inputs = {}
inputs['image'] = np.stack(imgs, axis=0).astyp... | generate input for different model type
Args:
imgs (list(numpy)): list of image (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
| create_inputs | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | Apache-2.0 |
def check_model(self, yml_conf):
"""
Raises:
ValueError: loaded model not in supported model type
"""
for support_model in KEYPOINT_SUPPORT_MODELS:
if support_model in yml_conf['arch']:
return True
raise ValueError("Unsupported arch: {}, e... |
Raises:
ValueError: loaded model not in supported model type
| check_model | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_infer.py | Apache-2.0 |
def warp_affine_joints(joints, mat):
"""Apply affine transformation defined by the transform matrix on the
joints.
Args:
joints (np.ndarray[..., 2]): Origin coordinate of joints.
mat (np.ndarray[3, 2]): The affine matrix.
Returns:
matrix (np.ndarray[..., 2]): Result coordinate ... | Apply affine transformation defined by the transform matrix on the
joints.
Args:
joints (np.ndarray[..., 2]): Origin coordinate of joints.
mat (np.ndarray[3, 2]): The affine matrix.
Returns:
matrix (np.ndarray[..., 2]): Result coordinate of joints.
| warp_affine_joints | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | Apache-2.0 |
def get_max_preds(self, heatmaps):
"""get predictions from score maps
Args:
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_... | get predictions from score maps
Args:
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the key... | get_max_preds | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | Apache-2.0 |
def dark_postprocess(self, hm, coords, kernelsize):
"""
refer to https://github.com/ilovepose/DarkPose/lib/core/inference.py
"""
hm = self.gaussian_blur(hm, kernelsize)
hm = np.maximum(hm, 1e-10)
hm = np.log(hm)
for n in range(coords.shape[0]):
for p ... |
refer to https://github.com/ilovepose/DarkPose/lib/core/inference.py
| dark_postprocess | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | Apache-2.0 |
def get_final_preds(self, heatmaps, center, scale, kernelsize=3):
"""the highest heatvalue location with a quarter offset in the
direction from the highest response to the second highest response.
Args:
heatmaps (numpy.ndarray): The predicted heatmaps
center (numpy.ndarr... | the highest heatvalue location with a quarter offset in the
direction from the highest response to the second highest response.
Args:
heatmaps (numpy.ndarray): The predicted heatmaps
center (numpy.ndarray): The boxes center
scale (numpy.ndarray): The scale factor
... | get_final_preds | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_postprocess.py | Apache-2.0 |
def get_affine_transform(center,
input_size,
rot,
output_size,
shift=(0., 0.),
inv=False):
"""Get the affine transform matrix, given the center/scale/rot/output_size.
Args:
cente... | Get the affine transform matrix, given the center/scale/rot/output_size.
Args:
center (np.ndarray[2, ]): Center of the bounding box (x, y).
scale (np.ndarray[2, ]): Scale of the bounding box
wrt [width, height].
rot (float): Rotation angle (degree).
output_size (np.ndarr... | get_affine_transform | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | Apache-2.0 |
def get_warp_matrix(theta, size_input, size_dst, size_target):
"""This code is based on
https://github.com/open-mmlab/mmpose/blob/master/mmpose/core/post_processing/post_transforms.py
Calculate the transformation matrix under the constraint of unbiased.
Paper ref: Huang et al. The Devil is in ... | This code is based on
https://github.com/open-mmlab/mmpose/blob/master/mmpose/core/post_processing/post_transforms.py
Calculate the transformation matrix under the constraint of unbiased.
Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased
Data Processing for Human Pose ... | get_warp_matrix | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | Apache-2.0 |
def rotate_point(pt, angle_rad):
"""Rotate a point by an angle.
Args:
pt (list[float]): 2 dimensional point to be rotated
angle_rad (float): rotation angle by radian
Returns:
list[float]: Rotated point.
"""
assert len(pt) == 2
sn, cs = np.sin(angle_rad), np.cos(angle_ra... | Rotate a point by an angle.
Args:
pt (list[float]): 2 dimensional point to be rotated
angle_rad (float): rotation angle by radian
Returns:
list[float]: Rotated point.
| rotate_point | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | Apache-2.0 |
def _get_3rd_point(a, b):
"""To calculate the affine matrix, three pairs of points are required. This
function is used to get the 3rd point, given 2D points a & b.
The 3rd point is defined by rotating vector `a - b` by 90 degrees
anticlockwise, using b as the rotation center.
Args:
a (np.n... | To calculate the affine matrix, three pairs of points are required. This
function is used to get the 3rd point, given 2D points a & b.
The 3rd point is defined by rotating vector `a - b` by 90 degrees
anticlockwise, using b as the rotation center.
Args:
a (np.ndarray): point(x,y)
b (np... | _get_3rd_point | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/keypoint_preprocess.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'pred_dets': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
... |
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'pred_dets': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
FairMOT(JDE)'s result inclu... | predict | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/mot_jde_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/mot_jde_infer.py | Apache-2.0 |
def hard_nms(box_scores, iou_threshold, top_k=-1, candidate_size=200):
"""
Args:
box_scores (N, 5): boxes in corner-form and probabilities.
iou_threshold: intersection over union threshold.
top_k: keep top_k results. If k <= 0, keep all the results.
candidate_size: only consider ... |
Args:
box_scores (N, 5): boxes in corner-form and probabilities.
iou_threshold: intersection over union threshold.
top_k: keep top_k results. If k <= 0, keep all the results.
candidate_size: only consider the candidates with the highest scores.
Returns:
picked: a list o... | hard_nms | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | Apache-2.0 |
def iou_of(boxes0, boxes1, eps=1e-5):
"""Return intersection-over-union (Jaccard index) of boxes.
Args:
boxes0 (N, 4): ground truth boxes.
boxes1 (N or 1, 4): predicted boxes.
eps: a small number to avoid 0 as denominator.
Returns:
iou (N): IoU values.
"""
overlap_lef... | Return intersection-over-union (Jaccard index) of boxes.
Args:
boxes0 (N, 4): ground truth boxes.
boxes1 (N or 1, 4): predicted boxes.
eps: a small number to avoid 0 as denominator.
Returns:
iou (N): IoU values.
| iou_of | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | Apache-2.0 |
def area_of(left_top, right_bottom):
"""Compute the areas of rectangles given two corners.
Args:
left_top (N, 2): left top corner.
right_bottom (N, 2): right bottom corner.
Returns:
area (N): return the area.
"""
hw = np.clip(right_bottom - left_top, 0.0, None)
return hw[... | Compute the areas of rectangles given two corners.
Args:
left_top (N, 2): left top corner.
right_bottom (N, 2): right bottom corner.
Returns:
area (N): return the area.
| area_of | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/picodet_postprocess.py | Apache-2.0 |
def decode_image(im_file, im_info):
"""read rgb image
Args:
im_file (str|np.ndarray): input can be image path or np.ndarray
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
if isinstance(... | read rgb image
Args:
im_file (str|np.ndarray): input can be image path or np.ndarray
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| decode_image | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im_channel = im.shape[2... |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def generate_scale(self, img):
"""
Args:
img (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
"""
limit_side_len = self.limit_side_len
h, w, c = img.shape
# limit the... |
Args:
img (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
| generate_scale | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
assert len(self.target_... |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def generate_scale(self, im):
"""
Args:
im (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
"""
origin_shape = im.shape[:2]
im_c = im.shape[2]
if self.keep_ratio:
... |
Args:
im (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
| generate_scale | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, img):
"""
Performs resize operations.
Args:
img (PIL.Image): a PIL.Image.
return:
resized_img: a PIL.Image after scaling.
"""
result_img = None
if isinstance(img, np.ndarray):
h, w, _ = img.shape
eli... |
Performs resize operations.
Args:
img (PIL.Image): a PIL.Image.
return:
resized_img: a PIL.Image after scaling.
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im = im.astype(np.float... |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im = im.transpose((2, 0... |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
coarsest_stride = self.... |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __init__(self, target_size):
"""
Resize image to target size, convert normalized xywh to pixel xyxy
format ([x_center, y_center, width, height] -> [x0, y0, x1, y1]).
Args:
target_size (int|list): image target size.
"""
super(LetterBoxResize, self).__init__... |
Resize image to target size, convert normalized xywh to pixel xyxy
format ([x_center, y_center, width, height] -> [x0, y0, x1, y1]).
Args:
target_size (int|list): image target size.
| __init__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
assert len(self.target_... |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __init__(self, size, fill_value=[114.0, 114.0, 114.0]):
"""
Pad image to a specified size.
Args:
size (list[int]): image target size
fill_value (list[float]): rgb value of pad area, default (114.0, 114.0, 114.0)
"""
super(Pad, self).__init__()
... |
Pad image to a specified size.
Args:
size (list[int]): image target size
fill_value (list[float]): rgb value of pad area, default (114.0, 114.0, 114.0)
| __init__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
img = cv2.cvtColor(im, ... |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/preprocess.py | Apache-2.0 |
def get_current_memory_mb():
"""
It is used to Obtain the memory usage of the CPU and GPU during the running of the program.
And this function Current program is time-consuming.
"""
import pynvml
import psutil
import GPUtil
gpu_id = int(os.environ.get('CUDA_VISIBLE_DEVICES', 0))
pid... |
It is used to Obtain the memory usage of the CPU and GPU during the running of the program.
And this function Current program is time-consuming.
| get_current_memory_mb | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/utils.py | Apache-2.0 |
def nms(dets, match_threshold=0.6, match_metric='iou'):
""" Apply NMS to avoid detecting too many overlapping bounding boxes.
Args:
dets: shape [N, 5], [score, x1, y1, x2, y2]
match_metric: 'iou' or 'ios'
match_threshold: overlap thresh for match metric.
"""
if de... | Apply NMS to avoid detecting too many overlapping bounding boxes.
Args:
dets: shape [N, 5], [score, x1, y1, x2, y2]
match_metric: 'iou' or 'ios'
match_threshold: overlap thresh for match metric.
| nms | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/utils.py | Apache-2.0 |
def visualize_box_mask(im, results, labels, threshold=0.5):
"""
Args:
im (str/np.ndarray): path of image/np.ndarray read by cv2
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
... |
Args:
im (str/np.ndarray): path of image/np.ndarray read by cv2
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's results include 'masks': np.ndarray:
... | visualize_box_mask | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/visualize.py | Apache-2.0 |
def get_color_map_list(num_classes):
"""
Args:
num_classes (int): number of class
Returns:
color_map (list): RGB color list
"""
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((... |
Args:
num_classes (int): number of class
Returns:
color_map (list): RGB color list
| get_color_map_list | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/visualize.py | Apache-2.0 |
def draw_mask(im, np_boxes, np_masks, labels, threshold=0.5):
"""
Args:
im (PIL.Image.Image): PIL image
np_boxes (np.ndarray): shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
np_masks (np.ndarray): shape:[N, im_h, im_w]
labels (... |
Args:
im (PIL.Image.Image): PIL image
np_boxes (np.ndarray): shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
np_masks (np.ndarray): shape:[N, im_h, im_w]
labels (list): labels:['class1', ..., 'classn']
threshold (float): th... | draw_mask | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/visualize.py | Apache-2.0 |
def draw_box(im, np_boxes, labels, threshold=0.5):
"""
Args:
im (PIL.Image.Image): PIL image
np_boxes (np.ndarray): shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
labels (list): labels:['class1', ..., 'classn']
... |
Args:
im (PIL.Image.Image): PIL image
np_boxes (np.ndarray): shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
labels (list): labels:['class1', ..., 'classn']
threshold (float): threshold of box
Returns:
... | draw_box | python | PaddlePaddle/models | modelcenter/PP-HumanV2/APP/python/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-HumanV2/APP/python/visualize.py | Apache-2.0 |
def __init__(self, cfg):
"""
Prepare for prediction.
The usage and docs of paddle inference, please refer to
https://paddleinference.paddlepaddle.org.cn/product_introduction/summary.html
"""
self.cfg = DeployConfig(cfg)
self._init_base_config()
self._ini... |
Prepare for prediction.
The usage and docs of paddle inference, please refer to
https://paddleinference.paddlepaddle.org.cn/product_introduction/summary.html
| __init__ | python | PaddlePaddle/models | modelcenter/PP-LiteSeg/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-LiteSeg/APP/app.py | Apache-2.0 |
def get_pseudo_color_map(self, pred, color_map=None):
"""
Get the pseudo color image.
Args:
pred (numpy.ndarray): the origin predicted image.
color_map (list, optional): the palette color map. Default: None,
use paddleseg's default color map.
Retur... |
Get the pseudo color image.
Args:
pred (numpy.ndarray): the origin predicted image.
color_map (list, optional): the palette color map. Default: None,
use paddleseg's default color map.
Returns:
(numpy.ndarray): the pseduo image.
| get_pseudo_color_map | python | PaddlePaddle/models | modelcenter/PP-LiteSeg/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-LiteSeg/APP/app.py | Apache-2.0 |
def get_color_map_list(self, num_classes, custom_color=None):
"""
Returns the color map for visualizing the segmentation mask,
which can support arbitrary number of classes.
Args:
num_classes (int): Number of classes.
custom_color (list, optional): Save images wit... |
Returns the color map for visualizing the segmentation mask,
which can support arbitrary number of classes.
Args:
num_classes (int): Number of classes.
custom_color (list, optional): Save images with a custom color map. Default: None, use paddleseg's default color map.
... | get_color_map_list | python | PaddlePaddle/models | modelcenter/PP-LiteSeg/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-LiteSeg/APP/app.py | Apache-2.0 |
def get_output(img, size, bg, download_size):
"""
Get the special size and background photo.
Args:
img(numpy:ndarray): The image array.
size(str): The size user specified.
bg(str): The background color user specified.
download_size(str): The size for image saving.
"""
... |
Get the special size and background photo.
Args:
img(numpy:ndarray): The image array.
size(str): The size user specified.
bg(str): The background color user specified.
download_size(str): The size for image saving.
| get_output | python | PaddlePaddle/models | modelcenter/PP-Matting/APP1/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Matting/APP1/app.py | Apache-2.0 |
def __init__(self, cfg):
"""
Prepare for prediction.
The usage and docs of paddle inference, please refer to
https://paddleinference.paddlepaddle.org.cn/product_introduction/summary.html
"""
self.cfg = DeployConfig(cfg)
self._init_base_config()
self._ini... |
Prepare for prediction.
The usage and docs of paddle inference, please refer to
https://paddleinference.paddlepaddle.org.cn/product_introduction/summary.html
| __init__ | python | PaddlePaddle/models | modelcenter/PP-Matting/APP1/predict.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Matting/APP1/predict.py | Apache-2.0 |
def is_url(path):
"""
Whether path is URL.
Args:
path (string): URL string or not.
"""
return path.startswith('http://') \
or path.startswith('https://') \
or path.startswith('paddlecv://') |
Whether path is URL.
Args:
path (string): URL string or not.
| is_url | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def get_model_path(path):
"""Get model path from WEIGHTS_HOME, if not exists,
download it from url.
"""
if not is_url(path):
return path
url = parse_url(path)
path, _ = get_path(url, WEIGHTS_HOME, path_depth=2)
return path | Get model path from WEIGHTS_HOME, if not exists,
download it from url.
| get_model_path | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def get_config_path(path):
"""Get config path from CONFIGS_HOME, if not exists,
download it from url.
"""
if not is_url(path):
return path
url = parse_url(path)
path, _ = get_path(url, CONFIGS_HOME)
return path | Get config path from CONFIGS_HOME, if not exists,
download it from url.
| get_config_path | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def get_dict_path(path):
"""Get config path from CONFIGS_HOME, if not exists,
download it from url.
"""
if not is_url(path):
return path
url = parse_url(path)
path, _ = get_path(url, DICTS_HOME)
return path | Get config path from CONFIGS_HOME, if not exists,
download it from url.
| get_dict_path | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def get_path(url, root_dir, md5sum=None, check_exist=True, path_depth=1):
""" Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url, return the path.
url (str): download url
root_dir (str): root ... | Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url, return the path.
url (str): download url
root_dir (str): root dir for downloading, it should be
WEIGHTS_HOME
md5sum (st... | get_path | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def _download(url, path, md5sum=None):
"""
Download from url, save to path.
url (str): download url
path (str): download to given path
"""
if not osp.exists(path):
os.makedirs(path)
fname = osp.split(url)[-1]
fullname = osp.join(path, fname)
retry_cnt = 0
while not (osp... |
Download from url, save to path.
url (str): download url
path (str): download to given path
| _download | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/download.py | Apache-2.0 |
def decode_image(im_file, im_info):
"""read rgb image
Args:
im_file (str|np.ndarray): input can be image path or np.ndarray
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
if isinstance(... | read rgb image
Args:
im_file (str|np.ndarray): input can be image path or np.ndarray
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| decode_image | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
assert len(self.target_... |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def generate_scale(self, im):
"""
Args:
im (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
"""
origin_shape = im.shape[:2]
im_c = im.shape[2]
if self.keep_ratio:
... |
Args:
im (np.ndarray): image (np.ndarray)
Returns:
im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
| generate_scale | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im = im.astype(np.float... |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
im = im.transpose((2, 0... |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def __call__(self, im, im_info):
"""
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
"""
coarsest_stride = self.... |
Args:
im (np.ndarray): image (np.ndarray)
im_info (dict): info of image
Returns:
im (np.ndarray): processed image (np.ndarray)
im_info (dict): info of processed image
| __call__ | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/preprocess.py | Apache-2.0 |
def get_color_map_list(num_classes):
"""
Args:
num_classes (int): number of class
Returns:
color_map (list): RGB color list
"""
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((... |
Args:
num_classes (int): number of class
Returns:
color_map (list): RGB color list
| get_color_map_list | python | PaddlePaddle/models | modelcenter/PP-PicoDet/APP/src/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-PicoDet/APP/src/visualize.py | Apache-2.0 |
def download_with_progressbar(url: str, save_path: str):
"""Download file from given url and decompress it
Args:
url (str): url
save_path (str): path for saving downloaded file
Raises:
Exception: exception
"""
print(f"Auto downloading {url} to {save_path}")
if os.path.e... | Download file from given url and decompress it
Args:
url (str): url
save_path (str): path for saving downloaded file
Raises:
Exception: exception
| download_with_progressbar | python | PaddlePaddle/models | modelcenter/PP-ShiTuV2/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-ShiTuV2/APP/app.py | Apache-2.0 |
def model_inference(image) -> tuple:
"""send given image to inference model and get result from output
Args:
image (gr.Image): input image
Returns:
tuple: (drawn image to display, result in json format)
"""
results = clas_engine.predict(image, print_pred=True, predict_type="shitu")... | send given image to inference model and get result from output
Args:
image (gr.Image): input image
Returns:
tuple: (drawn image to display, result in json format)
| model_inference | python | PaddlePaddle/models | modelcenter/PP-ShiTuV2/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-ShiTuV2/APP/app.py | Apache-2.0 |
def draw_bbox_results(image: Union[np.ndarray, Image.Image],
results: List[Dict[str, Any]]) -> np.ndarray:
"""draw bounding box(es)
Args:
image (Union[np.ndarray, Image.Image]): image to be drawn
results (List[Dict[str, Any]]): information for drawing bounding box
Ret... | draw bounding box(es)
Args:
image (Union[np.ndarray, Image.Image]): image to be drawn
results (List[Dict[str, Any]]): information for drawing bounding box
Returns:
np.ndarray: drawn image
| draw_bbox_results | python | PaddlePaddle/models | modelcenter/PP-ShiTuV2/APP/app.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-ShiTuV2/APP/app.py | Apache-2.0 |
def __init__(self,
config,
model_info: dict={},
data_info: dict={},
perf_info: dict={},
resource_info: dict={},
**kwargs):
"""
Construct PaddleInferBenchmark Class to format logs.
args:
... |
Construct PaddleInferBenchmark Class to format logs.
args:
config(paddle.inference.Config): paddle inference config
model_info(dict): basic model info
{'model_name': 'resnet50'
'precision': 'fp32'}
data_info(dict): input data info
... | __init__ | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/benchmark_utils.py | Apache-2.0 |
def parse_config(self, config) -> dict:
"""
parse paddle predictor config
args:
config(paddle.inference.Config): paddle inference config
return:
config_status(dict): dict style config info
"""
if isinstance(config, paddle_infer.Config):
... |
parse paddle predictor config
args:
config(paddle.inference.Config): paddle inference config
return:
config_status(dict): dict style config info
| parse_config | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/benchmark_utils.py | Apache-2.0 |
def report(self, identifier=None):
"""
print log report
args:
identifier(string): identify log
"""
if identifier:
identifier = f"[{identifier}]"
else:
identifier = ""
self.logger.info("\n")
self.logger.info(
... |
print log report
args:
identifier(string): identify log
| report | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/benchmark_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/benchmark_utils.py | Apache-2.0 |
def is_url(path):
"""
Whether path is URL.
Args:
path (string): URL string or not.
"""
return path.startswith('http://') \
or path.startswith('https://') \
or path.startswith('ppdet://') |
Whether path is URL.
Args:
path (string): URL string or not.
| is_url | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def _download(url, path, md5sum=None):
"""
Download from url, save to path.
url (str): download url
path (str): download to given path
"""
if not osp.exists(path):
os.makedirs(path)
fname = osp.split(url)[-1]
fullname = osp.join(path, fname)
retry_cnt = 0
while not (osp.... |
Download from url, save to path.
url (str): download url
path (str): download to given path
| _download | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def _move_and_merge_tree(src, dst):
"""
Move src directory to dst, if dst is already exists,
merge src to dst
"""
if not osp.exists(dst):
shutil.move(src, dst)
elif osp.isfile(src):
shutil.move(src, dst)
else:
for fp in os.listdir(src):
src_fp = osp.join(s... |
Move src directory to dst, if dst is already exists,
merge src to dst
| _move_and_merge_tree | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def _decompress(fname):
"""
Decompress for zip and tar file
"""
# For protecting decompressing interupted,
# decompress to fpath_tmp directory firstly, if decompress
# successed, move decompress files to fpath and delete
# fpath_tmp and remove download compress file.
fpath = osp.split(f... |
Decompress for zip and tar file
| _decompress | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def get_path(url, root_dir=WEIGHTS_HOME, md5sum=None, check_exist=True):
""" Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url and decompress it, return the path.
url (str): download url
root... | Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url and decompress it, return the path.
url (str): download url
root_dir (str): root dir for downloading
md5sum (str): md5 sum of download packa... | get_path | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def get_weights_path(url):
"""Get weights path from WEIGHTS_HOME, if not exists,
download it from url.
"""
url = parse_url(url)
md5sum = None
if url in MODEL_URL_MD5_DICT.keys():
md5sum = MODEL_URL_MD5_DICT[url]
path, _ = get_path(url, WEIGHTS_HOME, md5sum)
return path | Get weights path from WEIGHTS_HOME, if not exists,
download it from url.
| get_weights_path | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/download.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
... |
Args:
repeats (int): repeats number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's result include 'mask... | predict | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): 'segm': np.ndarray,shape:[N, im_h, im_w]
'cate_label': label of segm, shape:[N]
'cate_score': confidence sco... |
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): 'segm': np.ndarray,shape:[N, im_h, im_w]
'cate_label': label of segm, shape:[N]
'cate_score': confidence score of segm, shape:[N]
| predict | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def predict(self, repeats=1):
'''
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
'''
... |
Args:
repeats (int): repeat number for prediction
Returns:
result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
| predict | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def create_inputs(imgs, im_info):
"""generate input for different model type
Args:
imgs (list(numpy)): list of images (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
"""
inputs = {}
im_shape = []
scale_factor = []
if l... | generate input for different model type
Args:
imgs (list(numpy)): list of images (np.ndarray)
im_info (list(dict)): list of image info
Returns:
inputs (dict): input of model
| create_inputs | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def check_model(self, yml_conf):
"""
Raises:
ValueError: loaded model not in supported model type
"""
for support_model in SUPPORT_MODELS:
if support_model in yml_conf['arch']:
return True
raise ValueError("Unsupported arch: {}, expect {}"... |
Raises:
ValueError: loaded model not in supported model type
| check_model | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def load_predictor(model_dir,
run_mode='paddle',
batch_size=1,
device='CPU',
min_subgraph_size=3,
use_dynamic_shape=False,
trt_min_shape=1,
trt_max_shape=1280,
trt_opt_... | set AnalysisConfig, generate AnalysisPredictor
Args:
model_dir (str): root path of __model__ and __params__
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(paddle/trt_fp32/trt_fp16/trt_int8)
use_dynamic_shape (bo... | load_predictor | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
def get_test_images(infer_dir, infer_img):
"""
Get image path list in TEST mode
"""
assert infer_img is not None or infer_dir is not None, \
"--image_file or --image_dir should be set"
assert infer_img is None or os.path.isfile(infer_img), \
"{} is not a file".format(infer_img)
... |
Get image path list in TEST mode
| get_test_images | python | PaddlePaddle/models | modelcenter/PP-TinyPose/APP/infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/infer.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.