code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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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-TinyPose/APP/keypoint_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/keypoint_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/keypoint_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/keypoint_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/keypoint_preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/keypoint_preprocess.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-TinyPose/APP/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/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-TinyPose/APP/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-TinyPose/APP/visualize.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-Vehicle/APP/pipeline/attr_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/attr_infer.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-Vehicle/APP/pipeline/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/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-Vehicle/APP/pipeline/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/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-Vehicle/APP/pipeline/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/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-Vehicle/APP/pipeline/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/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-Vehicle/APP/pipeline/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/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-Vehicle/APP/pipeline/download.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/download.py | Apache-2.0 |
def get_model_dir(cfg):
"""
Auto download inference model if the model_path is a url link.
Otherwise it will use the model_path directly.
"""
for key in cfg.keys():
if type(cfg[key]) == dict and \
("enable" in cfg[key].keys() and cfg[key]['enable']
or "... |
Auto download inference model if the model_path is a url link.
Otherwise it will use the model_path directly.
| get_model_dir | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pipeline/pipeline.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/pipeline.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, \
"--infer_img or --infer_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-Vehicle/APP/pipeline/pipe_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/pipe_utils.py | Apache-2.0 |
def refine_keypoint_coordinary(kpts, bbox, coord_size):
"""
This function is used to adjust coordinate values to a fixed scale.
"""
tl = bbox[:, 0:2]
wh = bbox[:, 2:] - tl
tl = np.expand_dims(np.transpose(tl, (1, 0)), (2, 3))
wh = np.expand_dims(np.transpose(wh, (1, 0)), (2, 3))
targ... |
This function is used to adjust coordinate values to a fixed scale.
| refine_keypoint_coordinary | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pipeline/pipe_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/pipe_utils.py | Apache-2.0 |
def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
'''
_bitmap: single map with shape (1, H, W),
whose values are binarized as {0, 1}
'''
bitmap = _bitmap
height, width = bitmap.shape
outs = cv2.findContours((bitmap * 255).astype(np.uin... |
_bitmap: single map with shape (1, H, W),
whose values are binarized as {0, 1}
| boxes_from_bitmap | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicleplate_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicleplate_postprocess.py | Apache-2.0 |
def box_score_fast(self, bitmap, _box):
'''
box_score_fast: use bbox mean score as the mean score
'''
h, w = bitmap.shape[:2]
box = _box.copy()
xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int... |
box_score_fast: use bbox mean score as the mean score
| box_score_fast | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicleplate_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicleplate_postprocess.py | Apache-2.0 |
def box_score_slow(self, bitmap, contour):
'''
box_score_slow: use polyon mean score as the mean score
'''
h, w = bitmap.shape[:2]
contour = contour.copy()
contour = np.reshape(contour, (-1, 2))
xmin = np.clip(np.min(contour[:, 0]), 0, w - 1)
xmax = np.cl... |
box_score_slow: use polyon mean score as the mean score
| box_score_slow | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicleplate_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicleplate_postprocess.py | Apache-2.0 |
def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
""" convert text-index into text-label. """
result_list = []
ignored_tokens = self.get_ignored_tokens()
batch_size = len(text_index)
for batch_idx in range(batch_size):
selection = np.ones(len(te... | convert text-index into text-label. | decode | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicleplate_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicleplate_postprocess.py | Apache-2.0 |
def draw_ocr(image,
boxes,
txts=None,
scores=None,
drop_score=0.5,
font_path="./doc/fonts/simfang.ttf"):
"""
Visualize the results of OCR detection and recognition
args:
image(Image|array): RGB image
boxes(list): boxes with sha... |
Visualize the results of OCR detection and recognition
args:
image(Image|array): RGB image
boxes(list): boxes with shape(N, 4, 2)
txts(list): the texts
scores(list): txxs corresponding scores
drop_score(float): only scores greater than drop_threshold will be visualized
... | draw_ocr | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicle_plateutils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicle_plateutils.py | Apache-2.0 |
def str_count(s):
"""
Count the number of Chinese characters,
a single English character and a single number
equal to half the length of Chinese characters.
args:
s(string): the input of string
return(int):
the number of Chinese characters
"""
import string
count_zh =... |
Count the number of Chinese characters,
a single English character and a single number
equal to half the length of Chinese characters.
args:
s(string): the input of string
return(int):
the number of Chinese characters
| str_count | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicle_plateutils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicle_plateutils.py | Apache-2.0 |
def text_visual(texts,
scores,
img_h=400,
img_w=600,
threshold=0.,
font_path="./doc/simfang.ttf"):
"""
create new blank img and draw txt on it
args:
texts(list): the text will be draw
scores(list|None): correspon... |
create new blank img and draw txt on it
args:
texts(list): the text will be draw
scores(list|None): corresponding score of each txt
img_h(int): the height of blank img
img_w(int): the width of blank img
font_path: the path of font which is used to draw text
return(ar... | text_visual | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicle_plateutils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicle_plateutils.py | Apache-2.0 |
def get_rotate_crop_image(img, points):
'''
img_height, img_width = img.shape[0:2]
left = int(np.min(points[:, 0]))
right = int(np.max(points[:, 0]))
top = int(np.min(points[:, 1]))
bottom = int(np.max(points[:, 1]))
img_crop = img[top:bottom, left:right, :].copy()
points[:, 0] = points[... |
img_height, img_width = img.shape[0:2]
left = int(np.min(points[:, 0]))
right = int(np.max(points[:, 0]))
top = int(np.min(points[:, 1]))
bottom = int(np.max(points[:, 1]))
img_crop = img[top:bottom, left:right, :].copy()
points[:, 0] = points[:, 0] - left
points[:, 1] = points[:, 1] - ... | get_rotate_crop_image | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicle_plateutils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pipeline/ppvehicle/vehicle_plateutils.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]
| predict | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/det_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/det_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-Vehicle/APP/pptracking/python/det_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/det_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-Vehicle/APP/pptracking/python/det_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/det_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-Vehicle/APP/pptracking/python/det_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/det_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, \
"--infer_img or --infer_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-Vehicle/APP/pptracking/python/det_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/det_infer.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-Vehicle/APP/pptracking/python/mot_jde_infer.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot_jde_infer.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-Vehicle/APP/pptracking/python/mot_utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot_utils.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-Vehicle/APP/pptracking/python/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/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-Vehicle/APP/pptracking/python/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/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-Vehicle/APP/pptracking/python/picodet_postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/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-Vehicle/APP/pptracking/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/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-Vehicle/APP/pptracking/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/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-Vehicle/APP/pptracking/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/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-Vehicle/APP/pptracking/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/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-Vehicle/APP/pptracking/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/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-Vehicle/APP/pptracking/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/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-Vehicle/APP/pptracking/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/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-Vehicle/APP/pptracking/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/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-Vehicle/APP/pptracking/python/preprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/preprocess.py | Apache-2.0 |
def to_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)`.
| to_tlbr | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/utils.py | Apache-2.0 |
def to_xyah(self):
"""
Convert bounding box to format `(center x, center y, aspect ratio,
height)`, where the aspect ratio is `width / height`.
"""
ret = self.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`.
| to_xyah | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/utils.py | Apache-2.0 |
def update_object_info(object_in_region_info,
result,
region_type,
entrance,
fps,
illegal_parking_time,
distance_threshold_frame=3,
distance_threshold_interval... |
For consecutive frames, the distance between two frame is smaller than distance_threshold_frame, regard as parking
For parking in general, the move distance should smaller than distance_threshold_interval
The moving distance of the vehicle is scaled according to the y, which is inversely proportional to y.... | update_object_info | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/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]
lab... |
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]
labels (list): labels:['class1', ..., 'classn']
threshold (flo... | visualize_box_mask | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/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-Vehicle/APP/pptracking/python/mot/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/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-Vehicle/APP/pptracking/python/mot/visualize.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/visualize.py | Apache-2.0 |
def iou_1toN(bbox, candidates):
"""
Computer intersection over union (IoU) by one box to N candidates.
Args:
bbox (ndarray): A bounding box in format `(top left x, top left y, width, height)`.
candidates (ndarray): A matrix of candidate bounding boxes (one per row) in the
sa... |
Computer intersection over union (IoU) by one box to N candidates.
Args:
bbox (ndarray): A bounding box in format `(top left x, top left y, width, height)`.
candidates (ndarray): A matrix of candidate bounding boxes (one per row) in the
same format as `bbox`.
Returns:
... | iou_1toN | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | Apache-2.0 |
def iou_cost(tracks, detections, track_indices=None, detection_indices=None):
"""
IoU distance metric.
Args:
tracks (list[Track]): A list of tracks.
detections (list[Detection]): A list of detections.
track_indices (Optional[list[int]]): A list of indices to tracks that
... |
IoU distance metric.
Args:
tracks (list[Track]): A list of tracks.
detections (list[Detection]): A list of detections.
track_indices (Optional[list[int]]): A list of indices to tracks that
should be matched. Defaults to all `tracks`.
detection_indices (Optional[list... | iou_cost | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | Apache-2.0 |
def _nn_euclidean_distance(s, q):
"""
Compute pair-wise squared (Euclidean) distance between points in `s` and `q`.
Args:
s (ndarray): Sample points: an NxM matrix of N samples of dimensionality M.
q (ndarray): Query points: an LxM matrix of L samples of dimensionality M.
Returns:
... |
Compute pair-wise squared (Euclidean) distance between points in `s` and `q`.
Args:
s (ndarray): Sample points: an NxM matrix of N samples of dimensionality M.
q (ndarray): Query points: an LxM matrix of L samples of dimensionality M.
Returns:
distances (ndarray): A vector of leng... | _nn_euclidean_distance | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | Apache-2.0 |
def _nn_cosine_distance(s, q):
"""
Compute pair-wise cosine distance between points in `s` and `q`.
Args:
s (ndarray): Sample points: an NxM matrix of N samples of dimensionality M.
q (ndarray): Query points: an LxM matrix of L samples of dimensionality M.
Returns:
distances (n... |
Compute pair-wise cosine distance between points in `s` and `q`.
Args:
s (ndarray): Sample points: an NxM matrix of N samples of dimensionality M.
q (ndarray): Query points: an LxM matrix of L samples of dimensionality M.
Returns:
distances (ndarray): A vector of length M that con... | _nn_cosine_distance | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | Apache-2.0 |
def partial_fit(self, features, targets, active_targets):
"""
Update the distance metric with new data.
Args:
features (ndarray): An NxM matrix of N features of dimensionality M.
targets (ndarray): An integer array of associated target identities.
active_targ... |
Update the distance metric with new data.
Args:
features (ndarray): An NxM matrix of N features of dimensionality M.
targets (ndarray): An integer array of associated target identities.
active_targets (List[int]): A list of targets that are currently
... | partial_fit | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | Apache-2.0 |
def distance(self, features, targets):
"""
Compute distance between features and targets.
Args:
features (ndarray): An NxM matrix of N features of dimensionality M.
targets (list[int]): A list of targets to match the given `features` against.
Returns:
... |
Compute distance between features and targets.
Args:
features (ndarray): An NxM matrix of N features of dimensionality M.
targets (list[int]): A list of targets to match the given `features` against.
Returns:
cost_matrix (ndarray): a cost matrix of shape le... | distance | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | Apache-2.0 |
def min_cost_matching(distance_metric,
max_distance,
tracks,
detections,
track_indices=None,
detection_indices=None):
"""
Solve linear assignment problem.
Args:
distance_metric :
... |
Solve linear assignment problem.
Args:
distance_metric :
Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
The distance metric is given a list of tracks and detections as
well as a list of N track indices and M detection indices. The
... | min_cost_matching | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | Apache-2.0 |
def matching_cascade(distance_metric,
max_distance,
cascade_depth,
tracks,
detections,
track_indices=None,
detection_indices=None):
"""
Run matching cascade.
Args:
distance_... |
Run matching cascade.
Args:
distance_metric :
Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
The distance metric is given a list of tracks and detections as
well as a list of N track indices and M detection indices. The
metric ... | matching_cascade | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | Apache-2.0 |
def gate_cost_matrix(kf,
cost_matrix,
tracks,
detections,
track_indices,
detection_indices,
gated_cost=INFTY_COST,
only_position=False):
"""
Invalidate infeasible en... |
Invalidate infeasible entries in cost matrix based on the state
distributions obtained by Kalman filtering.
Args:
kf (object): The Kalman filter.
cost_matrix (ndarray): The NxM dimensional cost matrix, where N is the
number of track indices and M is the number of detection indi... | gate_cost_matrix | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/deepsort_matching.py | Apache-2.0 |
def iou_distance(atracks, btracks):
"""
Compute cost based on IoU between two list[STrack].
"""
if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) or (
len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
atlbrs = atracks
btlbrs = btracks
else:
atlb... |
Compute cost based on IoU between two list[STrack].
| iou_distance | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/jde_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/jde_matching.py | Apache-2.0 |
def embedding_distance(tracks, detections, metric='euclidean'):
"""
Compute cost based on features between two list[STrack].
"""
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray(
[track.c... |
Compute cost based on features between two list[STrack].
| embedding_distance | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/jde_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/jde_matching.py | Apache-2.0 |
def iou_batch(bboxes1, bboxes2):
"""
From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
"""
bboxes2 = np.expand_dims(bboxes2, 0)
bboxes1 = np.expand_dims(bboxes1, 1)
xx1 = np.maximum(bboxes1[..., 0], bboxes2[..., 0])
yy1 = np.maximum(bboxes1[..., 1], bboxes2[..., 1])
x... |
From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
| iou_batch | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/ocsort_matching.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/matching/ocsort_matching.py | Apache-2.0 |
def initiate(self, measurement):
"""
Create track from unassociated measurement.
Args:
measurement (ndarray): Bounding box coordinates (x, y, a, h) with
center position (x, y), aspect ratio a, and height h.
Returns:
The mean vector (8 dimensional... |
Create track from unassociated measurement.
Args:
measurement (ndarray): Bounding box coordinates (x, y, a, h) with
center position (x, y), aspect ratio a, and height h.
Returns:
The mean vector (8 dimensional) and covariance matrix (8x8
dim... | initiate | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/motion/kalman_filter.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/motion/kalman_filter.py | Apache-2.0 |
def predict(self, mean, covariance):
"""
Run Kalman filter prediction step.
Args:
mean (ndarray): The 8 dimensional mean vector of the object state
at the previous time step.
covariance (ndarray): The 8x8 dimensional covariance matrix of the
... |
Run Kalman filter prediction step.
Args:
mean (ndarray): The 8 dimensional mean vector of the object state
at the previous time step.
covariance (ndarray): The 8x8 dimensional covariance matrix of the
object state at the previous time step.
... | predict | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/motion/kalman_filter.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/motion/kalman_filter.py | Apache-2.0 |
def project(self, mean, covariance):
"""
Project state distribution to measurement space.
Args
mean (ndarray): The state's mean vector (8 dimensional array).
covariance (ndarray): The state's covariance matrix (8x8 dimensional).
Returns:
The projecte... |
Project state distribution to measurement space.
Args
mean (ndarray): The state's mean vector (8 dimensional array).
covariance (ndarray): The state's covariance matrix (8x8 dimensional).
Returns:
The projected mean and covariance matrix of the given state ... | project | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/motion/kalman_filter.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/motion/kalman_filter.py | Apache-2.0 |
def multi_predict(self, mean, covariance):
"""
Run Kalman filter prediction step (Vectorized version).
Args:
mean (ndarray): The Nx8 dimensional mean matrix of the object states
at the previous time step.
covariance (ndarray): The Nx8x8 dimensiona... |
Run Kalman filter prediction step (Vectorized version).
Args:
mean (ndarray): The Nx8 dimensional mean matrix of the object states
at the previous time step.
covariance (ndarray): The Nx8x8 dimensional covariance matrics of the
object sta... | multi_predict | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/motion/kalman_filter.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/motion/kalman_filter.py | Apache-2.0 |
def update(self, mean, covariance, measurement):
"""
Run Kalman filter correction step.
Args:
mean (ndarray): The predicted state's mean vector (8 dimensional).
covariance (ndarray): The state's covariance matrix (8x8 dimensional).
measurement (ndarray): The ... |
Run Kalman filter correction step.
Args:
mean (ndarray): The predicted state's mean vector (8 dimensional).
covariance (ndarray): The state's covariance matrix (8x8 dimensional).
measurement (ndarray): The 4 dimensional measurement vector
(x, y, a, h... | update | python | PaddlePaddle/models | modelcenter/PP-Vehicle/APP/pptracking/python/mot/motion/kalman_filter.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/motion/kalman_filter.py | Apache-2.0 |
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-Vehicle/APP/pptracking/python/mot/motion/kalman_filter.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/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-Vehicle/APP/pptracking/python/mot/mtmct/postprocess.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/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-Vehicle/APP/pptracking/python/mot/mtmct/utils.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/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-Vehicle/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/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-Vehicle/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/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-Vehicle/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/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-Vehicle/APP/pptracking/python/mot/tracker/base_jde_tracker.py | https://github.com/PaddlePaddle/models/blob/master/modelcenter/PP-Vehicle/APP/pptracking/python/mot/tracker/base_jde_tracker.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 Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
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.