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TexTeller/texteller/models/det_model/__pycache__/preprocess.cpython-310.pyc

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[chore] exclude paddleocr directory from pre-commit hooks
2025-02-28 19:56:49 +08:00
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<EFBFBD>Resizez<65>resize image by target_size and max_size
Args:
target_size (int): the target size of image
keep_ratio (bool): whether keep_ratio or not, default true
interp (int): method of resize
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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
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im (np.ndarray): image (np.ndarray)
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im_scale_x: the resize ratio of X
im_scale_y: the resize ratio of Y
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<EFBFBD>NormalizeImagez<65>normalize image
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mean (list): im - mean
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is_scale (bool): whether need im / 255
norm_type (str): type in ['mean_std', 'none']
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to_bgr (bool): whether convert RGB to BGR
channel_first (bool): whether convert HWC to CHW
cstt|<00><02><02>dSr!)<03>superrLr')r&<00><01> __class__rrr'<00>szPermute.__init__cCs|<01>d<01><01><01>}||fS)r()rrr)<02> transposerH)r&rr0rrrr3<00>s zPermute.__call__)r=r>r?r@r'r3<00> __classcell__rrrNrrL<00>s rLc@<00>"eZdZdZddd<04>Zdd<06>ZdS) <09> PadStridez<65> padding image for model with FPN, instead PadBatch(pad_to_stride) in original config
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stride (bool): model with FPN need image shape % stride == 0
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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.
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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)
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 "z Pad.__call__)r=r>r?r'r3rQrrrNrrt<00>s rtcCsbt|<00>dksJ<00>t<01>|<01>t<01>|<01>}}|d||d|}|d||d|}||g}|S)z<>Rotate a point by an angle.
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angle_rad (float): rotation angle by radian
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list[float]: Rotated point.
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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.ndarray): point(x,y)
Returns:
np.ndarray: The 3rd point.
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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.ndarray[2, ]): Size of the destination heatmaps.
shift (0-100%): Shift translation ratio wrt the width/height.
Default (0., 0.).
inv (bool): Option to inverse the affine transform direction.
(inv=False: src->dst or inv=True: dst->src)
Returns:
np.ndarray: The transform matrix.
rrrr<00><>r<><00><00><>)rxrN<><00>?) r,r rr<00>listrr<00>pir<69>rWr<>r<00>getAffineTransform)<10>center<65>
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WarpAffinezWarp affine the image
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|j }}tj|d|dgtjd<04>} t | |d||g<02>}
t<00> |||f<02>}tj||
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warpAffinerA) r&rr0rcrrrsr<>r<><00>s<>cZ trans_input<75>inprrrr3{s  <06>zWarpAffine.__call__N)Fr<46>r<>r<>r<>r^rKrrrrr<>is
<EFBFBD> r<>cCst<00>|<00>}tjdtjd<02>}|d|d}|d|d}t<00>|<00>||d<t<00>|<00> ||d<|d|dt<00>|<00>d|dt<00>|<00>d|d|d <t<00>|<00>||d
<t<00>|<00>||d <|d|dt<00>|<00>d|dt<00>|<00>d|d|d <|S) a<>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 Estimation (CVPR 2020).
Args:
theta (float): Rotation angle in degrees.
size_input (np.ndarray): Size of input image [w, h].
size_dst (np.ndarray): Size of output image [w, h].
size_target (np.ndarray): Size of ROI in input plane [w, h].
Returns:
matrix (np.ndarray): A matrix for transformation.
)rrxrrr)rr)rrr<>r<>)rr)rr)rr)rr)r<00>deg2radrWrr|r{)<07>thetaZ
size_inputZsize_dstZ size_target<65>matrix<69>scale_x<5F>scale_yrrr<00>get_warp_matrix<69>s.
<04>
<02><08><04>
<02><08>r<>c@rR) <09>TopDownEvalAffineaQapply affine transform to image and coords
Args:
trainsize (list): [w, h], the standard size used to train
use_udp (bool): whether to use Unbiased Data Processing.
records(dict): the dict contained the image and coords
Returns:
records (dict): contain the image and coords after tranformed
FcCs||_||_dSr!)<02> trainsize<7A>use_udp)r&r<>r<>rrrr'<00>s
zTopDownEvalAffine.__init__cCs<>d}|dddd<03>}d|vr|dn|d}d|vr|dn|}|jrSt||d|jdd|jddg|<06>}tj||t|jd<00>t|jd<00>ftjd <09>}||fSt||||j<02>}tj||t|jd<00>t|jd<00>ftjd <09>}||fS)
Nrr <00><><EFBFBD><EFBFBD><EFBFBD>r<EFBFBD>r<>rJrrr<>)r<>r<>r<>rr<>r"rAr<>)r&<00>imager0r<>Zimshaper<65>rJr<>rrrr3<00>s,<04><06> <10><06>zTopDownEvalAffine.__call__N)FrKrrrrr<><00>s
r<>c@seZdZdd<02>Zdd<04>ZdS)<06>ComposecCs@g|_|D]}|<02><01>}|<03>d<01>}|j<00>t|<04>di|<03><01><01>qdS)N<>typer)<05>
transformsrH<00>pop<6F>append<6E>eval)r&r<><00>op_infoZ new_op_info<66>op_typerrrr'<00>s 
<04>zCompose.__init__cCs<t|<01>\}}|jD] }|||<03>\}}q t<02>|<03>}||d<|S)Nr<4E>)rr<>rH<00>deepcopy)r&rrcr0<00>t<>inputsrrrr3<00>s 

zCompose.__call__N)r=r>r?r'r3rrrrr<><00>s r<>)r<>F)<13>numpyrrrHr<00>objectr rBrLrSr[rtr<>r<>r<>r<>r<>r<>r<>rrrr<00><module>s&C%6
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