Instructions to use Reza2kn/Bina-0.4-Rizeh-LiteRT-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use Reza2kn/Bina-0.4-Rizeh-LiteRT-INT4 with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 1,922 Bytes
a409515 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | """One 64px crop contract for training/inference; width is never clamped or squashed.
Margins and polarity follow the Apache-2.0 Indra rec_preproc implementation.
Only right-padding batches changes the canvas; widths retain the unpadded size.
"""
import math
import numpy as np
def crop_line64(gray,box,*,pad_y=.2,pad_x=.3,dark=None):
import cv2
from polarity import is_dark_line
if gray.ndim!=2 or gray.dtype!=np.uint8:raise ValueError('Original uint8 grayscale required')
x0,y0,x1,y1=map(float,box[:4])
if not all(math.isfinite(v)for v in [x0,y0,x1,y1])or x1<=x0 or y1<=y0:raise ValueError('Invalid line geometry')
height=y1-y0;ix0=math.floor(x0-pad_x*height);ix1=math.ceil(x1+pad_x*height);iy0=math.floor(y0-pad_y*height);iy1=math.ceil(y1+pad_y*height);cw,ch=ix1-ix0,iy1-iy0
H,W=gray.shape;sx0,sy0,sx1,sy1=max(0,ix0),max(0,iy0),min(W,ix1),min(H,iy1)
if dark is None:dark=is_dark_line(gray,box)
canvas=np.full((ch,cw),0 if dark else 255,dtype=np.uint8)
if sx1>sx0 and sy1>sy0:canvas[sy0-iy0:sy1-iy0,sx0-ix0:sx1-ix0]=gray[sy0:sy1,sx0:sx1]
width=max(1,round(cw*64/ch));resized=cv2.resize(canvas,(width,64),interpolation=cv2.INTER_AREA if ch>64 else cv2.INTER_LINEAR).astype(np.float32)/255.
ink=resized if dark else 1.-resized
receipt=dict(original_WH=[W,H],ink_box=[x0,y0,x1,y1],padded_source_extent=[ix0,iy0,ix1,iy1],padded_source_WH=[cw,ch],resized_WH=[width,64],scale_XY=[width/cw,64/ch],width_clamped=False,aspect_rounding_error_pixels=abs(width-cw*64/ch),dark_polarity=bool(dark))
return ink,receipt
def pad_batch(crops,multiple=8):
if not crops or any(c.ndim!=2 or c.shape[0]!=64 for c in crops):raise ValueError('Every recognizer crop must be 64px high')
widths=np.array([c.shape[1]for c in crops],dtype=np.int64);padded=math.ceil(int(widths.max())/multiple)*multiple;out=np.zeros((len(crops),1,64,padded),dtype=np.float32)
for i,c in enumerate(crops):out[i,0,:,:c.shape[1]]=c
return out,widths
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