Spaces:
Running
Running
File size: 1,665 Bytes
591ba45 1081f7c 591ba45 1081f7c 591ba45 1081f7c 591ba45 1081f7c 591ba45 |
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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import argparse
import os
import warnings
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from DocScanner.model import DocScanner
from DocScanner.seg import U2NETP
from PIL import Image
warnings.filterwarnings("ignore")
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.msk = U2NETP(3, 1)
self.bm = DocScanner() # 矫正
def forward(self, x):
msk, _1, _2, _3, _4, _5, _6 = self.msk(x)
msk = (msk > 0.5).float()
x = msk * x
bm = self.bm(x, iters=12, test_mode=True)
bm = (2 * (bm / 286.8) - 1) * 0.99
return bm, msk
def reload_seg_model(cuda, model, path=""):
if not bool(path):
return model
else:
model_dict = model.state_dict()
pretrained_dict = torch.load(path, map_location=cuda)
pretrained_dict = {
k[6:]: v for k, v in pretrained_dict.items() if k[6:] in model_dict
}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
return model
def reload_rec_model(cuda, model, path=""):
if not bool(path):
return model
else:
model_dict = model.state_dict()
pretrained_dict = torch.load(path, map_location=cuda)
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
return model |