inference переписан под CLI, удаление не рантайм файлов
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150
inference.py
150
inference.py
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import os, sys, argparse, warnings
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import torch, torch.nn as nn, torch.nn.functional as F
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import numpy as np, cv2
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from PIL import Image
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from model import DocScanner
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from seg import U2NETP
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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import cv2
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import os
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from PIL import Image
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import argparse
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import warnings
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warnings.filterwarnings('ignore')
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# Запоминаем CWD пользователя ДО смены директории
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USER_CWD = os.getcwd()
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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os.chdir(SCRIPT_DIR)
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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super().__init__()
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self.msk = U2NETP(3, 1)
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self.bm = DocScanner() # 矫正
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self.bm = DocScanner()
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def forward(self, x):
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msk, _1,_2,_3,_4,_5,_6 = self.msk(x)
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msk = (msk > 0.5).float()
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x = msk * x
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bm = self.bm(x, iters=12, test_mode=True)
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bm = (2 * (bm / 286.8) - 1) * 0.99
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return bm
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msk, *_ = self.msk(x)
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bm = self.bm((msk > 0.5).float() * x, iters=12, test_mode=True)
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return (2 * (bm / 286.8) - 1) * 0.99
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def reload_seg_model(model, path=""):
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if not bool(path):
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return model
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def load_model(model, path, strip_prefix=False):
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state_dict = model.state_dict()
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pretrained = torch.load(path, map_location='cuda:0')
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if strip_prefix:
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pretrained = {k[6:]: v for k, v in pretrained.items() if k[6:] in state_dict}
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else:
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model_dict = model.state_dict()
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pretrained_dict = torch.load(path, map_location='cuda:0')
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pretrained_dict = {k[6:]: v for k, v in pretrained_dict.items() if k[6:] in model_dict}
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model_dict.update(pretrained_dict)
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model.load_state_dict(model_dict)
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return model
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pretrained = {k: v for k, v in pretrained.items() if k in state_dict}
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state_dict.update(pretrained)
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model.load_state_dict(state_dict)
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return model
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def reload_rec_model(model, path=""):
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if not bool(path):
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return model
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else:
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model_dict = model.state_dict()
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pretrained_dict = torch.load(path, map_location='cuda:0')
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pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
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model_dict.update(pretrained_dict)
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model.load_state_dict(model_dict)
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return model
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def rec(seg_model_path, rec_model_path, distorrted_path, save_path):
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# distorted images list
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img_list = os.listdir(distorrted_path)
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# creat save path for rectified images
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if not os.path.exists(save_path):
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os.makedirs(save_path)
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# net init
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net = Net().cuda()
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# reload seg model
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reload_seg_model(net.msk, seg_model_path)
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# reload rec model
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reload_rec_model(net.bm, rec_model_path)
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net.eval()
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for img_path in img_list:
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name = img_path.split('.')[-2] # image name
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img_path = distorrted_path + img_path # image path
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im_ori = np.array(Image.open(img_path))[:, :, :3] / 255.
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h, w, _ = im_ori.shape
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im = cv2.resize(im_ori, (288, 288))
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im = im.transpose(2, 0, 1)
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im = torch.from_numpy(im).float().unsqueeze(0)
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with torch.no_grad():
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bm = net(im.cuda())
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bm = bm.cpu()
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# save rectified image
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bm0 = cv2.resize(bm[0, 0].numpy(), (w, h)) # x flow
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bm1 = cv2.resize(bm[0, 1].numpy(), (w, h)) # y flow
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bm0 = cv2.blur(bm0, (3, 3))
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bm1 = cv2.blur(bm1, (3, 3))
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lbl = torch.from_numpy(np.stack([bm0, bm1], axis=2)).unsqueeze(0) # h * w * 2
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out = F.grid_sample(torch.from_numpy(im_ori).permute(2, 0, 1).unsqueeze(0).float(), lbl, align_corners=True)
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cv2.imwrite(save_path + name + '_rec' + '.png', (((out[0]*255).permute(1, 2, 0).numpy())[:,:,::-1]).astype(np.uint8))
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def resolve_user_path(path):
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"""Если путь относительный — считаем его относительно CWD пользователя, не скрипта"""
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if os.path.isabs(path):
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return path
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return os.path.join(USER_CWD, path)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--seg_model_path', default='./model_pretrained/seg.pth')
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parser.add_argument('--rec_model_path', default='./model_pretrained/DocScanner-L.pth')
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parser.add_argument('--distorrted_path', default='./distorted/')
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parser.add_argument('--rectified_path', default='./rectified/')
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parser.add_argument('-i', '--input', required=True)
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parser.add_argument('-o', '--output', required=True)
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opt = parser.parse_args()
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rec(seg_model_path=opt.seg_model_path,
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rec_model_path=opt.rec_model_path,
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distorrted_path=opt.distorrted_path,
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save_path=opt.rectified_path)
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input_path = resolve_user_path(opt.input)
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output_path = resolve_user_path(opt.output)
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net = Net().cuda().eval()
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load_model(net.msk, f'{SCRIPT_DIR}/model_pretrained/seg.pth', strip_prefix=True)
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load_model(net.bm, f'{SCRIPT_DIR}/model_pretrained/DocScanner-L.pth')
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image = np.array(Image.open(input_path))[:, :, :3] / 255.0
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height, width = image.shape[:2]
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tensor = torch.from_numpy(cv2.resize(image, (288, 288)).transpose(2, 0, 1)).float().unsqueeze(0)
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with torch.no_grad():
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bm = net(tensor.cuda()).cpu()
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flow = torch.from_numpy(np.stack([
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cv2.blur(cv2.resize(bm[0, 0].numpy(), (width, height)), (3, 3)),
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cv2.blur(cv2.resize(bm[0, 1].numpy(), (width, height)), (3, 3))
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], axis=2)).unsqueeze(0)
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out = F.grid_sample(
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torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).float(),
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flow, align_corners=True
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)
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result = ((out[0] * 255).permute(1, 2, 0).numpy()[:, :, ::-1]).astype(np.uint8)
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out_dir = os.path.dirname(output_path)
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if out_dir:
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os.makedirs(out_dir, exist_ok=True)
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cv2.imwrite(output_path, result)
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print(f"[OK] {output_path}")
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if __name__ == "__main__":
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