inference переписан под CLI, удаление не рантайм файлов
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.gitignore
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.gitignore
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__pycache__
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43
OCR_eval.py
43
OCR_eval.py
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def Levenshtein_Distance(str1, str2):
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matrix = [[ i + j for j in range(len(str2) + 1)] for i in range(len(str1) + 1)]
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for i in range(1, len(str1)+1):
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for j in range(1, len(str2)+1):
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if(str1[i-1] == str2[j-1]):
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d = 0
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else:
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d = 1
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matrix[i][j] = min(matrix[i-1][j]+1, matrix[i][j-1]+1, matrix[i-1][j-1]+d)
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return matrix[len(str1)][len(str2)]
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def cal_cer_ed(path_ours, tail='_rec'):
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path_gt='./GT/'
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N=66
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cer1=[]
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cer2=[]
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ed1=[]
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ed2=[]
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check=[0 for _ in range(N+1)]
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lis=[1,2,3,4,5,6,7,9,10,21,22,23,24,27,30,31,32,36,38,40,41,44,45,46,47,48,50,51,52,53] # DocTr (Setting 1)
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# lis=[1,9,10,12,19,20,21,22,23,24,30,31,32,34,35,36,37,38,39,40,44,45,46,47,49] # DewarpNet (Setting 2)
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for i in range(1,N):
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if i not in lis:
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continue
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gt=Image.open(path_gt+str(i)+'.png')
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img1=Image.open(path_ours+str(i)+'_1' + tail)
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img2=Image.open(path_ours+str(i)+'_2' + tail)
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content_gt=pytesseract.image_to_string(gt)
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content1=pytesseract.image_to_string(img1)
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content2=pytesseract.image_to_string(img2)
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l1=Levenshtein_Distance(content_gt,content1)
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l2=Levenshtein_Distance(content_gt,content2)
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ed1.append(l1)
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ed2.append(l2)
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cer1.append(l1/len(content_gt))
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cer2.append(l2/len(content_gt))
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check[i]=cer1[-1]
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print('CER: ', (np.mean(cer1)+np.mean(cer2)) / 2.)
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print('ED: ', (np.mean(ed1)+np.mean(ed2)) / 2.)
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def evalu(path_ours, tail):
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cal_cer_ed(path_ours, tail)
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64
eval.m
64
eval.m
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path_rec = "xxx"; % rectified image path
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path_scan = './scan/'; % scan image path
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label_path = './layout/'; % layout result path
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tarea = 598400;
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ms1 = 0;
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ld1 = 0;
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lid1 = 0;
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ms2 = 0;
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ld2 = 0;
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lid2 = 0;
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wv = 0;
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wh = 0;
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sprintf(path_rec)
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for i=1:65
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path_rec_1 = sprintf("%s%d%s", path_rec, i, '_1 copy_rec.png'); % rectified image path
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path_rec_2 = sprintf("%s%d%s", path_rec, i, '_2 copy_rec.png'); % rectified image path
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path_scan_new = sprintf("%s%d%s", path_scan, i, '.png'); % corresponding scan image path
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bbox_i_path = sprintf("%s%d%s", label_path, i, '.txt'); % corresponding layout txt path
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% imread and rgb2gray
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A1 = imread(path_rec_1);
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A2 = imread(path_rec_2);
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% if i == 64
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% A1 = rot90(A1,-2);
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% A2 = rot90(A2,-2);
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% end
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ref = imread(path_scan_new);
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A1 = rgb2gray(A1);
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A2 = rgb2gray(A2);
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ref = rgb2gray(ref);
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bbox_i = read_txt(bbox_i_path);
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bbox_i = bbox_i + 1; % python index starts from 0
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% resize
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b = sqrt(tarea/size(ref,1)/size(ref,2));
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ref = imresize(ref,b);
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A1 = imresize(A1,[size(ref,1),size(ref,2)]);
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A2 = imresize(A2,[size(ref,1),size(ref,2)]);
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scaled_bbox_i = bbox_i * b * 0.5;
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scaled_bbox_i = round(scaled_bbox_i);
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scaled_bbox_i = max(scaled_bbox_i, 1);
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% calculate
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[ms_1, ld_1, lid_1, W_v_1, W_h_1] = evalUnwarp(A1, ref, scaled_bbox_i);
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[ms_2, ld_2, lid_2, W_v_2, W_h_2] = evalUnwarp(A2, ref, scaled_bbox_i);
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ms1 = ms1 + ms_1;
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ms2 = ms2 + ms_2;
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ld1 = ld1 + ld_1;
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ld2 = ld2 + ld_2;
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lid1 = lid1 + lid_1;
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lid2 = lid2 + lid_2;
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wv = wv + W_v_1 + W_v_2;
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wh = wh + W_h_1 + W_h_2;
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end
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ms = (ms1 + ms2) / 130 % MS-SSIM
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ld = (ld1 + ld2) / 130 % local distortion
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li_d = (lid1 + lid2) / 130 % line distortion
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wv = wv / 130 % wv index
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wh = wh / 130 % wh index
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102
evalUnwarp.m
102
evalUnwarp.m
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function [ms, ld, li_d, wv, wh] = evalUnwarp(A, ref, data)
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%EVALUNWARP compute MSSSIM and LD between the unwarped image and the scan
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% A: unwarped image
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% ref: reference image, the scan image
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% ms: returned MS-SSIM value
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% ld: returned local distortion value
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% Matlab image processing toolbox is necessary to compute ssim. The weights
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% for multi-scale ssim is directly adopted from:
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%
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% Wang, Zhou, Eero P. Simoncelli, and Alan C. Bovik. "Multiscale structural
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% similarity for image quality assessment." In Signals, Systems and Computers,
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% 2004. Conference Record of the Thirty-Seventh Asilomar Conference on, 2003.
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%
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% Local distortion relies on the paper:
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% Liu, Ce, Jenny Yuen, and Antonio Torralba. "Sift flow: Dense correspondence
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% across scenes and its applications." In PAMI, 2010.
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%
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% and its implementation:
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% https://people.csail.mit.edu/celiu/SIFTflow/
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x = A;
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y = ref;
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im1=imresize(imfilter(y,fspecial('gaussian',7,1.),'same','replicate'),0.5,'bicubic');
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im2=imresize(imfilter(x,fspecial('gaussian',7,1.),'same','replicate'),0.5,'bicubic');
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im1=im2double(im1);
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im2=im2double(im2);
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cellsize=3;
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gridspacing=1;
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sift1 = mexDenseSIFT(im1,cellsize,gridspacing);
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sift2 = mexDenseSIFT(im2,cellsize,gridspacing);
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SIFTflowpara.alpha=2*255;
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SIFTflowpara.d=40*255;
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SIFTflowpara.gamma=0.005*255;
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SIFTflowpara.nlevels=4;
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SIFTflowpara.wsize=2;
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SIFTflowpara.topwsize=10;
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SIFTflowpara.nTopIterations = 60;
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SIFTflowpara.nIterations= 30;
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[vx,vy,~]=SIFTflowc2f(sift1,sift2,SIFTflowpara);
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rows1p = size(im1,1);
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cols1p = size(im1,2);
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% Li-D
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rowstd_sum = 0;
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for i = 1:rows1p
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rowstd = std(vy(i, :),1);
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rowstd_sum = rowstd_sum + rowstd;
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end
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rowstd_mean = rowstd_sum / rows1p;
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colstd_sum = 0;
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for i = 1:cols1p
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colstd = std(vx(:, i),1);
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colstd_sum = colstd_sum + colstd;
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end
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colstd_mean = colstd_sum / cols1p;
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li_d = (rowstd_mean + colstd_mean) / 2;
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% LD
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d = sqrt(vx.^2 + vy.^2);
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ld = mean(d(:));
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% MS-SSIM
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wt = [0.0448 0.2856 0.3001 0.2363 0.1333];
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ss = zeros(5, 1);
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for s = 1 : 5
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ss(s) = ssim(x, y);
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x = impyramid(x, 'reduce');
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y = impyramid(y, 'reduce');
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end
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ms = wt * ss;
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% wv and wh
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rowstd_sum = 0;
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for i = 1:size(data, 1)
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rowstd_top = std(vy(data(i,2), data(i,1):data(i,3)),1) / (data(i,3)-data(i,1));
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rowstd_bot = std(vy(data(i,4), data(i,1):data(i,3)),1) / (data(i,3)-data(i,1));
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rowstd_sum = rowstd_sum + rowstd_top + rowstd_bot;
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end
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wv = rowstd_sum / (2 * size(data, 1));
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colstd_sum = 0;
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for i = 1:size(data, 1)
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colstd_left = std(vx(data(i,2):data(i,4), data(i,1)),1) / (data(i,4)- data(i,2));
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colstd_right = std(vx(data(i,2):data(i,4), data(i,3)),1) / (data(i,4)- data(i,2));
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colstd_sum = colstd_sum + colstd_left + colstd_right;
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end
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wh = colstd_sum / (2 * size(data, 1));
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end
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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 model import DocScanner
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from seg import U2NETP
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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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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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class Net(nn.Module):
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def __init__(self):
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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.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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def forward(self, x):
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msk, _1,_2,_3,_4,_5,_6 = self.msk(x)
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msk, *_ = self.msk(x)
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msk = (msk > 0.5).float()
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bm = self.bm((msk > 0.5).float() * x, iters=12, test_mode=True)
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x = msk * x
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return (2 * (bm / 286.8) - 1) * 0.99
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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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def reload_seg_model(model, path=""):
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def load_model(model, path, strip_prefix=False):
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if not bool(path):
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state_dict = model.state_dict()
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return model
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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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else:
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model_dict = model.state_dict()
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pretrained = {k: v for k, v in pretrained.items() if k in state_dict}
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pretrained_dict = torch.load(path, map_location='cuda:0')
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state_dict.update(pretrained)
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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.load_state_dict(state_dict)
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model_dict.update(pretrained_dict)
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return model
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model.load_state_dict(model_dict)
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return model
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def reload_rec_model(model, path=""):
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def resolve_user_path(path):
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if not bool(path):
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"""Если путь относительный — считаем его относительно CWD пользователя, не скрипта"""
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return model
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if os.path.isabs(path):
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else:
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return path
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model_dict = model.state_dict()
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return os.path.join(USER_CWD, path)
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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 main():
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def main():
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parser = argparse.ArgumentParser()
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parser = argparse.ArgumentParser()
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parser.add_argument('--seg_model_path', default='./model_pretrained/seg.pth')
|
parser.add_argument('-i', '--input', required=True)
|
||||||
parser.add_argument('--rec_model_path', default='./model_pretrained/DocScanner-L.pth')
|
parser.add_argument('-o', '--output', required=True)
|
||||||
parser.add_argument('--distorrted_path', default='./distorted/')
|
|
||||||
parser.add_argument('--rectified_path', default='./rectified/')
|
|
||||||
opt = parser.parse_args()
|
opt = parser.parse_args()
|
||||||
|
|
||||||
rec(seg_model_path=opt.seg_model_path,
|
input_path = resolve_user_path(opt.input)
|
||||||
rec_model_path=opt.rec_model_path,
|
output_path = resolve_user_path(opt.output)
|
||||||
distorrted_path=opt.distorrted_path,
|
|
||||||
save_path=opt.rectified_path)
|
net = Net().cuda().eval()
|
||||||
|
load_model(net.msk, f'{SCRIPT_DIR}/model_pretrained/seg.pth', strip_prefix=True)
|
||||||
|
load_model(net.bm, f'{SCRIPT_DIR}/model_pretrained/DocScanner-L.pth')
|
||||||
|
|
||||||
|
image = np.array(Image.open(input_path))[:, :, :3] / 255.0
|
||||||
|
height, width = image.shape[:2]
|
||||||
|
tensor = torch.from_numpy(cv2.resize(image, (288, 288)).transpose(2, 0, 1)).float().unsqueeze(0)
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
bm = net(tensor.cuda()).cpu()
|
||||||
|
|
||||||
|
flow = torch.from_numpy(np.stack([
|
||||||
|
cv2.blur(cv2.resize(bm[0, 0].numpy(), (width, height)), (3, 3)),
|
||||||
|
cv2.blur(cv2.resize(bm[0, 1].numpy(), (width, height)), (3, 3))
|
||||||
|
], axis=2)).unsqueeze(0)
|
||||||
|
|
||||||
|
out = F.grid_sample(
|
||||||
|
torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).float(),
|
||||||
|
flow, align_corners=True
|
||||||
|
)
|
||||||
|
result = ((out[0] * 255).permute(1, 2, 0).numpy()[:, :, ::-1]).astype(np.uint8)
|
||||||
|
|
||||||
|
out_dir = os.path.dirname(output_path)
|
||||||
|
if out_dir:
|
||||||
|
os.makedirs(out_dir, exist_ok=True)
|
||||||
|
cv2.imwrite(output_path, result)
|
||||||
|
print(f"[OK] {output_path}")
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
|
|||||||
BIN
model_pretrained/DocScanner-L.pth
Normal file
BIN
model_pretrained/DocScanner-L.pth
Normal file
Binary file not shown.
BIN
model_pretrained/seg.pth
Normal file
BIN
model_pretrained/seg.pth
Normal file
Binary file not shown.
62
ocr_img.txt
62
ocr_img.txt
@ -1,62 +0,0 @@
|
|||||||
The images for OCR evaluation of DocUNet Benchmark.
|
|
||||||
# Setting 1 (Setting from DocTr)
|
|
||||||
# Total 30 * 2 = 60 images.
|
|
||||||
./scan/1.png
|
|
||||||
./scan/2.png
|
|
||||||
./scan/3.png
|
|
||||||
./scan/4.png
|
|
||||||
./scan/5.png
|
|
||||||
./scan/6.png
|
|
||||||
./scan/7.png
|
|
||||||
./scan/9.png
|
|
||||||
./scan/10.png
|
|
||||||
./scan/21.png
|
|
||||||
./scan/22.png
|
|
||||||
./scan/23.png
|
|
||||||
./scan/24.png
|
|
||||||
./scan/27.png
|
|
||||||
./scan/30.png
|
|
||||||
./scan/31.png
|
|
||||||
./scan/32.png
|
|
||||||
./scan/36.png
|
|
||||||
./scan/38.png
|
|
||||||
./scan/40.png
|
|
||||||
./scan/41.png
|
|
||||||
./scan/44.png
|
|
||||||
./scan/45.png
|
|
||||||
./scan/46.png
|
|
||||||
./scan/47.png
|
|
||||||
./scan/48.png
|
|
||||||
./scan/50.png
|
|
||||||
./scan/51.png
|
|
||||||
./scan/52.png
|
|
||||||
./scan/53.png
|
|
||||||
|
|
||||||
# Setting 2 (Setting from DewarpNet)
|
|
||||||
# Link: https://github.com/cvlab-stonybrook/DewarpNet/blob/master/eval/ocr_eval/ocr_files.txt
|
|
||||||
# Total 25 * 2 = 50 images.
|
|
||||||
./scan/1.png
|
|
||||||
./scan/9.png
|
|
||||||
./scan/10.png
|
|
||||||
./scan/12.png
|
|
||||||
./scan/19.png
|
|
||||||
./scan/20.png
|
|
||||||
./scan/21.png
|
|
||||||
./scan/22.png
|
|
||||||
./scan/23.png
|
|
||||||
./scan/24.png
|
|
||||||
./scan/30.png
|
|
||||||
./scan/31.png
|
|
||||||
./scan/32.png
|
|
||||||
./scan/34.png
|
|
||||||
./scan/35.png
|
|
||||||
./scan/36.png
|
|
||||||
./scan/37.png
|
|
||||||
./scan/38.png
|
|
||||||
./scan/39.png
|
|
||||||
./scan/40.png
|
|
||||||
./scan/44.png
|
|
||||||
./scan/45.png
|
|
||||||
./scan/46.png
|
|
||||||
./scan/47.png
|
|
||||||
./scan/49.png
|
|
||||||
Reference in New Issue
Block a user