doc_category_map = {}
for category, docs in categories.items():
for doc in docs:
doc_category_map[doc] = category.upper()
print('#documents in D1 (per categories): to = {}, lg = {} fg = {}'.format(
len(categories['to']), len(categories['lg']), len(categories['fg'])))
# data to be analyzed
results = json.load(open('results/results_proposed.json', 'r'))
for doc in results:
_, dataset, _, doc_ = doc.split('/')
solution = results[doc]['solution']
comp = results[doc]['compatibilities']
if dataset == 'D2':
print(comp)
continue
acc = accuracy(solution)
print('{}-{} {:.2f}% {}'.format(
dataset, doc_, 100 * acc,
doc_category_map[doc_] if dataset == 'D1' else ''))
if acc < 0.8:
if dataset == 'D1':
src1 = '{}/{}.jpg'.format(doc, doc_)
else:
src1 = 'datasets/D2/integral/{}.TIF'.format(doc_)
shutil.copy(src1, 'ignore/{}-{}'.format(dataset,
os.path.basename(src1)))
if os.path.exists('ignore/{}-{}'.format(dataset, doc_)):
shutil.rmtree('ignore/{}-{}'.format(dataset, doc_))
shutil.copytree(doc, 'ignore/{}-{}'.format(dataset, doc_))
import cv2
import numpy as np
import json
from docrec.strips.strips import Strips
from docrec.metrics.solution import accuracy
results_prop = json.load(open('results/results_proposed.json', 'r'))
results_marques = json.load(open('results/results_best_marques.json', 'r'))
for dataset in ['D1', 'D2']:
avg_prop = 0
avg_marques = 0
perfect_ls = 0
cnt = 0
for doc in results_prop:
_, dataset_, _, doc_ = doc.split('/')
if dataset == dataset_:
solution_prop = results_prop[doc]['solution']
solution_marques = results_marques[doc]['solution']
avg_prop += accuracy(solution_prop)
avg_marques += accuracy(solution_marques)
cnt += 1
avg_prop /= cnt
avg_marques /= cnt
print('{}: avg_prop={} - avg_marques={} => {} '.format(
dataset, 100 * avg_prop, 100 * avg_marques,
100 * (avg_prop - avg_marques)))
import os
import argparse
import matplotlib.pyplot as plt
from docrec.metrics.solution import accuracy
from docrec.strips.strips import Strips
from docrec.compatibility.proposed import Proposed
from docrec.solver.solverlocal import SolverLS
from docrec.pipeline import Pipeline
#path_model = '{}/{}'.format(os.getcwd(), open('best_model.txt').read())
path_model = 'model.npy'
# reconstruction pipeline (compatibility algorithm + solver)
algorithm = Proposed(path_model, 10, (3000, 31), num_classes=2)
solver = SolverLS(maximize=True)
pipeline = Pipeline(algorithm, solver)
parser = argparse.ArgumentParser(description='Score.')
parser.add_argument('-d',
'--d',
action='store',
dest='doc',
required=False,
type=str,
default='datasets/D1/artificial/D001',
help='Document.')
args = parser.parse_args()
strips = Strips(path=args.doc, filter_blanks=True)
solution, compatibilities, displacements = pipeline.run(strips)
print(compatibilities, displacements, solution)
print(accuracy(solution))
import cv2
import numpy as np
import json
from docrec.strips.strips import Strips
from docrec.metrics.solution import accuracy
from docrec.metrics.matrix import precision_mc
results = json.load(open('results/results_proposed.json', 'r'))
total = 0
for doc in results:
_, dataset, _, doc_ = doc.split('/')
solution = results[doc]['solution']
N = len(solution)
last = False
for i in range(N - 1):
if solution[i] == N - 1 and solution[i + 1] == 0:
last = True
mistakes = 0
for i in range(N - 1):
if solution[i] + 1 != solution[i + 1]:
mistakes += 1
if (mistakes == 1) and last:
total += 1
else:
print(doc, N, '{:.2f}'.format(100 * accuracy(solution)))
print('#last={}'.format(total))
import cv2
import numpy as np
import json
from docrec.strips.strips import Strips
from docrec.metrics.solution import accuracy
from docrec.metrics.matrix import precision_mc
results = json.load(open('results/results_proposed.json', 'r'))
perfect_kbh = 0
perfect_ls = 0
for doc in results:
_, dataset, _, doc_ = doc.split('/')
solution = results[doc]['solution']
compatibilities = np.asarray(results[doc]['compatibilities'])
if accuracy(solution) == 1:
perfect_ls += 1
if precision_mc(compatibilities, True, True) == 1:
perfect_kbh += 1
print('#perfect_kbh={} #perfect_ls={}'.format(perfect_kbh, perfect_ls))