def digit_recognize():
mb = MultipleSvm.load_variables(Smo, RESULT_PATH)
# file_path = '../resource/svm_wrong_digits/pic04_no17_real8_cal3.dataset'
file_path = '../resource/svm_wrong_digits/pic04_no33_real8_cal3.dataset'
# file_path = '../resource/svm_wrong_digits/pic15_no19_real5_cal6_1.dataset'
# file_path = '../resource/svm_wrong_digits/pic15_no19_real5_cal6.dataset'
number_ragion = numpy.mat(Image.read_from_number_file(file_path))
transfered_ragion = numpy_helper.transfer_1to255(number_ragion)
# adjusted_ragion = main_sudoku.adjust_number_ragion(transfered_ragion)
adjusted_ragion = adjust_number_ragion2(transfered_ragion)
# adjusted_ragion = transfered_ragion
Display.ragions([transfered_ragion, adjusted_ragion])
adjusted_ragion = numpy_helper.transfer_255to1(adjusted_ragion)
number_matrix = main_sudoku.transfer_to_digit_matrix(adjusted_ragion)
mb.dag_classify(number_matrix).ppl()
# square_ragion.mean().ppl()
threshold_value = Ragion.cal_threshold_value(the_ragion, square_ragion, 0.69)
thresholded_ragion = Image.threshold_white(the_ragion, threshold_value)
# thresholded_ragion = cv2.adaptiveThreshold(the_ragion, 255,
# cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY_INV, blockSize=7, C=2)
cell_rect = nonzero_rect.analyze_from_center(thresholded_ragion)
if cell_rect:
cell_ragion = Rect.get_ragion(cell_rect, thresholded_ragion)
cell_rect.pl()
# Display.image(cell_ragion)
file_path = Resource.get_test_path("sample_19_07_05_image.jpg")
the_ragion = cv2.imread(file_path, 0)
# the_ragion.mean().ppl()
file_path = Resource.get_test_path("sample_19_square.jpg")
square_ragion = cv2.imread(file_path, 0)
# square_ragion.mean().ppl()
threshold_value = Ragion.cal_threshold_value(the_ragion, square_ragion, 0.8)
thresholded_ragion = Image.threshold_white(the_ragion, threshold_value)
cell_rect = nonzero_rect.analyze_from_center(thresholded_ragion)
if cell_rect:
cell_ragion = Rect.get_ragion(cell_rect, thresholded_ragion)
number_ragion = numpy_helper.transfer_255to1(cell_ragion)
number_matrix = main_sudoku.transfer_to_digit_matrix(number_ragion)
som_svm.dag_classify(number_matrix).pl()
# thresholded_ragion.ppl()
# Display.image(thresholded_ragion)
if __name__ == "__main__":
from minitest import *
import data_file_helper
from picture_sudoku.helpers import numpy_helper
from picture_sudoku.cv2_helpers.display import Display
from picture_sudoku.cv2_helpers.image import Image
from picture_sudoku.helpers.common import Resource
from picture_sudoku import main_sudoku
def show_number_matrix(number_matrix):
binary_number_image = number_matrix.reshape((data_file_helper.IMG_SIZE, data_file_helper.IMG_SIZE))
number_image = numpy_helper.transfer_values_quickly(binary_number_image, {1: 255})
number_image = numpy.array(number_image, dtype=numpy.uint8)
# Image.save_to_txt(number_image,'test1.dataset')
Display.image(number_image)
""" how to use training, please see the generator.py"""
with test(MultipleSvm.dag_classify):
dataset_matrix_hash = data_file_helper.get_dataset_matrix_hash(data_file_helper.FONT_TRAINING_PATH, (9,))
mb = MultipleSvm.load_variables(Smo, data_file_helper.FONT_RESULT_PATH)
mb.dag_classify(dataset_matrix_hash[9][0]).must_equal(9)
with test("special number"):
file_name = "sample_18_01_26_resized.dataset"
issue_ragion = Image.load_from_txt(Resource.get_path("test", file_name))
transfered_ragion = main_sudoku.transfer_to_digit_matrix(issue_ragion)
mb = MultipleSvm.load_variables(Smo, data_file_helper.FONT_RESULT_PATH)
mb.dag_classify(transfered_ragion).must_equal(1)
# Display.binary_image(main_sudoku.resize_to_cell_size(issue_ragion))
