def test_generate_supplement_number_ragion(file_name, simple_svm, supplement_svm):
file_path = os.path.join(data_file_helper.SUPPLEMENT_TRAINING_PATH, file_name)
the_ragion = Image.load_from_txt(file_path)
the_number_matrix = numpy.matrix(the_ragion.reshape(1, FULL_SIZE))
# simple_svm.dag_classify(the_number_matrix).must_equal(
# extract_cal_digit(file_path),
# failure_msg="simple file path: {0}".format(file_path))
supplement_svm.dag_classify(the_number_matrix).must_equal(
extract_real_digit(file_path), failure_msg="supplement file path: {0}".format(file_path)
)
def generate_standard_supplement_number_ragions(
ragion_file_path, the_digit, save_path=data_file_helper.SUPPLEMENT_TRAINING_PATH
):
issue_ragion = Image.load_from_txt(ragion_file_path)
# os.path.basename(ragion_file_path).pl()
# ragion_file_path.pl()
resize_interpolation_arr = (
(cv2.INTER_AREA, "INTER_AREA"),
(cv2.INTER_LINEAR, "INTER_LINEAR"),
(cv2.INTER_CUBIC, "INTER_CUBIC"),
)
def resize_and_save_file(interpolation_item):
interpolation_value, interpolation_name = interpolation_item
result_file_path = os.path.join(
save_path, str(the_digit) + "_" + interpolation_name + "_" + os.path.basename(ragion_file_path)
)
resized_ragion = main_sudoku.resize_to_cell_size(issue_ragion, interpolation_value)
Image.save_to_txt(resized_ragion, result_file_path)
result_file_path.pl()
return result_file_path
return map(resize_and_save_file, resize_interpolation_arr)
inject(numpy.allclose, 'must_close')
with test("Ragion.fill"):
the_ragion = numpy.ones((3,4))
Ragion.fill(the_ragion, (6,6)).must_close(
numpy.array( [[ 0., 0., 0., 0., 0., 0.],
[ 0., 1., 1., 1., 1., 0.],
[ 0., 1., 1., 1., 1., 0.],
[ 0., 1., 1., 1., 1., 0.],
[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.]]))
with test(Ragion.review_classified_number_ragion_for_8):
the_pic_path = '../../resource/test/pic17_no08_real6_cal8.dataset'
the_ragion = Image.load_from_txt(the_pic_path)
Ragion.review_classified_number_ragion_for_8(the_ragion, 8).must_equal(6)
the_pic_path = '../../resource/test/pic16_no05_real8_cal6.dataset'
the_ragion = Image.load_from_txt(the_pic_path)
Ragion.review_classified_number_ragion_for_8(the_ragion, 8).must_equal(8)
# Display.binary_image(the_ragion)
# save file: ../resource/svm_wrong_digits/pic16_no10_real8_cal6.dataset
# save file: ../resource/svm_wrong_digits/pic16_no20_real8_cal6.dataset
pass
with test("Ragions.fill_to_same_size"):
ragions = (numpy.ones((3,2)), numpy.ones((2,1)), numpy.ones((2,4)))
Ragions.fill_to_same_size(ragions).must_close(
[numpy.array([[ 0., 1., 1., 0.],
def generate_number(index):
file_path = font_training_path+"/"+str(index)+suffix_file_name
return Image.load_from_txt(file_path)
import cv2
from picture_sudoku.cv2_helpers.image import Image
from picture_sudoku.cv2_helpers.display import Display
from picture_sudoku.helpers.common import Resource
if __name__ == '__main__':
from minitest import *
with test("show freeman"):
the_pic_path = Resource.get_path('test/pic16_no05_real8_cal6.dataset')
the_image = Image.load_from_txt(the_pic_path)
# contours,not_use = cv2.findContours(the_image.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
# # Display.contours(the_image, contours)
# contours.ppl()
cv2.CHAIN_APPROX_NONE.pl()
cv2.CHAIN_APPROX_SIMPLE.pl()
contours,not_use = cv2.findContours(the_image.copy(),cv2.RETR_LIST,0)
# Display.contours(the_image, contours)
contours.ppl()
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))
final_left = left_x + 1
final_right = right_x - 1
return Rect.create(final_left, final_top, final_right, final_bottom)
if __name__ == '__main__':
from minitest import *
from picture_sudoku.cv2_helpers.display import Display
from picture_sudoku.helpers import numpy_helper
from picture_sudoku.helpers.common import Resource
inject(numpy.allclose, 'must_close')
test_image_path = '../../resource/test/'
image_14_07_path = test_image_path+'sample_14_07.dataset'
image_14_07 = Image.load_from_txt(image_14_07_path)
image_14_07_255 = numpy_helper.transfer_values_quickly(image_14_07, {1:255})
with test(analyze_from_center):
rect_14_07 = analyze_from_center(image_14_07)
rect_14_07.must_equal((15, 11, 26, 39))
# Display.binary_rect(image_14_07, rect_14_07)
# Image.save_to_txt(Rect.get_ragion(rect_14_07, image_14_07),
# test_image_path+'sample_14_07_no.dataset')
image_01_03_path = test_image_path+'sample_01_03.dataset'
image_01_03 = Image.load_from_txt(image_01_03_path)
rect_01_03 = analyze_from_center(image_01_03)
rect_01_03.must_equal((27, 26, 12, 17))
# Display.binary_rect(image_01_03, rect_01_03)
