def save_dataset(gray_image, file_name):
from picture_sudoku.helpers.common import Resource
file_path = Resource.get_path('test',file_name)
# file_path = '../../resource/test/' + file_name
transfered_image = numpy_helper.transfer_values_quickly(gray_image,{255:1})
print(file_path+" has been saved!")
return Image.save_to_txt(transfered_image,file_path)
def test_the_sample(sample_index):
pic_filepath = Resource.get_path('example_pics/sample'+
str(sample_index).zfill(2)+'.dataset.jpg')
# pic_filepath = '../resource/example_pics/sample01.dataset.jpg'
result = requests.post(IMAGE_RESULT_URL,
files={'files':open(pic_filepath,'rb')})
answer_result = json.loads(result.content)
return answer_result
import numpy
import os
from picture_sudoku.helpers.common import Resource, OtherResource
IMG_SIZE = 32
FULL_SIZE = 1024
FONT_TRAINING_PATH = OtherResource.get_path('font_training')
FONT_RESULT_PATH = OtherResource.get_path('font_training_result')
SUPPLEMENT_RESULT_PATH = OtherResource.get_path('font_supplement_result')
HAND_TRAINING_PATH = OtherResource.get_path('hand_training')
HAND_TESTING_PATH = OtherResource.get_path('hand_testing')
HAND_RESULT_PATH = Resource.get_path('hand_training_result')
SUPPLEMENT_TRAINING_PATH = OtherResource.get_path('supplement_training')
def get_dataset_matrix_hash(the_path, start_with_numbers):
'''
main method
'''
return {i:numpy.mat(get_dataset_from_filenames(the_path,
filter_filenames_with_nums(the_path,(i,)))) for i in start_with_numbers}
def get_dataset_matrix_hash_with_supplement(
the_path, supplement_path, start_with_numbers):
'''
main method
'''
# # gray_image = cv2.imread(image_path, 0)
# # Display.image(gray_image)
# # /Users/colin/work/picture_sudoku/other_resource/font_training_result
# with test("for having more than two border lines"):
# # image_path = Resource.get_path('example_pics/sample11.dataset.jpg')
# # image_path = Resource.get_path('example_pics/sample08.dataset.jpg')
# # image_path = Resource.get_path('example_pics/sample16.dataset.jpg')
# image_path = Resource.get_path('for_issues/cannot_recognize.jpg')
# main_sudoku.answer_quiz_with_pic(image_path).pl()
# # gray_image = cv2.imread(image_path, 0)
# # Display.image(gray_image)
with test("get clear number ragion"):
som_svm = MultipleSvm.load_variables(Smo, data_file_helper.SUPPLEMENT_RESULT_PATH)
file_path = Resource.get_test_path("sample_15_null_38_image.jpg")
the_ragion = cv2.imread(file_path, 0)
# the_ragion.mean().ppl()
# the_ragion.ppl()
# thresholded_ragion = Image.threshold_white_with_mean_percent(the_ragion, 0.8)
# thresholded_ragion.ppl()
# Display.image(thresholded_ragion)
file_path = Resource.get_test_path("sample_15_square.jpg")
square_ragion = cv2.imread(file_path, 0)
# 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)
def main():
contour = numpy.load(Resource.get_path("test/max_contour_with_noises.npy"))
extract_square_from_contour(contour)
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))
