def solve(filename,mode = 'product'):
original_img, binary_img = read_img_and_convert_to_binary(filename)
symbols = binary_img_segment(binary_img, original_img)
sort_symbols = sort_characters(symbols)
process.detect_uncontinous_symbols(sort_symbols, binary_img)
length = len(symbols)
column = length/3+1
index = 1
# for symbol in symbols:
# # print(symbol)
# plt.subplot(column,3,index)
# plt.imshow(symbol['src_img'], cmap='gray')
# plt.title(index), plt.xticks([]), plt.yticks([])
# index += 1
# temp_img = original_img[:, :, ::-1]
# # cv2.imshow('img',temp_img)
# # cv2.waitKey(0)
# # cv2.destroyAllWindows()
# plt.subplot(column,3,index)
# plt.imshow(temp_img, cmap = 'gray', interpolation = 'bicubic')
# plt.title(index),plt.xticks([]), plt.yticks([])
# plt.show()
symbols_to_be_predicted = normalize_matrix_value([x['src_img'] for x in symbols])
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": np.array(symbols_to_be_predicted)},
shuffle=False)
predictions = cnn_symbol_classifier.predict(input_fn=predict_input_fn)
characters = []
for i,p in enumerate(predictions):
# print(p['classes'],FILELIST[p['classes']])
candidates = get_candidates(p['probabilities'])
characters.append({'location':symbols[i]['location'],'candidates':candidates})
print([x['location'] for x in characters])
modify_characters(characters)
# print('排序后的字符序列')
# print([[x['location'], x['candidates']] for x in characters])
tokens = process.group_into_tokens(characters)
print('识别出的token')
print(tokens)
string_out = ""
#转换为string类型的序列
count_arrow = 0
i = 0
eq = False
while i < len(tokens):
if tokens[i]['token_string'] == 'f':
if i < 2:
tokens.insert(0,{'location': [39, 30, 118, 90], 'token_string': '(', 'token_type': 9})
tokens.insert(i + 3, {'location': [39, 30, 118, 90], 'token_string': ')', 'token_type': 9})
else:
tokens.insert(i - 1, {'location': [39, 30, 118, 90], 'token_string': '(', 'token_type': 9})
tokens.insert(i + 3, {'location': [39, 30, 118, 90], 'token_string': ')', 'token_type': 9})
i = i + 3
if tokens[i]['token_string'] == '=':
eq = True
i = i + 1
i = 0
while i < len(tokens):
#print(i,"")
string_temp = tokens[i]['token_string']
if string_temp =='f' and tokens[i]['token_type'] == 0:
string_temp = '/'
if string_temp == 'times' and tokens[i]['token_type'] == 0:
string_temp = '*'
if string_temp == 'x' and tokens[i]['token_type'] == 4:
string_temp = 'x'
if string_temp == 'log':
string_temp = tokens[i+1]['token_string']+'~'+tokens[i+2]['token_string']
string_out += str(string_temp)
i = i + 3
continue
if string_temp == 'sqrt' and tokens[i]['token_type'] == 0:
string_temp = '2%'
if string_temp == 'div' and tokens[i]['token_type'] == 0:
string_temp = '/'
if i>=1 and tokens[i-1]['token_type'] == 1 and tokens[i]['token_type'] == 1 :
string_temp = '^' + string_temp
if tokens[i]['token_type'] == 9 and string_temp == 'rightarrow' and count_arrow%2 == 0:
string_temp = '('
count_arrow +=1
if tokens[i]['token_type'] == 9 and string_temp == 'rightarrow' and count_arrow % 2 == 1:
string_temp = ')'
count_arrow += 1
i = i + 1
string_out += str(string_temp)
print(string_out, "string_out")
y_start = 0.9
y_stride = 0.2
speak.Speak(string_out)
if eq:
result1 = equation(string_out)
else:
result1 = (calculate_new(string_out))
solution = r'$solution:' + str(result1) + '$'
speak.Speak("答案是")
speak.Speak(result1)
print('答案：', solution)
print('处理结果请到static文件夹下的最新生成的图片查看')
plt.text(0.1, y_start, solution, fontsize=18)
plt.xticks([]), plt.yticks([])
# print(filename.rsplit('.',1)[1])
save_filename = str(int(time.time()))
save_filename_dir = SAVE_FOLDER + save_filename
plt.savefig(save_filename_dir)
# plt.show()
plt.close()
if mode == 'product':
return save_filename
elif mode == 'test':
return latex_str, answer
import process
from config import FILELIST
import numpy as np
from matplotlib import pyplot as plt
from tools.img_preprocess import read_img_and_convert_to_binary, binary_img_segment
import cv2
import parser
import tools
from calculator import *
#test
original_img, binary_img = read_img_and_convert_to_binary(
'./testImgs/easy +/3.jpg')
symbols = binary_img_segment(binary_img, original_img)
symbols = process.detect_uncontinous_symbols(symbols, binary_img)
length = len(symbols)
column = length / 3 + 1
index = 1
for symbol in symbols:
# print(symbol)
plt.subplot(column, 3, index)
plt.imshow(symbol['src_img'], cmap='gray')
plt.title(index), plt.xticks([]), plt.yticks([])
index += 1
temp_img = original_img[:, :, ::-1]
# cv2.imshow('img',temp_img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
plt.subplot(column, 3, index)
plt.imshow(temp_img, cmap='gray', interpolation='bicubic')
def solve(filename, mode='product'):
original_img, binary_img = read_img_and_convert_to_binary(filename)
symbols = binary_img_segment(binary_img, original_img)
sort_symbols = sort_characters(symbols)
process.detect_uncontinous_symbols(sort_symbols, binary_img)
length = len(symbols)
column = length / 3 + 1
index = 1
# for symbol in symbols:
# # print(symbol)
# plt.subplot(column,3,index)
# plt.imshow(symbol['src_img'], cmap='gray')
# plt.title(index), plt.xticks([]), plt.yticks([])
# index += 1
# temp_img = original_img[:, :, ::-1]
# # cv2.imshow('img',temp_img)
# # cv2.waitKey(0)
# # cv2.destroyAllWindows()
# plt.subplot(column,3,index)
# plt.imshow(temp_img, cmap = 'gray', interpolation = 'bicubic')
# plt.title(index),plt.xticks([]), plt.yticks([])
# plt.show()
symbols_to_be_predicted = normalize_matrix_value(
[x['src_img'] for x in symbols])
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": np.array(symbols_to_be_predicted)}, shuffle=False)
predictions = cnn_symbol_classifier.predict(input_fn=predict_input_fn)
characters = []
for i, p in enumerate(predictions):
# print(p['classes'],FILELIST[p['classes']])
candidates = get_candidates(p['probabilities'])
characters.append({
'location': symbols[i]['location'],
'candidates': candidates
})
#print([x['location'] for x in characters])
modify_characters(characters)
# print('排序后的字符序列')
# print([[x['location'], x['candidates']] for x in characters])
tokens = process.group_into_tokens(characters)
# print('识别出的token')
# print(tokens)
# 先将每一个token初始化成一个树节点，得到一个节点列表node_list
node_list = parser.characters_to_nodes(characters)
parser_tree = parser.decompose(node_list)
# print(parser_tree)
set_forward_step(0)
post_order(parser_tree)
y_start = 0.9
y_stride = 0.2
if parser_tree['status'] == STATUS['solved']:
latex_strs = []
i = 5
j = 0
while j < i and isinstance(parser_tree['structure'], list):
set_forward_step(1)
latex_str = post_order(parser_tree)
latex_strs.append(latex_str)
j = j + 1
# for latex_str in latex_strs:
# print(latex_str)
# print(parser_tree)
for i, latex_str in enumerate(latex_strs):
if i == 0:
expression_str = r'$expression:' + latex_str + '$'
else:
expression_str = r'$step' + str(i) + ':' + latex_str + '$'
# print(expression_str)
font_size = 18
if len(latex_str) > 12:
font_size = 15
plt.text(0.1, y_start, expression_str, fontsize=font_size)
y_start = y_start - y_stride
latex_str = latex_strs[0]
else:
set_forward_step(0)
latex_str = post_order(parser_tree)
expression_str = r'$expression:' + latex_str + '$'
font_size = 18
if len(latex_str) > 12:
font_size = 15
plt.text(0.1, y_start, expression_str, fontsize=font_size)
y_start = y_start - y_stride
# print(solve_expression(parser_tree))
solution = ''
answer = ''
if parser_tree['status'] == STATUS['solved']:
# print(latex(parser_tree['value']))
if isinstance(parser_tree['value'], int) or isinstance(
parser_tree['value'], float):
solution = r'$result:' + str(parser_tree['value']) + '$'
answer = str(parser_tree['value'])
else:
solution = r'$result:' + str(latex(parser_tree['value'])) + '$'
answer = str(latex(parser_tree['value']))
elif parser_tree['type'] == NODE_TYPE['derivation'] or parser_tree[
'type'] == NODE_TYPE['limitation']:
solution = r'$result:' + str(latex(parser_tree['value'])) + '$'
answer = str(latex(parser_tree['value']))
elif parser_tree['status'] == STATUS['eq1'] or parser_tree[
'status'] == STATUS['eq2']:
result = solve_expression(parser_tree)
# print(result)
solution = r'$result:' + result_to_str(result) + '$'
answer = result
elif parser_tree['status'] == STATUS['other']:
answer = latex(parser_tree['value'])
# print(answer)
else:
result = solve_expression(parser_tree)
# print(str(result))
solution = r'$solution:' + latex_str + '$'
print('答案：', solution)
print('处理结果请到static文件夹下的最新生成的图片查看')
plt.text(0.1, y_start, solution, fontsize=18)
#
# expression_str = r'$expression:' + latex_str + '$'
# print(expression_str)
# plt.text(0.1, 0.9, expression_str, fontsize=20)
# # print(solve_expression(parser_tree))
# solution = ''
# answer =''
# if parser_tree['status'] == STATUS['solved']:
# if isinstance(parser_tree['value'], int) or isinstance(parser_tree['value'], float):
# solution = r'$result:' + str(parser_tree['value']) + '$'
# answer = str(parser_tree['value'])
# else:
# solution = r'$result:' + str(latex(parser_tree['value'])) + '$'
# answer = str(latex(parser_tree['value']))
#
#
# elif parser_tree['status'] == STATUS['eq1'] or parser_tree['status'] == STATUS['eq2']:
# solution = r'$result:' + str(latex(parser_tree['value'])) + '$'
# result = solve_expression(parser_tree)
# print(result)
# answer = result
# elif parser_tree['status'] == STATUS['other']:
# answer = latex(parser_tree['value'])
# print(answer)
# else:
# solution = r'$solution:' + latex_str + '$'
# answer = latex_str
# plt.text(0.1, 0.5, solution, fontsize=20)
plt.xticks([]), plt.yticks([])
# print(filename.rsplit('.',1)[1])
save_filename = str(int(time.time()))
save_filename_dir = SAVE_FOLDER + save_filename
plt.savefig(save_filename_dir)
# plt.show()
plt.close()
if mode == 'product':
return save_filename
elif mode == 'test':
return latex_str, answer