├── .gitignore ├── data └── .gitignore ├── model └── .gitignore ├── test.jpg ├── test ├── 1.jpg ├── 10.jpg ├── 11.jpg ├── 12.jpg ├── 13.jpg ├── 14.jpg ├── 15.jpg ├── 16.jpg ├── 17.jpg ├── 18.jpg ├── 19.jpg ├── 2.jpg ├── 20.jpg ├── 21.jpg ├── 22.jpg ├── 23.jpg ├── 24.jpg ├── 25.jpg ├── 26.jpg ├── 27.jpg ├── 28.jpg ├── 29.jpg ├── 3.jpg ├── 30.jpg ├── 31.jpg ├── 32.jpg ├── 33.jpg ├── 34.jpg ├── 35.jpg ├── 36.jpg ├── 37.jpg ├── 38.jpg ├── 39.jpg ├── 4.jpg ├── 40.jpg ├── 41.jpg ├── 42.jpg ├── 43.jpg ├── 44.jpg ├── 45.jpg ├── 46.jpg ├── 47.jpg ├── 48.jpg ├── 49.jpg ├── 5.jpg ├── 50.jpg ├── 51.jpg ├── 52.jpg ├── 53.jpg ├── 54.jpg ├── 55.jpg ├── 56.jpg ├── 57.jpg ├── 58.jpg ├── 59.jpg ├── 6.jpg ├── 60.jpg ├── 61.jpg ├── 62.jpg ├── 63.jpg ├── 64.jpg ├── 65.jpg ├── 66.jpg ├── 67.jpg ├── 68.jpg ├── 69.jpg ├── 7.jpg ├── 70.jpg ├── 71.jpg ├── 72.jpg ├── 73.jpg ├── 74.jpg ├── 75.jpg ├── 76.jpg ├── 77.jpg ├── 78.jpg ├── 79.jpg ├── 8.jpg ├── 80.jpg ├── 81.jpg ├── 82.jpg ├── 83.jpg ├── 84.jpg ├── 85.jpg ├── 86.jpg ├── 87.jpg ├── 88.jpg ├── 89.jpg ├── 9.jpg ├── 90.jpg ├── 91.jpg ├── 92.jpg ├── 93.jpg ├── 94.jpg ├── 95.jpg ├── 96.jpg ├── 97.jpg ├── 98.jpg ├── 99.jpg ├── 100.jpg ├── 101.jpg ├── 102.jpg ├── 103.jpg ├── 104.jpg ├── 105.jpg ├── 106.jpg ├── 107.jpg ├── 108.jpg ├── 109.jpg ├── 110.jpg ├── 111.jpg ├── 112.jpg ├── 113.jpg ├── 114.jpg ├── 115.jpg ├── 116.jpg ├── 117.jpg ├── 118.jpg ├── 119.jpg ├── 120.jpg ├── 121.jpg ├── 122.jpg ├── 123.jpg ├── 124.jpg ├── 125.jpg ├── 126.jpg ├── 127.jpg ├── 128.jpg ├── 129.jpg ├── 130.jpg ├── 131.jpg ├── 132.jpg ├── 133.jpg ├── 134.jpg ├── 135.jpg ├── 136.jpg ├── 137.jpg ├── 138.jpg ├── 139.jpg ├── 140.jpg ├── 141.jpg ├── 142.jpg ├── 143.jpg ├── 144.jpg ├── 145.jpg ├── 146.jpg ├── 147.jpg ├── 148.jpg ├── 149.jpg ├── 150.jpg ├── 151.jpg ├── 152.jpg ├── 153.jpg ├── 154.jpg ├── 155.jpg ├── 156.jpg ├── 157.jpg ├── 158.jpg ├── 159.jpg ├── 160.jpg ├── 161.jpg ├── 162.jpg ├── 163.jpg ├── 164.jpg ├── 165.jpg ├── 166.jpg ├── 167.jpg ├── 168.jpg ├── 169.jpg ├── 170.jpg ├── 171.jpg ├── 172.jpg ├── 173.jpg ├── 174.jpg ├── 175.jpg ├── 176.jpg ├── 177.jpg ├── 178.jpg ├── 179.jpg ├── 180.jpg ├── 181.jpg ├── 182.jpg ├── 183.jpg ├── 184.jpg ├── 185.jpg ├── 186.jpg ├── 187.jpg ├── 188.jpg ├── 189.jpg ├── 190.jpg ├── 191.jpg ├── 192.jpg ├── 193.jpg ├── 194.jpg ├── 195.jpg ├── 196.jpg ├── 197.jpg ├── 198.jpg ├── 199.jpg ├── 200.jpg ├── 201.jpg ├── 202.jpg ├── 203.jpg ├── 204.jpg ├── 205.jpg ├── 206.jpg ├── 207.jpg ├── 208.jpg ├── 209.jpg ├── 210.jpg ├── 211.jpg ├── 212.jpg ├── 213.jpg ├── 214.jpg ├── 215.jpg ├── 216.jpg ├── 217.jpg ├── 218.jpg ├── 219.jpg ├── 220.jpg ├── 221.jpg ├── 222.jpg ├── 223.jpg ├── 224.jpg ├── 225.jpg ├── 226.jpg ├── 227.jpg ├── 228.jpg ├── 229.jpg ├── 230.jpg ├── 231.jpg ├── 232.jpg ├── 233.jpg ├── 234.jpg ├── 235.jpg ├── 236.jpg ├── 237.jpg ├── 238.jpg ├── 239.jpg ├── 240.jpg ├── 241.jpg ├── 242.jpg ├── 243.jpg ├── 244.jpg ├── 245.jpg ├── 246.jpg ├── 247.jpg ├── 248.jpg ├── 249.jpg └── 250.jpg ├── train ├── 1.jpg ├── 2.jpg ├── 3.jpg ├── 4.jpg ├── 5.jpg ├── 6.jpg ├── 7.jpg ├── 8.jpg ├── 9.jpg ├── 10.jpg ├── 100.jpg ├── 101.jpg ├── 102.jpg ├── 103.jpg ├── 104.jpg ├── 105.jpg ├── 106.jpg ├── 107.jpg ├── 108.jpg ├── 109.jpg ├── 11.jpg ├── 110.jpg ├── 111.jpg ├── 112.jpg ├── 113.jpg ├── 114.jpg ├── 115.jpg ├── 116.jpg ├── 117.jpg ├── 118.jpg ├── 119.jpg ├── 12.jpg ├── 120.jpg ├── 121.jpg ├── 122.jpg ├── 123.jpg ├── 124.jpg ├── 125.jpg ├── 126.jpg ├── 127.jpg ├── 128.jpg ├── 129.jpg ├── 13.jpg ├── 130.jpg ├── 131.jpg ├── 132.jpg ├── 133.jpg ├── 134.jpg ├── 135.jpg ├── 136.jpg ├── 137.jpg ├── 138.jpg ├── 139.jpg ├── 14.jpg ├── 140.jpg ├── 141.jpg ├── 142.jpg ├── 143.jpg ├── 144.jpg ├── 145.jpg ├── 146.jpg ├── 147.jpg ├── 148.jpg ├── 149.jpg ├── 15.jpg ├── 150.jpg ├── 151.jpg ├── 152.jpg ├── 153.jpg ├── 154.jpg ├── 155.jpg ├── 156.jpg ├── 157.jpg ├── 158.jpg ├── 159.jpg ├── 16.jpg ├── 160.jpg ├── 161.jpg ├── 162.jpg ├── 163.jpg ├── 164.jpg ├── 165.jpg ├── 166.jpg ├── 167.jpg ├── 168.jpg ├── 169.jpg ├── 17.jpg ├── 170.jpg ├── 171.jpg ├── 172.jpg ├── 173.jpg ├── 174.jpg ├── 175.jpg ├── 176.jpg ├── 177.jpg ├── 178.jpg ├── 179.jpg ├── 18.jpg ├── 180.jpg ├── 181.jpg ├── 182.jpg ├── 183.jpg ├── 184.jpg ├── 185.jpg ├── 186.jpg ├── 187.jpg ├── 188.jpg ├── 189.jpg ├── 19.jpg ├── 190.jpg ├── 191.jpg ├── 192.jpg ├── 193.jpg ├── 194.jpg ├── 195.jpg ├── 196.jpg ├── 197.jpg ├── 198.jpg ├── 199.jpg ├── 20.jpg ├── 200.jpg ├── 201.jpg ├── 202.jpg ├── 203.jpg ├── 204.jpg ├── 205.jpg ├── 206.jpg ├── 207.jpg ├── 208.jpg ├── 209.jpg ├── 21.jpg ├── 210.jpg ├── 211.jpg ├── 212.jpg ├── 213.jpg ├── 214.jpg ├── 215.jpg ├── 216.jpg ├── 217.jpg ├── 218.jpg ├── 219.jpg ├── 22.jpg ├── 220.jpg ├── 221.jpg ├── 222.jpg ├── 223.jpg ├── 224.jpg ├── 225.jpg ├── 226.jpg ├── 227.jpg ├── 228.jpg ├── 229.jpg ├── 23.jpg ├── 230.jpg ├── 231.jpg ├── 232.jpg ├── 233.jpg ├── 234.jpg ├── 235.jpg ├── 236.jpg ├── 237.jpg ├── 238.jpg ├── 239.jpg ├── 24.jpg ├── 240.jpg ├── 241.jpg ├── 242.jpg ├── 243.jpg ├── 244.jpg ├── 245.jpg ├── 246.jpg ├── 247.jpg ├── 248.jpg ├── 249.jpg ├── 25.jpg ├── 250.jpg ├── 251.jpg ├── 252.jpg ├── 253.jpg ├── 254.jpg ├── 255.jpg ├── 256.jpg ├── 257.jpg ├── 258.jpg ├── 259.jpg ├── 26.jpg ├── 260.jpg ├── 261.jpg ├── 262.jpg ├── 263.jpg ├── 264.jpg ├── 265.jpg ├── 266.jpg ├── 267.jpg ├── 268.jpg ├── 269.jpg ├── 27.jpg ├── 270.jpg ├── 271.jpg ├── 272.jpg ├── 273.jpg ├── 274.jpg ├── 275.jpg ├── 276.jpg ├── 277.jpg ├── 278.jpg ├── 279.jpg ├── 28.jpg ├── 280.jpg ├── 281.jpg ├── 282.jpg ├── 283.jpg ├── 284.jpg ├── 285.jpg ├── 286.jpg ├── 287.jpg ├── 288.jpg ├── 289.jpg ├── 29.jpg ├── 290.jpg ├── 291.jpg ├── 292.jpg ├── 293.jpg ├── 294.jpg ├── 295.jpg ├── 296.jpg ├── 297.jpg ├── 298.jpg ├── 299.jpg ├── 30.jpg ├── 300.jpg ├── 301.jpg ├── 302.jpg ├── 303.jpg ├── 304.jpg ├── 305.jpg ├── 306.jpg ├── 307.jpg ├── 308.jpg ├── 309.jpg ├── 31.jpg ├── 310.jpg ├── 311.jpg ├── 312.jpg ├── 313.jpg ├── 314.jpg ├── 315.jpg ├── 316.jpg ├── 317.jpg ├── 318.jpg ├── 319.jpg ├── 32.jpg ├── 320.jpg ├── 321.jpg ├── 322.jpg ├── 323.jpg ├── 324.jpg ├── 325.jpg ├── 326.jpg ├── 327.jpg ├── 328.jpg ├── 329.jpg ├── 33.jpg ├── 330.jpg ├── 331.jpg ├── 332.jpg ├── 333.jpg ├── 334.jpg ├── 335.jpg ├── 336.jpg ├── 337.jpg ├── 338.jpg ├── 339.jpg ├── 34.jpg ├── 340.jpg ├── 341.jpg ├── 342.jpg ├── 343.jpg ├── 344.jpg ├── 345.jpg ├── 346.jpg ├── 347.jpg ├── 348.jpg ├── 349.jpg ├── 35.jpg ├── 350.jpg ├── 351.jpg ├── 352.jpg ├── 353.jpg ├── 354.jpg ├── 355.jpg ├── 356.jpg ├── 357.jpg ├── 358.jpg ├── 359.jpg ├── 36.jpg ├── 360.jpg ├── 361.jpg ├── 362.jpg ├── 363.jpg ├── 364.jpg ├── 365.jpg ├── 366.jpg ├── 367.jpg ├── 368.jpg ├── 369.jpg ├── 37.jpg ├── 370.jpg ├── 371.jpg ├── 372.jpg ├── 373.jpg ├── 374.jpg ├── 375.jpg ├── 376.jpg ├── 377.jpg ├── 378.jpg ├── 379.jpg ├── 38.jpg ├── 380.jpg ├── 381.jpg ├── 382.jpg ├── 383.jpg ├── 384.jpg ├── 385.jpg ├── 386.jpg ├── 387.jpg ├── 388.jpg ├── 389.jpg ├── 39.jpg ├── 390.jpg ├── 391.jpg ├── 392.jpg ├── 393.jpg ├── 394.jpg ├── 395.jpg ├── 396.jpg ├── 397.jpg ├── 398.jpg ├── 399.jpg ├── 40.jpg ├── 400.jpg ├── 401.jpg ├── 402.jpg ├── 403.jpg ├── 404.jpg ├── 405.jpg ├── 406.jpg ├── 407.jpg ├── 408.jpg ├── 409.jpg ├── 41.jpg ├── 410.jpg ├── 411.jpg ├── 412.jpg ├── 413.jpg ├── 414.jpg ├── 415.jpg ├── 416.jpg ├── 417.jpg ├── 418.jpg ├── 419.jpg ├── 42.jpg ├── 420.jpg ├── 421.jpg ├── 422.jpg ├── 423.jpg ├── 424.jpg ├── 425.jpg ├── 426.jpg ├── 427.jpg ├── 428.jpg ├── 429.jpg ├── 43.jpg ├── 430.jpg ├── 431.jpg ├── 432.jpg ├── 433.jpg ├── 434.jpg ├── 435.jpg ├── 436.jpg ├── 437.jpg ├── 438.jpg ├── 439.jpg ├── 44.jpg ├── 440.jpg ├── 441.jpg ├── 442.jpg ├── 443.jpg ├── 444.jpg ├── 445.jpg ├── 446.jpg ├── 447.jpg ├── 448.jpg ├── 449.jpg ├── 45.jpg ├── 450.jpg ├── 451.jpg ├── 452.jpg ├── 453.jpg ├── 454.jpg ├── 455.jpg ├── 456.jpg ├── 457.jpg ├── 458.jpg ├── 459.jpg ├── 46.jpg ├── 460.jpg ├── 461.jpg ├── 462.jpg ├── 463.jpg ├── 464.jpg ├── 465.jpg ├── 466.jpg ├── 467.jpg ├── 468.jpg ├── 469.jpg ├── 47.jpg ├── 470.jpg ├── 471.jpg ├── 472.jpg ├── 473.jpg ├── 474.jpg ├── 475.jpg ├── 476.jpg ├── 477.jpg ├── 478.jpg ├── 479.jpg ├── 48.jpg ├── 480.jpg ├── 481.jpg ├── 482.jpg ├── 483.jpg ├── 484.jpg ├── 485.jpg ├── 486.jpg ├── 487.jpg ├── 488.jpg ├── 489.jpg ├── 49.jpg ├── 490.jpg ├── 491.jpg ├── 492.jpg ├── 493.jpg ├── 494.jpg ├── 495.jpg ├── 496.jpg ├── 497.jpg ├── 498.jpg ├── 499.jpg ├── 50.jpg ├── 500.jpg ├── 51.jpg ├── 52.jpg ├── 53.jpg ├── 54.jpg ├── 55.jpg ├── 56.jpg ├── 57.jpg ├── 58.jpg ├── 59.jpg ├── 60.jpg ├── 61.jpg ├── 62.jpg ├── 63.jpg ├── 64.jpg ├── 65.jpg ├── 66.jpg ├── 67.jpg ├── 68.jpg ├── 69.jpg ├── 70.jpg ├── 71.jpg ├── 72.jpg ├── 73.jpg ├── 74.jpg ├── 75.jpg ├── 76.jpg ├── 77.jpg ├── 78.jpg ├── 79.jpg ├── 80.jpg ├── 81.jpg ├── 82.jpg ├── 83.jpg ├── 84.jpg ├── 85.jpg ├── 86.jpg ├── 87.jpg ├── 88.jpg ├── 89.jpg ├── 90.jpg ├── 91.jpg ├── 92.jpg ├── 93.jpg ├── 94.jpg ├── 95.jpg ├── 96.jpg ├── 97.jpg ├── 98.jpg └── 99.jpg ├── README.md ├── Pipfile ├── train.py ├── work.py ├── prepare.py ├── test.txt └── train.txt /.gitignore: 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-------------------------------------------------------------------------------- 1 | [[source]] 2 | name = "pypi" 3 | url = "https://pypi.tuna.tsinghua.edu.cn/simple" 4 | verify_ssl = true 5 | 6 | [dev-packages] 7 | 8 | [packages] 9 | pillow = "*" 10 | numpy = "*" 11 | matplotlib = "*" 12 | tensorflow = "*" 13 | 14 | [requires] 15 | python_version = "3.6" 16 | 17 | [scripts] 18 | prepare = "python prepare.py" 19 | train = "python train.py" 20 | work = "python work.py" 21 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | from data import train_images, train_labels, test_labels, test_images 3 | 4 | DLEN = len(train_images.data[0]) 5 | DNUM = len(train_images.data) 6 | 7 | x = tf.placeholder(tf.float32, [None, DLEN]) 8 | 9 | W = tf.Variable(tf.zeros([DLEN, 10])) 10 | b = tf.Variable(tf.zeros([10])) 11 | y = tf.nn.softmax(tf.matmul(x, W) + b) 12 | 13 | y_ = tf.placeholder("float", [None, 10]) 14 | 15 | cross_entropy = -tf.reduce_sum(y_*tf.log(y)) 16 | 17 | train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy) 18 | 19 | saver = tf.train.Saver() 20 | 21 | sess = tf.Session() 22 | sess.run(tf.global_variables_initializer()) 23 | 24 | for i in range(DNUM): 25 | batch_xs = [train_images.data[i]] 26 | batch_ys = [train_labels.data[i]] 27 | sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) 28 | 29 | correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) 30 | accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) 31 | print(sess.run(accuracy, feed_dict={x: test_images.data, y_: test_labels.data})) 32 | 33 | saver.save(sess, 'model/model') 34 | 35 | sess.close() 36 | -------------------------------------------------------------------------------- /work.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | from PIL import Image 3 | import numpy as np 4 | import sys 5 | import os 6 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 7 | 8 | img = Image.open(sys.stdin.buffer.raw).convert('L') # 读取图片并灰度化 9 | 10 | img = img.crop((2, 1, 66, 22)) # 裁掉边变成 64x21 11 | 12 | # 分离数字 13 | img1 = img.crop((0, 0, 16, 21)) 14 | img2 = img.crop((16, 0, 32, 21)) 15 | img3 = img.crop((32, 0, 48, 21)) 16 | img4 = img.crop((48, 0, 64, 21)) 17 | 18 | img1 = np.array(img1).flatten() 19 | img1 = list(map(lambda x: 1 if x <= 180 else 0, img1)) 20 | img2 = np.array(img2).flatten() 21 | img2 = list(map(lambda x: 1 if x <= 180 else 0, img2)) 22 | img3 = np.array(img3).flatten() 23 | img3 = list(map(lambda x: 1 if x <= 180 else 0, img3)) 24 | img4 = np.array(img4).flatten() 25 | img4 = list(map(lambda x: 1 if x <= 180 else 0, img4)) 26 | 27 | 28 | x = tf.placeholder(tf.float32, [None, 336]) 29 | 30 | W = tf.Variable(tf.zeros([336, 10])) 31 | b = tf.Variable(tf.zeros([10])) 32 | y = tf.nn.softmax(tf.matmul(x, W) + b) 33 | 34 | saver = tf.train.Saver() 35 | 36 | sess = tf.Session() 37 | sess.run(tf.global_variables_initializer()) 38 | 39 | save_path="model/model" 40 | 41 | saver.restore(sess, save_path) 42 | 43 | correct_prediction = tf.argmax(y, 1) 44 | result = sess.run(correct_prediction, feed_dict={x: [img1, img2, img3, img4]}) 45 | sess.close() 46 | 47 | for num in result: 48 | print(num, end='') 49 | -------------------------------------------------------------------------------- /prepare.py: -------------------------------------------------------------------------------- 1 | import os 2 | from PIL import Image 3 | import numpy as np 4 | import matplotlib.pyplot as plt 5 | 6 | # 保存输入数据 7 | def px(prefix, img1, img2, img3, img4): 8 | with open('./data/' + prefix + '_images.py', 'a+') as f: 9 | print(img1, file=f, end=",\n") 10 | print(img2, file=f, end=",\n") 11 | print(img3, file=f, end=",\n") 12 | print(img4, file=f, end=",\n") 13 | 14 | # 保存标签数据 15 | def py(prefix, code): 16 | with open('./data/' + prefix + '_labels.py', 'a+') as f: 17 | for x in range(4): 18 | tmp = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] 19 | tmp[int(code[x])] = 1 20 | print(tmp, file=f, end=",\n") 21 | 22 | # 预处理图片 23 | def paa(file): 24 | img = Image.open(file).convert('L') # 读取图片并灰度化 25 | 26 | img = img.crop((2, 1, 66, 22)) # 裁掉边变成 64x21 27 | 28 | # 分离数字 29 | img1 = img.crop((0, 0, 16, 21)) 30 | img2 = img.crop((16, 0, 32, 21)) 31 | img3 = img.crop((32, 0, 48, 21)) 32 | img4 = img.crop((48, 0, 64, 21)) 33 | 34 | img1 = np.array(img1).flatten() # 扁平化，把二维弄成一维度 35 | img1 = list(map(lambda x: 1 if x <= 180 else 0, img1)) # 二值化 36 | img2 = np.array(img2).flatten() 37 | img2 = list(map(lambda x: 1 if x <= 180 else 0, img2)) 38 | img3 = np.array(img3).flatten() 39 | img3 = list(map(lambda x: 1 if x <= 180 else 0, img3)) 40 | img4 = np.array(img4).flatten() 41 | img4 = list(map(lambda x: 1 if x <= 180 else 0, img4)) 42 | 43 | return (img1, img2, img3, img4) 44 | 45 | def work(prefix): 46 | results = {} 47 | files = [] 48 | with open('./' + prefix + '.txt') as f: 49 | for x in f.readlines(): 50 | tmp = x.split(':') 51 | files.append(tmp[0]) 52 | results[tmp[0]] = tmp[1] 53 | 54 | with open('./data/' + prefix + '_images.py', 'w') as f: 55 | print("data = [", file=f) 56 | with open('./data/' + prefix + '_labels.py', 'w') as f: 57 | print("data = [", file=f) 58 | 59 | for file in files: 60 | print(file) 61 | img1, img2, img3, img4 = paa(file); 62 | px(prefix, img1, img2, img3, img4) 63 | py(prefix, results[file]) 64 | 65 | with open('./data/' + prefix + '_images.py', 'a+') as f: 66 | print("]", file=f) 67 | with open('./data/' + prefix + '_labels.py', 'a+') as f: 68 | print("]", file=f) 69 | 70 | work('test') 71 | work('train') 72 | -------------------------------------------------------------------------------- /test.txt: -------------------------------------------------------------------------------- 1 | ./test/1.jpg:0286 2 | ./test/2.jpg:4646 3 | ./test/3.jpg:9450 4 | ./test/4.jpg:8060 5 | 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