import tensorflow as tf
import sys
import cv2
import os
import numpy as np
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2'
# sess = tf.InteractiveSession()
#
def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

x = tf.placeholder("float", shape=[None, 784])

y_ = tf.placeholder("float", shape=[None, 10])

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [-1,28,28,1])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

saver = tf.train.Saver()

def get_rec(result):
    row, col = np.where(result==np.max(result))
    return col[0]

if __name__ == '__main__':
    if len(sys.argv) != 2:
        sys.exit('请输入正确的参数')
    filname = sys.argv[1]
    img = cv2.imread(filname, 0)
    thr, img = cv2.threshold(img, 250, 1, cv2.THRESH_BINARY_INV)
    img_numpy = np.array(img, np.float)
    img = img_numpy.reshape(-1, 784)
    with tf.Session() as sess:
        saver.restore(sess, './Model/model.ckpt')
        res = sess.run(y_conv, feed_dict={x: img, keep_prob:1.0})
    res = get_rec(res)
    print('预测的结果是：', res)
