github地址 https://github.com/gzydominating/Camera-Mnist-with-Tensorflow-OpenCV
花了一晚上搞好了摄像头捕获图像做手写数字识别，代码基于tensorflow的mnist代码实现，作为学习tensorflow的一个过程。
先在mnist数据集上训练好网络，并保存模型。
import numpy as np import tensorflow as tf import tensorflow.examples.tutorials.mnist.input_data as input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) x_input = tf.placeholder(tf.float32, [None, 784]) y_actual = tf.placeholder(tf.float32, shape=[None, 10]) 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(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') #input -> conv -> pool -> conv -> pool -> fc -> dropout -> softmax def network(x): x_image = tf.reshape(x, [-1,28,28,1]) #-1 means arbitrary W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) #conv1 h_pool1 = max_pool(h_conv1) #max_pool1 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) #conv2 h_pool2 = max_pool(h_conv2) #max_pool2 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) #fc1 keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) #dropout W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_predicts=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) #fc2 output return y_predicts y_predict=network(x_input) #see http://www.tensorfly.cn/tfdoc/tutorials/mnist_pros.html cross_entropy = -tf.reduce_sum(y_actual*tf.log(y_predict)) train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_predict,1), tf.argmax(y_actual,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) saver = tf.train.Saver() sess=tf.InteractiveSession() sess.run(tf.initialize_all_variables()) for i in range(20000): #iteration 20000 steps = (epochs * train_size) / batch_size ,epochs=21 batch = mnist.train.next_batch(64) #batch_size=64 if i0 == 0: train_acc = accuracy.eval(feed_dict={x_input:batch[0], y_actual: batch[1], keep_prob: 1.0}) print('step',i,'training accuracy',train_acc) train_step.run(feed_dict={x_input: batch[0], y_actual: batch[1], keep_prob: 0.5}) saver.save(sess, "model_save.ckpt") #save model #test accuracy in mnist.test dataset test_acc=accuracy.eval(feed_dict={x_input: mnist.test.images, y_actual: mnist.test.labels, keep_prob: 1.0}) print("test accuracy",test_acc)预测时使用opencv来打开摄像头捕获图像，设置ROI区域，将ROI区域图像输入加载好参数的cnn网络来识别
import numpy as np import tensorflow as tf import cv2 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(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') def network(x): x_image = tf.reshape(x, [-1,28,28,1]) #-1 means arbitrary h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) #conv1 h_pool1 = max_pool(h_conv1) #max_pool1 h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) #conv2 h_pool2 = max_pool(h_conv2) #max_pool2 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) #fc1 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) #dropout y_predict=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) #fc2 output return y_predict keep_prob = tf.placeholder("float") W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) sess=tf.InteractiveSession() saver = tf.train.Saver() saver.restore(sess, "./model_save.ckpt") #load model file must have ./ with tensorflow1.0 cap = cv2.VideoCapture(1) while(1): ret, frame = cap.read() cv2.rectangle(frame,(270,200),(340,270),(0,0,255),2) cv2.imshow("capture", frame) roiImg = frame[200:270,270:340] img = cv2.resize(roiImg,(28,28)) img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) np_img = img.astype(np.float32) netoutput = network(np_img) predictions = sess.run(netoutput,feed_dict={keep_prob: 0.5}) predicts=predictions.tolist() #tensorflow output is numpy.ndarray like [[0 0 0 0]] label=predicts[0] result=label.index(max(label)) print('result num:') print(result) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows()
测试图片
随便找几个数字测试，识别还挺准的。
