def predict(img_dir, model_dir, img_size=64, colour_channels=3, batch_size=1):
checkpoint_dir = os.path.join(os.path.abspath(model_dir), 'tensorflow/cnn/model')
data, category_ref = read_img_sets(img_dir + '/predict', img_size)
flat_img_size = flat_img_shape(img_size, colour_channels)
num_classes = len(category_ref)
x, y_true, keep_prob = variables(flat_img_size, num_classes)
logits = model(x, keep_prob, img_size, colour_channels, filter_size=3, neurons=2*img_size, num_classes=num_classes)
predict_op = softmax(logits)
with tf.Session() as sess:
saver = tf.train.Saver()
restore_or_initialize(sess, saver, checkpoint_dir)
x_predict_batch, y_predict_batch, _, cls_predict_batch = data.train.next_batch(batch_size=1)
x_predict_batch = x_predict_batch.reshape(batch_size, flat_img_size)
prediction = sess.run([tf.argmax(predict_op, dimension=1)], feed_dict={x: x_predict_batch, keep_prob: 1.0})
return category_ref[prediction[0][0]], cls_predict_batch[0]
def train(img_dir, model_dir, img_size=64, colour_channels=3, batch_size=10, training_epochs=50):
log_dir = os.path.join(os.path.abspath(model_dir), 'tensorflow/cnn/logs/cnn_with_summaries')
checkpoint_dir = os.path.join(os.path.abspath(model_dir), 'tensorflow/cnn/model')
data, category_ref = image_loading.read_img_sets(img_dir + '/train', img_size, validation_size=.2)
print(data)
flat_img_size = flat_img_shape(img_size, colour_channels)
num_classes = len(category_ref)
x, y_true, keep_prob = variables(flat_img_size, num_classes)
logits = model(x, keep_prob, img_size, colour_channels, filter_size=3, neurons=2*img_size, num_classes=num_classes)
cost = calulate_cost(logits, y_true)
training_op = optimizer(cost)
accuracy = calculate_accuracy(logits, y_true)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver()
writer = tf.summary.FileWriter(log_dir, graph=tf.get_default_graph())
with tf.Session() as sess:
restore_or_initialize(sess, saver, checkpoint_dir)
print("\n\n" + str(training_epochs))
print("\n\n" + str(data.train.num_examples))
print("\n\n" + str(batch_size))
for epoch in range(training_epochs):
batch_count = int(data.train.num_examples / batch_size)
for i in range(batch_count):
x_batch, y_true_batch, _, cls_batch = data.train.next_batch(batch_size)
x_batch = x_batch.reshape(batch_size, flat_img_size)
x_test_batch, y_test_batch, _, cls_test_batch = data.test.next_batch(batch_size)
x_test_batch = x_test_batch.reshape(batch_size, flat_img_size)
_, summary = sess.run([training_op, summary_op],
feed_dict={x: x_batch, y_true: y_true_batch, keep_prob: 0.5})
writer.add_summary(summary, epoch * batch_count + i)
if epoch % 5 == 0:
log_progress(sess, saver, cost, accuracy, epoch,
test_feed_dict={x: x_test_batch, y_true: y_test_batch, keep_prob: 1.0},
checkpoint_path=os.path.join(checkpoint_dir, 'model.ckpt'))
def predict(img_dir, model_dir, img_size=64, colour_channels=3, batch_size=1):
checkpoint_dir = os.path.join(os.path.abspath(model_dir), 'tensorflow/cnn/model')
data, category_ref = image_loading.read_img_sets(img_dir + '/predict', img_size)
flat_img_size = flat_img_shape(img_size, colour_channels)
num_classes = len(category_ref)
x, y_true, keep_prob = variables(flat_img_size, num_classes)
logits = model(x, keep_prob, img_size, colour_channels, filter_size=3, neurons=2*img_size, num_classes=num_classes)
predict_op = softmax(logits)
with tf.Session() as sess:
saver = tf.train.Saver()
restore_or_initialize(sess, saver, checkpoint_dir)
acertou = 0
errou = 0
batch_count = int(data.train.num_examples / batch_size)
for i in range(batch_count):
x_predict_batch, y_predict_batch, _, cls_predict_batch = data.train.next_batch(batch_size=1)
x_predict_batch = x_predict_batch.reshape(batch_size, flat_img_size)
prediction = sess.run([tf.argmax(predict_op, dimension=1)], feed_dict={x: x_predict_batch, keep_prob: 1.0})
if category_ref[prediction[0][0]] == cls_predict_batch[0]:
print("Acertou: ", category_ref[prediction[0][0]], cls_predict_batch[0])
acertou += 1
else:
print("Errou: ", category_ref[prediction[0][0]], cls_predict_batch[0])
errou += 1
print("Porcentagem = %d", (acertou * 100 / (acertou + errou)))
return category_ref[prediction[0][0]], cls_predict_batch[0]
def predict(img_dir,
model_dir,
img_size=64,
colour_channels=3,
batch_size=803):
checkpoint_dir = os.path.join(os.path.abspath(model_dir),
'tensorflow/cnn/model/')
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
checkpoint_dir2 = os.path.join(os.path.abspath(model_dir),
'tensorflow/cnn/model2')
ckpt2 = tf.train.get_checkpoint_state(checkpoint_dir2)
log_dir = os.path.join(os.path.abspath(model_dir),
'tensorflow/cnn/logs/cnn_with_summaries')
data, category_ref = read_img_sets(img_dir + '/predict', img_size)
flat_img_size = flat_img_shape(img_size, colour_channels)
num_classes = len(category_ref)
x, y_true = variables(flat_img_size, num_classes)
logits = model(x,
img_size,
colour_channels,
filter_size=3,
neurons=2 * img_size,
num_classes=num_classes)
predict_op = softmax(logits)
with tf.Session() as sess:
saver = tf.train.Saver()
writer = tf.summary.FileWriter(log_dir, graph=tf.get_default_graph())
writer.add_graph(tf.get_default_graph())
saver.restore(sess, ckpt.model_checkpoint_path)
saver.save(sess, checkpoint_dir2)
x_predict_batch, y_predict_batch, idss, cls_predict_batch = data.train.next_batch(
batch_size=803)
x_predict_batch = x_predict_batch.reshape(batch_size, flat_img_size)
start = time.time()
prediction = sess.run([tf.argmax(predict_op, dimension=1)],
feed_dict={x: x_predict_batch})
end = time.time()
timepershot = (end - start) / batch_size
idsspath = img_dir + '/predict/' + cls_predict_batch[0] + '/' + idss[0]
#CV2 part
font = cv2.FONT_HERSHEY_SIMPLEX
topleftCornerOfText = (10, 40)
topleftCornerOfText2 = (10, 70)
topleftCornerOfText3 = (10, 100)
bottomleft = (10, 350)
fontScale = 1
fontColor = (0, 0, 0)
lineType = 2
img2 = cv2.imread(idsspath, cv2.IMREAD_COLOR)
cv2.putText(img2, 'Prediction: %s' % (category_ref[prediction[0][0]]),
topleftCornerOfText, font, fontScale, fontColor, lineType)
cv2.putText(img2, 'Truth: %s' % (cls_predict_batch[0]),
topleftCornerOfText2, font, fontScale, fontColor, lineType)
cv2.putText(img2, 'Time: %s' % (timepershot), topleftCornerOfText3,
font, fontScale, fontColor, lineType)
cv2.putText(img2, 'Path: %s' % (idss[0]), bottomleft, font, fontScale,
fontColor, lineType)
cv2.imshow('imgtest', img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
print(img_dir + '/predict/' + cls_predict_batch[0] + '/' + idss[0])
j = 0
for i in range(batch_size):
if (category_ref[prediction[0][i]] == cls_predict_batch[i]):
j += 1
print(j, batch_size)
return category_ref[prediction[0][0]], cls_predict_batch[0]
def predict(img_dir, model_dir, img_size=224, colour_channels=3, batch_size=1):
checkpoint_dir = os.path.join(os.path.abspath(model_dir), 'tensorflow/cnn/model/')
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
checkpoint_dir2 = os.path.join(os.path.abspath(model_dir), 'tensorflow/cnn/modelpredict')
ckpt2 = tf.train.get_checkpoint_state(checkpoint_dir2)
log_dir = os.path.join(os.path.abspath(model_dir), 'tensorflow/cnn/logspredict/cnn_with_summaries')
data, category_ref = read_img_sets(img_dir + '/predict', img_size)
predimgs=data.train.x()
print("This number of images in image predict folder: ",predimgs)
flat_img_size = flat_img_shape(img_size, colour_channels)
num_classes = len(category_ref)
x, y_true,keep_prob= variables(flat_img_size, num_classes)
logits = model(x, keep_prob, img_size, colour_channels, filter_size=3, neurons=2*img_size, num_classes=num_classes)
predict_op = softmax(logits)
with tf.Session() as sess:
saver = tf.train.Saver(save_relative_paths=True)
#restore_or_initialize(sess, saver, checkpoint_dir)
writer = tf.summary.FileWriter(log_dir, graph=tf.get_default_graph())
writer.add_graph(tf.get_default_graph())
saver.restore(sess, ckpt.model_checkpoint_path)
saver.save(sess, checkpoint_dir2)
#print(data._num_examples)
x_predict_batch, y_predict_batch, idss, cls_predict_batch = data.train.next_batch(batch_size=predimgs)
x_predict_batch = x_predict_batch.reshape(predimgs, flat_img_size)
#print(idss[0],idss[1])
print(x_predict_batch)
testimg = cv2.imread('test.png')
testimg = cv2.resize(testimg, (img_size,img_size), cv2.INTER_LINEAR)
#img=img.astype(np.float32)
testbin = np.empty((1,(img_size*img_size*3)),np.float32)
#testbin=np.vstack((testbin,testimg.flatten()))
testbin[0]=testimg.flatten()
testbin=np.multiply(testbin, 1.0/255.0)
print(testbin)
#print(testimg.flatten().shape)
start = time.time()
prediction = sess.run([tf.argmax(predict_op, dimension=1)], feed_dict={x: x_predict_batch})
#prediction = sess.run([tf.argmax(predict_op, dimension=1)], feed_dict={x: testbin})
#print(prediction[0],cls_predict_batch)
#if category_ref[prediction[0][0]] == cls_predict_batch[0]:
# print("it is correct")
end = time.time()
timepershot=(end-start)/predimgs
idsspath=img_dir + '/predict/'+cls_predict_batch[0]+'/'+idss[0]
font = cv2.FONT_HERSHEY_SIMPLEX
topleftCornerOfText = (10,40)
topleftCornerOfText2 = (10,70)
topleftCornerOfText3 = (10,100)
bottomleft = (10,350)
fontScale = 1
fontColor = (0,0,0)
lineType = 2
img2 = cv2.imread(idsspath, cv2.IMREAD_COLOR)
cv2.putText(img2,'Prediction: %s' % (category_ref[prediction[0][0]]), topleftCornerOfText, font, fontScale,fontColor,lineType)
cv2.putText(img2,'Truth: %s' % (cls_predict_batch[0]), topleftCornerOfText2, font, fontScale,fontColor,lineType)
cv2.putText(img2,'Time: %s' % (timepershot), topleftCornerOfText3, font, fontScale,fontColor,lineType)
cv2.putText(img2,'Path: %s' % (idss[0]),bottomleft, font, fontScale,fontColor,lineType)
cv2.imshow('imgtest',img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
print(img_dir + '/predict/'+cls_predict_batch[0]+'/'+idss[0])
j=0
for i in range(predimgs):
if (category_ref[prediction[0][i]] == cls_predict_batch[i]):
j+=1
print(j,predimgs)
return category_ref[prediction[0][0]], cls_predict_batch[0]