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test_LSTM3.py
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test_LSTM3.py
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import tensorflow as tf
import numpy as np
import cv2
import glob
def predict(frame, frames_passed, u, v, lstm_state):
batch_size = 1
max_time = 2000
num_size = 128
#test_data creation here (x and input_label)
#test_file_path = './coords/10.txt'
frame_size = frame.shape[:2]
#test_file = open(test_file_path, 'r')
#stop_sequence = []
#test_data_in = np.zeros((0, 2))
test_data_in = np.array(np.array(u,v))
center_coords = []
#test_data_out = np.zeros((0, 2))
#lines = test_file.read().splitlines()
#test_data_in = np.append(test_data_in, np.array([[u,v]]))
#test_data_out = np.append(test_data_out, np.array([[u_prev,v_prev]]))
#u_f, v_f = (u,v)
#u_f1, v_f1 = (u_prev, v_prev)
#for j in range(len(lines) - 1):
# u, v, m, n = lines[j].split(',')
# new_u, new_v, new_m, new_n = lines[j + 1].split(',')
# u_f1 = float(new_u) - float(u);
# v_f1 = float(new_v) - float(v)
# test_data_in = np.append(test_data_in, np.array([[u_f, v_f]]), axis=0)
# center_coords.append( (int(float(u) + float(m)/2), int(float(v) + float(n)/2)) )
# test_data_out = np.append(test_data_out, np.array([[u_f1, v_f1]]), axis=0)
# u_f, v_f = (u_f1, v_f1)
#stop_sequence.append(len(lines))
#for j in range(max_time - len(lines) + 1):
# test_data_in = np.append(test_data_in, np.array([[-1,-1]]), axis = 0)
# test_data_out = np.append(test_data_out, np.array([[0,0]]), axis = 0)
stop_sequence = [1]
x = tf.placeholder(tf.float32, [batch_size, max_time, 2], name='x')
#input_labels = tf.placeholder(tf.float32, [batch_size, max_time, 2], name='input_labels')
seq_length = tf.placeholder(tf.int32, [batch_size], name='seq_length')
W1 = tf.get_variable('W1', [2, num_size], initializer = tf.initializers.random_normal())
b1 = tf.get_variable('b1', [1, num_size], initializer = tf.initializers.zeros(dtype=tf.float32))
x = tf.reshape(x, [max_time, 2])
rnn_input = tf.matmul(x, W1) + b1
rnn_input = tf.reshape(rnn_input, [1, max_time, num_size])
lstm_cells = [tf.nn.rnn_cell.LSTMCell(num_size), tf.nn.rnn_cell.LSTMCell(num_size)]
print('\n')
print(lstm_cells[0].variables)
print('\n')
u_and_v_cell = tf.nn.rnn_cell.MultiRNNCell(lstm_cells)
#init_state = u_and_v_cell.zero_state(batch_size, tf.float32)
if frames_passed == 0:
init_state = u_and_v_cell.zero_state(batch_size, tf.float32)
else:
init_state = lstm_state
rnn_outputs, final_state = tf.nn.dynamic_rnn(u_and_v_cell, rnn_input, initial_state=init_state, sequence_length=seq_length)
W2 = tf.get_variable('W2', [num_size, 2], initializer = tf.initializers.random_normal())
b2 = tf.get_variable('b2', [1, 2], initializer = tf.initializers.zeros(dtype=tf.float32))
rnn_outputs_list = tf.split(rnn_outputs, batch_size)
output_coords = []
for i in range(len(rnn_outputs_list)):
o = rnn_outputs_list[i]
o = tf.reshape(o, [max_time, num_size])
oi = tf.matmul(o, W2) + b2
oix = tf.split(oi, max_time)
oix = [oix[j] for j in range(stop_sequence[i])]
for j in range(max_time - stop_sequence[i]):
oix.append(tf.zeros(dtype = tf.float32, shape = [1,2]))
oix = tf.reshape(tf.stack(oix), shape = [max_time, 2])
output_coords.append(oix)
output_coords = tf.stack(output_coords)
print('graph ', output_coords)
output_coords = tf.reshape(output_coords, [max_time,2])
print('graph2 ', output_coords)
saver = tf.train.Saver()
out_vec = 0
with tf.Session() as sess:
W1.initializer.run()
b1.initializer.run()
for var in u_and_v_cell.variables:
print(var, ' hi')
var.initializer.run()
W2.initializer.run()
b2.initializer.run()
saver.restore(sess, "/tmp/model.ckpt")
print("Model restored.")
print(output_coords)
print('feede_dict')
feed_dict = {x: test_data_in, seq_length: stop_sequence}
#feed_dict = {x: test_data_in}
print(feed_dict)
out_vec = sess.run([output_coords], feed_dict = feed_dict)
out_vec = out_vec[0]
#list_of_imgs = glob.glob('./Tiger1/img/*.jpg')
#list_of_imgs.sort()
#k = 0
#for img in list_of_imgs:
# frame = cv2.imread(img)
# frame = cv2.circle(frame, center_coords[k], 2, (0, 0, 255), -1)
# frame = cv2.circle(frame, center_coords[k+1], 2, (0,255,0), -1)
# frame = cv2.circle(frame, (int(center_coords[k][0] + out_vec[k][0]),int(center_coords[k][1] + out_vec[k][1])), 2, (255, 0, 0), -1)
# frame = cv2.circle(frame, center_coords[k+1], 12, (0,0,0), 1)
# cv2.imshow('frame', frame)
# cv2.waitKey(0)
# k+=1
#cv2.destroyAllWindows()
return out_vec, final_state