CHANNEL_NUM = 3
CLIP_LENGTH = 16
EPOCH_NUM = 50
test_num = data_processing.get_test_num(TEST_LIST_PATH)
test_video_indices = range(test_num)
with tf.Graph().as_default():
batch_clips = tf.placeholder(
tf.float32,
[BATCH_SIZE, CLIP_LENGTH, CROP_SZIE, CROP_SZIE, CHANNEL_NUM],
name='X')
batch_labels = tf.placeholder(tf.int32, [BATCH_SIZE, NUM_CLASSES],
name='Y')
keep_prob = tf.placeholder(tf.float32)
logits = C3D_model.C3D(batch_clips, NUM_CLASSES, keep_prob)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(logits, 1), tf.argmax(batch_labels, 1)),
np.float32))
restorer = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
restorer.restore(sess, TRAIN_CHECK_POINT)
accuracy_epoch = 0
batch_index = 0
for i in range(test_num // BATCH_SIZE):
if i % 10 == 0:
CROP_SZIE = 112
CHANNEL_NUM = 3
CLIP_LENGTH = 16
EPOCH_NUM = 50
test_num = data_processing.get_test_num(TEST_LIST_PATH)
test_video_indices = range(test_num)
with tf.Graph().as_default():
batch_clips = tf.placeholder(
np.float32,
[BATCH_SIZE, CLIP_LENGTH, CROP_SZIE, CROP_SZIE, CHANNEL_NUM],
name='X')
batch_labels = tf.placeholder(np.int32, [BATCH_SIZE, NUM_CLASSES],
name='Y')
logits = C3D_model.C3D(batch_clips, NUM_CLASSES, 0.5)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(logits, 1), tf.argmax(batch_labels, 1)),
np.float32))
restorer = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
restorer.restore(sess, TRAIN_CHECK_POINT)
accuracy_epoch = 0
batch_index = 0
for i in range(test_num // BATCH_SIZE):
if i % 10 == 0:
def main():
with open('classInd.txt', 'r') as f:
class_names = f.readlines()
f.close()
with tf.Graph().as_default():
batch_clips = tf.placeholder(tf.float32, [BATCH_SIZE, CLIP_LENGTH, CROP_SZIE, CROP_SZIE, CHANNEL_NUM], name='X')
batch_labels = tf.placeholder(tf.int32, [BATCH_SIZE, NUM_CLASSES], name='Y')
keep_prob = tf.placeholder(tf.float32)
logits = C3D_model.C3D(batch_clips, NUM_CLASSES, keep_prob)
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), tf.argmax(batch_labels, 1)), np.float32))
restorer = tf.train.Saver()
classifier=tf.nn.softmax(logits) #class
restorer = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
restorer.restore(sess, TRAIN_CHECK_POINT)
accuracy_epoch = 0
batch_index = 0
video = 'videos/Clip1/Clip1_1.mp4'
cap = cv2.VideoCapture(video)
out_width=int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
out_height=int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps=cap.get(cv2.CAP_PROP_FPS)
fourcc=cv2.VideoWriter_fourcc(*'XVID')
out=cv2.VideoWriter('out.mp4',fourcc,fps,(out_width,out_height))
#cap.set(cv2.CAP_PROP_FPS,16)
print(cap.get(cv2.CAP_PROP_FPS))
clip = []
n=0
while True:
ret, frame = cap.read()
if ret:
tmp = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
clip.append(tmp)
if len(clip) == 16:
#print clip.shape
#print np.asarray(clip).shape
clip2=data_processing.frame_process(clip,16,112,3)
#print(clip2.shape)
newclip=[]
newclip.append(clip2)
newclip=np.array(newclip).astype(np.float32)
#print(newclip.shape)
classification=sess.run(classifier,feed_dict={batch_clips:newclip,keep_prob: 1.0})
label=np.argmax(classification[0])
cv2.putText(frame, class_names[label].split(' ')[-1].strip(), (40, 80),cv2.FONT_HERSHEY_SIMPLEX, 3,(0, 0, 255), 3)
print(class_names[label])
clip.pop(0)
cv2.imshow('result', frame)
out.write(frame)
cv2.waitKey(10)
out.write(frame)
n=n+1
#print(n)
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()