def main(argv):
tf_device = '/cpu:0'
with tf.device(tf_device):
"""Build graph
"""
if FLAGS.color_channel == 'RGB':
input_data = tf.placeholder(dtype=tf.float32,
shape=[None, FLAGS.input_size, FLAGS.input_size, 3],
name='input_image')
else:
input_data = tf.placeholder(dtype=tf.float32,
shape=[None, FLAGS.input_size, FLAGS.input_size, 1],
name='input_image')
center_map = tf.placeholder(dtype=tf.float32,
shape=[None, FLAGS.input_size, FLAGS.input_size, 1],
name='center_map')
model = cpm_body_slim.CPM_Model(FLAGS.stages, FLAGS.joints + 1)
model.build_model(input_data, center_map, 1)
saver = tf.train.Saver()
"""Create session and restore weights
"""
sess = tf.Session()
sess.run(tf.global_variables_initializer())
if FLAGS.model_path.endswith('pkl'):
model.load_weights_from_file(FLAGS.model_path, sess, False)
else:
saver.restore(sess, FLAGS.model_path)
test_center_map = cpm_utils.gaussian_img(FLAGS.input_size,
FLAGS.input_size,
FLAGS.input_size / 2,
FLAGS.input_size / 2,
FLAGS.cmap_radius)
test_center_map = np.reshape(test_center_map, [1, FLAGS.input_size,
FLAGS.input_size, 1])
# Check weights
for variable in tf.trainable_variables():
with tf.variable_scope('', reuse=True):
var = tf.get_variable(variable.name.split(':0')[0])
print(variable.name, np.mean(sess.run(var)))
# Create kalman filters
if FLAGS.KALMAN_ON:
kalman_filter_array = [cv2.KalmanFilter(4, 2) for _ in range(FLAGS.joints)]
for _, joint_kalman_filter in enumerate(kalman_filter_array):
joint_kalman_filter.transitionMatrix = np.array([[1, 0, 1, 0],
[0, 1, 0, 1],
[0, 0, 1, 0],
[0, 0, 0, 1]],
np.float32)
joint_kalman_filter.measurementMatrix = np.array([[1, 0, 0, 0],
[0, 1, 0, 0]],
np.float32)
joint_kalman_filter.processNoiseCov = np.array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]],
np.float32) * FLAGS.kalman_noise
else:
kalman_filter_array = None
# read in video / flow frames
if FLAGS.DEMO_TYPE.endswith(('avi', 'flv', 'mp4')):
# OpenCV can only read in '.avi' files
cam = imageio.get_reader(FLAGS.DEMO_TYPE)
else:
cam = cv2.VideoCapture(FLAGS.cam_num)
# iamge processing
with tf.device(tf_device):
if FLAGS.DEMO_TYPE.endswith(('avi', 'flv', 'mp4')):
ori_fps = cam.get_meta_data()['fps']
print('This video fps is %f' % ori_fps)
video_length = cam.get_length()
writer_path = os.path.join('results', os.path.basename(FLAGS.DEMO_TYPE))
# !! OpenCV can only write in .avi
cv_writer = cv2.VideoWriter(writer_path + '.avi',
# cv2.cv.CV_FOURCC('M', 'J', 'P', 'G'),
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
ori_fps,
(FLAGS.input_size, FLAGS.input_size))
# imageio_writer = imageio.get_writer(writer_path, fps=ori_fps)
try:
for it, im in enumerate(cam):
test_img_t = time.time()
test_img = cpm_utils.read_image(im, [], FLAGS.input_size, 'VIDEO')
test_img_resize = cv2.resize(test_img, (FLAGS.input_size, FLAGS.input_size))
print('img read time %f' % (time.time() - test_img_t))
if FLAGS.color_channel == 'GRAY':
test_img_resize = mgray(test_img_resize, test_img)
test_img_input = test_img_resize / 256.0 - 0.5
test_img_input = np.expand_dims(test_img_input, axis=0)
# Inference
fps_t = time.time()
predict_heatmap, stage_heatmap_np = sess.run([model.current_heatmap,
model.stage_heatmap,
],
feed_dict={'input_image:0': test_img_input,
'center_map:0': test_center_map})
# Show visualized image
demo_img = visualize_result(test_img, FLAGS, stage_heatmap_np, kalman_filter_array)
cv2.imshow('demo_img', demo_img.astype(np.uint8))
if (cv2.waitKey(1) == ord('q')): break
print('fps: %.2f' % (1 / (time.time() - fps_t)))
cv_writer.write(demo_img.astype(np.uint8))
# imageio_writer.append_data(demo_img[:, :, 1])
except KeyboardInterrupt:
print('Stopped! {}/{} frames captured!'.format(it, video_length))
finally:
cv_writer.release()
# imageio_writer.close()
else:
while True:
test_img_t = time.time()
if FLAGS.DEMO_TYPE.endswith(('png', 'jpg')):
test_img = cpm_utils.read_image(FLAGS.DEMO_TYPE, [], FLAGS.input_size, 'IMAGE')
else:
test_img = cpm_utils.read_image([], cam, FLAGS.input_size, 'WEBCAM')
test_img_resize = cv2.resize(test_img, (FLAGS.input_size, FLAGS.input_size))
print('img read time %f' % (time.time() - test_img_t))
if FLAGS.color_channel == 'GRAY':
test_img_resize = mgray(test_img_resize, test_img)
test_img_input = test_img_resize / 256.0 - 0.5
test_img_input = np.expand_dims(test_img_input, axis=0)
if FLAGS.DEMO_TYPE.endswith(('png', 'jpg')):
# Inference
fps_t = time.time()
predict_heatmap, stage_heatmap_np = sess.run([model.current_heatmap,
model.stage_heatmap, ],
feed_dict={'input_image:0': test_img_input,
'center_map:0': test_center_map})
# Show visualized image
demo_img = visualize_result(test_img, FLAGS, stage_heatmap_np, kalman_filter_array)
cv2.imshow('demo_img', demo_img.astype(np.uint8))
if cv2.waitKey(0) == ord('q'): break
print('fps: %.2f' % (1 / (time.time() - fps_t)))
elif FLAGS.DEMO_TYPE == 'MULTI':
# Inference
fps_t = time.time()
predict_heatmap, stage_heatmap_np = sess.run([model.current_heatmap,
model.stage_heatmap,
],
feed_dict={'input_image:0': test_img_input,
'center_map:0': test_center_map})
# Show visualized image
demo_img = visualize_result(test_img, FLAGS, stage_heatmap_np, kalman_filter_array)
cv2.imshow('demo_img', demo_img.astype(np.uint8))
if cv2.waitKey(1) == ord('q'): break
print('fps: %.2f' % (1 / (time.time() - fps_t)))
elif FLAGS.DEMO_TYPE == 'SINGLE':
# Inference
fps_t = time.time()
stage_heatmap_np = sess.run([model.stage_heatmap[5]],
feed_dict={'input_image:0': test_img_input,
'center_map:0': test_center_map})
# Show visualized image
demo_img = visualize_result(test_img, FLAGS, stage_heatmap_np, kalman_filter_array)
cv2.imshow('current heatmap', (demo_img).astype(np.uint8))
if cv2.waitKey(1) == ord('q'): break
print('fps: %.2f' % (1 / (time.time() - fps_t)))
elif FLAGS.DEMO_TYPE == 'HM':
# Inference
fps_t = time.time()
stage_heatmap_np = sess.run([model.stage_heatmap[FLAGS.stages - 1]],
feed_dict={'input_image:0': test_img_input,
'center_map:0': test_center_map})
print('fps: %.2f' % (1 / (time.time() - fps_t)))
# demo_stage_heatmap = stage_heatmap_np[len(stage_heatmap_np) - 1][0, :, :, 0:FLAGS.joints].reshape(
# (FLAGS.hmap_size, FLAGS.hmap_size, FLAGS.joints))
demo_stage_heatmap = stage_heatmap_np[-1][0, :, :, 0:FLAGS.joints].reshape(
(FLAGS.hmap_size, FLAGS.hmap_size, FLAGS.joints))
demo_stage_heatmap = cv2.resize(demo_stage_heatmap, (FLAGS.input_size, FLAGS.input_size))
vertical_imgs = []
tmp_img = None
joint_coord_set = np.zeros((FLAGS.joints, 2))
for joint_num in range(FLAGS.joints):
# Concat until 4 img
if (joint_num % 4) == 0 and joint_num != 0:
vertical_imgs.append(tmp_img)
tmp_img = None
demo_stage_heatmap[:, :, joint_num] *= (255 / np.max(demo_stage_heatmap[:, :, joint_num]))
# Plot color joints
if np.min(demo_stage_heatmap[:, :, joint_num]) > -50:
joint_coord = np.unravel_index(np.argmax(demo_stage_heatmap[:, :, joint_num]),
(FLAGS.input_size, FLAGS.input_size))
joint_coord_set[joint_num, :] = joint_coord
color_code_num = (joint_num // 4)
if joint_num in [0, 4, 8, 12, 16]:
if PYTHON_VERSION == 3:
joint_color = list(
map(lambda x: x + 35 * (joint_num % 4), joint_color_code[color_code_num]))
else:
joint_color = map(lambda x: x + 35 * (joint_num % 4), joint_color_code[color_code_num])
cv2.circle(test_img, center=(joint_coord[1], joint_coord[0]), radius=3, color=joint_color,
thickness=-1)
else:
if PYTHON_VERSION == 3:
joint_color = list(
map(lambda x: x + 35 * (joint_num % 4), joint_color_code[color_code_num]))
else:
joint_color = map(lambda x: x + 35 * (joint_num % 4), joint_color_code[color_code_num])
cv2.circle(test_img, center=(joint_coord[1], joint_coord[0]), radius=3, color=joint_color,
thickness=-1)
# Put text
tmp = demo_stage_heatmap[:, :, joint_num].astype(np.uint8)
tmp = cv2.putText(tmp, 'Min:' + str(np.min(demo_stage_heatmap[:, :, joint_num])),
org=(5, 20), fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.3, color=150)
tmp = cv2.putText(tmp, 'Mean:' + str(np.mean(demo_stage_heatmap[:, :, joint_num])),
org=(5, 30), fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.3, color=150)
tmp_img = np.concatenate((tmp_img, tmp), axis=0) \
if tmp_img is not None else tmp
# Plot limbs
for limb_num in range(len(limbs)):
if np.min(demo_stage_heatmap[:, :, limbs[limb_num][0]]) > -2000 and np.min(
demo_stage_heatmap[:, :, limbs[limb_num][1]]) > -2000:
x1 = joint_coord_set[limbs[limb_num][0], 0]
y1 = joint_coord_set[limbs[limb_num][0], 1]
x2 = joint_coord_set[limbs[limb_num][1], 0]
y2 = joint_coord_set[limbs[limb_num][1], 1]
length = ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5
if length < 10000 and length > 5:
deg = math.degrees(math.atan2(x1 - x2, y1 - y2))
polygon = cv2.ellipse2Poly((int((y1 + y2) / 2), int((x1 + x2) / 2)),
(int(length / 2), 3),
int(deg),
0, 360, 1)
color_code_num = limb_num // 4
if PYTHON_VERSION == 3:
limb_color = list(
map(lambda x: x + 35 * (limb_num % 4), joint_color_code[color_code_num]))
else:
limb_color = map(lambda x: x + 35 * (limb_num % 4), joint_color_code[color_code_num])
cv2.fillConvexPoly(test_img, polygon, color=limb_color)
if tmp_img is not None:
tmp_img = np.lib.pad(tmp_img, ((0, vertical_imgs[0].shape[0] - tmp_img.shape[0]), (0, 0)),
'constant', constant_values=(0, 0))
vertical_imgs.append(tmp_img)
# Concat horizontally
output_img = None
for col in range(len(vertical_imgs)):
output_img = np.concatenate((output_img, vertical_imgs[col]), axis=1) if output_img is not None else \
vertical_imgs[col]
output_img = output_img.astype(np.uint8)
output_img = cv2.applyColorMap(output_img, cv2.COLORMAP_JET)
test_img = cv2.resize(test_img, (300, 300), cv2.INTER_LANCZOS4)
cv2.imshow('hm', output_img)
cv2.moveWindow('hm', 2000, 200)
cv2.imshow('rgb', test_img)
cv2.moveWindow('rgb', 2000, 750)
if cv2.waitKey(1) == ord('q'): break
def main(argv):
tf_device = '/gpu:0'
with tf.device(tf_device):
"""Build graph
"""
if FLAGS.color_channel == 'RGB':
input_data = tf.placeholder(
dtype=tf.float32,
shape=[None, FLAGS.input_size, FLAGS.input_size, 3],
name='input_image')
else:
input_data = tf.placeholder(
dtype=tf.float32,
shape=[None, FLAGS.input_size, FLAGS.input_size, 1],
name='input_image')
center_map = tf.placeholder(
dtype=tf.float32,
shape=[None, FLAGS.input_size, FLAGS.input_size, 1],
name='center_map')
model = cpm_body_slim.CPM_Model(FLAGS.stages, FLAGS.joints + 1)
model.build_model(input_data, center_map, 1)
saver = tf.train.Saver()
"""Create session and restore weights
"""
sess = tf.Session()
sess.run(tf.global_variables_initializer())
if FLAGS.model_path.endswith('pkl'):
model.load_weights_from_file(FLAGS.model_path, sess, False)
else:
saver.restore(sess, FLAGS.model_path)
test_center_map = cpm_utils.gaussian_img(FLAGS.input_size,
FLAGS.input_size,
FLAGS.input_size / 2,
FLAGS.input_size / 2,
FLAGS.cmap_radius)
test_center_map = np.reshape(test_center_map,
[1, FLAGS.input_size, FLAGS.input_size, 1])
# Check weights
for variable in tf.trainable_variables():
with tf.variable_scope('', reuse=True):
var = tf.get_variable(variable.name.split(':0')[0])
print(variable.name, np.mean(sess.run(var)))
# Create kalman filters
if FLAGS.KALMAN_ON:
kalman_filter_array = [
cv2.KalmanFilter(4, 2) for _ in range(FLAGS.joints)
]
for _, joint_kalman_filter in enumerate(kalman_filter_array):
joint_kalman_filter.transitionMatrix = np.array(
[[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]],
np.float32)
joint_kalman_filter.measurementMatrix = np.array(
[[1, 0, 0, 0], [0, 1, 0, 0]], np.float32)
joint_kalman_filter.processNoiseCov = np.array(
[[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
np.float32) * FLAGS.kalman_noise
else:
kalman_filter_array = None
# read in video / flow frames
if FLAGS.DEMO_TYPE.endswith(('avi', 'flv', 'mp4')):
# OpenCV can only read in '.avi' files
cam = imageio.get_reader(FLAGS.DEMO_TYPE)
else:
cam = cv2.VideoCapture(FLAGS.cam_num)
# iamge processing
with tf.device(tf_device):
test_img_t = time.time()
if FLAGS.DEMO_TYPE.endswith(('png', 'jpg')):
test_img = cpm_utils.read_image(FLAGS.DEMO_TYPE, [],
FLAGS.input_size, 'IMAGE')
else:
test_img = cpm_utils.read_image([], cam, FLAGS.input_size,
'WEBCAM')
test_img_resize = cv2.resize(test_img,
(FLAGS.input_size, FLAGS.input_size))
print('img read time %f' % (time.time() - test_img_t))
if FLAGS.color_channel == 'GRAY':
test_img_resize = mgray(test_img_resize, test_img)
test_img_input = test_img_resize / 256.0 - 0.5
test_img_input = np.expand_dims(test_img_input, axis=0)
if FLAGS.DEMO_TYPE.endswith(('png', 'jpg')):
# Inference
fps_t = time.time()
predict_heatmap, stage_heatmap_np = sess.run([
model.current_heatmap,
model.stage_heatmap,
],
feed_dict={
'input_image:0':
test_img_input,
'center_map:0':
test_center_map
})
# Show visualized image
demo_img = visualize_result(test_img, FLAGS, stage_heatmap_np,
kalman_filter_array)
cv2.imshow('demo_img', demo_img.astype(np.uint8))
if cv2.waitKey(0) == ord('q'): exit()
print('fps: %.2f' % (1 /
(time.time() - fps_t))) # iamge processing
def main(argv):
"""Build graph
"""
batch_x, batch_c, batch_y, batch_x_orig = tf_utils.read_batch_cpm(
FLAGS.tfr_data_files, FLAGS.input_size, FLAGS.heatmap_size,
FLAGS.num_of_joints, FLAGS.center_radius, FLAGS.batch_size)
if FLAGS.color_channel == 'RGB':
input_placeholder = tf.placeholder(dtype=tf.float32,
shape=(FLAGS.batch_size,
FLAGS.input_size,
FLAGS.input_size, 3),
name='input_placeholer')
elif FLAGS.color_channel == 'GRAY':
input_placeholder = tf.placeholder(dtype=tf.float32,
shape=(FLAGS.batch_size,
FLAGS.input_size,
FLAGS.input_size, 1),
name='input_placeholer')
cmap_placeholder = tf.placeholder(dtype=tf.float32,
shape=(FLAGS.batch_size,
FLAGS.input_size,
FLAGS.input_size, 1),
name='cmap_placeholder')
hmap_placeholder = tf.placeholder(
dtype=tf.float32,
shape=(FLAGS.batch_size, FLAGS.heatmap_size, FLAGS.heatmap_size,
FLAGS.num_of_joints + 1),
name='hmap_placeholder')
model = cpm_body_slim.CPM_Model(FLAGS.stages, FLAGS.num_of_joints + 1)
model.build_model(input_placeholder, cmap_placeholder, FLAGS.batch_size)
model.build_loss(hmap_placeholder, FLAGS.lr, FLAGS.lr_decay_rate,
FLAGS.lr_decay_step)
print('=====Model Build=====\n')
"""Training
"""
with tf.Session() as sess:
# Create dataset queue
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
## Create summary
tf_writer = tf.summary.FileWriter(FLAGS.log_dir,
sess.graph,
filename_suffix=FLAGS.log_file_name)
## Create model saver
saver = tf.train.Saver(max_to_keep=None)
# Init
init = tf.global_variables_initializer()
sess.run(init)
# Restore weights
if FLAGS.pretrained_model is not None:
if FLAGS.pretrained_model.endswith('.pkl'):
model.load_weights_from_file(FLAGS.pretrained_model,
sess,
finetune=True)
# Check weights
for variable in tf.trainable_variables():
with tf.variable_scope('', reuse=True):
var = tf.get_variable(variable.name.split(':0')[0])
print(variable.name, np.mean(sess.run(var)))
else:
saver.restore(sess, FLAGS.pretrained_model)
# check weights
for variable in tf.trainable_variables():
with tf.variable_scope('', reuse=True):
var = tf.get_variable(variable.name.split(':0')[0])
print(variable.name, np.mean(sess.run(var)))
while True:
# Read in batch data
batch_x_np, batch_y_np, batch_c_np = sess.run(
[batch_x, batch_y, batch_c])
# Warp training images
for img_num in range(batch_x_np.shape[0]):
deg1 = (2 * np.random.rand() - 1) * 50
deg2 = (2 * np.random.rand() - 1) * 50
batch_x_np[img_num,
...] = cpm_utils.warpImage(batch_x_np[img_num, ...],
0, deg1, deg2, 1, 30)
batch_y_np[img_num,
...] = cpm_utils.warpImage(batch_y_np[img_num, ...],
0, deg1, deg2, 1, 30)
batch_y_np[img_num, :, :, FLAGS.num_of_joints] = np.ones(shape=(FLAGS.input_size, FLAGS.input_size)) - \
np.max(
batch_y_np[img_num, :, :, 0:FLAGS.num_of_joints],
axis=2)
batch_c_np[img_num, ...] = cpm_utils.warpImage(
batch_c_np[img_num, ...], 0, deg1, deg2, 1, 30).reshape(
(FLAGS.input_size, FLAGS.input_size, 1))
# Convert image to grayscale
if FLAGS.color_channel == 'GRAY':
batch_x_gray_np = np.zeros(
(batch_x_np.shape[0], FLAGS.input_size, FLAGS.input_size,
1))
for img_num in range(batch_x_np.shape[0]):
tmp = batch_x_np[img_num, ...]
tmp += 0.5
tmp *= 255
tmp = np.dot(tmp[..., :3], [0.114, 0.587, 0.299])
tmp /= 255
tmp -= 0.5
batch_x_gray_np[img_num, ...] = tmp.reshape(
(FLAGS.input_size, FLAGS.input_size, 1))
batch_x_np = batch_x_gray_np
# Recreate heatmaps
gt_heatmap_np = cpm_utils.make_gaussian_batch(
batch_y_np, FLAGS.heatmap_size, 3)
# Update once
stage_losses_np, total_loss_np, _, summary, current_lr, \
stage_heatmap_np, global_step = sess.run([model.stage_loss,
model.total_loss,
model.train_op,
model.merged_summary,
model.lr,
model.stage_heatmap,
model.global_step
],
feed_dict={input_placeholder: batch_x_np,
cmap_placeholder: batch_c_np,
hmap_placeholder: gt_heatmap_np})
# Write logs
tf_writer.add_summary(summary, global_step)
# Draw intermediate results
if global_step % 50 == 0:
# if True:
if FLAGS.color_channel == 'GRAY':
demo_img = np.repeat(batch_x_np[0], 3, axis=2)
demo_img += 0.5
elif FLAGS.color_channel == 'RGB':
demo_img = batch_x_np[0] + 0.5
demo_stage_heatmaps = []
for stage in range(FLAGS.stages):
demo_stage_heatmap = stage_heatmap_np[stage][
0, :, :, 0:FLAGS.num_of_joints].reshape(
(FLAGS.heatmap_size, FLAGS.heatmap_size,
FLAGS.num_of_joints))
demo_stage_heatmap = cv2.resize(
demo_stage_heatmap,
(FLAGS.input_size, FLAGS.input_size))
demo_stage_heatmap = np.amax(demo_stage_heatmap, axis=2)
demo_stage_heatmap = np.reshape(
demo_stage_heatmap,
(FLAGS.input_size, FLAGS.input_size, 1))
demo_stage_heatmap = np.repeat(demo_stage_heatmap,
3,
axis=2)
demo_stage_heatmaps.append(demo_stage_heatmap)
demo_gt_heatmap = gt_heatmap_np[0, :, :,
0:FLAGS.num_of_joints].reshape(
(FLAGS.heatmap_size,
FLAGS.heatmap_size,
FLAGS.num_of_joints))
demo_gt_heatmap = cv2.resize(
demo_gt_heatmap, (FLAGS.input_size, FLAGS.input_size))
demo_gt_heatmap = np.amax(demo_gt_heatmap, axis=2)
demo_gt_heatmap = np.reshape(
demo_gt_heatmap, (FLAGS.input_size, FLAGS.input_size, 1))
demo_gt_heatmap = np.repeat(demo_gt_heatmap, 3, axis=2)
if FLAGS.stages > 4:
upper_img = np.concatenate(
(demo_stage_heatmaps[0], demo_stage_heatmaps[1],
demo_stage_heatmaps[2]),
axis=1)
blend_img = 0.5 * demo_gt_heatmap + 0.5 * demo_img
lower_img = np.concatenate(
(demo_stage_heatmaps[FLAGS.stages - 1],
demo_gt_heatmap, blend_img),
axis=1)
demo_img = np.concatenate((upper_img, lower_img), axis=0)
cv2.imshow('current heatmap',
(demo_img * 255).astype(np.uint8))
cv2.waitKey()
else:
upper_img = np.concatenate(
(demo_stage_heatmaps[FLAGS.stages - 1],
demo_gt_heatmap, demo_img),
axis=1)
cv2.imshow('current heatmap',
(upper_img * 255).astype(np.uint8))
cv2.waitKey()
print('##========Iter {:>6d}========##'.format(global_step))
print('Current learning rate: {:.8f}'.format(current_lr))
for stage_num in range(FLAGS.stages):
print('Stage {} loss: {:>.3f}'.format(
stage_num + 1, stage_losses_np[stage_num]))
print('Total loss: {:>.3f}\n\n'.format(total_loss_np))
# Save models
if global_step % 5000 == 1:
save_path_str = 'models/' + FLAGS.saved_model_name
saver.save(sess=sess,
save_path=save_path_str,
global_step=global_step)
print('\nModel checkpoint saved...\n')
# Finish training
if global_step == FLAGS.training_iterations:
break
coord.request_stop()
coord.join(threads)
print('Training done.')
def main(argv):
tf_device = '/gpu:0'
with tf.device(tf_device):
"""
if FLAGS.color_channel == 'RGB':
input_data = tf.placeholder(dtype=tf.float32,
shape=[None, FLAGS.input_size, FLAGS.input_size, 3],
name='input_image')
else:
input_data = tf.placeholder(dtype=tf.float32,
shape=[None, FLAGS.input_size, FLAGS.input_size, 1],
name='input_image')
center_map = tf.placeholder(dtype=tf.float32,
shape=[None, FLAGS.input_size, FLAGS.input_size, 1],
name='center_map')
"""
# model = cpm_body_slim.CPM_Model(FLAGS.stages, FLAGS.joints + 1)
# model.build_model(input_data, center_map, 1)
# 没有背景
model = cpm_body_slim.CPM_Model(FLAGS.input_size, FLAGS.hmap_size, FLAGS.stages, FLAGS.joints, 1, img_type = FLAGS.color_channel)
saver = tf.train.Saver()
"""Create session and restore weights
"""
sess = tf.Session()
sess.run(tf.global_variables_initializer())
if FLAGS.model_path.endswith('pkl'):
model.load_weights_from_file(FLAGS.model_path, sess, False)
else:
saver.restore(sess, FLAGS.model_path)
# 不要centermap, 意义不明
"""
# 创建center_map，正方形的中心放置高斯响应
test_center_map = cpm_utils.gaussian_img(FLAGS.input_size,
FLAGS.input_size,
FLAGS.input_size / 2,
FLAGS.input_size / 2,
FLAGS.cmap_radius)
test_center_map = np.reshape(test_center_map, [1, FLAGS.input_size,
FLAGS.input_size, 1])
"""
# read in video / flow frames
if FLAGS.DEMO_TYPE.endswith(('avi', 'flv', 'mp4')):
# OpenCV can only read in '.avi' files
cam = imageio.get_reader(FLAGS.DEMO_TYPE)
# iamge processing
with tf.device(tf_device):
if FLAGS.DEMO_TYPE.endswith(('avi', 'flv', 'mp4')):
ori_fps = cam.get_meta_data()['fps']
cap = cv2.VideoCapture(FLAGS.DEMO_TYPE)
total_frame = cap.get(cv2.CAP_PROP_FRAME_COUNT)
(W,H) = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
pbar = tqdm(total=total_frame)
print('This video fps is %f' % ori_fps)
video_length = cam.get_length()
# writer_path = os.path.join('results', os.path.basename(FLAGS.DEMO_TYPE))
# !! OpenCV can only write in .avi
cv_writer = cv2.VideoWriter('results/result.mp4',
# cv2.cv.CV_FOURCC('M', 'J', 'P', 'G'),
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
ori_fps,
(W,H))
# imageio_writer = imageio.get_writer(writer_path, fps=ori_fps)
try:
for it, im in enumerate(cam):
test_img_t = time.time()
full_img = im.copy() # 把结果画在原图上
# test_img是原图像resize得到，并且进行了padding
test_img = cpm_utils.read_image(im, [], FLAGS.input_size, 'VIDEO')
# 多余的resize
# test_img_resize = cv2.resize(test_img, (FLAGS.input_size, FLAGS.input_size))
# print('img read time %f' % (time.time() - test_img_t))
if FLAGS.color_channel == 'GRAY':
test_img = cv2.cvtColor(test_img, cv2.COLOR_RGB2GRAY)
test_img_input = test_img / 256.0 - 0.5
test_img_input = np.expand_dims(test_img_input, axis=0)
# Inference
# fps_t = time.time()
"""
predict_heatmap, stage_heatmap_np = sess.run([model.current_heatmap,
model.stage_heatmap,
],
feed_dict={'input_placeholder:0': test_img_input,
'cmap_placeholder:0': test_center_map})
"""
predict_heatmap, stage_heatmap_np = sess.run([model.current_heatmap,
model.stage_heatmap,
],
feed_dict={'input_placeholder:0': test_img_input})
# Show visualized image
demo_img = visualize_result(test_img, full_img, FLAGS, stage_heatmap_np)
#cv2.putText(demo_img, "FPS: %.1f" % (1 / (time.time() - fps_t)), (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 1)
cv_writer.write(demo_img.astype(np.uint8))
pbar.update(1)
# print(1/(time.time()-test_img_t))
cv2.imwrite('results/pics_test/{}.png'.format(str(it).zfill(5)), demo_img[:,:,::-1])
#exit(0)
# imageio_writer.append_data(demo_img[:, :, 1])
except KeyboardInterrupt:
print('Stopped! {}/{} frames captured!'.format(it, video_length))
finally:
cv_writer.release()
# imageio_writer.close()
elif FLAGS.DEMO_TYPE.endswith(('png', 'jpg')):
test_img = cpm_utils.read_image(FLAGS.DEMO_TYPE, [], FLAGS.input_size, 'IMAGE')
test_img_input = test_img / 256.0 - 0.5
test_img_input = np.expand_dims(test_img_input, axis=0)
# Inference
fps_t = time.time()
"""
stage_heatmap_np = sess.run([model.stage_heatmap[5]],
feed_dict={'input_placeholder:0': test_img_input,
'cmap_placeholder:0': test_center_map})
"""
stage_heatmap_np = sess.run([model.stage_heatmap[5]],
feed_dict={'input_placeholder:0': test_img_input})
# Show visualized image
ori_img = cv2.imread(FLAGS.DEMO_TYPE)
demo_img = visualize_result(test_img, ori_img, FLAGS, stage_heatmap_np)
cv2.imwrite('results/test.jpg', demo_img)
print('fps: %.1f' % (1 / (time.time() - fps_t)))
else:
print('Demo type is not defined, please check it!')
