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')
# tensorflow slim model
model = cpm_hand_slim_dw.CPM_Model(FLAGS.stages, FLAGS.joints + 1)
model.build_model(input_data, center_map, 1)
# keras model
# model = cpm_hand_keras.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)))
if not FLAGS.DEMO_TYPE.endswith(('png', 'jpg')):
cam = cv2.VideoCapture(FLAGS.cam_num)
# ------------------------------------------ save model-------------------------------
# save_dir = 'checkpoints/'
# if not os.path.exists(save_dir):
# os.makedirs(save_dir)
# save_path = os.path.join(save_dir, 'best_validation')
# saver.save(sess=sess, save_path=save_path)
# builder = tf.saved_model.builder.SavedModelBuilder('./SavedModel/')
# signature = predict_signature_def(inputs={'input_image:0': input_data,
# 'center_map:0': center_map})
# builder.add_meta_graph_and_variables(sess,
# [tf.saved_model.tag_constants.TRAINING],
# signature_def_map={'predict': signature},
# assets_collection=None,
# strip_default_attrs=True)
# builder.add_meta_graph([tf.saved_model.tag_constants.SERVING], strip_default_attrs=True)
# builder.save()
signature = None
# 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
with tf.device(tf_device):
while True:
t1 = 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(
"----------------------------------------start to read img--------------------"
)
print('img read time %f' % (time.time() - t1))
if FLAGS.color_channel == 'GRAY':
test_img_resize = np.dot(test_img_resize[..., :3],
[0.299, 0.587, 0.114]).reshape(
(FLAGS.input_size,
FLAGS.input_size, 1))
cv2.imshow('color', test_img.astype(np.uint8))
cv2.imshow('gray', test_img_resize.astype(np.uint8))
cv2.waitKey(1)
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
t1 = time.time()
# print("=========================test image============================")
# print(test_img_input)
# print("=========================test image shape============================")
# print(test_img_input.shape)
# slim prediction
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
})
# keras prediction
# predict_heatmap, stage_heatmap_np = model.predict(test_img_input)
print("---------------stage_heatmap_np:------------------")
# print(predict_heatmap)
print(np.array(stage_heatmap_np).shape)
# print(stage_heatmap_np)
inputa = tf.saved_model.utils.build_tensor_info(input_data)
predict_heatmap = tf.convert_to_tensor(predict_heatmap)
outputa = tf.saved_model.utils.build_tensor_info(
predict_heatmap)
# signatureA = (
# tf.saved_model.signature_def_utils.build_signature_def(
# inputs={"aaa": inputA},
# outputs={"bbb": outputA},
# method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME)
# )
# print('input:')
# print(input_data)
# print('output:')
# print(predict_heatmap)
# signature = predict_signature_def(inputs={'input_image:0': input_data}
# ,
# outputs={'Const:0': predict_heatmap})
#
# print("signature content:")
# print(signature)
# 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))
# break
if cv2.waitKey(0) == ord('q'): break
print('fps: %.2f' % (1 / (time.time() - t1)))
elif FLAGS.DEMO_TYPE == 'MULTI':
# Inference
t1 = 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() - t1)))
elif FLAGS.DEMO_TYPE == 'SINGLE':
# Inference
t1 = 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() - t1)))
elif FLAGS.DEMO_TYPE == 'HM':
# Inference
t1 = 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() - t1)))
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 = 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):
"""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 = cpm_hand_slim_dw.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_2, 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)))
# tf_device = '/gpu:0'
# with tf.device(tf_device):
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 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(100)
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(1000)
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.')