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demo_cpm_body.py
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demo_cpm_body.py
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import numpy as np
from utils import cpm_utils
import cv2
import time
import math
import sys
import os
import imageio
import tensorflow as tf
from models.nets import cpm_body_slim
"""Parameters
"""
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('DEMO_TYPE',
# default_value='test_imgs/roger.png',
default_value='test_imgs/single_gym.mp4',
# default_value='SINGLE',
docstring='MULTI: show multiple stage,'
'SINGLE: only last stage,'
'HM: show last stage heatmap,'
'paths to .jpg or .png image'
'paths to .avi or .flv or .mp4 video')
tf.app.flags.DEFINE_string('model_path',
default_value='models/weights/cpm_body.pkl',
docstring='Your model')
tf.app.flags.DEFINE_integer('input_size',
default_value=368,
docstring='Input image size')
tf.app.flags.DEFINE_integer('hmap_size',
default_value=46,
docstring='Output heatmap size')
tf.app.flags.DEFINE_integer('cmap_radius',
default_value=21,
docstring='Center map gaussian variance')
tf.app.flags.DEFINE_integer('joints',
default_value=14,
docstring='Number of joints')
tf.app.flags.DEFINE_integer('stages',
default_value=6,
docstring='How many CPM stages')
tf.app.flags.DEFINE_integer('cam_num',
default_value=0,
docstring='Webcam device number')
tf.app.flags.DEFINE_bool('KALMAN_ON',
default_value=False,
docstring='enalbe kalman filter')
tf.app.flags.DEFINE_integer('kalman_noise',
default_value=3e-2,
docstring='Kalman filter noise value')
tf.app.flags.DEFINE_string('color_channel',
default_value='RGB',
docstring='')
# Set color for each finger
joint_color_code = [[139, 53, 255],
[0, 56, 255],
[43, 140, 237],
[37, 168, 36],
[147, 147, 0],
[70, 17, 145]]
limbs = [[0, 1],
[2, 3],
[3, 4],
[5, 6],
[6, 7],
[8, 9],
[9, 10],
[11, 12],
[12, 13]]
if sys.version_info.major == 3:
PYTHON_VERSION = 3
else:
PYTHON_VERSION = 2
def mgray(test_img_resize, test_img):
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)
return test_img_resize
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):
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 visualize_result(test_img, FLAGS, stage_heatmap_np, kalman_filter_array):
hm_t = time.time()
demo_stage_heatmaps = []
if FLAGS.DEMO_TYPE == 'MULTI':
for stage in range(len(stage_heatmap_np)):
demo_stage_heatmap = stage_heatmap_np[stage][0, :, :, 0:FLAGS.joints].reshape(
(FLAGS.hmap_size, FLAGS.hmap_size, FLAGS.joints))
demo_stage_heatmap = cv2.resize(demo_stage_heatmap, (test_img.shape[1], test_img.shape[0]))
demo_stage_heatmap = np.amax(demo_stage_heatmap, axis=2)
demo_stage_heatmap = np.reshape(demo_stage_heatmap, (test_img.shape[1], test_img.shape[0], 1))
demo_stage_heatmap = np.repeat(demo_stage_heatmap, 3, axis=2)
demo_stage_heatmap *= 255
demo_stage_heatmaps.append(demo_stage_heatmap)
# last_heatmap = stage_heatmap_np[len(stage_heatmap_np) - 1][0, :, :, 0:FLAGS.joints].reshape(
# (FLAGS.hmap_size, FLAGS.hmap_size, FLAGS.joints))
last_heatmap = stage_heatmap_np[-1][0, :, :, 0:FLAGS.joints].reshape(
(FLAGS.hmap_size, FLAGS.hmap_size, FLAGS.joints))
last_heatmap = cv2.resize(last_heatmap, (test_img.shape[1], test_img.shape[0]))
else:
# last_heatmap = stage_heatmap_np[len(stage_heatmap_np) - 1][0, :, :, 0:FLAGS.joints].reshape(
# (FLAGS.hmap_size, FLAGS.hmap_size, FLAGS.joints))
last_heatmap = stage_heatmap_np[-1][0, :, :, 0:FLAGS.joints].reshape(
(FLAGS.hmap_size, FLAGS.hmap_size, FLAGS.joints))
last_heatmap = cv2.resize(last_heatmap, (test_img.shape[1], test_img.shape[0]))
print('hm resize time %f' % (time.time() - hm_t))
joint_t = time.time()
joint_coord_set = np.zeros((FLAGS.joints, 2))
# Plot joint colors
if kalman_filter_array is not None:
for joint_num in range(FLAGS.joints):
joint_coord = np.unravel_index(np.argmax(last_heatmap[:, :, joint_num]),
(test_img.shape[0], test_img.shape[1]))
# add a dimension for kalman filter
joint_coord = np.array(joint_coord).reshape((2, 1)).astype(np.float32)
kalman_filter_array[joint_num].correct(joint_coord)
kalman_pred = kalman_filter_array[joint_num].predict()
joint_coord_set[joint_num, :] = np.array([kalman_pred[0], kalman_pred[1]]).reshape((2))
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)
else:
for joint_num in range(FLAGS.joints):
joint_coord = np.unravel_index(np.argmax(last_heatmap[:, :, joint_num]),
(test_img.shape[0], test_img.shape[1]))
joint_coord_set[joint_num, :] = [joint_coord[0], joint_coord[1]]
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)
print('plot joint time %f' % (time.time() - joint_t))
limb_t = time.time()
# Plot limb colors
for limb_num in range(len(limbs)):
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 < 200 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), 6),
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)
print('plot limb time %f' % (time.time() - limb_t))
if FLAGS.DEMO_TYPE == 'MULTI':
upper_img = np.concatenate((demo_stage_heatmaps[0], demo_stage_heatmaps[1], demo_stage_heatmaps[2]), axis=1)
lower_img = np.concatenate((demo_stage_heatmaps[3], demo_stage_heatmaps[len(stage_heatmap_np) - 1], test_img),
axis=1)
demo_img = np.concatenate((upper_img, lower_img), axis=0)
return demo_img
else:
return test_img
if __name__ == '__main__':
tf.app.run()