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demo_cpm_hand.py
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import tensorflow as tf
from models.nets import cpm_hand_slim
import numpy as np
from utils import cpm_utils
import cv2
import time
import math
import sys
import tensorflow.contrib.slim as slim
import os
import csv
import numpy as np
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('DEMO_TYPE',
default='test_imgs/longhand.jpg',
# default='SINGLE',
help='MULTI: show multiple stage,'
'SINGLE: only last stage,'
'HM: show last stage heatmap,'
'paths to .jpg or .png image')
tf.app.flags.DEFINE_string('model_path',
default='models/weights/cpm_hand.pkl',
help='Your model')
tf.app.flags.DEFINE_integer('input_size',
default=368,
help='Input image size')
tf.app.flags.DEFINE_integer('hmap_size',
default=46,
help='Output heatmap size')
tf.app.flags.DEFINE_integer('cmap_radius',
default=21,
help='Center map gaussian variance')
tf.app.flags.DEFINE_integer('joints',
default=21,
help='Number of joints')
tf.app.flags.DEFINE_integer('stages',
default=6,
help='How many CPM stages')
tf.app.flags.DEFINE_integer('cam_num',
default=0,
help='Webcam device number')
tf.app.flags.DEFINE_bool('KALMAN_ON',
default=True,
help='enalbe kalman filter')
tf.app.flags.DEFINE_float('kalman_noise',
default=3e-2,
help='Kalman filter noise value')
tf.app.flags.DEFINE_string('color_channel',
default='RGB',
help='')
# 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],
[1, 2],
[2, 3],
[3, 4],
[0, 5],
[5, 6],
[6, 7],
[7, 8],
[0, 9],
[9, 10],
[10, 11],
[11, 12],
[0, 13],
[13, 14],
[14, 15],
[15, 16],
[0, 17],
[17, 18],
[18, 19],
[19, 20]
]
m_avg=np.zeros((10,4))
avg_i=0
def main(argv):
tf_device = '/gpu:0'
with tf.device(tf_device):
"""Build graph
"""
input_data = tf.placeholder(dtype=tf.float32, shape=[None, FLAGS.input_size, FLAGS.input_size, 3],name='input_image')
center_map = tf.placeholder(dtype=tf.float32, shape=[None, FLAGS.input_size, FLAGS.input_size, 1],name='center_map')
model = cpm_hand_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())
model.load_weights_from_file(FLAGS.model_path, sess, False)
#model_vars = tf.trainable_variables()
#slim.model_analyzer.analyze_vars(model_vars, print_info=True)
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])
if not FLAGS.DEMO_TYPE.endswith(('png', 'jpg')):
cam = cv2.VideoCapture(FLAGS.cam_num)
# Create kalman filters
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
with tf.device(tf_device):
#filedem = open("testfile.txt","w")
#filedem.close()
if FLAGS.DEMO_TYPE == 'SINGLE':
#read default image coords
v=[]
with open('test_file.csv') as file:
reader = csv.reader(file, delimiter=',')
for row in reader:
for entries in row:
v.append(entries)
'''
f=open("testfile.txt", 'r')
v=f.readline()
v=v.strip().split(' ')
'''
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('img read time %f' % (time.time() - t1))
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()
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,coords = visualize_result(test_img, FLAGS, stage_heatmap_np, kalman_filter_array)
cv2.imshow('demo_img', demo_img.astype(np.uint8))
'''
filedem = open("testfile.txt","w")
for i in [0,5,7,9,11,13,15,17,19]:
for j in [0,1]:
filedem.write(str(coords[i][j])+' ')
filedem.close()
'''
with open('test_file.csv', mode='w') as file:
writer = csv.writer(file, delimiter=',')
for i in [1,5,8,9,12,13,16,17,20]:
writer.writerow([coords[i][0],coords[i][1]])
if cv2.waitKey(0) == 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[2]],
feed_dict={'input_image:0': test_img_input,
'center_map:0': test_center_map})
# Show visualized image
demo_img,coords = visualize_result(test_img, FLAGS, stage_heatmap_np, kalman_filter_array)
cv2.imshow('current heatmap', (demo_img).astype(np.uint8))
debug(coords,v)
global avg_i, m_avg
avg_i=(avg_i+1)%10
print(np.mean(m_avg,axis=0))
'''
filedem = open("testfile.txt","a")
for i in [0,5,7,9,11,13,15,17,19]:
for j in [0,1]:
filedem.write(str(coords[i][j])+' ')
filedem.close()
'''
if cv2.waitKey(1) == ord('q'): break
print('fps: %.2f' % (1 / (time.time() - t1)))
def visualize_result(test_img, FLAGS, stage_heatmap_np, kalman_filter_array):
t1 = time.time()
demo_stage_heatmaps = []
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 = cv2.resize(last_heatmap, (test_img.shape[1], test_img.shape[0]))
#print('hm resize time %f' % (time.time() - t1))
t1 = time.time()
joint_coord_set = np.zeros((FLAGS.joints, 2))
# Plot joint colors
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 = 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]:
joint_color = list(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:
joint_color = list(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() - t1))
t1 = 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 < 150 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
limb_color = list(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() - t1))
return test_img,joint_coord_set
def debug(coords, v):
global m_avg
degs=[]
coords1 = []
for i in [1,5,8,9,12,13,16,17,20]:
coords1.append(coords[i])
Xc1=(coords1[0][0]+coords1[1][0]+coords1[3][0]+coords1[5][0]+coords1[7][0])/5
Yc1=(coords1[0][1]+coords1[1][1]+coords1[3][1]+coords1[5][1]+coords1[7][1])/5
for i in range(4):
length1=((coords1[2*i+1][0]-Xc1)**2+(coords1[2*i+1][1]-Yc1)**2)**0.5
length2=((coords1[2*i+2][0]-coords1[2*i+1][0])**2+(coords1[2*i+2][1]-coords1[2*i+1][1])**2)**0.5
r=length2
#r=length2/length1
#print(r, v[i])
r=r/float(v[i])
if(r>1):
r=1
if(r<-1):
r=-1
degs.append(math.degrees(math.acos((r))))
#print(degs)
for i in range(4):
m_avg[avg_i][i]=degs[i]
if __name__ == '__main__':
tf.app.run()