def format_img(img, C):
img_min_side = float(C.im_size)
(height, width, _) = img.shape
if width <= height:
f = img_min_side / width
new_height = int(f * height)
new_width = int(img_min_side)
else:
f = img_min_side / height
new_width = int(f * width)
new_height = int(img_min_side)
fx = width / float(new_width)
fy = height / float(new_height)
img = cv.resize(img, (new_width, new_height), interpolation=cv.INTER_CUBIC)
img = img[:, :, (2, 1, 0)]
img = img.astype(np.float32)
img[:, :, 0] -= C.img_channel_mean[0]
img[:, :, 1] -= C.img_channel_mean[1]
img[:, :, 2] -= C.img_channel_mean[2]
img /= C.img_scaling_factor
img = np.transpose(img, (2, 0, 1))
img = np.expand_dims(img, axis=0)
return img, fx, fy
import opencv as cv2
import numpy as np
import time
imgF = cv2.imread("/home/vishav/Documents/Project/DepthCal/Data/front.png", 0)
imgR = cv2.imread("/home/vishav/Documents/Project/DepthCal/Data/rear.png", 0)
imgF = cv2.resize(imgF, (480, 720))
imgR = cv2.resize(imgR, (480, 720))
imgF = imgF[300:338, 230:279]
imgR = imgR[227:260, 289:327]
start = time.time()
dC = -50
f = 532
liF = []
for i in range(imgF.shape[0]):
for j in range(imgF.shape[1]):
if imgF[i][j] >= 180 and imgF[i][j] <= 255:
imgF[i][j] = 255
else:
imgF[i][j] = 0
liF.append(i)
fy0 = min(liF)
fy1 = max(liF)
liR = []
for i in range(imgR.shape[0]):
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_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())
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)
# 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('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()
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() - 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 processImage(screen):
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)
gray = cv2.resize(screen, (80, 60))
return gray
def visualize_result(test_img, FLAGS, stage_heatmap_np, kalman_filter_array):
t1 = 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 = 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 = 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
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]))
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() - 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
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() - t1))
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