Duplicateflagerror：标志'DEMO_TYPE'定义了两次。 [英] Duplicateflagerror: the flag 'DEMO_TYPE' is defined twice.
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问题描述
raise _exceptions.DuplicateFlagError.from_flag(name, self)
DuplicateFlagError: The flag 'DEMO_TYPE' is defined twice. First from G:/convolutional-pose-machines-tensorflow-master/demo_cpm_hand.py, Second from G:/convolutional-pose-machines-tensorflow-master/demo_cpm_hand.py. Description from first occurrence: MULTI: show multiple stage,SINGLE: only last stage,HM: show last stage heatmap,paths to .jpg or .png image
这是下面的代码
This is the below code
# For single hand and no body part in the picture
# ======================================================
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
"""Parameters
"""
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('DEMO_TYPE',
#default_value='test_imgs/roger.png',
#default_value='test_imgs/longhand.jpg',
default_value='SINGLE',
docstring='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_value='models/weights/cpm_hand.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=21,
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=True,
docstring='enalbe kalman filter')
tf.app.flags.DEFINE_float('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],
[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]
]
if sys.version_info.major == 3:
PYTHON_VERSION = 3
else:
PYTHON_VERSION = 2
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 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
if __name__ == '__main__':
tf.app.run()
我的尝试：
我找不到任何解决方案。我通过更改环境尝试了很多次但是无法纠正错误
What I have tried:
I could'nt find any solution. I tried it many times by changing the enviornment too but could'nt rectify the error
推荐答案
如@ 011111100010所述，错误非常清楚...你有多次定义DEMO_TYPE。在您提交给我们的代码中，它只定义一次，这意味着其他定义在您导入的其他代码模块中的某处...
As stated by @011111100010, the error is pretty clear ... you have defined DEMO_TYPE more than once. In the code you have presented to us it is only defined once which implies that the "other" definition is somewhere in the other code modules you are importing...
tensorflow
cpm_hand_slim
numpy
cv2
time
math
sys
尝试删除此代码来自您的源模块G：/convolutional-pose-machines-tensorflow-master/demo_cpm_hand.py
Try removing this code below from your source module G:/convolutional-pose-machines-tensorflow-master/demo_cpm_hand.py
tf.app.flags.DEFINE_string('DEMO_TYPE',
#default_value='test_imgs/roger.png',
#default_value='test_imgs/longhand.jpg',
default_value='SINGLE',
docstring='MULTI: show multiple stage,'
'SINGLE: only last stage,'
'HM: show last stage heatmap,'
'paths to .jpg or .png image')或找出定义所在的导入模块，并删除 import
OR work out which of the import modules the definition is in and remove the import
这篇关于Duplicateflagerror：标志'DEMO_TYPE'定义了两次。的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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