使用多处理的代码会导致永久循环。
我正在使用一个构建虹膜识别系统。 这是matching函数。 一切正常，直到multiprocessing零件。
我在下面附上错误输出的屏幕截图，以便您获得更好的主意。
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
法典：
##-----------------------------------------------------------------------------
## Import
##-----------------------------------------------------------------------------
import numpy as np
from os import listdir
from fnmatch import filter
import scipy.io as sio
from multiprocessing import Pool, cpu_count
from itertools import repeat
import warnings
warnings.filterwarnings("ignore")
##-----------------------------------------------------------------------------
## Function
##-----------------------------------------------------------------------------
def matching(template_extr, mask_extr, temp_dir, threshold=0.38):
"""
Description:
Match the extracted template with database.
Input:
template_extr - Extracted template.
mask_extr - Extracted mask.
threshold - Threshold of distance.
temp_dir - Directory contains templates.
Output:
List of strings of matched files, 0 if not, -1 if no registered sample.
"""
# Get the number of accounts in the database
n_files = len(filter(listdir(temp_dir), '*.mat'))
if n_files == 0:
return -1
# Use all cores to calculate Hamming distances
args = zip(
sorted(listdir(temp_dir)),
repeat(template_extr),
repeat(mask_extr),
repeat(temp_dir),
)
with Pool(processes=cpu_count()) as pools:
result_list = pools.starmap(matchingPool, args)
filenames = [result_list[i][0] for i in range(len(result_list))]
hm_dists = np.array([result_list[i][1] for i in range(len(result_list))])
# Remove NaN elements
ind_valid = np.where(hm_dists>0)[0]
hm_dists = hm_dists[ind_valid]
filenames = [filenames[idx] for idx in ind_valid]
# Threshold and give the result ID
ind_thres = np.where(hm_dists<=threshold)[0]
# Return
if len(ind_thres)==0:
return 0
else:
hm_dists = hm_dists[ind_thres]
filenames = [filenames[idx] for idx in ind_thres]
ind_sort = np.argsort(hm_dists)
return [filenames[idx] for idx in ind_sort]
#------------------------------------------------------------------------------
def calHammingDist(template1, mask1, template2, mask2):
"""
Description:
Calculate the Hamming distance between two iris templates.
Input:
template1 - The first template.
mask1 - The first noise mask.
template2 - The second template.
mask2 - The second noise mask.
Output:
hd - The Hamming distance as a ratio.
"""
# Initialize
hd = np.nan
# Shift template left and right, use the lowest Hamming distance
for shifts in range(-8,9):
template1s = shiftbits(template1, shifts)
mask1s = shiftbits(mask1, shifts)
mask = np.logical_or(mask1s, mask2)
nummaskbits = np.sum(mask==1)
totalbits = template1s.size - nummaskbits
C = np.logical_xor(template1s, template2)
C = np.logical_and(C, np.logical_not(mask))
bitsdiff = np.sum(C==1)
if totalbits==0:
hd = np.nan
else:
hd1 = bitsdiff / totalbits
if hd1 < hd or np.isnan(hd):
hd = hd1
# Return
return hd
#------------------------------------------------------------------------------
def shiftbits(template, noshifts):
"""
Description:
Shift the bit-wise iris patterns.
Input:
template - The template to be shifted.
noshifts - The number of shift operators, positive for right
direction and negative for left direction.
Output:
templatenew - The shifted template.
"""
# Initialize
templatenew = np.zeros(template.shape)
width = template.shape[1]
s = 2 * np.abs(noshifts)
p = width - s
# Shift
if noshifts == 0:
templatenew = template
elif noshifts < 0:
x = np.arange(p)
templatenew[:, x] = template[:, s + x]
x = np.arange(p, width)
templatenew[:, x] = template[:, x - p]
else:
x = np.arange(s, width)
templatenew[:, x] = template[:, x - s]
x = np.arange(s)
templatenew[:, x] = template[:, p + x]
# Return
return templatenew
#------------------------------------------------------------------------------
def matchingPool(file_temp_name, template_extr, mask_extr, temp_dir):
"""
Description:
Perform matching session within a Pool of parallel computation
Input:
file_temp_name - File name of the examining template
template_extr - Extracted template
mask_extr - Extracted mask of noise
Output:
hm_dist - Hamming distance
"""
# Load each account
data_template = sio.loadmat('%s%s'% (temp_dir, file_temp_name))
template = data_template['template']
mask = data_template['mask']
# Calculate the Hamming distance
hm_dist = calHammingDist(template_extr, mask_extr, template, mask)
return (file_temp_name, hm_dist)
如何删除多处理并使代码仍然正常工作？
截图保管箱链接
使用 python 的itertools.starmap()
希望对您有所帮助