def find_best_piece(input_data):
# 保存之前的五个结果，求方差
rate_res = list()
num_pic = 1
best_pic = 1
best_rate = 0
best_rates = None
while True:
# 创建分析对象
packer = PackerSolution(input_data['shape_data'],
input_data['bin_data'],
border=int(input_data['border']),
num_pic=num_pic)
if packer.is_valid():
# 选择几种经常用的算法
res = packer.find_solution(algo_list=[0, 4, 40, 8, 20, 44, 24])
# 平均使用率
total_rate = 0
for data in res:
total_rate += data['rate']
tmp_avg_rate = total_rate / len(res)
# 记录最大值
if best_rate < tmp_avg_rate:
best_rate = tmp_avg_rate
best_pic = num_pic
best_rates = [(data['bin_key'], data['rate']) for data in res]
if num_pic > my_settings.NUM_SAVE:
rate_res.append(tmp_avg_rate)
np_arr = np.array(rate_res[-1 * my_settings.NUM_SAVE:])
var_rate = np_arr.var()
if var_rate < my_settings.MAX_VAR_RATE:
# 少于阈值返回最佳值
return {
'error': False,
'piece': best_pic,
'rates': best_rates
}
else:
rate_res.append(tmp_avg_rate)
else:
return {'error': True, 'info': packer.error_info()}
num_pic += 1
def find_best_piece(shape_data, bin_data, border=5):
rate_res = list()
num_pic = 1
best_pic = 1
best_rate = 0
best_rates = {}
while True:
# 创建分析对象
packer = PackerSolution(
shape_data,
bin_data,
border=border,
num_pic=num_pic
)
if packer.is_valid():
# 选择几种经常用的算法
res = packer.find_solution(algo_list=[0, 4, 40, 8, 20, 44, 24])
# 平均使用率
total_rate = 0
for data in res:
total_rate += data['rate']
tmp_avg_rate = total_rate / len(res)
# 记录最大值
if best_rate < tmp_avg_rate:
best_rate = tmp_avg_rate
best_pic = num_pic
for data in res:
best_rates[data['bin_key']] = data['rate']
if num_pic > my_settings.NUM_SAVE:
rate_res.append(tmp_avg_rate)
np_arr = np.array(rate_res[-1 * my_settings.NUM_SAVE:])
var_rate = np_arr.var()
if var_rate < my_settings.MAX_VAR_RATE:
# 少于阈值返回最佳值
return False, {'piece': best_pic, 'rates': best_rates}
else:
rate_res.append(tmp_avg_rate)
else:
return True, {'info': packer.error_info()}
num_pic += 1
shape_data = '[{"SkuCode":"32050093","Length":1019.0,"Width":30.3,"Amount":4},{"SkuCode":"32050093","Length":319.0,"Width":30.0,"Amount":20},{"SkuCode":"32050076","Length":406.0,"Width":291.0,"Amount":20},{"SkuCode":"32050434","Length":396.0,"Width":110.0,"Amount":20},{"SkuCode":"32050434","Length":295.0,"Width":110.0,"Amount":40},{"SkuCode":"32050038","Length":484.0,"Width":190.0,"Amount":20},{"SkuCode":"32050076","Length":396.0,"Width":456.0,"Amount":10},{"SkuCode":"32050093","Length":386.0,"Width":362.0,"Amount":20},{"SkuCode":"32050093","Length":446.0,"Width":75.0,"Amount":10},{"SkuCode":"32050519","Length":516.0,"Width":397.0,"Amount":10},{"SkuCode":"32050052","Length":516.0,"Width":397.0,"Amount":10},{"SkuCode":"32050038","Length":340.0,"Width":55.0,"Amount":10},{"SkuCode":"32050038","Length":1294.0,"Width":398.0,"Amount":9},{"SkuCode":"32050093","Length":1444.0,"Width":296.0,"Amount":9},{"SkuCode":"32010004","Length":1056.0,"Width":279.0,"Amount":9},{"SkuCode":"32010004","Length":1200.0,"Width":330.0,"Amount":9},{"SkuCode":"32050051","Length":2010.0,"Width":128.0,"Amount":18},{"SkuCode":"32050052","Length":1444.0,"Width":456.0,"Amount":9},{"SkuCode":"32050052","Length":1444.0,"Width":163.0,"Amount":9}]'
bin_data = u'[{"SkuCode":"32050093","ItemName":"三聚氰胺板-双面仿古白哑光(18mm)","SkuName":"2440*1220*18mm","HasGrain":"否"},{"SkuCode":"32050076","ItemName":"三聚氰胺板-双面仿古白哑光(5mm)","SkuName":"2440*1220*5mm","HasGrain":"否"},{"SkuCode":"32050434","ItemName":"三聚氰胺板-2#8834双面仿橡胶木哑光(12mm)","SkuName":"2440*1220*12mm","HasGrain":"是"},{"SkuCode":"32050038","ItemName":"三聚氰胺板-双面仿古白哑光单保(18mm)","SkuName":"2440*1220*18mm","HasGrain":"否"},{"SkuCode":"32050519","ItemName":"三聚氰胺板-双面仿古白哑光双保(25mm)","SkuName":"2440*1220*25mm","HasGrain":"否"},{"SkuCode":"32050052","ItemName":"三聚氰胺板-双面仿古白哑光单保(25mm)","SkuName":"2440*1220*25mm","HasGrain":"否"},{"SkuCode":"32010004","ItemName":"中纤板(E1)-B(25mm)","SkuName":"2440*1220*25mm","HasGrain":"否"},{"SkuCode":"32050051","ItemName":"三聚氰胺板-双面仿古白哑光(25mm)","SkuName":"2440*1220*25mm","HasGrain":"否"}]'
packer = PackerSolution(shape_data, bin_data, border=5, num_pic=2,
empty_section_min_size=100000)
if packer.is_valid():
res = packer.find_solution(algo_list=[0, 4, 40, 8, 20])
print res
for data in res:
best_solution = data['solution']
bins_list = data['bins_list']
shape_list = packer.get_bin_data(data['bin_key'], key='shape_list')
# 计算使用率
rate_list = list()
for s_id in range(0, len(best_solution)):
r = use_rate(best_solution[s_id], bins_list[s_id][0], bins_list[s_id][1])
rate_list.append(r)
title = u'平均利用率: %s' % str(data['rate'])
# 返回唯一的排版列表，以及数量
same_bin_list = find_the_same_position(best_solution)
draw_one_pic(best_solution, rate_list, width=2430, height=1210,
path='package_min_b' + data['bin_key'], border=1, num_list=same_bin_list, title=title,
shapes=shape_list, empty_positions=data['empty_sections'])
else:
print packer.error_info()
def package_main_function(input_data, pathname):
bins_num = None
min_size = None
min_height = None
min_width = None
cut_linear_p = 30 # 切割线重要系数
empty_section_p = 70 # 余料重要系数
if 'bins_num' in input_data.keys():
if input_data['bins_num'] != '':
bins_num = input_data['bins_num']
min_size = int(input_data['min_size'])
min_height = int(input_data['min_height'])
min_width = int(input_data['min_width'])
if 'use_rate_p' in input_data.keys():
cut_linear_p = int(input_data['cut_linear_p'])
empty_section_p = int(input_data['empty_section_p'])
if 'effective_rate' in input_data.keys():
effective_rate = float(input_data['empty_section_p'])
else:
effective_rate = EFFECTIVE_RATE
# 创建分析对象
packer = PackerSolution(input_data['shape_data'],
input_data['bin_data'],
border=int(input_data['border']),
bins_num=bins_num,
empty_section_min_size=min_size,
empty_section_min_height=min_height,
empty_section_min_width=min_width,
cut_linear_p=cut_linear_p,
empty_section_p=empty_section_p)
algo_list = None
if 'algo_list' in input_data.keys():
algo_list = [int(x) for x in input_data.getlist('algo_list')]
# 若数据没有错，返回结果
if packer.is_valid():
res = packer.find_solution(algo_list=algo_list)
statistics_data = [] # 汇总报告
for data in res:
best_solution = data['solution']
empty_sections = data['empty_sections']
bins_list = data['bins_list']
shape_list = packer.get_bin_data(data['bin_key'], key='shape_list')
shape_num = packer.get_bin_data(data['bin_key'], key='shape_num')
name = packer.get_bin_data(data['bin_key'], key='name')
# 计算总利用率 = 余料使用率/2 + 组件利用率 ，余料面积的1/2 有效余料
rate_list = list() # 组件使用率
total_rate_list = list() # 总利用率
empty_ares_list = list() # 余料面积
for s_id in range(0, len(best_solution)):
r = use_rate(best_solution[s_id], bins_list[s_id][0],
bins_list[s_id][1])
empty_r = use_rate(empty_sections[s_id], bins_list[s_id][0],
bins_list[s_id][1])
rate_list.append(r)
total_rate_list.append(
float("%0.4f" % (empty_r * effective_rate + r)))
# 余料总面积
empty_ares_list.append(empty_ares(empty_sections[s_id]))
title = 'average rate: %s' % str(data['rate'])
# 返回唯一的排版列表，以及数量
same_bin_list = find_the_same_position(best_solution)
draw_one_pic(best_solution,
rate_list,
bins_list=bins_list,
path=pathname + data['bin_key'],
border=1,
num_list=same_bin_list,
title=title,
shapes=shape_list,
empty_positions=data['empty_sections'])
# 保存统计信息
statistics_data.append({
'error':
False,
'rate':
data['rate'],
'num_sheet':
len(best_solution),
'detail':
detail_text(shape_list, best_solution, same_bin_list),
'num_shape':
str(shape_num)[1:-1],
'same_bin_list':
str(same_bin_list)[1:-1],
'sheet_num_shape':
str([len(s) for s in best_solution])[1:-1],
'rates':
str(rate_list)[1:-1],
'sheet':
name,
'name':
data['bin_key'] + ' ' + name +
u' 切割线(mm):%s' % str(data['cut_linear']),
'bin_type':
data['bin_key'],
'pic_url':
pathname + data['bin_key'] + '.png',
'empty_sections':
detail_empty_sections(
empty_sections, shape_list, packer.get_border(),
packer.get_bin_data(data['bin_key'], key='is_texture'),
packer.get_bin_data(data['bin_key'], key='is_vertical')),
'algo_id':
data['algo_id'],
'total_rates':
str(total_rate_list)[1:-1],
'empty_section_ares':
str(empty_ares_list)[1:-1],
})
# 返回结果
return {'statistics_data': statistics_data, 'error': False}
else:
# 有错误，返回错误信息
return {'error': True, 'info': packer.error_info()}