Agg2图形引擎：C++中的高保真2D图形渲染

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资源摘要信息:"Agg2.zip是一个包含High Fidelity agg_graphics engine的压缩包。该引擎是一个高质量的2D图形渲染引擎，专门为C++语言开发。它的核心是Anti-Grain Geometry (AGG)库，这是一个强大的2D图形处理库。" Anti-Grain Geometry (AGG)是一个开源的2D图形渲染库，它提供了一套丰富的工具和算法，用于高质量的图形渲染。AGG的目的是提供一个高度优化、面向对象的API，可以满足各种图形处理需求，如字体渲染、图像处理等。 High Fidelity agg_graphics engine是基于AGG库开发的，旨在为C++用户提供一个高性能的2D图形渲染解决方案。它具有高保真度，能够处理复杂的图形任务，并保持图像的质量和清晰度。 agg2.zip中可能包含了这个引擎的所有相关文件，包括源代码、示例程序、文档等。这些文件可以帮助开发者快速理解和使用该引擎。 Agg2.zip的标签"high_fidelity"和"agg"进一步强调了这个引擎的特点。"high_fidelity"表示这个引擎在图形渲染上具有高保真度，能够生成高质量的图像。"agg"则表明这个引擎是基于AGG库开发的。 总的来说，agg2.zip是一个非常有价值的资源，尤其是对于需要进行高质量2D图形渲染的C++开发者。它提供了一个强大的工具集，可以处理各种复杂的图形任务，同时保持了图像的高质量和清晰度。

SCons 3.0.1: Agg库与Mapnik Python绑定教程

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2. bindings/python: 这个部分是Python绑定，提供了与Mapnik交互的接口。有mapnik_symbolizer.os、mapnik_font_engine.os、mapnik_view_transform.os、mapnik_image.os等，用于处理地图符号化、字体渲染、视图变换和...

Scons V3.0.5：Mapnik符号化引擎与agg库升级

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3. `./src/graphics.os`, `./src/font_set.os`, `./src/font_engine_freetype.os`：与图形和字体处理相关的模块，可能涉及字体渲染和选择，以及图形符号和图标管理。 4. `./bindings/python` 目录下的多个文件：...

深入了解开源2D图形库AGG的高效编程接口

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根据提供的文件信息，"copy-agg-jj-2.5.zip_JJ"是一个AGG库的压缩包，包含了名为"main.c"的主源文件和AGG库的版本2.5。这个压缩包可能是某个项目在使用AGG库时的源代码快照或版本备份，文件名中的"JJ"可能是该项目的...

copy-agg-jj-2.5.zip_JJ

2022-09-21 上传
2. **可扩展性**：AGG的模块化设计允许开发者根据需求选择和组合不同的组件，如渲染后台（可以是内存位图、OpenGL、PDF等）、画笔和刷子样式、滤镜等。 3. **高质量渲染**：AGG支持多种抗锯齿级别，可以产生平滑的...

解读--P0 select date(repay_date) repay_dt, count(1) cnt, sum(case when repay_status=2 then 1 end) repay_cnt, sum(plan_principal+plan_interest+plan_period_service+plan_financing_consulting_fee+coalesce(plan_other_fee,0)) plan_amt, sum(actual_principal+actual_interest+actual_period_service+actual_financing_consulting_fee+coalesce(actual_other_fee,0))actual_amount from (select agg.etl_date,agg.contr_num,agg.reloan_user,i.irr_flag from dm_fk.agg_speed_withhold_aggregation agg ---资产表 left join (select contr_num,irr_flag from dm_fk.agg_speed_withhold_irrflag --费率表 where etl_date='2023-01-31' ) i on agg.contr_num=i.contr_num where agg.etl_date in( '2022-12-31' )--上月底未结清C账户 and cast(overdue_pird_num as int)=0 and clear_flag='0' and agg.platform_nm not in('保理分期','恒易融')) t left join dm_fk.o_assets_xd_new_repay_plan_s p --还款计划表 on t.contr_num =p.contract_number where p.etl_date>=date('2023-01-01') and date(date_trunc('month',repay_date))=date_add('day',1,date(t.etl_date))--账单日为1月 --and date_diff('day',date(repay_date),p.etl_date) >=-6 and date_diff('day',date(repay_date),p.etl_date)=0 --账单日当天分区 根据还款计算获取实还金额 group by 1

2023-06-01 上传
2. count：表示统计某个字段的数量。 3. sum：表示求和某个字段的值。 4. case when ... then ... end：表示条件语句，根据条件返回不同的值。 5. left join：表示左连接操作，将两个表按照某个字段进行连接。 6...

class TemporalBlock(nn.Module): """ Temporal block with the following layers: - 2x3x3, 1x3x3, spatio-temporal pyramid pooling - dropout - skip connection. """ def __init__(self, in_channels, out_channels=None, use_pyramid_pooling=False, pool_sizes=None): super().__init__() self.in_channels = in_channels self.half_channels = in_channels // 2 self.out_channels = out_channels or self.in_channels self.kernels = [(2, 3, 3), (1, 3, 3)] # Flag for spatio-temporal pyramid pooling self.use_pyramid_pooling = use_pyramid_pooling # 3 convolution paths: 2x3x3, 1x3x3, 1x1x1 self.convolution_paths = [] for kernel_size in self.kernels: self.convolution_paths.append( nn.Sequential( conv_1x1x1_norm_activated(self.in_channels, self.half_channels), CausalConv3d(self.half_channels, self.half_channels, kernel_size=kernel_size), ) ) self.convolution_paths.append(conv_1x1x1_norm_activated(self.in_channels, self.half_channels)) self.convolution_paths = nn.ModuleList(self.convolution_paths) agg_in_channels = len(self.convolution_paths) * self.half_channels if self.use_pyramid_pooling: assert pool_sizes is not None, "setting must contain the list of kernel_size, but is None." reduction_channels = self.in_channels // 3 self.pyramid_pooling = PyramidSpatioTemporalPooling(self.in_channels, reduction_channels, pool_sizes) agg_in_channels += len(pool_sizes) * reduction_channels # Feature aggregation self.aggregation = nn.Sequential( conv_1x1x1_norm_activated(agg_in_channels, self.out_channels),) if self.out_channels != self.in_channels: self.projection = nn.Sequential( nn.Conv3d(self.in_channels, self.out_channels, kernel_size=1, bias=False), nn.BatchNorm3d(self.out_channels), ) else: self.projection = None网络结构是什么？

2023-06-10 上传
- 3 个卷积路径: 2x3x3 卷积、1x3x3 卷积和 1x1x1 卷积 - dropout 层 - skip 连接 - 可选的 spatio-temporal pyramid pooling 层 - 最后是特征聚合和投影层（如果输入和输出通道数不同） 其中，卷积路径通过 nn....

class Metrics(): def __init__(self): self.reset() def reset(self): self.running_agg = {} self.running_count = {} @torch.no_grad() def step(self, **additional_terms): for term in additional_terms: if term not in self.running_agg: self.running_agg[term] = additional_terms[term].detach() self.running_count[term] = 1 else: self.running_agg[term] += additional_terms[term].detach() self.running_count[term] += 1是什么意思

2023-07-14 上传
它将两个实例变量 `self.running_agg` 和 `self.running_count` 设置为空字典。 3. `@torch.no_grad()` 是一个装饰器，用于指示接下来的 `step()` 方法不需要进行梯度计算。 4. `step(self, **additional_terms)` ...

优化SQL select round( ohbmc.after_actual_amount/zz,0) cost_moneyi ,count(distinct case when ddp.orig_plan_rid = -1 then null else ddp.orig_plan_rid end) AS orig_num ,array_agg (dlt.state) AS loading_state ,count(DISTINCT CASE WHEN ddp.sale_planid = -1 THEN NULL ELSE ddp.sale_planid END) AS saleid_num--销地已计划数量 ,array_agg(dto.state) AS saletransport_state from ( SELECT id AS origin_planid , unnest(cabinet_rule_id) cabinet_rule_id -- 判断 next_plan_id 本身是空和 next_plan_id 为 {} ,unnest(case when (next_plan_id is null or next_plan_id[1] is null) then ARRAY[-1]::integer[] else next_plan_id end) as sale_planid --销地计划 , case when dp.plan_receiver_id is null then -1 else dp.plan_receiver_id end orig_plan_rid --产地计划 FROM ods.ods_durian_delivery_plan as dp left join ods.ods_hl_commodity_category as hcc on hcc.category_id = dp.category_id WHERE dp.type = 'ORIGIN' AND dp.deleted = 99 AND dp.tenant_id = 1 and cabinet_rule_id='{8}'or cabinet_rule_id='{9}'or cabinet_rule_id='{10000005}'---取白心火龙果 AND hcc.category_name = '火龙果') as ddp LEFT JOIN ods.ods_durian_loading_task AS dlt ON dlt.plan_id = ddp.origin_planid and dlt.plan_type='ORIGIN' AND dlt.deleted = 99 LEFT JOIN ods.ods_durian_transport_order AS dto ON dto.plan_id = ddp.sale_planid AND dto.deleted = 99 LEFT JOIN ods.ods_durian_receipt_task AS drt ON drt.plan_id = ddp.sale_planid AND drt.deleted = 99 LEFT JOIN ods.ods_durian_transport_order AS dto1 ON dto1.plan_id = ddp.sale_planid AND dto1.sort_no = 1 AND dto1.deleted = 99 left join (select odlsi.plan_id,sum(odlsi.quantity) zz from ods.ods_durian_loading_sku_item odlsi group by 1) odlsi on odlsi.plan_id=dlt.plan_id left join (select *, unnest(case when ( odbr.bill_main_id is null or odbr.bill_main_id is null) then ARRAY[-1]::integer[] else odbr.bill_main_id end) bill_main_id_r from ods.ods_durian_bill_rel odbr) odbr on odbr.data_id= dlt.plan_id and odbr.data_type='ORIGIN_FEE' left join ods.ods_hl_bill_main_currency ohbmc on ohbmc.bill_main_id=odbr.bill_main_id_r and ohbmc.deleted=99 group by 1;

2023-06-13 上传
2. 合理使用索引：根据实际情况，选择合适的索引可以大大提升查询效率。可以对WHERE子句中的条件表达式所涉及的列添加索引。 3. 减少DISTINCT关键字的使用：DISTINCT关键字会增加查询的开销，如果不必要，可以考虑...

帮我优化这个pgsql select info.pot_type_name,info.ttpw_addr as twlPortName,info.ttpw_addr_code as twlPortCode,info.tmr_material_name,info.ids --库存 ,(select sum(twl_leave_stock) from tms_whe_control where twl_material_id = any(STRING_TO_ARRAY(ids, ','))and twl_port_code=ttpw_addr_code and pot_type_name='合作港口') as twl_leave_stock from ( select port.pot_type_name,ttpw.ttpw_addr ,ttpw.ttpw_addr_code,tmrGroup.tmr_material_name, array_to_string( ARRAY( SELECT unnest(string_to_array(array_to_string(array_agg(distinct(tmr.tmr_id)),','), ',')) ), ',' ) ids from tms_transport_plan_work ttpw,tms_material_ref tmr,tms_material_ref tmrGroup,pub_ports port,tms_transport_plan plan where tmr.tmr_id=ttpw_tmr_id and tmr.parent_tmr_id=tmrGroup.tmr_id and port.pot_regcd=ttpw.ttpw_addr_code and port.pot_cnname=ttpw.ttpw_addr and plan.ttp_id=ttpw.ttpw_ttp_id and plan.ttp_waybill_status >0 AND ttpw.ttpw_plan_amount > 0 GROUP BY pot_type_name,ttpw_addr,ttpw_addr_code,tmrGroup.tmr_material_name ORDER BY ttpw_addr ) as info

2023-06-07 上传
2. 避免使用 `array_agg` 和 `unnest` 函数。这些函数需要进行大量的数组转换操作，非常耗费资源。如果可以，可以考虑使用其他方式将数组数据按逗号拼接。 3. 在使用 GROUP BY 时，只选择必要的列。不需要 GROUP BY...

select tu.id as userId,tu."name",tm.totalUploaded,tm.categories,tm.totalProcessed from t_user tu left join ( select tm.user_id userId, count(tm.user_id) totalUploaded, string_agg(distinct tm.label_id,',') categories, COUNT(CASE WHEN tm.status != 10 THEN 1 END) totalProcessed from t_monitor tm where date(tm.create_time) = CURRENT_DATE group by tm.user_id ) tm on tu.id = tm.userId union all select 0 as userId, 'python上传' as name, count(tm.user_id) totalUploaded, string_agg(distinct tm.label_id,',') categories, COUNT(CASE WHEN tm.status != 10 THEN 1 END) totalProcessed from t_monitor tm where date(tm.create_time) = CURRENT_DATE and tm.user_id = 0 group by tm.user_id;

2023-05-27 上传
具体来说，子查询中的 `count(tm.user_id)` 统计了每个用户上传的文件总数，`string_agg(distinct tm.label_id,',')` 统计了每个用户上传文件所属的类别，`COUNT(CASE WHEN tm.status != 10 THEN 1 END)` 统计了每个...
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