如何根據(jù)視野范圍選擇遠心鏡頭

視野指使用照相機以后看到的物體側(cè)的范圍，首先我們先了解一下視野的計算方法。

如何根據(jù)視野范圍選擇遠心鏡頭

視野指使用照相機以后看到的物體側(cè)的范圍，首先我們先了解一下視野的計算方法。視野的大小通常與照相機的有效區(qū)域及倍率有關(guān)，一般知其二便可選擇，不同的相機靶面大小不一，其所呈視野也不盡相同，通常來說視野可以從以下公式算得：有了視野之后我們即可找到滿足要求視野的鏡頭，通常來說，對于雙遠心鏡頭和物方遠心鏡頭其最大視野不變，我們只需再確定使用相機之后便可選擇合適的倍率。 例如光虎視覺的OTL11.5-20-65C，其倍率，相機靶面和工作距都已明確標(biāo)明，11.5表示2/3”靶面的相機，20指其倍率為2，65為其工作距離（單位mm），那么如何根據(jù)這些參數(shù)看是否滿足我們的需求呢？ 例如，我們需要檢測一個尺寸為4mm的螺母外徑，但是由于位置關(guān)系可能需要5.5mm左右的視野，相機就用2/3”靶面的那么其2倍的遠心鏡頭是否滿足要求只需把視場和傳感器尺寸帶入公式（1）即可，算出其倍率為是2.09。而2倍的視野能達到5.75，所以滿足要求。當(dāng)然，客戶的需求千變?nèi)f化，為了更好的服務(wù)顧客，我們還有0.5倍、0.8倍、1倍的等等，為了滿足差異化需求我們還有高分辨率和大景深兩種類型！ 當(dāng)然還有鏡頭像面與靶面不一樣的時候，其視野該如何計算呢？ 第一種，鏡頭的像面大于靶面 比如，我們選擇TTL18.5-45-65C鏡頭，其像面尺寸為18.5，倍率為0.411但是相機靶面為11.5，由于相機為呈像設(shè)備其視野=相機靶面尺寸/倍率也就是11.5/0.411= 27.98mm，而該鏡頭視野能達到45mm。所以會出現(xiàn)如圖1所示情況，其鏡頭的部分視野會被浪費。圖1.像面尺寸大于靶面尺寸第二種，像面尺寸小于相機靶面尺寸 圖2.像面尺寸小于靶面尺寸比如我們用TTL11.5-45-65C，其倍率為0.256，但是我們用1’’的相機時，帶入公式（1），算得其最大視野為18.5/0.256=72.27m。 然而，如圖2所示，sensor上的四周會有光線盲點，其所成現(xiàn)出來的圖像四角為黑色，俗稱暗角，黑影。 第三種，優(yōu)選鏡頭與相機靶面契合 所以我們在選擇鏡頭時要注意與相機的配合，只有合適的相機與鏡頭配合才會既不浪費鏡頭性能也不影響成像質(zhì)量，如圖3，一般鏡頭靶面尺寸稍大于sensor傳感器對角線尺寸即可，這樣既不浪費鏡頭性能，也能有更好的成像質(zhì)量。 圖3. 像面尺寸等于靶面尺寸 當(dāng)然這只是鏡頭選擇要素的一部分，具體還要考慮分辨率和工作距離等參數(shù)，詳情可參考往期內(nèi)容“遠心鏡頭如進何進行參數(shù)選型”。 光虎光學(xué)專業(yè)生產(chǎn)由德國設(shè)計的工業(yè)鏡頭。以高精度雙遠心鏡頭為核心，涵蓋高性能FA定焦鏡頭、變倍鏡頭等產(chǎn)品?？蓪崿F(xiàn)為客戶定制化研發(fā)生產(chǎn)。光虎光學(xué)還代理歐美日機器視覺全系列產(chǎn)品。如面陣與線掃工業(yè)相機、智能相機、3D相機、紅外與紫外相機、光源、圖像采集卡、機器視覺軟件及其他周邊產(chǎn)品。http://spoilthemsilly.com/

【視覺知識】紫外線在機器視覺中的使用

紫外線在機器視覺中的使用 什么是UV紫外線？ UV是波長范圍為10-400nm的電磁輻射，分為三個不同的波段。在300-400nm之間，光譜的波段稱為近UV波段，分為UV-A（315-400nm）和UV-B（280-315）子波段。在300nm以下，UV-C波段覆蓋100-280nm的波長。在機器視覺應(yīng)用中，最常用的是UV-A波段中的波長，最常見的是365nm和395nm波長。 紫外線可用于機器視覺應(yīng)用中，以檢測使用可見光無法檢測到的特征。由于紫外線被許多材料吸收，因此可以捕獲產(chǎn)品表面的圖像，并且由于它的波長比可見光短，因此會被產(chǎn)品上的表面特征所散射。 光虎視覺認為紫外線可以以兩種不同的方式應(yīng)用于機器視覺系統(tǒng)。在UV照明反射成像應(yīng)用中，將UV光施加到對象并使用對UV敏感的單色或彩色相機捕獲。在紫外熒光成像中，對象的表面再次用紫外光照射。在添加了熒光增白劑的產(chǎn)品中，例如油漆，塑料，印刷油墨和染料，這些熒光材料將吸收紫外線輻射，然后再輻射更長的擴散波長。吸收光譜和發(fā)射光譜的最大譜帶位置之間的波長差稱為斯托克斯位移（圖1）。<img 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

視覺引導(dǎo)抓取應(yīng)用中的手眼系統(tǒng)介紹

什么是手眼系統(tǒng) 在機器人技術(shù)中，手眼系統(tǒng)指的是一個視覺系統(tǒng)直接連接到機械臂上，通常位于最后一個關(guān)節(jié)的后面。換句話說，視覺系統(tǒng)與機械臂一起移動。這種方法在機器人技術(shù)中是相當(dāng)新的，并且為較為傳統(tǒng)的視覺系統(tǒng)的固定安裝提供了替代方案，通常高于機器人的工作距離。雖然手眼方法可能使許多應(yīng)用受益，但它的潛力在很大程度上受到標(biāo)準(zhǔn)視覺系統(tǒng)的限制。 手眼系統(tǒng)的優(yōu)點 手眼方法在特定的應(yīng)用中非常有用，因為它比固定視覺系統(tǒng)方法有更多的優(yōu)勢。首先，它可以覆蓋更大的掃描量，因為它可以專門針對感興趣的領(lǐng)域，靈活性更好。因此，視覺系統(tǒng)只受機器人的范圍限制而不受其自身掃描體積的限制。 由于掃描距離是決定掃描精度的主要參數(shù)之一，相對于掃描距離較大的固定視覺系統(tǒng)，掃描距離較短的手眼視覺系統(tǒng)可以提供更高層次的細節(jié)。如果客戶有一個裝滿小零件的大箱子，那么通過使用安裝在機械臂上的掃描范圍較短的小型掃描儀，就可以實現(xiàn)最高水平的細節(jié)。因此，機器人可以通過掃描儀從最佳距離拍攝箱子內(nèi)的物體，并從不同的角度和角度掃描各個零件。但是，當(dāng)一個固定的視覺系統(tǒng)能夠提供符合預(yù)期的細節(jié)水平時(如Photoneo PhoXi 3D Scanner XL)，建議選擇固定的視覺系統(tǒng)的方法。 從近距離和任何角度掃描料框的能力也有效地消除了與陰影相關(guān)的挑戰(zhàn)。用固定的視覺系統(tǒng)安裝在料框上方很可能會導(dǎo)致料框的某些部分投射陰影，從而妨礙對某些部件進行相應(yīng)的點云采集。在這種情況下，需要找到掃描儀相對于料框的最佳位置，有時甚至需要手動重新放置零件。手眼系統(tǒng)就可以輕松克服這一挑戰(zhàn)。 光虎視覺認為手眼系統(tǒng)是一種越來越受歡迎的方法，在越來越多的機器人應(yīng)用中站穩(wěn)了腳跟。隨著協(xié)作機器人的興起，手眼系統(tǒng)的優(yōu)勢變得更加明顯。 手眼系統(tǒng)的缺點 首先，相對于固定的視覺系統(tǒng)而言，手眼系統(tǒng)的安裝更加困難。此外，要找到一種最佳的方式來處理之前已部署好的線纜也更加困難。 手眼系統(tǒng)的其中一個缺點是與傳感器發(fā)生碰撞的風(fēng)險較高，特別是當(dāng)它的尺寸較大時，很難在機器人手臂上找到視覺系統(tǒng)的最佳安裝位置。另一個主要的缺點是機器人的運動會引起振動，這是機器視覺系統(tǒng)無法處理的。因此，在掃描采集過程中，機械臂需要保持靜止，這可能會延長周期時間。因此，對于時間緊迫的應(yīng)用場合，手眼方法并不是最恰當(dāng)?shù)慕鉀Q方案。 在光虎視覺看來，許多情況下，與其選擇安裝步驟比較復(fù)雜的手眼系統(tǒng)，不如選擇帶有高質(zhì)量3D視覺系統(tǒng)的方法，如Photoneo PhoXi 3D Scanner XL。 當(dāng)然，在一些應(yīng)用程序中，手眼法是最好的選擇。在這些情況下，上述手眼系統(tǒng)的缺點可以通過Photoneo的革命性的“平行結(jié)構(gòu)光”技術(shù)來克服，這是唯一一種能夠有效抵抗振動的3D傳感方法，從而實現(xiàn)對移動物體的高質(zhì)量3D掃描，而不需要運動工件。 手眼協(xié)調(diào)運動 在Photoneo 3D相機MotionCam-3D中實現(xiàn)的“平行結(jié)構(gòu)光”技術(shù)，可以實現(xiàn)手眼協(xié)調(diào)運動，無需任何權(quán)衡。MotionCam-3D是唯一能夠在機械臂運動期間提供高質(zhì)量掃描的3D視覺系統(tǒng)。該相機高度抗震動，不需要停止機器人。 由于該技術(shù)能夠捕獲移動速度高達144公里/小時的物體，所以機械臂運動引起的振動對3D點云數(shù)據(jù)的質(zhì)量沒有任何影響。使用該技術(shù)最大的好處是，機器人不需要停下來進行掃描，與市場上所有其他技術(shù)相比，這大大縮短了循環(huán)時間。因此，它為新的應(yīng)用打開了大門，例如物體位置的即時跟蹤。 這一技術(shù)在協(xié)作機器人的背景下也仍然有意義，因為使用固定視覺系統(tǒng)能檢測的缺陷通常非常有限。協(xié)作機器人本身比工業(yè)機器人慢，當(dāng)與手眼方法結(jié)合時，周期時間甚至更長。MotionCam-3D克服了這一限制，如果連接到協(xié)作機器人的手臂上，也能完美地工作。 Motioncam-3D是任何機器人任務(wù)的終極解決方案——無論是靜態(tài)場景還是動態(tài)場景?；谝曈X系統(tǒng)的手眼方法的局限性現(xiàn)在是一個過去的問題。MotionCam-3D為快速掃描提供了很高的分辨率和精度，有效抵抗振動，比以往更短的周期。 【來源：Photoneo 官網(wǎng)】光虎光學(xué)專業(yè)生產(chǎn)由德國設(shè)計的工業(yè)鏡頭。以高精度雙遠心鏡頭為核心，涵蓋高性能FA定焦鏡頭、變倍鏡頭等產(chǎn)品?？蓪崿F(xiàn)為客戶定制化研發(fā)生產(chǎn)。光虎光學(xué)還代理歐美日機器視覺全系列產(chǎn)品。如面陣與線掃工業(yè)相機、智能相機、3D相機、紅外與紫外相機、光源、圖像采集卡、機器視覺軟件及其他周邊產(chǎn)品。http://spoilthemsilly.com/

視覺引導(dǎo)抓取應(yīng)用中的手眼系統(tǒng)介紹

什么是手眼系統(tǒng) 在機器人技術(shù)中，手眼系統(tǒng)指的是一個視覺系統(tǒng)直接連接到機械臂上，通常位于最后一個關(guān)節(jié)的后面。換句話說，視覺系統(tǒng)與機械臂一起移動。這種方法在機器人技術(shù)中是相當(dāng)新的，并且為較為傳統(tǒng)的視覺系統(tǒng)的固定安裝提供了替代方案，通常高于機器人的工作距離。雖然手眼方法可能使許多應(yīng)用受益，但它的潛力在很大程度上受到標(biāo)準(zhǔn)視覺系統(tǒng)的限制。 手眼系統(tǒng)的優(yōu)點 手眼方法在特定的應(yīng)用中非常有用，因為它比固定視覺系統(tǒng)方法有更多的優(yōu)勢。首先，它可以覆蓋更大的掃描量，因為它可以專門針對感興趣的領(lǐng)域，靈活性更好。因此，視覺系統(tǒng)只受機器人的范圍限制而不受其自身掃描體積的限制。 由于掃描距離是決定掃描精度的主要參數(shù)之一，相對于掃描距離較大的固定視覺系統(tǒng)，掃描距離較短的手眼視覺系統(tǒng)可以提供更高層次的細節(jié)。如果客戶有一個裝滿小零件的大箱子，那么通過使用安裝在機械臂上的掃描范圍較短的小型掃描儀，就可以實現(xiàn)最高水平的細節(jié)。因此，機器人可以通過掃描儀從最佳距離拍攝箱子內(nèi)的物體，并從不同的角度和角度掃描各個零件。但是，當(dāng)一個固定的視覺系統(tǒng)能夠提供符合預(yù)期的細節(jié)水平時(如Photoneo PhoXi 3D Scanner XL)，建議選擇固定的視覺系統(tǒng)的方法。 從近距離和任何角度掃描料框的能力也有效地消除了與陰影相關(guān)的挑戰(zhàn)。用固定的視覺系統(tǒng)安裝在料框上方很可能會導(dǎo)致料框的某些部分投射陰影，從而妨礙對某些部件進行相應(yīng)的點云采集。在這種情況下，需要找到掃描儀相對于料框的最佳位置，有時甚至需要手動重新放置零件。手眼系統(tǒng)就可以輕松克服這一挑戰(zhàn)。 光虎視覺認為手眼系統(tǒng)是一種越來越受歡迎的方法，在越來越多的機器人應(yīng)用中站穩(wěn)了腳跟。隨著協(xié)作機器人的興起，手眼系統(tǒng)的優(yōu)勢變得更加明顯。 手眼系統(tǒng)的缺點 首先，相對于固定的視覺系統(tǒng)而言，手眼系統(tǒng)的安裝更加困難。此外，要找到一種最佳的方式來處理之前已部署好的線纜也更加困難。 手眼系統(tǒng)的其中一個缺點是與傳感器發(fā)生碰撞的風(fēng)險較高，特別是當(dāng)它的尺寸較大時，很難在機器人手臂上找到視覺系統(tǒng)的最佳安裝位置。另一個主要的缺點是機器人的運動會引起振動，這是機器視覺系統(tǒng)無法處理的。因此，在掃描采集過程中，機械臂需要保持靜止，這可能會延長周期時間。因此，對于時間緊迫的應(yīng)用場合，手眼方法并不是最恰當(dāng)?shù)慕鉀Q方案。 在光虎視覺看來，許多情況下，與其選擇安裝步驟比較復(fù)雜的手眼系統(tǒng)，不如選擇帶有高質(zhì)量3D視覺系統(tǒng)的方法，如Photoneo PhoXi 3D Scanner XL。 當(dāng)然，在一些應(yīng)用程序中，手眼法是最好的選擇。在這些情況下，上述手眼系統(tǒng)的缺點可以通過Photoneo的革命性的“平行結(jié)構(gòu)光”技術(shù)來克服，這是唯一一種能夠有效抵抗振動的3D傳感方法，從而實現(xiàn)對移動物體的高質(zhì)量3D掃描，而不需要運動工件。 手眼協(xié)調(diào)運動 在Photoneo 3D相機MotionCam-3D中實現(xiàn)的“平行結(jié)構(gòu)光”技術(shù)，可以實現(xiàn)手眼協(xié)調(diào)運動，無需任何權(quán)衡。MotionCam-3D是唯一能夠在機械臂運動期間提供高質(zhì)量掃描的3D視覺系統(tǒng)。該相機高度抗震動，不需要停止機器人。 由于該技術(shù)能夠捕獲移動速度高達144公里/小時的物體，所以機械臂運動引起的振動對3D點云數(shù)據(jù)的質(zhì)量沒有任何影響。使用該技術(shù)最大的好處是，機器人不需要停下來進行掃描，與市場上所有其他技術(shù)相比，這大大縮短了循環(huán)時間。因此，它為新的應(yīng)用打開了大門，例如物體位置的即時跟蹤。 這一技術(shù)在協(xié)作機器人的背景下也仍然有意義，因為使用固定視覺系統(tǒng)能檢測的缺陷通常非常有限。協(xié)作機器人本身比工業(yè)機器人慢，當(dāng)與手眼方法結(jié)合時，周期時間甚至更長。MotionCam-3D克服了這一限制，如果連接到協(xié)作機器人的手臂上，也能完美地工作。 Motioncam-3D是任何機器人任務(wù)的終極解決方案——無論是靜態(tài)場景還是動態(tài)場景?；谝曈X系統(tǒng)的手眼方法的局限性現(xiàn)在是一個過去的問題。MotionCam-3D為快速掃描提供了很高的分辨率和精度，有效抵抗振動，比以往更短的周期。 【來源：Photoneo 官網(wǎng)】光虎光學(xué)專業(yè)生產(chǎn)由德國設(shè)計的工業(yè)鏡頭。以高精度雙遠心鏡頭為核心，涵蓋高性能FA定焦鏡頭、變倍鏡頭等產(chǎn)品?？蓪崿F(xiàn)為客戶定制化研發(fā)生產(chǎn)。光虎光學(xué)還代理歐美日機器視覺全系列產(chǎn)品。如面陣與線掃工業(yè)相機、智能相機、3D相機、紅外與紫外相機、光源、圖像采集卡、機器視覺軟件及其他周邊產(chǎn)品。http://spoilthemsilly.com/

光虎光學(xué)雙遠心鏡頭和遠心鏡頭手冊2023年第2版

光虎光學(xué)技術(shù)手冊 2021年第4版

為什么使用圖像采集卡？

達到數(shù)據(jù)傳輸所能達到的最大吞吐量是工業(yè)和工廠自動化的關(guān)鍵標(biāo)準(zhǔn)之一。提高傳感器分辨率和幀率有助于實現(xiàn)針對更加高速和高精度物體的捕捉，但與此同時，會導(dǎo)致帶寬達到極限，從而帶來傳輸上的問題。目前實現(xiàn)的高帶寬接口，例如25GigE、50GigE接口，就需要將相機連接到PC上配置的圖像采集卡，從而完成圖像數(shù)據(jù)的傳輸。所以在工業(yè)環(huán)境中使用圖像采集卡，在高傳輸速率和多相機同步處理方面的應(yīng)用中，是有實際意義的。 圖像采集卡的意義 了解圖像采集卡的功能和意義是比較重要的，這樣就可以更好地了解其是否可以用于特定的成像系統(tǒng)或應(yīng)用。從本質(zhì)上來說，圖像采集卡能夠?qū)崿F(xiàn)具有同步特性的高分辨率圖像的高速實時采集。 圖像采集卡利用PCI總線的功能來管理從相機到PC存儲器的圖像數(shù)據(jù)負載，直接將圖像數(shù)據(jù)從直接存儲器訪問（DMA）移動到PC的RAM內(nèi)存，避免了CPU的重載，從而釋放CPU來執(zhí)行其他任務(wù)。 同時他它可以接收觸發(fā)信號和編碼器信號的輸入，來達到和運動目標(biāo)的無縫同步。 圖像采集卡的優(yōu)勢 使用圖像采集卡最大的優(yōu)勢在于能夠幫助視覺系統(tǒng)實現(xiàn)最好的傳輸質(zhì)量，即最大化傳輸速度。 可以將所有的采集和I/O同步任務(wù)交給單個圖像采集卡 可以管理連接的所有外部設(shè)備，包括觸發(fā)和同步 高傳輸速率，CoaXPress采集卡目前可達50Gbps 可以使用圖像采集卡進行圖像預(yù)處理 可以釋放CPU來進行其他任務(wù) <section style="margin:0px

為什么使用圖像采集卡？

光虎視覺代理的圖像采集卡最常用于高速應(yīng)用場景，其對圖像傳輸速率的要求最大。

丁丁（蘇州）

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運動分析介紹及其應(yīng)用

運動分析基于計算機視覺、圖像處理、高速攝影和機器視覺，研究來自圖像序列的兩個或多個連續(xù)圖像的方法和應(yīng)用，例如由攝像機或高速相機捕獲圖像中的明顯運動的信息。 在最簡單的情況下，運動分析處理可以是檢測運動，即找到圖像中物體正在移動的點。更復(fù)雜的處理類型可以是隨著時間的推移，來跟蹤圖像中的特定對象，將場景中移動的同一剛性對象的點進行分組，或者確定圖像中每個點的運動幅度和方向。產(chǎn)生的信息通常與序列中特定時間點的特定圖像相關(guān)，這意味著運動分析可以產(chǎn)生關(guān)于運動的時間相關(guān)信息。 運動分析的應(yīng)用范圍相當(dāng)廣泛，例如監(jiān)視、醫(yī)學(xué)、電影業(yè)、汽車碰撞安全、彈道槍支研究、生物科學(xué)、火焰?zhèn)鞑サ取?運動分析的應(yīng)用 最簡單的運動分析應(yīng)用之一是檢測與場景中運動點相關(guān)的圖像點。這種處理的典型結(jié)果是一個二值圖像，其中與場景中移動點相關(guān)的所有圖像點（像素）都設(shè)置為1，所有其他點都設(shè)置為0，然后對該二值圖像進行進一步處理，例如，刪除噪聲、對相鄰像素進行分組并標(biāo)記對象。分析可以使用多種方法完成：兩個主要方法為差分法和背景分割法。 1.人體運動分析 在醫(yī)學(xué)、運動、視頻監(jiān)控、物理治療、和運動機能學(xué)等領(lǐng)域，人體運動分析已成為一種調(diào)查和診斷工具。人體運動分析可分為人體活動識別、人體運動跟蹤、身體及身體部位運動分析三類。 人類活動識別最常用于視頻監(jiān)控，特別是用于安全目的的自動運動監(jiān)控。該領(lǐng)域的大多數(shù)努力都依賴于狀態(tài)空間方法，其中對靜態(tài)姿勢序列進行統(tǒng)計分析并與建模運動進行比較。模板匹配是一種替代方法，可將靜態(tài)形狀模式與預(yù)先存在的原型進行比較。 人體運動跟蹤可以在兩個或三個維度上進行。根據(jù)分析的復(fù)雜性，人體的表示范圍從基本的簡筆畫到體積模型。跟蹤依賴于視頻連續(xù)幀之間圖像特征的對應(yīng)關(guān)系，同時考慮位置、顏色、形狀和紋理等信息。邊緣檢測可以通過比較相鄰像素的顏色或?qū)Ρ榷葋韴?zhí)行，特別是尋找不連續(xù)性或快速變化。三維跟蹤與二維跟蹤基本相同，但增加了空間校準(zhǔn)因素。 身體部位的運動分析在醫(yī)學(xué)領(lǐng)域至關(guān)重要。在姿勢和步態(tài)分析中，關(guān)節(jié)角度用于跟蹤身體部位的位置和方向。步態(tài)分析還用于運動以優(yōu)化運動表現(xiàn)或識別可能導(dǎo)致受傷或拉傷的運動。不需要使用光學(xué)標(biāo)記的跟蹤軟件在這些領(lǐng)域尤其重要，在這些領(lǐng)域中使用標(biāo)記可能會阻礙自然運動。 2.制造中的運動分析 運動分析也適用于制造過程。使用高速攝像機和運動分析軟件，人們可以監(jiān)控和分析裝配線和生產(chǎn)機器，以檢測效率低下或故障。棒球棒和曲棍球棒等運動器材的制造商也使用高速視頻分析來研究拋射物的影響。此類研究的實驗設(shè)置通常使用觸發(fā)設(shè)備、外部傳感器（例如加速度計、應(yīng)變儀）、數(shù)據(jù)采集模塊、高速攝像機和用于存儲同步視頻和數(shù)據(jù)的計算機。運動分析軟件計算距離、速度、加速度和變形角等參數(shù)作為時間的函數(shù)。然后使用這些數(shù)據(jù)來設(shè)計設(shè)備以獲得最佳性能。 3.運動分析的其他應(yīng)用 運動分析軟件的物體和特征檢測能力可用于計數(shù)和跟蹤粒子，如細菌、病毒、離子聚合物-金屬復(fù)合材料、微米大小的聚苯乙烯珠、蚜蟲等。 【來源：光虎光學(xué)內(nèi)部培訓(xùn)資料】 光虎光學(xué)專業(yè)生產(chǎn)由德國設(shè)計的工業(yè)鏡頭。以高精度雙遠心鏡頭為核心，涵蓋高性能FA定焦鏡頭、變倍鏡頭等產(chǎn)品。可實現(xiàn)為客戶定制化研發(fā)生產(chǎn)。光虎光學(xué)還代理歐美日機器視覺全系列產(chǎn)品。如面陣與線掃工業(yè)相機、智能相機、3D相機、紅外與紫外相機、光源、圖像采集卡、機器視覺軟件及其他周邊產(chǎn)品。http://spoilthemsilly.com/