qq_35275007的博客

inspect_solar_fingers.hdev* For the solar industry, a common problem is the damage of* fingers of solar cells. This example shows how to* extract the broken fingers using morphology.* dev_update_off ()dev_close_window ()ImageName := 'solar_cell/

inner_rectangle1.hdev思路：先将脏点剔除，再用最大内接矩形的方法不断分割木板inner_rectangle1* This example program shows how to use inner_rectangle1 to find knot-free* rectangles in a piece of wood. Typically, the piece of wood is then cut along* the rectangles to obtain kn

*求交叉区域intersection (Region1, Region2, RegionIntersection)*求circle与轮廓的交叉点intersection_circle_contour_xld (Contour, CircleRow, CircleColumn, CircleRadius, 0, 6.28318, 'positive', Row, Column)*求直线与轮廓的交叉点（计算的时候会自动延长直线，得到直线的延长线与轮廓的交叉点）intersection_lin.

read_image (Image66134E9b13f720b82e9b, 'G:/6_6134_e9b13f720b82e9b.png')*彩色转灰度rgb1_to_gray(Image66134E9b13f720b82e9b, GrayImage)*提取中间圆区域threshold (GrayImage, Regions, 60, 255)*抠出亮圆区域reduce_domain(GrayImage, Regions, ImageReduced)*图像增强，对比度拉伸emphasiz.

crystal.hdev* crystal.hdev: extraction of hexagonally shaped crystals via local thresholding and region post-processing* dev_close_window ()dev_update_window ('off')* ***** step: acquire image* ****read_image (Image, 'crystal')get_image_size (

count_pellets.hdev思想：核心代码很简单，但这里有个技巧；阈值分割后有石头连通在一起，先打散，腐蚀缩小区域，再打散，以同样参数膨胀恢复区域，能很好的分开重叠区域* This programs demonstrates the use of basic morphology* operators.* The aim of the program is to detect each single pellet* (bright particle on a darker backg

color_fuses.hdev将图像拆分为RGB，再转到HSV，抠出区域后，根据饱和度划分阈值断，以此分辨不同的颜色* color_fuses.hdev: classify fuses by colordev_update_window ('off')* ***** step: set up fuse properties and hue ranges* ****FuseColors := ['Orange','Red','Blue','Yellow','Green']FuseTyp

clip.hdev这个案例的核心是求物体的角度，核心算子是orientation_region为什么它能判定物体的方向勒，可能是halcon制定了某种规则；具体分析见以下两篇博文：Halcon学习（5.1）：orientation_region算子分析_SeventhBlue-优快云博客_halcon orientation_regionhalcon区域方向算子orientation_region参数Phi理解_hsjuzhn的博客-优快云博客_halcon orientation_re

//判断一张halcon图像是否为空int Halcon_img_if_empty(HObject img){ //↓↑↓↓↓↓判断图像是否为空 if (!img.IsInitialized())//IsInitialized只能用来判断图像有无初始化,不能判断图像空不空或有没有 return -1; HTuple hv_n; HObject ho_Null; GenEmptyObj(&ho_Null); TestEqualObj(img, ho_Null, &hv_.

circles.hdev* * The edges in the image are segmented into lines and circles.* For the edges that are part of a circle, the circle parameters* are estimated and the resulting circle is displayed.read_image (Image, 'double_circle')* * Init windowde

check_hazelnut_wafer.hdev* This example demonstrates a quality inspection on hazelnut wavers.* Using the morphology tools the waver is extracted and examined* according to a few shape features like Rectangularity and AreaHoles.* This program also sho

check_fish_stick_dimension.hdev说明： 这个案例不详细分析了，方法很简单，用旋转矩形框测量长度；但里面有一张图片需要注意，图片里面有4个鱼棒，有一个鱼棒在图片的边缘，没有拍全，里面的处理方法能把这个介于边缘的图片筛选出来。...

check_bottle_crate.hdev* This example counts bottles in bottle crates of returned empties.* With diffuse front light, correctly inserted bottles can* be segmented relatively easy with a simple thresholding operation,* followed by some basic morpholog

check_blister_mixed.hdev说明：这个案例很有意思，既运用了Blob分析定位+决策判断，也运用了高斯混合模型进行训练分类；把这个案例反复读几遍，抠细节，体会整个解题思路；* This example demonstrates an application from the pharmaceutical* industry. The task is to check the content of manually filled* blisters. The first i

check_blister.hdev* This example demonstrates an application from the pharmaceutical* industry. The task is to check the content of automatically filled* blisters. The first image (reference) is used to locate the chambers* within a blister shape as.

ball_seq.hdev* ball_seq.hdev: Inspection of Ball Bonding* dev_update_off ()ImageNames := 'die/' + ['die_02','die_03','die_04','die_07']dev_set_colored (12)read_image (Bond, ImageNames[0])get_image_size (Bond, Width, Height)dev_close_window ()dev

ball.hdev* ball.hdev: Inspection of Ball Bonding* dev_update_window ('off')dev_close_window ()dev_open_window (0, 0, 728, 512, 'black', WindowID)read_image (Bond, 'die/die_03')dev_display (Bond)set_display_font (WindowID, 14, 'mono', 'true', 'fal

autobahn.hdev* autobahn.hdev: Fast detection of lane markers* dev_update_window ('off')dev_close_window ()dev_open_window (0, 0, 768, 575, 'black', WindowID)MinSize := 30get_system ('init_new_image', Information)set_system ('init_new_image', 'fal

read_image (Image002, 'G:/learning_halcon_file/img/001.png')decompose3(Image002, R, G, B)trans_from_rgb(R, G, B, H, S, V, 'hsv')binary_threshold(H, Region1, 'max_separability', 'light', UsedThreshold)connection(Region1, ConnectedRegions1)select_s.

dev_close_window ()dev_update_off ()set_system ('seed_rand', 42)* * *** 0) SET INPUT/OUTPUT PATHS **** get_system ('example_dir', PathExample)* Give all folders that contain images.ImageBaseFolder := PathExample + '/images/food/'RawImageFold..

* * This example is part of a series of examples, which summarizes* the workflow for DL classification. It uses the MVTec pill dataset.* The four parts are:* 1. Dataset preprocessing* 2. Training of the model.* 3. Evaluation of the trained model.* .

* * This example is part of a series of examples, which summarizes* the workflow for DL classification. It uses the MVTec pill dataset.* * The four parts are:* 1. Dataset preprocessing.* 2. Training of the model.* 3. Evaluation of the trained model.

* * This example is part of a series of examples, which summarizes* the workflow for DL classification. It uses the MVTec pill dataset.* * The four parts are:* 1. Dataset preprocessing.* 2. Training of the model.* 3. Evaluation of the trained model.

assign：对数据赋值，对数组的初始化。但不能对数组中的某一个值进行赋值。举例：Tuple1:=[1,0,3,4,5,6,7,8,9]//对数组进行初始化Val:=sin(1.2)+cos(1.2) //对某一个值进行赋值 Tuple2:=[] //数组定义assign_at：对数组中的某一个值进行赋值。举例：Tuple1...

read_image (T0153a1b02bef525641, '../img/car_id.jpg')dev_close_window ()dev_open_window (0, 0, 512, 512, 'black', WindowHandle)*拆分为三个通道decompose3 (T0153a1b02bef525641, R, G, B)*转换到hsv控件trans_from_rgb (R, G, B, ImageResult1, ImageResult2, ImageResu.

find_pads.hdevdev_update_pc ('off')dev_update_window ('off')dev_update_var ('off')read_image (Image, 'die_pads')dev_close_window ()get_image_size (Image, Width, Height)dev_open_window (0, 0, Width * 2, Height * 2, 'black', WindowHandle)dev_set_pa

check_hazelnut_wafers.hdevread_image (Image, 'food/hazelnut_wafer_01')dev_close_window ()dev_open_window_fit_image (Image, 0, 0, -1, -1, WindowHandle)dev_update_window ('off')dev_set_line_width (3)dev_set_draw ('margin')set_display_font (WindowHan

circles.hdevread_image (Image, 'double_circle')* * Init windowdev_close_window ()get_image_size (Image, Width, Height)dev_open_window (0, 0, Width, Height, 'black', WindowHandle)* * Segment a region containing the edges*快速阈值分割fast_threshold (Im

connection.hdevread_image (Image, 'mreut')*二值化，可用灰度直方图进行可视化threshold (Image, Region, 190, 255)*将不相连的区域都分割成单独的区域connection (Region, ConnectedRegions)*根据面积进行特征选择，可用特征直方图进行可视化，只选出有道路的区域select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 5

count_fish_sticks.hdev* This program performs a completeness check by counting the* fish sticks contained in each package.* Here, one-dimensional measuring tools are used to determine* the boundary between adjacent fish sticks.* dev_update_off (.

count_pellets.hdev* This programs demonstrates the use of basic morphology* operators.* The aim of the program is to detect each single pellet* (bright particle on a darker background).* dev_update_off ()read_image (Image, 'pellets')dev_close_win

crystal.hdevdev_close_window ()dev_update_window ('off')* ***** step: acquire image* ****read_image (Image, 'crystal')get_image_size (Image, Width, Height)dev_open_window_fit_image (Image, 0, 0, Width, Height, WindowID)set_display_font (WindowID

思路：边缘提取---》阈值分割（1-255）---》分割连通域---》将每个小区域，根据线的曲率分割成多个线段---》根据线段生成多边形edge_segments.hdevdev_update_off ()dev_close_window ()* ***** step: acquire image* ****read_image (Image, 'mreut')get_image_size (Image, Width, Height)dev_open_window_fit_im.

思路：二进制阈值分割图像---》用圆形区域进行闭运算---》背景差分，得出不同点---》用矩形开操作去除小点，保留真正的差异点---》得到真正差异点的面积和中心点fin.hdevdev_update_window ('off')read_image (Fins, 'fin' + [1:3])get_image_size (Fins, Width, Height)dev_close_window ()dev_open_window (0, 0, Width[0], Height[0], 'b

measure_ball_bond.hdevdev_update_off ()dev_close_window ()FileName := 'bonds/ball_bond_ccd_'read_image (Image, FileName + 1$'02')dev_open_window_fit_image (Image, 0, 0, -1, -1, WindowHandle)dev_set_line_width (2)set_display_font (WindowHandle, .

inspect_bottle_mouth.hdev巧妙运用了极坐标变换法，细节很精细，值得学习* tuning parametersSmoothX := 501ThresholdOffset := 25MinDefectSize := 50* * initializationPolarResolution := 640RingSize := 70get_system ('store_empty_region', StoreEmptyRegion)set_system ('stor

对应示例程序：detect_mura_defects_texture.hdev目标：实现高纹理图像中缺陷的检测（黑色）。思路为：1.对彩色图像进行R G B分解，选取B作为后续图像。2. 生成背景模板，将图像傅里叶变换到频域中，通过高斯滤波，然后傅里叶反变换回来，得到的图像就是背景模板。3 .背景差分。采用sub_image函数进行图像差分,增强两幅图像的差异4 .分水岭算法分割，在分割之前采用中值滤波来抑制小斑点或细线。分水岭后，图像分割为多个轮廓（region）。5 .计算多个轮廓re

思路：读取图片--》图片转化为灰度---》快速傅里叶变换把图像转到频域---》频域滤波器进行卷积---》快速傅里叶反变换---》用一个矩形掩膜计算像素点的灰度范围---》求图片灰度值的最大和最小值和变换范围---》利用全局阈值对图像进行分割---》连通区域---》根据面积筛选区域---》合并区域---》使用圆形元素对区域进行闭运算---》连通区域---》根据面积筛选区域---》计算区域的面积以及中心位置---》显示结果例程：detect_indent_fft.hdev说明：这个程序展示了如何利用快速

*读取披萨图片，一个碗中好多披萨read_image(Image1,'./color/pizza_03.png') *将图片拆分成RGB的三通道decompose3(Image1, red, green, blue)*将RGB分别转到HSVtrans_from_rgb(red, green, blue, ImageH, ImageS, ImageV, 'hsv')*阈值分割S通道图片threshold (ImageS, Regions1, 46, 60)*连接各区域connection(.