用OpenMV自动识别颜色序列
主控：OpenMV3 M7摄像头(STM32F765)
IDE：OPENMV官方IDE
我将Capstone期间做的代码上传到Gitee啦，后续还会陆续上传代码上去的，地址：Capstone，如果对你有帮助的话不妨点个⭐支持一下~
OpenMV需要识别颜色顺序的位置是第一个区域，区域中的物料摆放如下图所示
对于OpenMV识别颜色，很大一部分人都是采用了使用阈值调节，通过拍回来的照片修改阈值得到想要的区域，这种情况一般都会存在调试时间长，受光照影响大的限制。
通过分析本项目的颜色识别部分，小车到达粗加工区后开始颜色识别，识别到序列之后在开始抓取，车子在识别颜色时的位置是大致不变的，所以本文将阈值->颜色识别的流程转化为区域->颜色的识别，可以减少调试时间，但解决不了受光照影响的问题。
OpenMV使用的是LAB色域，如下图所示
根据任务要求，物料颜色分别有红绿蓝三色，则对上图进行观察对比，可以发现红绿蓝三色均在球形的外端，有非明显的区别，比如红色的a值为正数，蓝色的b值为负数，绿色的a值为负数。因此可以根据这些特征筛选出所需颜色。
在实际运行过程中，我们将OpenMV的画面分成上下两部分，然后在每次运行到粗加工区位置时都运行一遍识别程序，避免因颜色识别不全卡在粗加工区。
在此处放出获取区域颜色的代码
function：GetColor
def GetColor(Rev_Num): global Left_x global Middle_x global Right_x global Left_y global Middle_y global Right_y global LeftMean_Shang global MiddleMean_Shang global RightMean_Shang global LeftMean_Xia global MiddleMean_Xia global RightMean_Xia if (Rev_Num == 1): #获取左中右三个位置的X坐标和Y坐标 Left_x = LeftROI_Shang[0] + (LeftROI_Shang[2] / 2) Middle_x = MiddleROI_Shang[0] + (MiddleROI_Shang[2] / 2) Right_x = RightROI_Shang[0] + (RightROI_Shang[2] / 2) Left_y = LeftROI_Shang[1] + (LeftROI_Shang[3] / 2) Middle_y = MiddleROI_Shang[1] + (MiddleROI_Shang[3] / 2) Right_y = RightROI_Shang[1] + (RightROI_Shang[3] / 2) for i in range(100): img = sensor.snapshot() img.draw_rectangle(LeftROI_Shang) img.draw_rectangle(MiddleROI_Shang) img.draw_rectangle(RightROI_Shang) img.draw_cross(int(Left_x), int(Left_y)) img.draw_cross(int(Middle_x), int(Middle_y)) img.draw_cross(int(Right_x), int(Right_y)) Left_Stat = img.get_statistics(roi = LeftROI_Shang) Middle_Stat = img.get_statistics(roi = MiddleROI_Shang) Right_Stat = img.get_statistics(roi = RightROI_Shang) #获取上层左侧ROI的LAB均值 LeftMean_Shang[0] = LeftMean_Shang[0] + (Left_Stat.l_mean() / 100) LeftMean_Shang[1] = LeftMean_Shang[1] + (Left_Stat.a_mean() / 100) LeftMean_Shang[2] = LeftMean_Shang[2] + (Left_Stat.b_mean() / 100) #获取上层中间ROI的LAB均值 MiddleMean_Shang[0] = MiddleMean_Shang[0] + (Middle_Stat.l_mean() / 100) MiddleMean_Shang[1] = MiddleMean_Shang[1] + (Middle_Stat.a_mean() / 100) MiddleMean_Shang[2] = MiddleMean_Shang[2] + (Middle_Stat.b_mean() / 100) #获取上层右侧ROI的LAB均值 RightMean_Shang[0] = RightMean_Shang[0] + (Right_Stat.l_mean() / 100) RightMean_Shang[1] = RightMean_Shang[1] + (Right_Stat.a_mean() / 100) RightMean_Shang[2] = RightMean_Shang[2] + (Right_Stat.b_mean() / 100) elif (Rev_Num == 2): #获取左中右三个位置的X坐标和Y坐标 Left_x = LeftROI_Xia[0] + (LeftROI_Xia[2] / 2) Middle_x = MiddleROI_Xia[0] + (MiddleROI_Xia[2] / 2) Right_x = RightROI_Xia[0] + (RightROI_Xia[2] / 2) Left_y = LeftROI_Xia[1] + (LeftROI_Xia[3] / 2) Middle_y = MiddleROI_Xia[1] + (MiddleROI_Xia[3] / 2) Right_y = RightROI_Xia[1] + (RightROI_Xia[3] / 2) for i in range(100): img = sensor.snapshot() img.draw_rectangle(LeftROI_Xia) img.draw_rectangle(MiddleROI_Xia) img.draw_rectangle(RightROI_Xia) img.draw_cross(int(Left_x), int(Left_y)) img.draw_cross(int(Middle_x), int(Middle_y)) img.draw_cross(int(Right_x), int(Right_y)) Left_Stat = img.get_statistics(roi = LeftROI_Xia) Middle_Stat = img.get_statistics(roi = MiddleROI_Xia) Right_Stat = img.get_statistics(roi = RightROI_Xia) #获取下层左侧ROI的LAB均值 LeftMean_Xia[0] = LeftMean_Xia[0] + (Left_Stat.l_mean() / 100) LeftMean_Xia[1] = LeftMean_Xia[1] + (Left_Stat.a_mean() / 100) LeftMean_Xia[2] = LeftMean_Xia[2] + (Left_Stat.b_mean() / 100) #获取下层中间ROI的LAB均值 MiddleMean_Xia[0] = MiddleMean_Xia[0] + (Middle_Stat.l_mean() / 100) MiddleMean_Xia[1] = MiddleMean_Xia[1] + (Middle_Stat.a_mean() / 100) MiddleMean_Xia[2] = MiddleMean_Xia[2] + (Middle_Stat.b_mean() / 100) #获取下层右侧ROI的LAB均值 RightMean_Xia[0] = RightMean_Xia[0] + (Right_Stat.l_mean() / 100) RightMean_Xia[1] = RightMean_Xia[1] + (Right_Stat.a_mean() / 100) RightMean_Xia[2] = RightMean_Xia[2] + (Right_Stat.b_mean() / 100)
简单介绍一下函数，函数内容挺简单的，将画面中三个区域中的LAB阈值提出来做均分再相加，就得到了三个区域中L、A、B三个通道的颜色均值，后面再做对比就行
还有调试用的函数，这个函数是用来调试找到三个区域的
function：Get_ROI
def Get_ROI(Rev_Num): global Left_x global Middle_x global Right_x global Left_y global Middle_y global Right_y if (Rev_Num == 1): #裁剪窗口 sensor.set_windowing(ROI_Shang) #获取左中右三个位置的X坐标和Y坐标 Left_x = LeftROI_Shang[0] + (LeftROI_Shang[2] / 2) Middle_x = MiddleROI_Shang[0] + (MiddleROI_Shang[2] / 2) Right_x = RightROI_Shang[0] + (RightROI_Shang[2] / 2) Left_y = LeftROI_Shang[1] + (LeftROI_Shang[3] / 2) Middle_y = MiddleROI_Shang[1] + (MiddleROI_Shang[3] / 2) Right_y = RightROI_Shang[1] + (RightROI_Shang[3] / 2) img = sensor.snapshot() img.draw_rectangle(LeftROI_Shang) img.draw_rectangle(MiddleROI_Shang) img.draw_rectangle(RightROI_Shang) img.draw_cross(int(Left_x), int(Left_y)) img.draw_cross(int(Middle_x), int(Middle_y)) img.draw_cross(int(Right_x), int(Right_y)) elif (Rev_Num == 2): #裁剪窗口 #sensor.set_windowing(ROI_Xia) #获取左中右三个位置的X坐标和Y坐标 Left_x = LeftROI_Xia[0] + (LeftROI_Xia[2] / 2) Middle_x = MiddleROI_Xia[0] + (MiddleROI_Xia[2] / 2) Right_x = RightROI_Xia[0] + (RightROI_Xia[2] / 2) Left_y = LeftROI_Xia[1] + (LeftROI_Xia[3] / 2) Middle_y = MiddleROI_Xia[1] + (MiddleROI_Xia[3] / 2) Right_y = RightROI_Xia[1] + (RightROI_Xia[3] / 2) img = sensor.snapshot() img.draw_rectangle(LeftROI_Xia) img.draw_rectangle(MiddleROI_Xia) img.draw_rectangle(RightROI_Xia) img.draw_cross(int(Left_x), int(Left_y)) img.draw_cross(int(Middle_x), int(Middle_y)) img.draw_cross(int(Right_x), int(Right_y))
完整的代码发在了Gitee上
程序的运行结果如下图，可以成功的识别得到颜色的序列
手上没有openmv，测试图就这一张
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