用OpenMV自動識別顏色序列
新年假期過去啦~
主控：OpenMV3 M7攝像頭(STM32F765)
IDE：OPENMV官方IDE
我將Capstone期間做的代碼上傳到Gitee啦，后續還會陸續上傳代碼上去的，地址：Capstone，如果對你有幫助的話不妨點個⭐支持一下~
OpenMV需要識別顏色順序的位置是第一個區域，區域中的物料擺放如下圖所示
對於OpenMV識別顏色，很大一部分人都是采用了使用閾值調節，通過拍回來的照片修改閾值得到想要的區域，這種情況一般都會存在調試時間長，受光照影響大的限制。
通過分析本項目的顏色識別部分，小車到達粗加工區后開始顏色識別，識別到序列之后在開始抓取，車子在識別顏色時的位置是大致不變的，所以本文將閾值->顏色識別的流程轉化為區域->顏色的識別，可以減少調試時間，但解決不了受光照影響的問題。
OpenMV使用的是LAB色域，如下圖所示
根據任務要求，物料顏色分別有紅綠藍三色，則對上圖進行觀察對比，可以發現紅綠藍三色均在球形的外端，有非明顯的區別，比如紅色的a值為正數，藍色的b值為負數，綠色的a值為負數。因此可以根據這些特征篩選出所需顏色。
在實際運行過程中，我們將OpenMV的畫面分成上下兩部分，然后在每次運行到粗加工區位置時都運行一遍識別程序，避免因顏色識別不全卡在粗加工區。
在此處放出獲取區域顏色的代碼
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):
#獲取左中右三個位置的X坐標和Y坐標
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)
#獲取上層左側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)
#獲取上層中間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)
#獲取上層右側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):
#獲取左中右三個位置的X坐標和Y坐標
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)
#獲取下層左側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)
#獲取下層中間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)
#獲取下層右側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)
簡單介紹一下函數，函數內容挺簡單的，將畫面中三個區域中的LAB閾值提出來做均分再相加，就得到了三個區域中L、A、B三個通道的顏色均值，后面再做對比就行
還有調試用的函數，這個函數是用來調試找到三個區域的
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)
#獲取左中右三個位置的X坐標和Y坐標
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)
#獲取左中右三個位置的X坐標和Y坐標
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))
完整的代碼發在了Gitee上
程序的運行結果如下圖，可以成功的識別得到顏色的序列
手上沒有openmv，測試圖就這一張