应用实例 | 手把手教你用OpenCV实现餐盘水果识别计价程序(附代码)
新机器视觉
共 8993字，需浏览 18分钟
·
2021-07-08 20:38
测试图像与说明
使用图像如下，拍摄环境有待改善(存在光照不均和拍摄角度的影响):
餐盘/菜品识别一般方法：
(1)识别餐盘---传统方法和机器学习/深度学习方法；
(2)识别菜品---机器学习/深度学习方法；
本文使用传统方法识别餐盘。
效果演示：
算法思路与实现步骤
思路：传统方法识别餐盘---依据颜色和形状来区分。
具体步骤：
(1)餐盘颜色共三种：白色、绿色、橙色，形状共两种：圆形和方形。区别颜色使用HSV阈值范围筛选即可，圆形与方形通过轮廓面积与轮廓最小外接圆面积的比值来筛选，圆形rate>=0.9，方形<0.9;
(2)水果共三种：苹果、香蕉、橙子，通过颜色可以区分苹果和橙子，通过轮廓最小外接矩形的宽高比可以区分香蕉和橙子；
(3)计价：盘子和水果的数量乘以对应的单价即可；
(4)设计UI，计价时显示收款码。
Python-OpenCV实现算法核心代码与效果如下：
def Recognize_Dish(self,img):#-------------------香蕉检测-----------------#banana_num = 0hsv_img=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)lower_yellow = np.array([15,30,145])#颜色范围低阈值upper_yellow = np.array([35,255,255])#颜色范围高阈值mask = cv2.inRange(hsv_img,lower_yellow,upper_yellow)#根据颜色范围删选mask = cv2.medianBlur(mask, 5)#中值滤波#cv2.imshow('mask_banana', mask)contours,hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)for cnt in contours:rect = cv2.minAreaRect(cnt)box = cv2.boxPoints(rect)box = np.int0(box)width = max(rect[1][0],rect[1][1])height = min(rect[1][0],rect[1][1])center = (int(rect[0][0]),int(rect[0][1]))if width > 180 and height > 80 and height < 130:#print(width,height)img = cv2.drawContours(img,[box],0,(0,0,255),2)cv2.putText(img,'banana',center,font,1,(255,0,255), 2)banana_num += 1item_0 = QTableWidgetItem("%d"%banana_num)self.tableWidget.setItem(8, 0, item_0)#-------------------苹果检测-----------------#apple_num = 0lower_apple = np.array([0,50,50])#颜色范围低阈值upper_apple = np.array([30,255,255])#颜色范围高阈值mask_apple = cv2.inRange(hsv_img,lower_apple,upper_apple)#根据颜色范围删选mask_apple = cv2.medianBlur(mask_apple, 9)#中值滤波#cv2.imshow('mask_apple', mask_apple)#cv2.imwrite('mask_apple.jpg', mask_apple)contours2,hierarchy2 = cv2.findContours(mask_apple, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)for cnt2 in contours2:center,radius = cv2.minEnclosingCircle(cnt2)area = cv2.contourArea(cnt2)#print(radius)rate = area / (math.pi * radius *radius)if radius > 50 and radius < 75 and rate < 0.91:#print(radius)cv2.circle(img,(int(center[0]),int(center[1])),int(radius),(0,255,0),2)cv2.putText(img,'apple',(int(center[0]),int(center[1])),font,1,(255,0,0), 2)apple_num += 1item_1 = QTableWidgetItem("%d"%apple_num)self.tableWidget.setItem(6, 0, item_1)#-------------------橘子检测-----------------#orange_num = 0lower_orange = np.array([0,90,60])#颜色范围低阈值upper_orange = np.array([60,255,255])#颜色范围高阈值mask_orange = cv2.inRange(hsv_img,lower_orange,upper_orange)#根据颜色范围删选mask_orange = cv2.medianBlur(mask_orange, 5)#中值滤波#cv2.imshow('mask_orange', mask_orange)#cv2.imwrite('mask_orange.jpg', mask_orange)contours3,hierarchy3 = cv2.findContours(mask_orange, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)for cnt3 in contours3:center,radius = cv2.minEnclosingCircle(cnt3)area = cv2.contourArea(cnt3)#print(radius)rate = area / (math.pi * radius *radius)if radius > 50 and radius < 75 and rate > 0.85:#print(radius)cv2.circle(img,(int(center[0]),int(center[1])),int(radius),(255,0,255),2)cv2.putText(img,'orange',(int(center[0]),int(center[1])),font,1,(255,255,0), 2)orange_num += 1item_2 = QTableWidgetItem("%d"%orange_num)self.tableWidget.setItem(7, 0, item_2)#-------------------白色餐盘检测-----------------#white_circle_num = 0white_rect_num = 0lower_white = np.array([0,0,150])#颜色范围低阈值upper_white= np.array([100,55,255])#颜色范围高阈值mask_white = cv2.inRange(hsv_img,lower_white,upper_white)#根据颜色范围删选mask_white = cv2.medianBlur(mask_white, 5)#中值滤波#cv2.imshow('mask_white', mask_white)#cv2.imwrite('mask_white.jpg', mask_white)contours4,hierarchy4 = cv2.findContours(mask_white, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)for cnt4 in contours4:area = cv2.contourArea(cnt4)center,radius = cv2.minEnclosingCircle(cnt4)#print(radius)rate = area / (math.pi * radius *radius)if radius > 100 and radius < 160:#print(radius)if rate >= 0.9:cv2.circle(img,(int(center[0]),int(center[1])),int(radius),(255,255,0),2)cv2.putText(img,'white_circle',(int(center[0]),int(center[1])),font,1,(0,255,0), 2)white_circle_num += 1elif rate >0.6 and rate < 0.9:rect = cv2.minAreaRect(cnt4)box = cv2.boxPoints(rect)box = np.int0(box)#cv2.circle(img,(int(center[0]),int(center[1])),int(radius),(255,0,255),5)img = cv2.drawContours(img,[box],0,(255,255,0),2)cv2.putText(img,'white_rect',(int(center[0]),int(center[1])),font,1,(0,255,0), 2)white_rect_num += 1item_3 = QTableWidgetItem("%d"%white_circle_num)self.tableWidget.setItem(0, 0, item_3)item_4 = QTableWidgetItem("%d"%white_rect_num)self.tableWidget.setItem(1, 0, item_4)#-------------------绿色餐盘检测-----------------#green_circle_num = 0green_rect_num = 0lower_green = np.array([30,65,65])#颜色范围低阈值upper_green= np.array([80,255,255])#颜色范围高阈值mask_green = cv2.inRange(hsv_img,lower_green,upper_green)#根据颜色范围删选mask_green = cv2.medianBlur(mask_green, 5)#中值滤波#cv2.imshow('mask_green', mask_green)#cv2.imwrite('mask_green.jpg', mask_green)contours5,hierarchy5 = cv2.findContours(mask_green, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)for cnt5 in contours5:area = cv2.contourArea(cnt5)center,radius = cv2.minEnclosingCircle(cnt5)#print(radius)rate = area / (math.pi * radius *radius)if radius > 100 and radius < 160:#print(radius)if rate >= 0.9:cv2.circle(img,(int(center[0]),int(center[1])),int(radius),(0,255,0),2)cv2.putText(img,'green_circle',(int(center[0]),int(center[1])),font,1,(0,255,255), 2)green_circle_num += 1elif rate >0.6 and rate < 0.9:rect = cv2.minAreaRect(cnt5)box = cv2.boxPoints(rect)box = np.int0(box)#cv2.circle(img,(int(center[0]),int(center[1])),int(radius),(255,0,255),5)img = cv2.drawContours(img,[box],0,(0,255,0),2)cv2.putText(img,'green_rect',(int(center[0]),int(center[1])),font,1,(0,255,255), 2)green_rect_num += 1item_5 = QTableWidgetItem("%d"%green_circle_num)self.tableWidget.setItem(4, 0, item_5)item_6 = QTableWidgetItem("%d"%green_rect_num)self.tableWidget.setItem(5, 0, item_6)#-------------------橙色餐盘检测-----------------#orange_circle_num = 0orange_rect_num = 0lower_orange_dish = np.array([0,100,100])#颜色范围低阈值upper_orange_dish= np.array([15,255,255])#颜色范围高阈值mask_orange_dish = cv2.inRange(hsv_img,lower_orange_dish,upper_orange_dish)#根据颜色范围删选mask_orange_dish = cv2.medianBlur(mask_orange_dish, 5)#中值滤波#cv2.imshow('mask_green', mask_green)#cv2.imwrite('mask_orange_dish.jpg', mask_orange_dish)contours6,hierarchy6 = cv2.findContours(mask_orange_dish, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)for cnt6 in contours6:area = cv2.contourArea(cnt6)center,radius = cv2.minEnclosingCircle(cnt6)#print('----------------')#print(radius)rate = area / (math.pi * radius *radius)if radius > 100 and radius < 160:#print(rate)if rate >= 0.8:cv2.circle(img,(int(center[0]),int(center[1])),int(radius),(0,255,0),2)cv2.putText(img,'orange_circle',(int(center[0]),int(center[1])),font,1,(255,0,255), 2)orange_circle_num += 1elif rate >0.3 and rate < 0.8:rect = cv2.minAreaRect(cnt6)box = cv2.boxPoints(rect)box = np.int0(box)#cv2.circle(img,(int(center[0]),int(center[1])),int(radius),(255,0,255),5)img = cv2.drawContours(img,[box],0,(0,255,0),2)cv2.putText(img,'orange_rect',(int(center[0]),int(center[1])),font,1,(255,0,255), 2)orange_rect_num += 1item_7 = QTableWidgetItem("%d"%orange_circle_num)self.tableWidget.setItem(2, 0, item_7)item_8 = QTableWidgetItem("%d"%orange_rect_num)self.tableWidget.setItem(3, 0, item_8)for i in range(0,9):self.tableWidget.item(i,0).setTextAlignment(QtCore.Qt.AlignHCenter|QtCore.Qt.AlignVCenter)self.tableWidget.item(i,1).setTextAlignment(QtCore.Qt.AlignHCenter|QtCore.Qt.AlignVCenter)#----------------计算价格--------------#self.price = self.price_white_circle * white_circle_num + \self.price_white_rect * white_rect_num + \self.price_orange_circle * orange_circle_num + \self.price_orange_rect * orange_rect_num + \self.price_green_circle * green_circle_num + \self.price_green_rect * green_rect_num + \self.price_apple * apple_num + \self.price_orange * orange_num +\self.price_banana * banana_numprint(self.price)return img
结尾语
(1) 算法只针对水果和餐盘数量和形态较少的情形，方法供参考；
(2) 实际应用将更复杂，要求更高，一般开源的目标检测网络也很难满足要求；
(3) 常见菜品识别的实际应用要求：一个菜只用一张图片训练或做模板，训练和识别时间尽量短，能够及时更新使用。所以真正类似的产品并不好做，如果你有好的方法欢迎留言。
—版权声明—
来源： OpenCV与AI深度学习
仅用于学术分享，版权属于原作者。
若有侵权，请联系微信号:yiyang-sy 删除或修改！
评论