导读

本文主要介绍使用Python-OpenCV实现餐盘水果识别与计价的应用。

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：

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 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 += 1 item_0 = QTableWidgetItem("%d"%banana_num) self.tableWidget.setItem(8, 0, item_0) #-------------------苹果检测-----------------# apple_num = 0 lower_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 += 1 item_1 = QTableWidgetItem("%d"%apple_num) self.tableWidget.setItem(6, 0, item_1) #-------------------橘子检测-----------------# orange_num = 0 lower_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 += 1 item_2 = QTableWidgetItem("%d"%orange_num) self.tableWidget.setItem(7, 0, item_2) #-------------------白色餐盘检测-----------------# white_circle_num = 0 white_rect_num = 0 lower_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 += 1 elif 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 += 1 item_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 = 0 green_rect_num = 0 lower_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 += 1 elif 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 += 1 item_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 = 0 orange_rect_num = 0 lower_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 += 1 elif 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 += 1 item_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_num print(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。