0、项目介绍

有一段时间没有更新专栏了，本次项目是我的软件课设，自主命题。智能计数指的是对特定物体进行计数（对于比较规整的物体效果很好，例如硬币、钥匙、方块等），表单信息指的是对图片进行计数并写入excel表格当中。从投入到应用角度来看，使用深度学习的方法是更加的准确，而使用opencv的方法有局限性，仍然有改进的空间。

使用步骤：

1、图片检测：

（1）运行data_collection脚本，将对应的物体放在红框当中，轨迹栏可以调整其大小，对框外进行高斯模糊，使用摄像头对圆签进行拍照。（拍照背景最好是纯色，且与物体的颜色有较大的对比）

（2）运行Picdetect.py文件，窗口显示后，如果显示数值与实际值有所不同，可以调整轨迹栏，调整到合适的值后，要记得修改轨迹栏初始化的值。点击键盘s即可保存图片到指定文件夹。

2、摄像头检测：

仅仅只运行Videodetect.py文件，窗口展示后，可以实时调整轨迹栏的滑动条，调整到在这个环境下适合的值，记得修改。点击键盘s即可保存图片，再次按下空格键即可继续检测。

3、控制台交互检测

模式功能可选，包括了Real-time、Mutil-image模式，Real-time模式即为摄像头检测，Mutil-image检测下还有Single-image模式可选，点击Esc键即可返回到是否进行Single-image。

1、效果展示

图片检测

摄像头检测

控制台交互检测

表单信息

2、项目搭建

关于这一步其实是很有必要的，因为我曾经看过很多博主直接就将代码贴在文章中，代码文件没有明确的说明在哪个目录下，创建时候还要通过import去琢磨一番。

———— 23 Intelligent Count with form information—— photodata—— Pic-img-data.xlsx—— Video-img-data.xlsx—— CountMain.py—— data_collection.py—— main2ship.py—— Picdetect.py—— tally.py—— Videodetect.py

3、项目代码展示与部分讲解

拍照脚本data_collection.py

"""
author : Auorui(夏天是冰红茶)
time : 2023-6-3
function:It is used for data acquisition. Place the round sticksin the red box, and perform Gaussian filter processingoutside the box. Adjust the brightness and size of thebox adaptively through the track bar. Press the keyboard"s" to take photos of the round sticks. The number is unlimited.
"""
import cv2
import tally as taVcap = ta.VideoCap()
Vcap.CapInit(mode=1, w=640, h=480)
count = 1cv2.namedWindow("photodata")
cv2.createTrackbar("bbox_scale", "photodata", 100, 900, ta.onTrackbarChange)
cv2.createTrackbar("brightness_factor", "photodata", 50, 100, ta.onTrackbarChange)while True:img = Vcap.read()img = cv2.flip(img,1)bbox_scale = cv2.getTrackbarPos("bbox_scale", "photodata") / 1000.0 + 0.1brightness_factor = cv2.getTrackbarPos("brightness_factor", "photodata") / 100.0 + 0.5position, img_with_box = ta.Bbox_img(img, bbox_scale=bbox_scale)img_with_blur = ta.maskBbox(img, position)img_with_blur_and_light = ta.Adjusted_image(img_with_blur, brightness_factor=brightness_factor)imgStacked = ta.stackImages(1, [img_with_box,img_with_blur_and_light])cv2.imshow("photodata", imgStacked)k = cv2.waitKey(1) & 0xFFif k == ord('s'):filename = "./photodata/image{:03d}.png".format(count)cv2.imwrite(filename, img_with_blur_and_light)print("image{:03d}.png保存成功！".format(count))count += 1elif k == 27:break

使用注意：

用于数据采集，将物体放在红框中的位置，框外进行了高斯滤波处理，通过轨迹栏自适应的调整亮度和框的大小，按下键盘“s”对圆签进行拍照，数量不限。

展示：第一行为调整框的大小，也就是识别的区域，第二行为亮度调整，根据当前的环境适当的调整亮度。原图在左，数据图在右，将目标对准摄像头后就可以按下键“s”，保存右图。

图片检测Picdetect.py

"""
author : Auorui(夏天是冰红茶)
time : 2023-6-18
"""
import cv2
import tally as ta
import os
from datetime import datetimecv2.namedWindow("Settings")
cv2.resizeWindow("Settings", 640, 240)
cv2.createTrackbar("Threshold1", "Settings", 82, 255, ta.empty)
cv2.createTrackbar("Threshold2", "Settings", 101, 255, ta.empty)img = cv2.imread("./photodata/image005.png")
success_text = "Successfully saved"
success_text_color = (0, 0, 255)
text_size = 20save_dir = "./Pic/img"
excel_path = "./Pic/data.xlsx"image_count = 1
data = []
while True:imgPre = ta.preProcessing(img)imgStacked,total,imgContours=ta.drawContour(img, imgPre, minArea=15)k = cv2.waitKey(1) & 0xFFif k == ord('s'):current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")image_name = f"Picimg_{str(image_count).zfill(3)}.png"image_path = os.path.join(save_dir, image_name)cv2.imwrite(image_path, imgContours)data.append([current_time, image_name, total])image_count += 1cv2.rectangle(imgStacked, (480, 140),(785, 190),(255, 0, 0), -1)cv2.putText(imgStacked,success_text,(485, 180),cv2.FONT_HERSHEY_SIMPLEX, text_size / 20, success_text_color, 2)cv2.imshow("Settings", imgStacked)while True:k = cv2.waitKey(1)if k == ord(' '):breakta.excelmation(excel_path,data)elif k == 27:breakcv2.destroyAllWindows()

摄像头检测Videodetect.py

"""
author : Auorui(夏天是冰红茶)
time : 2023-6-18
"""
import cv2
import tally as ta
import os
from datetime import datetimeVcap = ta.VideoCap()
Vcap.CapInit(mode=1, w=845, h=480)cv2.namedWindow("Settings")
cv2.resizeWindow("Settings", 640, 240)
cv2.createTrackbar("Threshold1", "Settings", 82, 255, ta.empty)
cv2.createTrackbar("Threshold2", "Settings", 101, 255, ta.empty)success_text = "Successfully saved"
success_text_color = (0, 0, 255)
text_size = 20save_dir = "./Video/img"
excel_path = "./Video/data.xlsx"image_count = 1
data = []while True:img = Vcap.read()imgPre = ta.preProcessing(img)imgStacked,total,imgContours=ta.drawContour(img, imgPre, minArea=15)k = cv2.waitKey(1) & 0xFFif k == ord('s'):current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")image_name = f"Videoimg_{str(image_count).zfill(3)}.png"image_path = os.path.join(save_dir, image_name)cv2.imwrite(image_path, imgContours)data.append([current_time, image_name, total])image_count += 1cv2.rectangle(imgStacked, (700, 140),(1005, 190),(255, 0, 0), -1)cv2.putText(imgStacked,success_text,(705, 180),cv2.FONT_HERSHEY_SIMPLEX, text_size / 20, success_text_color, 2)cv2.imshow("Settings", imgStacked)while True:k = cv2.waitKey(1)if k == ord(' '):breakta.excelmation(excel_path,data)elif k == 27:breakcv2.destroyAllWindows()

主函数CountMain.py

import cv2
import tally as ta
import os
from datetime import datetime# 通用
success_text = "Successfully saved"
success_text_color = (0, 0, 255)
text_size = 20
image_count = 1
data = []####################----------轨迹栏初始化----------#####################
cv2.namedWindow("Settings")
cv2.resizeWindow("Settings", 640, 240)
cv2.createTrackbar("Threshold1", "Settings", 82, 255, ta.empty)
cv2.createTrackbar("Threshold2", "Settings", 101, 255, ta.empty)
##################################################################################-----模式检测-----##########
print("Real-time | Multi-image")    #
print("请选择检测识别模式：",end=' ')  #
recongnitionMode = input()          #
#####################################save_dir, excel_path=ta.ImagePath(recongnitionMode)if recongnitionMode=='Real-time':Vcap = ta.VideoCap()Vcap.CapInit(mode=0, w=845, h=480)while True:img = Vcap.read()imgPre = ta.preProcessing(img)imgStacked, total, imgContours = ta.drawContour(img, imgPre, minArea=15)k = cv2.waitKey(1) & 0xFFif k == ord('s'):current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")image_name = f"Videoimg_{str(image_count).zfill(3)}.png"image_path = os.path.join(save_dir, image_name)cv2.imwrite(image_path, imgContours)data.append([current_time, image_name, total])image_count += 1cv2.rectangle(imgStacked, (700, 140),(1005, 190),(255, 0, 0), -1)cv2.putText(imgStacked,success_text,(705, 180),cv2.FONT_HERSHEY_SIMPLEX, text_size / 20, success_text_color, 2)cv2.imshow("Settings", imgStacked)while True:k = cv2.waitKey(1)if k == ord(' '):breakta.excelmation(excel_path, data)elif k == 27:breakif recongnitionMode == 'Multi-image':while True:print("是否经过Single-image检测(Y or N)（或按下Esc键退出）:", end=' ')Single_mode = input()if Single_mode.lower() == 'esc':breakif Single_mode == 'Y':while True:print("请输入要检测图片的路径（或按下Esc键返回）:", end=' ')path = input().strip()  # ./photodata/image005.pngif path.lower() == 'esc':breakif path.lower() == 'exit()':breakimg = cv2.imread(path)while True:imgPre = ta.preProcessing(img)imgStacked, total, imgContours = ta.drawContour(img, imgPre, minArea=15)k = cv2.waitKey(1)if k == 27:breakelif Single_mode == 'N':print("请输入图片文件夹路径:", end=' ')folder_path = input().strip()if folder_path.lower() == 'esc':breakif folder_path.lower() == 'exit()':breakimage_files = ta.readPath(folder_path)# print(image_files)for image in image_files:print(image)img = cv2.imread(image)imgPre = ta.preProcessing(img)imgStacked, total, imgContours = ta.drawContour(img, imgPre, minArea=15)current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")image_name = f"Picimg_{str(image_count).zfill(3)}.png"image_path = os.path.join(save_dir, image_name)cv2.imwrite(image_path, imgContours)data.append([current_time, image_name, total])image_count += 1ta.excelmation(excel_path, data)print("多文件检测结束！")breakelif Single_mode.lower() == 'exit()':break