目錄

• 技術介紹
• • 運行效果
  • 關鍵代碼
  • 寫在最后

技術介紹

你好！ python flask圖片識別系統使用到的技術有：圖片背景切割、圖片格式轉換（pdf轉png）、圖片模板匹配、圖片區別標識。

運行效果

第一組：

圖片1：

圖片2：

開始上傳：

上傳成功、圖片預覽：
（emmm…抱歉圖片大小未處理，有點大哈）

識別效果：

成功了。。。

第二組：

這會搞個復雜些的，也是實用的圖片

圖片1：（圖片僅供交流，侵權刪）

圖片2：

你會發現，其實圖片2是圖片1的子圖，這下我們看看程序處理的效果：

還可以哈，截取了圖片1中的匹配部分，然后標識出來了區別

關鍵代碼

圖片背景切割

from PIL import Image import cv2 import os from common.util import Util# 圖片去除周圍白色 def img_cut_white(img_path, cut_img_path, tagrt_rgb_x, tagrt_rgb_y):# img_path = "./images/notebook.png"img = Image.open(img_path)rgb_im = img.convert('RGB')width, height = img.size# 打印圖片的寬高print(width, height)# 把高度分為8份，后續用這8個點高度作為高度循環list_target_height = [height / 8, height / 4, 3 * height / 8, height / 2, 5 * height / 8, 3 * height / 4]x0,x1 = get_pointx(bypara="1",width=width,height=height,list_target_height=list_target_height,rgb_im=rgb_im,tagrt_rgb=tagrt_rgb_x)y0, y1 = get_pointx(bypara="2", width=width, height=height, list_target_height=list_target_height, rgb_im=rgb_im,tagrt_rgb=tagrt_rgb_y)print(x0, x1)print(y0, y1)# 按照兩個對角像素點切割圖片Util().cut_img_by_point(img_path=img_path,x0=x0,x1=x1,y0=y0,y1=y1,cut_img_path=cut_img_path)# 獲取x0,x1,y0,y1 def get_pointx(bypara=None,width=None,height=None,list_target_height=None,rgb_im=None,tagrt_rgb=None):''':param bypara: 1代表進行獲取x0，x1的邏輯，2代表進行獲取y0,y1的邏輯:param width: 圖片寬度:param height: 圖片高度:param list_target_height::param rgb_im: 轉換為“RGB”通道的圖片:param tagrt_rgb: rgb突變范圍值:return:'''x0 = 0x1 = 0y0 = 0y1 = 0# 多個目標高度，每個像素點的rgb之和multi_point_rgb_sum = 0# 多個目標高度像素點的所有像素點rgb總和的平均值list_rgb_sum_avg = []if bypara == '1':for i in range(width):for j in range(len(list_target_height)):# print("i:",i)# print("list_target_height[j]:",list_target_height[j])r, g, b = rgb_im.getpixel((i, list_target_height[j]))# 一個點的rgb和point_sum = r + g + bmulti_point_rgb_sum += point_sum# print(point_sum, multi_point_rgb_sum)list_rgb_sum_avg.append(multi_point_rgb_sum / 6)multi_point_rgb_sum = 0# 與白色背景圖像的差值listlist_white_sub = get_listwhitesub(list_rgb_sum_avg)list_white_sub_dup = list_white_sub.copy()list_white_sub.reverse()# 獲得x0for i in range(len(list_white_sub_dup)):if list_white_sub_dup[i] > tagrt_rgb:x0 = ibreak# 獲得x1for i in range(len(list_white_sub)):# print(list_white_sub[i])if list_white_sub[i] > tagrt_rgb:x1 = (width - i)breakreturn x0, x1elif bypara == '2':for i in range(height):for j in range(width):r, g, b = rgb_im.getpixel((j, i))# r, g, b = rgb_im.getpixel(j, i)# 一個點的rgb和point_sum = r + g + bmulti_point_rgb_sum += point_sum# print(point_sum, multi_point_rgb_sum)list_rgb_sum_avg.append(multi_point_rgb_sum / width)multi_point_rgb_sum = 0# 與白色背景圖像的差值listlist_white_sub = get_listwhitesub(list_rgb_sum_avg)list_white_sub_dup = list_white_sub.copy()list_white_sub.reverse()# 獲得y0for i in range(len(list_white_sub_dup)):if list_white_sub_dup[i] > tagrt_rgb:y0 = ibreak# 獲得y1for i in range(len(list_white_sub)):# print(list_white_sub[i])if list_white_sub[i] > tagrt_rgb:y1 = (height - i)breakreturn y0, y1# 獲得list中相鄰元素的差值list def get_listsub(list2):list3 = []for i in range(len(list2)):if i <= len(list2) - 2:cha = list2[i + 1] - list2[i]list3.append(abs(cha))return list3# 與白色rgb的差值 list def get_listwhitesub(list2):list3 = []for i in range(len(list2)):print(abs(list2[i]-765))list3.append(abs(list2[i]-765))return list3if __name__=="__main__":# img_path = "./images/notebook.png"# cut_img_path = './images/notebookcut4.png'tagrt_rgb_x = 300tagrt_rgb_y = 10# tagrt_rgb_x = 180# tagrt_rgb_y = 180# img_path = "../images/UIyuantu.png"# cut_img_path = '../images/yuantucut0.png'# img_path = "../images/00.png"img_path = "IMG_0.jpg"cut_img_path = 'IMG_0_cut.jpg'img_cut_white(img_path, cut_img_path, tagrt_rgb_x, tagrt_rgb_y)

pdf轉png代碼：

import fitz import os import datetime from common.util import Util from pdf2image import convert_from_path,convert_from_bytesdef pyMuPDF_fitz(pdfPath, imagePath):startTime_pdf2img = datetime.datetime.now() # 開始時間# print("imagePath=" + imagePath)# pdfDoc = fitz.open(pdfPath)# print(pdfPath)images = convert_from_path(pdfPath)for index, img in enumerate(images):# for pg in range(pdfDoc.pageCount):# page = pdfDoc[pg]rotate = int(0)# 每個尺寸的縮放系數為1.3，這將為我們生成分辨率提高2.6的圖像。# 此處若是不做設置，默認圖片大小為：792X612, dpi=96zoom_x = 1.33333333 # (1.33333333-->1056x816) (2-->1584x1224)zoom_y = 1.33333333# zoom_x = 1 # (1.33333333-->1056x816) (2-->1584x1224)# zoom_y = 1# mat = fitz.Matrix(zoom_x, zoom_y).preRotate(rotate)# pix = img.getPixmap(matrix=mat, alpha=False)# img.save('%s/page_%s.png' % (outputDir, index))if not os.path.exists(imagePath): # 判斷存放圖片的文件夾是否存在os.makedirs(imagePath) # 若圖片文件夾不存在就創建img.save(imagePath + '/' + 'images_%s.png' % index)# pix.writePNG(imagePath + '/' + 'images_%s.png' % index) # 將圖片寫入指定的文件夾內endTime_pdf2img = datetime.datetime.now() # 結束時間# print('pdf2img時間=', (endTime_pdf2img - startTime_pdf2img).seconds)def single_pyMuPDF_fitz(pdfPath, imagePath):startTime_pdf2img = datetime.datetime.now() # 開始時間# print("imagePath=" + imagePath)# pdfDoc = fitz.open(pdfPath)images = convert_from_path(pdfPath)for index, img in enumerate(images):# page = pdfDoc[pg]rotate = int(0)# 每個尺寸的縮放系數為1.3，這將為我們生成分辨率提高2.6的圖像。# 此處若是不做設置，默認圖片大小為：792X612, dpi=96zoom_x = 1.33333333 # (1.33333333-->1056x816) (2-->1584x1224)zoom_y = 1.33333333# zoom_x = 1 # (1.33333333-->1056x816) (2-->1584x1224)# zoom_y = 1# mat = fitz.Matrix(zoom_x, zoom_y).preRotate(rotate)# pix = img.getPixmap(matrix=mat, alpha=False)# pix.writePNG(imagePath) # 將圖片寫入指定的文件夾內img.save(imagePath)endTime_pdf2img = datetime.datetime.now() # 結束時間# print('pdf2img時間=', (endTime_pdf2img - startTime_pdf2img).seconds)if __name__ == "__main__":# pdfPath = '../images/EWSC007.pdf'pdfPath = 'SCAN855.PDF'##隨機文件夾名字imagePath = 'SCAN855.png'# imagePath = '../images/image'+str(Util().random_num())+'.png'# imagePath = '../images/SCAN003.PDF'single_pyMuPDF_fitz(pdfPath, imagePath)# # 遍歷文件夾下所有文件# work_dir = imagePath# for parent, dirnames, filenames in os.walk(work_dir, followlinks=True):# for filename in filenames:# file_path = os.path.join(parent, filename)# print('文件名：%s' % filename)# print('文件完整路徑：%s\n' % file_path)

圖片比較不同：

# import the necessary packages from skimage.measure import compare_ssim import argparse import imutils import cv2def get_img_result(path1, path2, path3, path4):# construct the argument parse and parse the arguments# ap = argparse.ArgumentParser()# ap.add_argument("-f", "--first", required=True,# help="first input image")# ap.add_argument("-s", "--second", required=True,# help="second")# args = vars(ap.parse_args())# load the two input imagesimageA = cv2.imread(path1)imageB = cv2.imread(path2)# convert the images to grayscalegrayA = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)grayB = cv2.cvtColor(imageB, cv2.COLOR_BGR2GRAY)# compute the Structural Similarity Index (SSIM) between the two# images, ensuring that the difference image is returned(score, diff) = compare_ssim(grayA, grayB, full=True)diff = (diff * 255).astype("uint8")print("SSIM: {}".format(score))# threshold the difference image, followed by finding contours to# obtain the regions of the two input images that differthresh = cv2.threshold(diff, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)cnts = imutils.grab_contours(cnts)# loop over the contoursfor c in cnts:# compute the bounding box of the contour and then draw the# bounding box on both input images to represent where the two# images differ(x, y, w, h) = cv2.boundingRect(c)cv2.rectangle(imageA, (x, y), (x + w, y + h), (0, 0, 255), 2)cv2.rectangle(imageB, (x, y), (x + w, y + h), (0, 0, 255), 2)# show the output images# cv2.imshow("Original", imageA)cv2.imwrite(path3, imageA)# cv2.imshow("Modified", imageB)cv2.imwrite(path4, imageB)# cv2.imshow("Diff", diff)# cv2.imshow("Thresh", thresh)# cv2.waitKey(0)if __name__=='__main__':get_img_result('static/images/modified_03.png', 'static/images/original_03.png', 'static/images/test1.png', 'static/images/test2.png')

flask路由部分：

from flask import Flask, redirect, url_for, jsonify import base64 from flask import request import os from flask import render_template from basicclass import image_diff import time from datetime import timedelta from werkzeug.utils import secure_filename from common.image_util import random_num from basicclass.pdfconvertpng import pyMuPDF_fitz, single_pyMuPDF_fitz from common.util import Util from basicclass.autocutpic import img_cut_white from basicclass.teamplatemath import match_target from common.globalparam import tagrt_rgb_x, tagrt_rgb_y, host_ip, port from basicclass.imagediff import dif_two_pic,dif_mark from basicclass.image_diff import get_img_result import os import shutil from basicclass.getbackcolor import replace_border_color,get_dominant_color, replace_color from basicclass.newimgcut import get_parts_similar,get_parts from basicclass.hashdiff import compare_image_with_hashapp = Flask(__name__)bl_files = ['logo.jpg','meixin2.jpg'] bl_dirs = []# 定義路由 @app.route('/hello/<name1>/<name2>') def hello(name1, name2):# # 接收圖片# upload_file = request.files['file']# # 獲取圖片名# file_name = upload_file.filename# # 文件保存目錄（桌面）# file_path = r'images/'# if upload_file:# # 地址拼接# file_paths = os.path.join(file_path, file_name)# # 保存接收的圖片到桌面# upload_file.save(file_paths)# # 隨便打開一張其他圖片作為結果返回，# with open(r'images/yp1.jpg', 'rb') as f:# res = base64.b64encode(f.read())# return res# with open("images/original_01.png", "rb") as f:# # b64encode是編碼，b64decode是解碼# base64_data = base64.b64encode(f.read())# # base64.b64decode(base64data)# print(base64_data)# with open("images/original_01.png", "rb") as f:# # b64encode是編碼，b64decode是解碼# base64_data = base64.b64encode(f.read())# print(base64_data)# whj = {"name":'老王'}# return render_template('static/index.html',**whj)return 'Hello %s!' % name1 + name2# return "hello"# ls_f = redi.get(photo)# ls_f1 = base64.b64decode(ls_f)# # 將字符流寫入BytesIO(主要用于讀取緩存中的數據)# by = BytesIO(ls_f1)# return send_file(by, mimetype='image/png')@app.route('/blog/<int:postID>') def show_blog(postID):return 'Blog Number %d' % postID@app.route('/rev/<float:revNo>') def revision(revNo):return 'Revision Number %f' % revNo@app.route('/admin') def hello_admin():# name = request.args['name']print('1111111111111')# print(name)return '222222'@app.route('/guest/<guest>') def hello_guest(guest):return 'Hello %s as Guest' % guest@app.route('/user/<name>') def user(name):if name == 'admin':return redirect(url_for('hello_admin'))else:return redirect(url_for('hello_guest', guest=name))@app.route('/popopo/<user>') def hello_name(user):return render_template('hello.html', name=user)@app.route('/') def index():return render_template("index.html")# return render_template("recog_result.html")@app.route('/success/<name>') def success(name):return 'welcome %s' % name@app.route('/login', methods=['POST', 'GET']) def login():if request.method == 'POST':user = request.form['name']return redirect(url_for('success', name=user))else:print("111111111111")user = request.args.get('name') + "111111"return redirect(url_for('success', name=user))@app.route('/getimg/<filename1>/<filename2>') def get_img(filename1, filename2):path3 = 'static/images/' + str(random_num()) + '.png'path4 = 'static/images/test4.png' + str(random_num() + 1) + '.png'image_diff.get_img_result('static/images/' +filename1,'static/images/' +filename2,path3,path4)time.sleep(5)img_path1 = path3.replace('static', '.')img_path2 = path4.replace('static', '.')# img_stream = return_img_stream(img_path)return render_template('img.html', upload_img1='./images/' + filename1, upload_img2='./images/' + filename2,img_path1=img_path1, img_path2=img_path2)""" 這是一個展示Flask如何讀取服務器本地圖片, 并返回圖片流給前端顯示的例子 """def return_img_stream(img_local_path):"""工具函數:獲取本地圖片流:param img_local_path:文件單張圖片的本地絕對路徑:return: 圖片流"""base64_data = ''img_stream = ''with open(img_local_path, 'rb') as img_f:img_stream = img_f.read()img_stream = base64.b64encode(img_stream)return img_stream@app.route('/qingchutp/<destdir>/<yuandir>') def qingchu_imgs(destdir,yuandir):'''清楚系統圖片緩存:return:'''rootdir = r"static/images" # 選取刪除文件夾的路徑,最終結果刪除img文件夾# rootdir = r""+ url_for('static', filename='img2') # 選取刪除文件夾的路徑,最終結果刪除img文件夾filelist = os.listdir(rootdir) # 列出該目錄下的所有文件名for f in filelist:filepath = os.path.join(rootdir, f) # 將文件名映射成絕對路勁# if os.path.isfile(filepath): # 判斷該文件是否為文件或者文件夾# print(filepath)# os.remove(filepath) # 若為文件，則直接刪除# print(str(filepath) + " removed!")if os.path.isdir(filepath):print(filepath)if (destdir not in filepath) and (yuandir not in filepath):shutil.rmtree(filepath, True) # 若為文件夾，則刪除該文件夾及文件夾內所有文件print("dir " + str(filepath) + " removed!")return '清除成功'def qingchu_files(bl_files,bl_dirs):'''清楚系統圖片緩存:return:'''rootdir = r"static/images" # 選取刪除文件夾的路徑,最終結果刪除img文件夾# rootdir = r""+ url_for('static', filename='img2') # 選取刪除文件夾的路徑,最終結果刪除img文件夾filelist = os.listdir(rootdir) # 列出該目錄下的所有文件名for f in filelist:filepath = os.path.join(rootdir, f) # 將文件名映射成絕對路勁if os.path.isfile(filepath): # 判斷該文件是否為文件或者文件夾for i in range(len(bl_files)):if bl_files[i] not in filepath:filepath = filepath.replace('\\','/')os.remove(filepath) # 若為文件，則直接刪除print(str(filepath) + " removed!")# print(filepath)# os.remove(filepath) # 若為文件，則直接刪除# print(str(filepath) + " removed!")if os.path.isdir(filepath):print(filepath)for i in range(len(bl_dirs)):if bl_dirs[i] not in filepath:shutil.rmtree(filepath, True) # 若為文件夾，則刪除該文件夾及文件夾內所有文件print("dir " + str(filepath) + " removed!")# if destdir in filepath or yuandir in filepath:# return '清除成功'# 設置允許的文件格式 ALLOWED_EXTENSIONS = set(['png', 'jpg', 'JPG', 'PNG', 'bmp', 'pdf'])def allowed_file(filename):return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS# 設置靜態文件緩存過期時間 app.send_file_max_age_default = timedelta(seconds=1)# 添加路由 @app.route('/upload', methods=['POST', 'GET']) def upload():if request.method == 'POST':# 通過file標簽獲取文件f1 = request.files['file1']f2 = request.files['file2']# if not (f1 and allowed_file(f1.filename)):# return jsonify({"error": 1001, "msg": "圖片類型：png、PNG、jpg、JPG、bmp"})# if not (f2 and allowed_file(f2.filename)):# return jsonify({"error": 1001, "msg": "圖片類型：png、PNG、jpg、JPG、bmp"})# 當前文件所在路徑basepath = os.path.dirname(__file__)# 一定要先創建該文件夾，不然會提示沒有該路徑# upload_path1 = os.path.join(basepath, 'static/images', secure_filename(f1.filename))# upload_path2 = os.path.join(basepath, 'static/images', secure_filename(f2.filename))upload_path1 = os.path.join(basepath,'static/images',secure_filename(f1.filename))upload_path2 = os.path.join(basepath,'static/images',secure_filename(f2.filename))print('filename:', f1.filename)print('filename:', f2.filename)filename1 = f1.filenamefilename2 = f2.filenamefilename3 = str(Util().random_num())+'.png'filename4 = str(Util().random_num()+1) + '.png'# 保存文件f1.save(upload_path1)f2.save(upload_path2)single_pyMuPDF_fitz(pdfPath='static/images/' + filename1, imagePath='static/images/' + filename3)single_pyMuPDF_fitz(pdfPath='static/images/' + filename2, imagePath='static/images/' + filename4)# 返回上傳成功界面return render_template('upload_ok.html', filename1=filename1,filename2=filename2, filename3=filename3,filename4=filename4)# 重新返回上傳界面return render_template('upload.html')@app.route('/pdftopng/<filename1>/<filename2>') def pdftopng(filename1, filename2):# pdf圖片轉為png格式# pdfPath1 = './../images/saomiaotu.pdf'# pdfpath2 = './../images/yuantu.pdf'pdfPath1 = 'static/images/' +filename1pdfpath2 = 'static/images/' +filename2dest_png_path = 'static/images/destpng' + \str(Util().random_num()) # 目標png文件夾名稱yuantuPath = 'static/images/yuantu' + str(Util().random_num())# auto_cut_png_path = '../images/autocutpng'+str(self.util.random_num()+1)# #自動切割后的圖片文件夾print(dest_png_path)print(yuantuPath)pyMuPDF_fitz(pdfPath1, yuantuPath)pyMuPDF_fitz(pdfpath2, dest_png_path)recog_images = []img_part = 0# 遍歷文件夾下所有文件work_dir = dest_png_pathfor parent, dirnames, filenames in os.walk(work_dir, followlinks=True):for filename in filenames:file_path = os.path.join(parent, filename)# print('文件名：%s' % filename)# print('文件完整路徑：%s\n' % file_path)img_path = dest_png_path + '/' + filenamescann_cut_img_path = dest_png_path + '/' + 'cut_' + filenameimg_cut_white(img_path,scann_cut_img_path,tagrt_rgb_x,tagrt_rgb_y)# if not os.path.exists(auto_cut_png_path): # 判斷存放圖片的文件夾是否存在# os.makedirs(auto_cut_png_path) # 若圖片文件夾不存在就創建# 如果圖片切割完 進行模板匹配if os.path.exists(scann_cut_img_path):target_path = yuantuPath + "/images_0.png"template_path = scann_cut_img_path# match_path = "static/images/result.png"template_cut_img_path = dest_png_path + '/' + 'template_part_' + filename# 匹配目標圖片x0, y0, x1, y1 = match_target(target_path, template_path)# 根據返回的兩個像素點切割圖片obj = Util()obj.cut_img_by_point(img_path=target_path,x0=x0,x1=x1,y0=y0,y1=y1,cut_img_path=template_cut_img_path)# 將模板匹配到的圖片的邊框紅色去掉# replace_border_color(template_cut_img_path)## print(scann_cut_img_path,template_cut_img_path)# 改變圖片的背景顏色target_rgb = get_dominant_color(scann_cut_img_path)replace_path_scan = scann_cut_img_path.replace('.','_white.')replace_color(scann_cut_img_path, replace_path_scan, target_rgb)target_rgb = get_dominant_color(template_cut_img_path)replace_path_yuan = template_cut_img_path.replace('.', '_white.')replace_color(template_cut_img_path,replace_path_yuan,target_rgb)## 對圖片進行等分切割，進行每部分對比dest_folder_scan = dest_png_path+"/whitescan"+str(Util().random_num())dest_folder_yuan = dest_png_path + "/whiteyuan" + str(Util().random_num())dest_scan_points = get_parts(replace_path_scan,64)get_parts_similar(replace_path_scan, 256, dest_folder=dest_folder_scan)get_parts_similar(replace_path_yuan, 256, dest_folder=dest_folder_yuan)# 遍歷文件夾下所有文件work_dir = dest_folder_scandifflag = []for parent, dirnames, filenames in os.walk(work_dir, followlinks=True):for filename in filenames:file_path_scan = os.path.join(parent, filename)file_path_yuan = os.path.join(parent.replace(dest_folder_scan,dest_folder_yuan), filename)# print('文件名：%s' % filename)# print('文件完整路徑：%s\n' % file_path_scan)# print('文件完整路徑：%s\n' % file_path_yuan)dif = compare_image_with_hash(file_path_scan, file_path_yuan, max_dif=0)print(dif)if dif >= 30:# if dif >= 5 and dif <=15:print(dif)index = int(filename.replace('image-','').replace('.png',''))difflag.append(dest_scan_points[index-1])print(difflag)res_scan_path = dest_png_path+'/'+'scan'+str(Util().random_num())+'.png'res_yuan_path = dest_png_path + '/' + 'yuan'+str(Util().random_num())+'.png'# dif_mark(scann_cut_img_path,template_cut_img_path,res_scan_path,res_yuan_path,difflag)get_img_result(scann_cut_img_path,template_cut_img_path,res_scan_path,res_yuan_path)img_part += 1dit_image = {'scann': res_scan_path.replace('static/', ''),'temp': res_yuan_path.replace('static/', ''), 'part': '第' + str(img_part) + '部分對比圖片'}recog_images.append(dit_image)# result_path = dest_png_path + '/result' + \# str(Util().random_num()) # 目標png文件夾名稱# if not os.path.exists(result_path): # 判斷存放圖片的文件夾是否存在# os.makedirs(result_path) # 若圖片文件夾不存在就創建# # 進行圖片識別并標識圖片差異# imga_path = scann_cut_img_path# imgb_path = template_cut_img_path# print('imga_path:' +imga_path)# print('imga_path:' +imgb_path)# # scann_path = result_path + '/scann' + str(Util().random_num() + 1) + '.png'# # template_path = result_path + '/template' + str(Util().random_num() + 1) + '.png'# scann_path = result_path + '/scann' + \# str(Util().random_num() + 1) + '.png'# template_path = result_path + '/template' + \# str(Util().random_num() + 1) + '.png'# 識別兩張圖片并標識差異點# try:# dif_two_pic(imga_path, imgb_path, scann_path, template_path)# img_part += 1## dit_image = {'scann': scann_path.replace('static/', ''),# 'temp': template_path.replace('static/', ''), 'part': '第' + str(img_part) + '部分對比圖片'}## recog_images.append(dit_image)# except Exception as e:# print(e)# dif_two_pic(imga_path, imgb_path, scann_path, template_path)## img_part += 1## dit_image = {'scann': scann_path.replace('static/',''), 'temp':template_path.replace('static/',''), 'part':'第'+str(img_part)+'部分對比圖片'}## recog_images.append(dit_image)# 刪除多余的圖片bl_dirs = [dest_png_path,yuantuPath,'destpng7151565','yuantu7151565']# qingchu_files(bl_files,bl_dirs)if os.path.exists(dest_png_path) and os.path.exists(yuantuPath): # 判斷存放圖片的文件夾是否存在# os.makedirs(result_path) # 若圖片文件夾不存在就創建print('dest_png_path:'+dest_png_path)print('yuantuPath:' + yuantuPath)qingchu_imgs(dest_png_path.replace('static/images/',''), yuantuPath.replace('static/images/',''))return render_template("recog_result.html", recog_images=recog_images)if __name__ == '__main__':# app.run(host=host_ip, port=port, debug=True)app.run(host='127.0.0.1', port=5000, debug=True)

寫在最后

寫這個功能的代碼是費了很大勁的，路過的朋友點個贊哈。
需要源碼的，可以Q我交流：3459067873

總結

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