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python智能图片识别系统（图片切割、图片识别、区别标识）_python把一张白色背景图片中的图标识别并切割出来
作者：IT小白 | 2024-02-27 00:42:36
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python把一张白色背景图片中的图标识别并切割出来
技术介绍
你好！ python flask图片识别系统使用到的技术有：图片背景切割、图片格式转换（pdf转png）、图片模板匹配、图片区别标识。
运行效果
第一组：
图片1：
图片2：
开始上传：
上传成功、图片预览：
（emmm…抱歉图片大小未处理，有点大哈）
识别效果：
成功了。。。
第二组：
这会搞个复杂些的，也是实用的图片
图片1：（图片仅供交流，侵权删）
图片2：
你会发现，其实图片2是图片1的子图，这下我们看看程序处理的效果：
还可以哈，截取了图片1中的匹配部分，然后标识出来了区别
关键代码
图片背景切割
from PIL import Image import cv2 import os from common.util import Util # 图片去除周围白色 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 # 打印图片的宽高 print(width, height) # 把高度分为8份，后续用这8个点高度作为高度循环 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) # 按照两个对角像素点切割图片 Util().cut_img_by_point(img_path=img_path,x0=x0,x1=x1,y0=y0,y1=y1,cut_img_path=cut_img_path) # 获取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代表进行获取x0，x1的逻辑，2代表进行获取y0,y1的逻辑 :param width: 图片宽度 :param height: 图片高度 :param list_target_height: :param rgb_im: 转换为“RGB”通道的图片 :param tagrt_rgb: rgb突变范围值 :return: ''' x0 = 0 x1 = 0 y0 = 0 y1 = 0 # 多个目标高度，每个像素点的rgb之和 multi_point_rgb_sum = 0 # 多个目标高度像素点的所有像素点rgb总和的平均值 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])) # 一个点的rgb和 point_sum = r + g + b multi_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 # 与白色背景图像的差值list list_white_sub = get_listwhitesub(list_rgb_sum_avg) list_white_sub_dup = list_white_sub.copy() list_white_sub.reverse() # 获得x0 for i in range(len(list_white_sub_dup)): if list_white_sub_dup[i] > tagrt_rgb: x0 = i break # 获得x1 for i in range(len(list_white_sub)): # print(list_white_sub[i]) if list_white_sub[i] > tagrt_rgb: x1 = (width - i) break return x0, x1 elif 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) # 一个点的rgb和 point_sum = r + g + b multi_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 # 与白色背景图像的差值list list_white_sub = get_listwhitesub(list_rgb_sum_avg) list_white_sub_dup = list_white_sub.copy() list_white_sub.reverse() # 获得y0 for i in range(len(list_white_sub_dup)): if list_white_sub_dup[i] > tagrt_rgb: y0 = i break # 获得y1 for i in range(len(list_white_sub)): # print(list_white_sub[i]) if list_white_sub[i] > tagrt_rgb: y1 = (height - i) break return y0, y1 # 获得list中相邻元素的差值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 # 与白色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 list3 if __name__=="__main__": # img_path = "./images/notebook.png" # cut_img_path = './images/notebookcut4.png' tagrt_rgb_x = 300 tagrt_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)
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pdf转png代码：
import fitz import os import datetime from common.util import Util from pdf2image import convert_from_path,convert_from_bytes def pyMuPDF_fitz(pdfPath, imagePath): startTime_pdf2img = datetime.datetime.now() # 开始时间 # 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) # 每个尺寸的缩放系数为1.3，这将为我们生成分辨率提高2.6的图像。 # 此处若是不做设置，默认图片大小为：792X612, dpi=96 zoom_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): # 判断存放图片的文件夹是否存在 os.makedirs(imagePath) # 若图片文件夹不存在就创建 img.save(imagePath + '/' + 'images_%s.png' % index) # pix.writePNG(imagePath + '/' + 'images_%s.png' % index) # 将图片写入指定的文件夹内 endTime_pdf2img = datetime.datetime.now() # 结束时间 # print('pdf2img时间=', (endTime_pdf2img - startTime_pdf2img).seconds) def single_pyMuPDF_fitz(pdfPath, imagePath): startTime_pdf2img = datetime.datetime.now() # 开始时间 # print("imagePath=" + imagePath) # pdfDoc = fitz.open(pdfPath) images = convert_from_path(pdfPath) for index, img in enumerate(images): # page = pdfDoc[pg] rotate = int(0) # 每个尺寸的缩放系数为1.3，这将为我们生成分辨率提高2.6的图像。 # 此处若是不做设置，默认图片大小为：792X612, dpi=96 zoom_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) # 将图片写入指定的文件夹内 img.save(imagePath) endTime_pdf2img = datetime.datetime.now() # 结束时间 # print('pdf2img时间=', (endTime_pdf2img - startTime_pdf2img).seconds) if __name__ == "__main__": # pdfPath = '../images/EWSC007.pdf' pdfPath = 'SCAN855.PDF' ##随机文件夹名字 imagePath = 'SCAN855.png' # imagePath = '../images/image'+str(Util().random_num())+'.png' # imagePath = '../images/SCAN003.PDF' single_pyMuPDF_fitz(pdfPath, imagePath) # # 遍历文件夹下所有文件 # 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('文件完整路径：%s\n' % file_path)
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图片比较不同：
# import the necessary packages from skimage.measure import compare_ssim import argparse import imutils import cv2 def 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 images imageA = cv2.imread(path1) imageB = cv2.imread(path2) # convert the images to grayscale grayA = 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 differ thresh = 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 contours for 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')
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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_hash app = Flask(__name__) bl_files = ['logo.jpg','meixin2.jpg'] bl_dirs = [] # 定义路由 @app.route('/hello/<name1>/<name2>') def hello(name1, name2): # # 接收图片 # upload_file = request.files['file'] # # 获取图片名 # file_name = upload_file.filename # # 文件保存目录（桌面） # file_path = r'images/' # if upload_file: # # 地址拼接 # file_paths = os.path.join(file_path, file_name) # # 保存接收的图片到桌面 # upload_file.save(file_paths) # # 随便打开一张其他图片作为结果返回， # 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是编码，b64decode是解码 # base64_data = base64.b64encode(f.read()) # # base64.b64decode(base64data) # print(base64_data) # with open("images/original_01.png", "rb") as f: # # b64encode是编码，b64decode是解码 # 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) # # 将字符流写入BytesIO(主要用于读取缓存中的数据) # 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) """ 这是一个展示Flask如何读取服务器本地图片, 并返回图片流给前端显示的例子 """ def return_img_stream(img_local_path): """ 工具函数: 获取本地图片流 :param img_local_path:文件单张图片的本地绝对路径 :return: 图片流 """ 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): '''清楚系统图片缓存 :return: ''' rootdir = r"static/images" # 选取删除文件夹的路径,最终结果删除img文件夹 # rootdir = r""+ url_for('static', filename='img2') # 选取删除文件夹的路径,最终结果删除img文件夹 filelist = os.listdir(rootdir) # 列出该目录下的所有文件名 for f in filelist: filepath = os.path.join(rootdir, f) # 将文件名映射成绝对路劲 # if os.path.isfile(filepath): # 判断该文件是否为文件或者文件夹 # print(filepath) # os.remove(filepath) # 若为文件，则直接删除 # 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) # 若为文件夹，则删除该文件夹及文件夹内所有文件 print("dir " + str(filepath) + " removed!") return '清除成功' def qingchu_files(bl_files,bl_dirs): '''清楚系统图片缓存 :return: ''' rootdir = r"static/images" # 选取删除文件夹的路径,最终结果删除img文件夹 # rootdir = r""+ url_for('static', filename='img2') # 选取删除文件夹的路径,最终结果删除img文件夹 filelist = os.listdir(rootdir) # 列出该目录下的所有文件名 for f in filelist: filepath = os.path.join(rootdir, f) # 将文件名映射成绝对路劲 if os.path.isfile(filepath): # 判断该文件是否为文件或者文件夹 for i in range(len(bl_files)): if bl_files[i] not in filepath: filepath = filepath.replace('\\','/') os.remove(filepath) # 若为文件，则直接删除 print(str(filepath) + " removed!") # print(filepath) # os.remove(filepath) # 若为文件，则直接删除 # 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) # 若为文件夹，则删除该文件夹及文件夹内所有文件 print("dir " + str(filepath) + " removed!") # if destdir in filepath or yuandir in filepath: # return '清除成功' # 设置允许的文件格式 ALLOWED_EXTENSIONS = set(['png', 'jpg', 'JPG', 'PNG', 'bmp', 'pdf']) def allowed_file(filename): return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS # 设置静态文件缓存过期时间 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': # 通过file标签获取文件 f1 = request.files['file1'] f2 = request.files['file2'] # if not (f1 and allowed_file(f1.filename)): # return jsonify({"error": 1001, "msg": "图片类型：png、PNG、jpg、JPG、bmp"}) # if not (f2 and allowed_file(f2.filename)): # return jsonify({"error": 1001, "msg": "图片类型：png、PNG、jpg、JPG、bmp"}) # 当前文件所在路径 basepath = os.path.dirname(__file__) # 一定要先创建该文件夹，不然会提示没有该路径 # 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.filename filename2 = f2.filename filename3 = 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) # 返回上传成功界面 return render_template('upload_ok.html', filename1=filename1,filename2=filename2, filename3=filename3,filename4=filename4) # 重新返回上传界面 return render_template('upload.html') @app.route('/pdftopng/<filename1>/<filename2>') def pdftopng(filename1, filename2): # pdf图片转为png格式 # pdfPath1 = './../images/saomiaotu.pdf' # pdfpath2 = './../images/yuantu.pdf' pdfPath1 = 'static/images/' +filename1 pdfpath2 = 'static/images/' +filename2 dest_png_path = 'static/images/destpng' + \ str(Util().random_num()) # 目标png文件夹名称 yuantuPath = 'static/images/yuantu' + str(Util().random_num()) # auto_cut_png_path = '../images/autocutpng'+str(self.util.random_num()+1) # #自动切割后的图片文件夹 print(dest_png_path) print(yuantuPath) pyMuPDF_fitz(pdfPath1, yuantuPath) pyMuPDF_fitz(pdfpath2, dest_png_path) recog_images = [] img_part = 0 # 遍历文件夹下所有文件 work_dir = dest_png_path 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('文件完整路径：%s\n' % file_path) img_path = dest_png_path + '/' + filename scann_cut_img_path = dest_png_path + '/' + 'cut_' + filename img_cut_white( img_path, scann_cut_img_path, tagrt_rgb_x, tagrt_rgb_y) # if not os.path.exists(auto_cut_png_path): # 判断存放图片的文件夹是否存在 # os.makedirs(auto_cut_png_path) # 若图片文件夹不存在就创建 # 如果图片切割完 进行模板匹配 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 # 匹配目标图片 x0, y0, x1, y1 = match_target( target_path, template_path) # 根据返回的两个像素点切割图片 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) # 将模板匹配到的图片的边框红色去掉 # replace_border_color(template_cut_img_path) # # print(scann_cut_img_path,template_cut_img_path) # 改变图片的背景颜色 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) ## 对图片进行等分切割，进行每部分对比 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) # 遍历文件夹下所有文件 work_dir = dest_folder_scan difflag = [] 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('文件完整路径：%s\n' % file_path_scan) # print('文件完整路径：%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 += 1 dit_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()) # 目标png文件夹名称 # if not os.path.exists(result_path): # 判断存放图片的文件夹是否存在 # os.makedirs(result_path) # 若图片文件夹不存在就创建 # # 进行图片识别并标识图片差异 # 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' # 识别两张图片并标识差异点 # 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) # 删除多余的图片 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): # 判断存放图片的文件夹是否存在 # os.makedirs(result_path) # 若图片文件夹不存在就创建 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)
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写在最后
写这个功能的代码是费了很大劲的，路过的朋友点个赞哈。
需要源码的，可以Q我交流：3459067873
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