python智能图片识别系统（图片切割、图片识别、区别标识）

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目录

• 技术介绍
  • 运行效果
  • 关键代码
  • 写在最后

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  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)

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
' % file_path)

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')

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
' % 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
' % file_path_scan)
                        # print('文件完整路径：%s
' % 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)

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。
交流：3459067873