我正在构建一个应用程序，用于GLCM和RGB提取，并进行预测。但不知何故，如果我使用不同的数据，我总是得到以前的数据或第一个数据的结果。但对于RGB数据则不然，RGB数据总是设法根据上传的数据获得结果。除非我停止服务器并重新启动它，否则它应该工作。
下面是我在app.py中的代码

from flask import Flask, render_template, request
from keras.models import load_model
from io import BytesIO
import keras.utils as image
import numpy as np
from PIL import Image
import math
import pandas as pd
import csv
import requests
from io import BytesIO
import numpy as np
import keras.utils as image
import cv2 as cv
from data import Data
from glcm import Glcm
from sklearn.preprocessing import StandardScaler
import os

app = Flask(__name__)

model = load_model('klasifikasi_ayam_potong_berformalin3.h5', compile=False)

matriks = [
    [0,1],
    [1,0],
]

matriksIterasi = [
    [0,0,0,0],
    [0,0,0,0],
    [0,0,0,0],
    [0,0,0,0],
]

feature = []

def countFeatures(data, glcm):
    asm = glcm.asmMethod(data)
    energy = glcm.energy(asm)
    feature.append(energy)
    idm = glcm.idmMethod(data)
    feature.append(idm)
    cont = glcm.contrasMethod(data)
    feature.append(cont)
    corel = glcm.correlation(data)
    feature.append(corel)

def processImg(imgPat):

    imageObject  = Image.open(imgPat)
    cropped     = imageObject.crop((0,0,250,250))
    citra = np.array(cropped)
    citra = citra.transpose(2,0,1).reshape(3,-1)
    matriksIterasi = [[0 for i in range(256)] for j in range(256)]

    glcm = Glcm()
    data0 = glcm.forOder0(citra, matriksIterasi)
    countFeatures(data0, glcm)
    data45 = glcm.forOder45(citra, matriksIterasi)
    countFeatures(data45, glcm)
    data90 = glcm.forOder90(citra, matriksIterasi)
    countFeatures(data90, glcm)
    data135 = glcm.forOder135(citra, matriksIterasi)
    countFeatures(data135, glcm)

    for i in range(len(data0)-1):
        for j in range(len(data0[i])-1):
            data0[i][j]=(data0[i][j]+data45[i][j]+data90[i][j]+data135[i][j])/4
    
    countFeatures(data0, glcm)

    R = []
    G = []
    B = []

    cvImage = cv.imread(imgPat, cv.COLOR_RGB2BGR)
    rows, cols, _ = cvImage.shape

    color_B = 0
    color_G = 0
    color_R = 0
    color_N = 0 
    for i in range(rows):
        for j in range(cols):
            k = cvImage[i,j]
            if k[0] > k[1] and k[0] > k[2]:
                color_B = color_B + 1
                continue
            if k[1] > k[0] and k[1] > k[2]:
                color_G = color_G + 1
                continue
            if k[2] > k[0] and k[2] > k[1]:
                color_R = color_R + 1
                continue
            color_N = color_N + 1

        pix_total = rows * cols

    G.append(color_G/pix_total)
    B.append(color_B/pix_total)
    R.append(color_R/pix_total)
    print("    ")
    print(imgPat, '\n')
    df = pd.DataFrame({'Red': R, 'Green': G, 'Blue': B, 'energy0':feature[0], 'contras0':feature[1], 'homogenity0':feature[2], 'correlation0':feature[3], 'energy45':feature[4], 'contras45':feature[5], 'homogenity45':feature[6], 'correlation45':feature[7], 'energy90':feature[8], 'contras90':feature[9], 'homogenity90':feature[10], 'correlation90':feature[11], 'energy135':feature[12], 'contras135':feature[13], 'homogenity135':feature[14], 'correlation135':feature[15], 'energy_mean':feature[16], 'contras_mean':feature[17], 'homogenity_mean':feature[18], 'correlation_mean':feature[19]})
    print(df)

    sc = StandardScaler()
    xtesx = sc.fit_transform(df)
    prediction = model.predict(xtesx)
    data_pred = []
    pred = ''
    for i in prediction:
        if i > 0.5:
            data_pred.append(1)
            pred = 'Good'
        else:
             data_pred.append(0)
             pred = 'Bad'

    return pred

@app.route("/", methods=['GET', 'POST'])
def main():
    return render_template("index.html")

@app.route("/about")
def about_page():
    return render_template("about.html")

@app.route("/contact")
def contact_page():
    return render_template("contact.html")

@app.route("/submit", methods = ['POST'])

def get_output():
    if request.method == 'POST':
       
       img = request.files['my_image']
       img_path = "static/" + img.filename  
       img.save(img_path)
       
       pred = processImg(img_path)
       
    return render_template("index.html", prediction = pred, img_path = img_path)

if __name__ =='__main__':
    app.run(debug = True)

字符串
你可以在Red，Green，Blue之后看到相同的结果，这是在energy0，contras0等等。
results could be found here
如何使服务器不重启，GLCM数据根据上传的数据获取？