import joblib
import numpy as np
import os
import cv2
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression

classes = {"circle":1, 
           "rectangle":2, 
           "triangle":3 }
X=[]
Y=[]
path1="trainer dataset's folder"
main_folder=os.listdir(path1)
print(main_folder)
for folder in main_folder:
    path2=os.path.join(path1,folder)
    files=os.listdir(path2)
    for file in files:
        path3=os.path.join(path2,file)
        img=cv2.imread(path3,cv2.IMREAD_GRAYSCALE)
        img=cv2.resize(img,(32,32))
        edges = cv2.Canny(img, 100, 200)
        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        x, y, w, h = cv2.boundingRect(contours[0])
        img= img[y:y+h, x:x+w]
        img=cv2.resize(img,(32,32))
        X.append(img)
        Y.append(classes[folder])

X=np.array(X)
Y=np.array(Y)
X=X/255.0
X=X.reshape(300,-1)
model=LogisticRegression(max_iter=1000)
model=model.fit(X,Y)
joblib.dump(model,'model_name')