| 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') |
