import os
#from sklearn import datasets
#from sklearn.metrics import confusion_matrix
#from sklearn.model_selection import train_test_split
#from PIL import Image
import numpy as np
import cv2
import sys
from sklearn.svm import SVC

path = 'C:\\Users\Kushal\Desktop\TLdataset01\data01'
image_paths = [os.path.join(path, f) for f in os.listdir(path)]
# images list will contain image data. i.e. pixel intensities
images = []
# labels list will contain the label that is assigned to the image
labels = []
name_map={}

for image_path in image_paths:
    # Read the image and convert to grayscale
    image_pil = Image.open(image_path).convert('L')
    # Convert the image format into numpy array
 #   image = np.array(image_pil, 'uint8')

    # Get the label of the image
    nbr = int(os.path.split(image_path)[1].split("-")[0])
    print(nbr)
    # Get the subject name for the label
    name = os.path.split(image_path)[1].split("-")[1]
    print(name)
    name_map[nbr] = name
    images.append(image)
    labels.append(nbr)

(X_train, X_test, y_train, y_test) = train_test_split(images, labels, test_size=0.30)

# training a linear SVM classifier

svm_model_linear = SVC(kernel='linear', C=1).fit(X_train, y_train)
svm_predictions = svm_model_linear.predict(X_test)

# model accuracy for X_test
accuracy = svm_model_linear.score(X_test, y_test)
# creating a confusion matrix
cm = confusion_matrix(y_test, svm_predictions)