import os
import cv2
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

def preprocess(img):
    scale_percent = 10
    width = int(img.shape[1] * scale_percent / 100)
    height = int(img.shape[0] * scale_percent / 100)
    dim = (width, height)
    resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
    resized = resized.flatten()
    return resized

def readData(data_links):
    x, y = [], []
    for link in data_links:
        img = cv2.imread(link, cv2.IMREAD_COLOR)
        img = preprocess(img)
        label = link.split("/")[1].split('_')[1]
        x.append(img)
        y.append(label)

    return x, y

data_links = os.listdir("data/")
data_links = ["data/" + x for x in data_links]

x, y = readData(data_links)

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)

clf = MLPClassifier(solver='adam', alpha=1e-5, hidden_layer_sizes=(1000, 700), random_state=1, 
    activation='relu', batch_size='auto', shuffle=True, verbose=True, learning_rate='adaptive', n_iter_no_change=10)
clf.fit(X_train, y_train)

print("Score:")
print(clf.score(X_test, y_test))

print("Summary:")
Y_pred = clf.predict(X_test)
print(classification_report(y_test, Y_pred))