from m import *
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score


ds = tf.keras.preprocessing.image_dataset_from_directory(
    '../dataset-orig-aug-1-mini-1/',
    labels = 'inferred',
    label_mode = 'categorical',
    color_mode = 'rgb',
    image_size = (300, 300),
    batch_size = 32,
    verbose = True
)

p = []
r = []

with open('results.txt', 'r') as f:
    for i in f.read().split('\n'):
        if i == '':
            continue
        
        res = tuple(map(float, i.split()))
        p.append([res[0] > 0.5, res[1] > 0.5])
        r.append([res[2], res[3]])

print(f"Accuracy  : {accuracy_score(p, r)}")
print(f"Precision : {precision_score(p, r, average = 'micro')}")
print(f"Recall    : {recall_score(p, r, average = 'micro')}")
print(f"F1 Score  : {f1_score(p, r, average = 'micro')}")