from tensorflow.keras.models import load_model
import cv2
import os
import numpy as np
from preprocessor import Preprocessor
races = {0: 'white', 1: 'black', 2: 'asian', 3: 'indian'}
model = load_model('models\cnn_vgg_256.h5')
file_path = os.path.abspath(os.path.dirname(__file__))
print(1)
input_folder = 'original'
output_folder = 'classified'
preprocessor = Preprocessor(file_path)
imgs_array = preprocessor.batch_preprocess(input_folder)
print(imgs_array)
print(2)
for img_path, img_array in imgs_array.items():
img_name = os.path.basename(img_path)
print(img_name)
if img_array == 'faceless' or img_array == 'corrupted':
os.rename(img_path,
output_folder + '\\{}\\'.format(img_array) + img_name)
continue
prediction = model.predict(img_array, verbose=1)
predicted_class = prediction.argmax(axis=-1)
prediction = np.around(prediction[0], decimals=3)
os.rename(
for row in csv_f:
if not first_row:
first_row = True
continue
age = row[1]
if age == '3-9' or age == '60-69':
continue
name = os.path.basename(row[0])
race_mapping[name] = race_groups[row[3]]
print(race_mapping)
input_folder = 'val'
preprocessor = Preprocessor(file_path)
imgs_array = preprocessor.batch_preprocess(input_folder, verbose=True)
correct = 0
incorrect = 0
total = 0
result = np.zeros((5, 5))
totals = np.zeros(5)
for img_path, img_array in imgs_array.items():
if img_array == 'faceless' or img_array == 'corrupted':
continue
img_name = os.path.basename(img_path)
if img_name not in race_mapping:
continue
total += 1
race = race_mapping[img_name]
