Charlie Hewitt
87 linhas
3.0 KiB
Python
87 linhas
3.0 KiB
Python
#! /usr/bin/env python
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import csv
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import os
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import cv2
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import sys
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import numpy as np
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def process_images(p, limit=-1):
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with open(p, 'r') as csvfile:
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r = csv.reader(csvfile, delimiter=',')
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count = 0
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for row in r:
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if 'sub' in row[0]:
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continue
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try:
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valence = float(row[-2])
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arousal = float(row[-1])
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emotion = int(row[-3])
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if (arousal == -2 or valence == -2) and emotion > 7:
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continue
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x, y, w, h = [float(row[1]), float(row[2]), float(row[3]), float(row[4])] # x, y, w, h
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path = 'data/' + row[0]
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o_path = 'data_p/' + row[0]
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if not os.path.exists(o_path):
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image = cv2.imread(path)[int(y):int(y + h), int(x):int(x + w)]
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image = cv2.resize(image, (256, 256))
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o_dir = '/'.join(o_path.split('/')[:-1])
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if not os.path.exists(o_dir):
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os.makedirs(o_dir)
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cv2.imwrite(o_path, image)
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count += 1
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print('Processed:', count, end='\r')
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if limit > 0 and count == limit: # TEMP
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break
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except Exception:
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continue
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print('Processed:', count)
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def process_labels(p):
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labels = []
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paths = []
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count = 0
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with open(p, 'r') as csvfile:
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r = csv.reader(csvfile, delimiter=',')
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count = 0
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for row in r:
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if 'sub' in row[0]:
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continue
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valence = float(row[-2])
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arousal = float(row[-1])
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emotion = int(row[-3])
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path = 'data_p/' + row[0]
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if emotion > 7 and (arousal == -2 or valence == -2):
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continue
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if not os.path.exists(path):
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print('error: no image')
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continue
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labels.append((emotion, valence, arousal))
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paths.append(path)
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count += 1
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print('Loaded:', count, end='\r')
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print('Loaded:', count)
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return paths, labels
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if __name__ == '__main__':
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if '-i' not in sys.argv and '-l' not in sys.argv:
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print('Usage:')
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print('process.py')
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print(' -i process images from AffectNet training.csv + validation.csv')
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print(' requires raw images in data dir and outputs to data_p')
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print(' -l process labels from AffectNet training.csv + validation.csv')
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print(' outputs to [training/validation]_[paths/labels].npy')
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if '-i' in sys.argv:
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process_images('training.csv')
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process_images('validation.csv')
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if '-l' in sys.argv:
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tp, tl = process_labels('training.csv')
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np.save('training_paths', tp)
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np.save('training_lables', tl)
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vp, vl = process_labels('validation.csv')
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np.save('validation_paths', vp)
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np.save('validation_labels', vl)
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