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utils.py
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utils.py
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import cv2
import numpy as np
import pandas as pd
import os
import logging
IMG_SIZE_X = 48
IMG_SIZE_Y = 48
DIR_PREFIX = './data/'
logging.basicConfig(level=logging.DEBUG)
# 将csv中的信息保存为图片
# 0 anger 生气
# 1 disgust 厌恶
# 2 fear 恐惧
# 3 happy 开心
# 4 sad 伤心
# 5 surprised 惊讶
# 6 normal 中性
def csv2png_train(csv_path, img_dir_name):
# 检测是否存在图片文件夹
try:
os.mkdir(DIR_PREFIX + img_dir_name)
for each in range(7):
os.mkdir(DIR_PREFIX + img_dir_name + '/' + str(each))
# 为了确保文件夹是空的
except FileExistsError:
raise Exception(f'已存在目标文件夹，请删除目标文件夹 {DIR_PREFIX + img_dir_name + "/"} 再执行转换操作')
count_list = [0, 0, 0, 0, 0, 0, 0]
data = pd.read_csv(csv_path)
emotion_list = data['emotion']
img_list = data['pixels']
# print(len(img_list[0]))
# cv2.imwrite('tmp.png', img_list[0].split(' ').reshape(IMG_SIZE_X, IMG_SIZE_Y))
for each in range(len(emotion_list)):
img_path_and_filename = str(emotion_list[each])+'/'+str(count_list[int(emotion_list[each])])+'.png'
face = [int(pixel) for pixel in img_list[each].split(' ')]
face = np.asarray(face).reshape(IMG_SIZE_X, IMG_SIZE_Y)
cv2.imwrite(DIR_PREFIX + img_dir_name + '/' + img_path_and_filename, face)
logging.info(f'已转换图片文件{img_path_and_filename} 保存于{DIR_PREFIX+img_dir_name+"/"}')
count_list[int(emotion_list[each])] += 1
def csv2png_test(csv_path, img_dir_name):
# 检测是否存在图片文件夹
try:
os.mkdir(DIR_PREFIX+img_dir_name)
# 为了确保文件夹是空的
except FileExistsError:
raise Exception(f'已存在目标文件夹，请删除目标文件夹 {DIR_PREFIX + img_dir_name + "/"} 再执行转换操作')
data = pd.read_csv(csv_path)
img_list = data['pixels']
# print(len(img_list[0]))
# cv2.imwrite('tmp.png', img_list[0].split(' ').reshape(IMG_SIZE_X, IMG_SIZE_Y))
for each in range(len(img_list)):
img_filename = str(each) + '.png'
face = [int(pixel) for pixel in img_list[each].split(' ')]
face = np.asarray(face).reshape(IMG_SIZE_X, IMG_SIZE_Y)
cv2.imwrite(DIR_PREFIX + img_dir_name + '/' + img_filename, face)
logging.info(f'已转换图片文件{img_filename} 保存于{DIR_PREFIX+img_dir_name+"/"}')
def csv2png_icml(csv_path):
# 检测是否存在图片文件夹
try:
os.mkdir(DIR_PREFIX + 'icml_' + 'train')
for each in range(7):
os.mkdir(DIR_PREFIX + 'icml_' + 'train/' + str(each))
os.mkdir(DIR_PREFIX + 'icml_' + 'public_test')
for each in range(7):
os.mkdir(DIR_PREFIX + 'icml_' + 'public_test/' + str(each))
os.mkdir(DIR_PREFIX + 'icml_' + 'private_test')
for each in range(7):
os.mkdir(DIR_PREFIX + 'icml_' + 'private_test/' + str(each))
# 为了确保文件夹是空的
except FileExistsError:
raise Exception(f'请确认 data 目录下不存在 icml_train icml_public_test icml_private_test 文件夹后再执行转换操作')
data_set = pd.read_csv(csv_path)
usage_list = data_set[' Usage'].tolist()
emotion_list = data_set['emotion'].tolist()
pixels_list = data_set[' pixels']
count_dict = {'Training': [0, 0, 0, 0, 0, 0, 0],
'PublicTest': [0, 0, 0, 0, 0, 0, 0],
'PrivateTest': [0, 0, 0, 0, 0, 0, 0]}
dir_name_dict = {'Training': 'icml_train',
'PublicTest': 'icml_public_test',
'PrivateTest': 'icml_private_test'}
for each in range(data_set.shape[0]):
tmp_usage = usage_list[each]
tmp_emotion = emotion_list[each]
tmp_pixels = np.asarray([int(pixel) for pixel in pixels_list[each].split(' ')]).reshape(IMG_SIZE_X, IMG_SIZE_Y)
cv2.imwrite(DIR_PREFIX
+ dir_name_dict[tmp_usage] + '/'
+ str(tmp_emotion) + '/'
+ str(count_dict[tmp_usage][tmp_emotion]) + '.png'
, tmp_pixels)
logging.info(f"已转换图片文件{str(count_dict[tmp_usage][tmp_emotion]) + '.png'} "
f"保存于{dir_name_dict[tmp_usage] + '/' + str(tmp_emotion) + '/'}")
count_dict[tmp_usage][tmp_emotion] += 1