In this blog we will learn how to detect age using the given images, we are providing machine learning assignment help, and other related areas help of machine learning:
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Step 1 : Import all related libraries
import os
import random
from scipy.io import loadmat
import pandas as pd
from scipy.misc import imread
from sklearn.model_selection import train_test_split
Step 2:
root_dir = os.path.abspath('.')
data_dir = '/mnt/hdd/datasets/misc'
train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
fileData = loadmat('wiki_labeled.mat')
Show file data:
fileData['wiki_labeled']
## meta information of each of the 60327 images
ID: identification number of the subject (starting from 2002)
• dob: the date of birth of the subject. (It is Matlab’s datenum value calculated based on total number of days since January 0, 0000.)
• dob_str: the DD-MMM-YYYY format dob value.
• photo_taken: when the photo was taken (only the year value)
• full_path: directory path, including filename of the image
• gender: Gender of the subject (0: female, 1: male, NaN if unknown)
• name: name of the subject
• face_location: location of the face.
• face_score: detector score (the higher the better). Inf implies that no face was found in the image, and the face_location then just returns the entire image.
• second_face_score: detector score of the face with the second highest score. This is useful to ignore images with more than one face. second_face_score is NaN (not a number) if no second face was detected.
• age: age of the person (in years), and was calculated based on the “dob” value and the “photo_taken” values.
fileData['wiki_labeled'][0][0][0]
dob = fileData['wiki_labeled'][0][0][0]
photo_taken = fileData['wiki_labeled'][0][0][1]
full_path = fileData['wiki_labeled'][0][0][2]
gender = fileData['wiki_labeled'][0][0][3]
name = fileData['wiki_labeled'][0][0][4]
face_location = fileData['wiki_labeled'][0][0][5]
face_score = fileData['wiki_labeled'][0][0][6]
second_face_score = fileData['wiki_labeled'][0][0][7]
dob_str = fileData['wiki_labeled'][0][0][8]
age = fileData['wiki_labeled'][0][0][9]
IDs = fileData['wiki_labeled'][0][0][10]
Show Data:
print("IDs: ",IDs)
print("DOB:",dob)
print("photo_taken",photo_taken)
print("dob_str:",dob_str)
print("full_path:",full_path)
print("gender:",gender)
print("name:",name)
print("face_location:",face_location)
print("face_score:",face_score)
print("second_face_score:",second_face_score)
print("age:",age)
Choose Selected Column:
columns = ["id","dob","photo_taken","dob_str","full_path","gender","name","face_location","face_score","second_face_score","age"]
Show Using Dataframe:
df = pd.DataFrame({'id': IDs[0], 'dob': dob[0],"photo_taken":photo_taken[0],'dob_str':dob_str[0],"full_path":full_path[0],"gender":gender[0],"name":name[0],
"face_location":face_location[0],"face_score":face_score[0],
"second_face_score":second_face_score[0],'age':age[0]}, columns=columns)
Print Selected data
df.iloc[:,:-1]
## Displaying some images
import imageio
images= df['full_path'].values
print(len(images[:10]))
data_dir = 'wiki_labeled/'
#displaying 10 pictures
for i in range(len(images[:10])):
img_name = images[i][0]
img = imageio.imread(os.path.join(data_dir, img_name))
imshow(img)
#print(‘Age: ‘, train.Class[i])
Split Data
x = df.iloc[:,:-1]
y = df['age']
xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.2, random_state = 0)
Training first 200 images to reduce execution time you can set your own desired size
Backgrounds on PCA
Step 1: Obtain the 2D face images, I1,I2,...,Im (the training faces). All faces must be of the same resolution.
#from scipy.misc import imresize
from skimage.transform import resize
images= xTrain['full_path'].values
data_dir = 'wiki_labeled/'
temp = []
# training 200 images
m = 200
for i in range(m):
img_name = images[i][0]
img = imageio.imread(os.path.join(data_dir, img_name))
#img = resize(img, (32, 32))
img = img.astype('float32') # this will help us in later stage
temp.append(img)
#train_x = np.stack(temp)
def plot_gallery(images, h, w, rows=10, cols=10):
plt.figure()
for i in range(rows * cols):
plt.subplot(rows, cols, i + 1)
plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)
plt.xticks(())
plt.yticks(())
plot_gallery(temp,100,100)
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