face_keypt_detec.py

# -*- coding: utf-8 -*-
"""
Created on Sat Jun 13 17:30:49 2020

@author: dell
"""

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.utils import shuffle

os.chdir(r"C:\Users\dell\Desktop\Facial KeyPoint detection")

def data_loader():
    data_frame = pd.read_csv('training.csv')
    data_frame['Image'] = data_frame['Image'].apply(lambda i: np.fromstring(i, sep=' '))
    data_frame = data_frame.fillna(method='ffill')
    # data_frame = data_frame.dropna()  # Get only the data with 15 keypoints
    imgs_array = np.vstack(data_frame['Image'].values)/ 255.0
    imgs_array = imgs_array.astype(np.float32)    # Normalize, target values to (0, 1)
    imgs_array = imgs_array.reshape(-1, 96, 96, 1)
    
    labels_array = data_frame[data_frame.columns[:-1]].values
    labels_array = (labels_array - 48) / 48    # Normalize, traget cordinates to (-1, 1)
    labels_array = labels_array.astype(np.float32) 
    
    return imgs_array, labels_array


# imgs, labels = data_loader()
# print(imgs.shape)
# print(labels.shape)

# n=0
# labels[n] = (labels[n]*48)+48
# image = np.squeeze(imgs[n])
# plt.imshow(image, cmap='gray')
# plt.plot(labels[n][::2], labels[n][1::2], 'ro')
# plt.show()

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, GlobalAveragePooling2D, Activation
from keras.layers import Flatten, Dense
from keras.layers.normalization import BatchNormalization
from keras import optimizers
from keras.callbacks import ModelCheckpoint, History
# from keras.optimizers import Adam

def the_model():
    model = Sequential()
    
    model.add(Conv2D(16, (3,3), padding='same', activation='relu', input_shape=X_train.shape[1:])) # Input shape: (96, 96, 1)
    model.add(MaxPooling2D(pool_size=2))
    
    model.add(Conv2D(32, (3,3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=2))
    
    model.add(Conv2D(64, (3,3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=2))
    
    model.add(Conv2D(128, (3,3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=2))
    
    model.add(Conv2D(256, (3,3), padding='same', activation='relu'))
    model.add(MaxPooling2D(pool_size=2))
    
    # Convert all values to 1D array
    model.add(Flatten())
    
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.2))

    model.add(Dense(30))
    
    return model

X_train, y_train = data_loader()

epochs = 60
batch_size = 64

model = the_model()
hist = History()

checkpointer = ModelCheckpoint(filepath='checkpoint1.hdf5', 
                               verbose=1, save_best_only=True)

model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])

model_fit = model.fit(X_train, y_train, validation_split=0.2, epochs=epochs, batch_size=batch_size, callbacks=[checkpointer, hist], verbose=1)

model.save('model1.h5')