Human Segmentation using U-Net – with source code – easiest way – 2022

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So guys in today’s blog we will see that how we can perform Human Segmentation using U-Net. U-Net is a very special CNN architecture that was specially made for segmentation in mainly the medical field. It is called a U-Net because of its special architecture whose shape is like U. So without any further due, Let’s do it…

Step 1 – Importing required libraries.

import tensorflow as tf
import numpy as np
import os
from skimage.io import imread,imshow
from skimage.transform import resize
from skimage import color
from tensorflow.keras.models import load_model
import matplotlib.pyplot as plt
import cv2
from sklearn.model_selection import train_test_split

Step 2 – Create X and y arrays.

IMG_HEIGHT = 256
IMG_WIDTH  = 256
CHANNELS = 3


training_images_names = os.listdir('data/Training_Images/')
training_masks_names = os.listdir('data/masks/')

X = np.zeros((len(training_images_names),IMG_HEIGHT,IMG_WIDTH,CHANNELS),dtype='uint8')
y = np.zeros((len(training_masks_names),IMG_HEIGHT,IMG_WIDTH,1))
  • Here we are just creating X and y arrays initialized with 0s.
  • In the next step, we will fill up these arrays.

Step 3 – Fill X and y arrays.

for i,n in enumerate(training_images_names):
    img = imread(f'data/Training_Images/{n}')
    img = resize(img,(IMG_HEIGHT,IMG_WIDTH,CHANNELS),mode='constant',preserve_range=True)
    fn = str(n.split('.')[0]) + '.png'
    mask = imread(f'data/masks/{fn}')
    mask = resize(mask,(IMG_HEIGHT,IMG_WIDTH,1),mode='constant')
    
    X[i] = img
    y[i] = mask
    
X[0].shape
  • In this step, we will be filling X and y arrays.
  • In X we will fill 256*256 resized images.
  • In y we will fill 256*256 resized masks.
U-Net Human Segmentation

Step 4 – Randomly plot the image and its mask.

i = np.random.randint(0,len(X))
fig,(a1,a2)=plt.subplots(1,2)
a1.imshow(X[i])
a2.imshow(y[i].reshape(y[i].shape[:-1]),cmap='gray')
  • Taking a random index and plotting its image and mask.
U-Net Human Segmentation

Step 5 – Building U-Net.

inputs = tf.keras.layers.Input((IMG_HEIGHT,IMG_WIDTH,CHANNELS))
s = tf.keras.layers.Lambda(lambda x:x/255)(inputs)

#contracting path
c1 = tf.keras.layers.Conv2D(16,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(s)
c1 = tf.keras.layers.Dropout(0.1)(c1)
c1 = tf.keras.layers.Conv2D(16,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(c1)
p1 = tf.keras.layers.MaxPooling2D((2,2))(c1)


c2 = tf.keras.layers.Conv2D(32,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(p1)
c2 = tf.keras.layers.Dropout(0.1)(c2)
c2 = tf.keras.layers.Conv2D(32,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(c2)
p2 = tf.keras.layers.MaxPooling2D((2,2))(c2)


c3 = tf.keras.layers.Conv2D(64,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(p2)
c3 = tf.keras.layers.Dropout(0.2)(c3)
c3 = tf.keras.layers.Conv2D(64,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(c3)
p3 = tf.keras.layers.MaxPooling2D((2,2))(c3)


c4 = tf.keras.layers.Conv2D(128,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(p3)
c4 = tf.keras.layers.Dropout(0.2)(c4)
c4 = tf.keras.layers.Conv2D(128,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(c4)
p4 = tf.keras.layers.MaxPooling2D((2,2))(c4)


c5 = tf.keras.layers.Conv2D(256,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(p4)
c5 = tf.keras.layers.Dropout(0.3)(c5)
c5_1 = tf.keras.layers.Conv2D(256,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(c5)
c5_1 = tf.keras.layers.Dropout(0.3)(c5_1)
c5_2 = tf.keras.layers.Conv2D(256,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(c5_1)
c5_3 = tf.keras.layers.Conv2D(256,(3,3),activation='relu',kernel_initializer='he_normal',padding='same',dilation_rate=2)(c5_2)
c5_4 = tf.keras.layers.Conv2D(512,(3,3),activation='relu',kernel_initializer='he_normal',padding='same',dilation_rate=2)(c5_3 )
c5_5 = tf.keras.layers.concatenate([c5_1,c5_4])


#expanding path
u4 = tf.keras.layers.Conv2DTranspose(128,(2,2),strides=(2,2),padding='same')(c5_5)
u4 = tf.keras.layers.concatenate([u4,c4])
u4 = tf.keras.layers.Conv2D(128,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(u4)
u4 = tf.keras.layers.Dropout(0.2)(u4)
u4 = tf.keras.layers.Conv2D(128,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(u4)


u3 = tf.keras.layers.Conv2DTranspose(64,(2,2),strides=(2,2),padding='same')(u4)
u3 = tf.keras.layers.concatenate([u3,c3])
u3 = tf.keras.layers.Conv2D(64,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(u3)
u3 = tf.keras.layers.Dropout(0.2)(u3)
u3 = tf.keras.layers.Conv2D(64,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(u3)


u2 = tf.keras.layers.Conv2DTranspose(32,(2,2),strides=(2,2),padding='same')(u3)
u2 = tf.keras.layers.concatenate([u2,c2])
u2 = tf.keras.layers.Conv2D(32,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(u2)
u2 = tf.keras.layers.Dropout(0.2)(u2)
u2 = tf.keras.layers.Conv2D(32,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(u2)


u1 = tf.keras.layers.Conv2DTranspose(16,(2,2),strides=(2,2),padding='same')(u2)
u1 = tf.keras.layers.concatenate([u1,c1])
u1 = tf.keras.layers.Conv2D(16,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(u1)
u1 = tf.keras.layers.Dropout(0.2)(u1)
u1 = tf.keras.layers.Conv2D(16,(3,3),activation='relu',kernel_initializer='he_normal',padding='same')(u1)

output = tf.keras.layers.Conv2D(1,(1,1),activation='sigmoid')(u1)

model = tf.keras.Model(inputs=[inputs],outputs=[output])
model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
model.summary()
  • Here we are simply building the U-Net structure.
U-Net Human Segmentation
U-Net Human Segmentation

U-Net Human Segmentation

Step 6 – Train test split the data.

X_train, X_test, y_train, y_test = train_test_split(X,  y, test_size=0.1, random_state=42)

Step 7 – Training and saving the model.

results = model.fit(X_train,y_train,batch_size=16,epochs=100)
model.save('models/human_segmentation_non-aug_100_v2.h5')
U-Net Human Segmentation

Step 8 – Plot the loss and accuracy curves.

fig,(a1,a2) = plt.subplots(1,2,figsize=(17,8))
a1.plot(np.arange(0,100),results.history['loss'],label = 'loss')
a2.plot(np.arange(0,100),results.history['accuracy'],label='accuracy')
a1.legend()
a2.legend()
plt.savefig('losses_and_accuracies_100_v2.png')
U-Net Human Segmentation

Step 9 – Visualizing outputs.

k=np.random.randint(0,len(X_test))
fig,(a1,a2) = plt.subplots(1,2)

a1.imshow(X_test[k])

(h,w,c) = X_test[k].shape
i = X_test[k].reshape((1,h,w,c))

pred = model.predict(i)

a2.imshow(pred.reshape(pred.shape[1:-1]),cmap='gray')
  • Here we are checking the performance of our model.
  • And see it’s performing very well (obviously not great :)).
U-Net Human Segmentation

Download Source Code…

Do let me know if there’s any query regarding Human Segmentation using U-Net by contacting me by email or LinkedIn. You can also comment down below for any queries.

So this is all for this blog folks, thanks for reading it and I hope you are taking something with you after reading this and till the next time …

Read my previous post: MILK PRODUCTION PREDICTION FOR NEXT YEAR USING LSTM

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