Iceberg Detection

Keras Unet Segmentation

Tensorflow

Keras

Video segmentation

EParisot's Starter Code for IceBerg Detection

What we are going to Learn¶

How to build a DataGenerator using Tensoflow.Keras API
How to semantic segmentation using TensorFlow.
Working with videos.

Install packages¶

In [3]:

# Installing Scikit Video & FFMPEG
!pip install scikit-video
!pip install ffmpeg
!apt-get install ffmpeg gstreamer1.0-libav vlc
!apt-get update -qq && sudo apt-get -y install \
  autoconf \
  automake \
  build-essential \
  cmake \
  git-core \
  libass-dev \
  libfreetype6-dev \
  libgnutls28-dev \
  libsdl2-dev \
  libtool \
  libva-dev \
  libvdpau-dev \
  libvorbis-dev \
  libxcb1-dev \
  libxcb-shm0-dev \
  libxcb-xfixes0-dev \
  meson \
  ninja-build \
  pkg-config \
  texinfo \
  wget \
  yasm \
  zlib1g-dev
!apt-get install ffmpeg libsm6  libxext6 -y
!pip install sk-video p-tqdm
!apt-get update
# install opencv and tf
!apt-get install -y python3-opencv
!pip install tensorflow
# utility that will help in getting the files in the right order
!pip install natsort

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wget is already the newest version (1.19.4-1ubuntu2.2).
libsdl2-dev is already the newest version (2.0.8+dfsg1-1ubuntu1.18.04.4).
meson is already the newest version (0.45.1-2ubuntu0.18.04.2).
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In [52]:

# Tensorflow Keras
import tensorflow.keras as keras

# Reading Dataset, vis and miscellaneous
from PIL import Image
import matplotlib.pyplot as plt

import os
import numpy as np
from tqdm.notebook import tqdm
import cv2
from natsort import natsorted
from glob import glob
import skvideo.io

Dataloader¶

Ok Here we go for the real first work we have to do: A DataGenerator !

Why is it usefull ? Well, you may not want to load all the dataset in your RAM...

Using a generator, every single data will be readen and used on the go, so you never store more than a batch in memory... This gives you the ability to control your machine's ressources !

Here we go !

In [53]:

# Here we define the frame's shape 
# (note that we will use the same shape for input and output images, 
# since we want to operate segmentation from grayscale images, with the same size output)
img_dims = (128, 128, 1)
# and the number of frame we will find in every video
nb_frames = 23

class VideoDataGenerator(keras.utils.Sequence):
    
    def __init__(self, data_folder="data/train", task="train", split=0.8, img_dims=img_dims, batch_size=nb_frames, shuffle=True):
        # Initialization
        self.batch_size = batch_size
        self.dims = img_dims
        self.task = task
        self.data_directory = data_folder
        # here we grab the images from folder in correct order (filenames are indexes)
        # if not in test config, we also grab the segmentations from correct folder
        # then we build a new array with the images/segmentations pair (or just images for testing)
        self.image_videos_files = natsorted(glob(self.data_directory+"/image*"))
        if self.task != "test":
            self.segmentation_videos_files = natsorted(glob(self.data_directory+"/segmentation*"))
            self.img_list = list(zip(self.image_videos_files, self.segmentation_videos_files))
        else:
            self.img_list = [[f] for f in self.image_videos_files]
        # here we compute the total number of batches
        self.nb = len(self.img_list) * nb_frames
        # then we apply the split validation
        self.split = int(split * self.nb)
        self.shuffle = shuffle
        # execute on init because no epoch ends a start
        self.on_epoch_end()
        
    def __len__(self):
        # Return the number of batches for train, val and test
        if self.task == "train":
            l = self.split // self.batch_size
        elif self.task == "val":
            l = (self.nb - self.split) // self.batch_size
        else:
            l = self.nb / self.batch_size
        return l

    def __getitem__(self, idx):
        # Generate indexes of the batch
        indexes = self.indexes[idx * self.batch_size : (idx + 1) * self.batch_size]
        return self.__data_generation(indexes)

    def on_epoch_end(self):
        # Updates indexes after each epoch (and shuffle if needed)
        self.indexes = np.arange(self.nb)
        if self.task == "train":
            self.indexes = self.indexes[:self.split]
        elif self.task == "val":
            self.indexes = self.indexes[self.split:]
        if self.task != "test" and self.shuffle == True:
            np.random.shuffle(self.indexes)
    
    def __data_generation(self, indexes):
        # Arrays Initializations
        X = np.empty((self.batch_size, *self.dims), dtype=np.float32)
        if self.task != "test":
            Y = np.empty((self.batch_size, *self.dims), dtype=np.float32)
        # Generate data
        for i, idx in enumerate(indexes):
            # Reading The iceberg video
            img_video = cv2.VideoCapture(self.img_list[idx // nb_frames][0])
            if self.task != "test":
                seg_video = cv2.VideoCapture(self.img_list[idx // nb_frames][1])
            # compute the n of image from idx (nb_frames images per video, 1 video per idx)
            n = idx % nb_frames
            # grab the correct image and copy it as X
            img_video.set(1, n)
            ret, img = img_video.read()
            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            img = np.array(img, dtype=np.float32)
            img = cv2.resize(img, self.dims[:2])
            X[i,] = np.expand_dims(np.squeeze(img) / 255., axis=2) 

            # Reading the segmentation video
            if self.task != "test":
                seg_video.set(1, n)
                ret, mask = seg_video.read()
                mask = cv2.resize(mask, self.dims[:2])
                mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
                _, mask = cv2.threshold(mask,127,255,cv2.THRESH_BINARY)
                Y[i,] = np.expand_dims(np.squeeze(mask) / 255., axis=2).astype(np.float32)

        if self.task != "test":
            return X, Y
        else:
            return X

Downloading Dataset¶

Hre we are downloading the challange dataset using AIcrowd CLI

In [27]:

!pip install aicrowd-cli
%load_ext aicrowd.magic
%aicrowd login

WARNING: You are using pip version 21.1.3; however, version 21.2.2 is available.
You should consider upgrading via the 'c:\users\rock_\appdata\local\programs\python\python39\python.exe -m pip install --upgrade pip' command.

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Please login here: https://api.aicrowd.com/auth/n6B7dp2xXLmToFiIlaP9TrpEbc0MnbTCG82zB5tVnNc
API Key valid
Saved API Key successfully!

In [6]:

!rm -rf data
!mkdir data
%aicrowd ds dl -c iceberg-detection -o data

In [7]:

# Unzipping the files
!unzip data/train.zip -d data/train
!unzip data/test.zip -d data/test

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Instanciate our datagenerators¶

In [54]:

# Here we just define the split validation ratio and instantiate our generators 
# for both training and validation
split = 0.8
# so we will use 80% of data for training, 20% for validation
train_gen = VideoDataGenerator(task="train", split=split)
val_gen = VideoDataGenerator(task="val", split=split)

Visualizing Dataset¶

In [55]:

# Ok time to check we did a correct job on previous cells:
# select a batch and an frame index to change the image/seg couple
batch = 0
idx = 15

# grab corresponding images
# 0 and 1 indexes corresponds to X and Y respectively
img = np.squeeze(train_gen[batch][0][idx] * 255).astype(np.int)
seg = np.squeeze(train_gen[batch][1][idx] * 255).astype(np.int)

# and the show the images with matplotlib
fig, axes = plt.subplots(1,2)
axes[0].imshow(img, cmap='gray')
axes[1].imshow(seg)
plt.show()

Ok At this point, we made the hardest part, now we need to define a Segmentation Model

Luckily, Keras already did it for us, so here we go for a copy/pasta safari: https://keras.io/examples/vision/oxford_pets_image_segmentation/

With some minus adaptations, it can fit our task pretty well

Here is an example of a Unet architecture from wikipedia (different in/output shape)

Building the Model¶

In [56]:

# this line will clean the model out from memory so we dont reuse the previous ones we tested before...
keras.backend.clear_session()

# here we define the model's input shape from previously defined img_dims
inputs = keras.layers.Input(shape=img_dims)
### [First half of the network: downsampling inputs] ###

# Entry block
x = keras.layers.Conv2D(32, 3, strides=2, padding="same")(inputs)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Activation("relu")(x)

previous_block_activation = x  # Set aside residual

# Blocks 1, 2, 3 are identical apart from the feature depth.
for filters in [64, 128]:
    x = keras.layers.Activation("relu")(x)
    x = keras.layers.SeparableConv2D(filters, 3, padding="same")(x)
    x = keras.layers.BatchNormalization()(x)

    x = keras.layers.Activation("relu")(x)
    x = keras.layers.SeparableConv2D(filters, 3, padding="same")(x)
    x = keras.layers.BatchNormalization()(x)

    x = keras.layers.MaxPooling2D(3, strides=2, padding="same")(x)

    # Project residual
    residual = keras.layers.Conv2D(filters, 1, strides=2, padding="same")(
        previous_block_activation
    )
    x = keras.layers.add([x, residual])  # Add back residual
    previous_block_activation = x  # Set aside next residual

### [Second half of the network: upsampling inputs] ###

for filters in [128, 64, 32]:
    x = keras.layers.Activation("relu")(x)
    x = keras.layers.Conv2DTranspose(filters, 3, padding="same")(x)
    x = keras.layers.BatchNormalization()(x)

    x = keras.layers.Activation("relu")(x)
    x = keras.layers.Conv2DTranspose(filters, 3, padding="same")(x)
    x = keras.layers.BatchNormalization()(x)

    x = keras.layers.UpSampling2D(2)(x)

    # Project residual
    residual = keras.layers.UpSampling2D(2)(previous_block_activation)
    residual = keras.layers.Conv2D(filters, 1, padding="same")(residual)
    x = keras.layers.add([x, residual])  # Add back residual
    previous_block_activation = x  # Set aside next residual

# Add a per-pixel classification layer
outputs = keras.layers.Conv2D(1, 3, activation="sigmoid", padding="same")(x)
model = keras.Model(inputs, outputs)
model.compile(optimizer="adam", loss="binary_crossentropy") # Here I changed the loss to binary 
# let's print the model's summary
model.summary()

Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 128, 128, 1) 0                                            
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 64, 64, 32)   320         input_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 64, 64, 32)   128         conv2d[0][0]                     
__________________________________________________________________________________________________
activation (Activation)         (None, 64, 64, 32)   0           batch_normalization[0][0]        
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 64, 64, 32)   0           activation[0][0]                 
__________________________________________________________________________________________________
separable_conv2d (SeparableConv (None, 64, 64, 64)   2400        activation_1[0][0]               
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 64, 64, 64)   256         separable_conv2d[0][0]           
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 64, 64, 64)   0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
separable_conv2d_1 (SeparableCo (None, 64, 64, 64)   4736        activation_2[0][0]               
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 64, 64, 64)   256         separable_conv2d_1[0][0]         
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D)    (None, 32, 32, 64)   0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 32, 32, 64)   2112        activation[0][0]                 
__________________________________________________________________________________________________
add (Add)                       (None, 32, 32, 64)   0           max_pooling2d[0][0]              
                                                                 conv2d_1[0][0]                   
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 32, 32, 64)   0           add[0][0]                        
__________________________________________________________________________________________________
separable_conv2d_2 (SeparableCo (None, 32, 32, 128)  8896        activation_3[0][0]               
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 32, 32, 128)  512         separable_conv2d_2[0][0]         
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 32, 32, 128)  0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
separable_conv2d_3 (SeparableCo (None, 32, 32, 128)  17664       activation_4[0][0]               
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 32, 32, 128)  512         separable_conv2d_3[0][0]         
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 128)  0           batch_normalization_4[0][0]      
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 16, 16, 128)  8320        add[0][0]                        
__________________________________________________________________________________________________
add_1 (Add)                     (None, 16, 16, 128)  0           max_pooling2d_1[0][0]            
                                                                 conv2d_2[0][0]                   
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 16, 16, 128)  0           add_1[0][0]                      
__________________________________________________________________________________________________
conv2d_transpose (Conv2DTranspo (None, 16, 16, 128)  147584      activation_5[0][0]               
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 16, 16, 128)  512         conv2d_transpose[0][0]           
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 16, 16, 128)  0           batch_normalization_5[0][0]      
__________________________________________________________________________________________________
conv2d_transpose_1 (Conv2DTrans (None, 16, 16, 128)  147584      activation_6[0][0]               
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 16, 16, 128)  512         conv2d_transpose_1[0][0]         
__________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D)  (None, 32, 32, 128)  0           add_1[0][0]                      
__________________________________________________________________________________________________
up_sampling2d (UpSampling2D)    (None, 32, 32, 128)  0           batch_normalization_6[0][0]      
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 32, 32, 128)  16512       up_sampling2d_1[0][0]            
__________________________________________________________________________________________________
add_2 (Add)                     (None, 32, 32, 128)  0           up_sampling2d[0][0]              
                                                                 conv2d_3[0][0]                   
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 32, 32, 128)  0           add_2[0][0]                      
__________________________________________________________________________________________________
conv2d_transpose_2 (Conv2DTrans (None, 32, 32, 64)   73792       activation_7[0][0]               
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 32, 32, 64)   256         conv2d_transpose_2[0][0]         
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 32, 32, 64)   0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
conv2d_transpose_3 (Conv2DTrans (None, 32, 32, 64)   36928       activation_8[0][0]               
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 32, 32, 64)   256         conv2d_transpose_3[0][0]         
__________________________________________________________________________________________________
up_sampling2d_3 (UpSampling2D)  (None, 64, 64, 128)  0           add_2[0][0]                      
__________________________________________________________________________________________________
up_sampling2d_2 (UpSampling2D)  (None, 64, 64, 64)   0           batch_normalization_8[0][0]      
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 64, 64, 64)   8256        up_sampling2d_3[0][0]            
__________________________________________________________________________________________________
add_3 (Add)                     (None, 64, 64, 64)   0           up_sampling2d_2[0][0]            
                                                                 conv2d_4[0][0]                   
__________________________________________________________________________________________________
activation_9 (Activation)       (None, 64, 64, 64)   0           add_3[0][0]                      
__________________________________________________________________________________________________
conv2d_transpose_4 (Conv2DTrans (None, 64, 64, 32)   18464       activation_9[0][0]               
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 64, 64, 32)   128         conv2d_transpose_4[0][0]         
__________________________________________________________________________________________________
activation_10 (Activation)      (None, 64, 64, 32)   0           batch_normalization_9[0][0]      
__________________________________________________________________________________________________
conv2d_transpose_5 (Conv2DTrans (None, 64, 64, 32)   9248        activation_10[0][0]              
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 64, 64, 32)   128         conv2d_transpose_5[0][0]         
__________________________________________________________________________________________________
up_sampling2d_5 (UpSampling2D)  (None, 128, 128, 64) 0           add_3[0][0]                      
__________________________________________________________________________________________________
up_sampling2d_4 (UpSampling2D)  (None, 128, 128, 32) 0           batch_normalization_10[0][0]     
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 128, 128, 32) 2080        up_sampling2d_5[0][0]            
__________________________________________________________________________________________________
add_4 (Add)                     (None, 128, 128, 32) 0           up_sampling2d_4[0][0]            
                                                                 conv2d_5[0][0]                   
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 128, 128, 1)  289         add_4[0][0]                      
==================================================================================================
Total params: 508,641
Trainable params: 506,913
Non-trainable params: 1,728
__________________________________________________________________________________________________

In [57]:

# And let the magic happens ! (feel free to try different epoch's number)
history = model.fit(train_gen, validation_data=val_gen, epochs=1, verbose=1)

1600/1600 [==============================] - 1361s 850ms/step - loss: 0.0251 - val_loss: 0.0374

Visualize Predictions¶

OK ! Nice, now we can check if our model performs as desired !

In [59]:

# So for every batch until N
N = 1 # we just check one batch here
for batch in range(N):
    # for every image/segmentation pair in batch
    for img, seg in zip(val_gen[batch][0], val_gen[batch][1]):
        
        # we predict the model's segmenation on this image ("un normalized" so [0, 255] values)
        pred = model.predict(np.expand_dims(img, axis=0)) * 255
        
        # threshold it so we only have 0 - 255 values in our mask
        _, pred_img = cv2.threshold(np.squeeze(pred), 128, 255, cv2.THRESH_BINARY)
        # then convert the numpy array to a PIL Image
        pred_img = Image.fromarray(pred_img.astype(np.uint8))
        
        # and finaly show the image, the real segmentation and the model's estimation !
        fig, axes = plt.subplots(1, 3)
        axes[0].imshow(np.squeeze(img), cmap='gray')
        axes[1].imshow(np.squeeze(seg))
        axes[2].imshow(pred_img)
        fig.show()

C:\Users\Rock_\AppData\Local\Temp/ipykernel_3872/3655903108.py:20: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.
  fig.show()
C:\Users\Rock_\AppData\Local\Temp/ipykernel_3872/3655903108.py:16: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  fig, axes = plt.subplots(1, 3)

It Works !!¶

What to do next ?¶

Well, whereas the model works, it certainely won't fit the submission's machine specs to predict all the test images in less than 10 minutes... (honestly I did not try, maybe it will...)

Now you have to tune the model (along with the data generator) so it can predict faster, while keeping the accuracy high...

In [ ]:

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