cnnrnn.py

import tensorflow as tf
from tensorflow.keras.applications import densenet
from tensorflow.keras.applications.densenet import preprocess_input
from tensorflow.keras.layers import Dense, Dropout, Input, Conv2D
from tensorflow.keras.models import Model
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
import os
import cv2
import tensorflow as tf
import re
import pickle
from PIL import Image
from skimage.transform import resize
import warnings
warnings.filterwarnings('ignore')
import seaborn as sns
from tqdm import tqdm
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
import time
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, LSTM, Input, Embedding, Conv2D, Concatenate, Flatten, Add, Dropout, GRU
import random
import datetime
from nltk.translate.bleu_score import sentence_bleu



def getModel():
    embedding_matrix_vocab = np.load('my_embedding_matrix.npy')

    input1 = Input(shape=(2048), name='Image_input')
    dense1 = Dense(256, kernel_initializer=tf.keras.initializers.glorot_uniform(seed = 56), name='dense_encoder')(input1)

    input2 = Input(shape=(153), name='Text_Input')
    embedding_layer = Embedding(input_dim = 1427, output_dim = 300, input_length=153, mask_zero=True, trainable=False,
                weights=[embedding_matrix_vocab], name="Embedding_layer")
    emb = embedding_layer(input2)

    LSTM1 = LSTM(units=256, activation='tanh', recurrent_activation='sigmoid', use_bias=True,
            kernel_initializer=tf.keras.initializers.glorot_uniform(seed=23),
            recurrent_initializer=tf.keras.initializers.orthogonal(seed=7),
            bias_initializer=tf.keras.initializers.zeros(), return_sequences=True, name="LSTM1")(emb)
    #LSTM1_output = LSTM1(emb)

    LSTM2 = LSTM(units=256, activation='tanh', recurrent_activation='sigmoid', use_bias=True,
            kernel_initializer=tf.keras.initializers.glorot_uniform(seed=23),
            recurrent_initializer=tf.keras.initializers.orthogonal(seed=7),
            bias_initializer=tf.keras.initializers.zeros(), name="LSTM2")
    LSTM2_output = LSTM2(LSTM1)

    dropout1 = Dropout(0.5, name='dropout1')(LSTM2_output)

    dec =  tf.keras.layers.Add()([dense1, dropout1])

    fc1 = Dense(256, activation='relu', kernel_initializer=tf.keras.initializers.he_normal(seed = 63), name='fc1')
    fc1_output = fc1(dec)

    dropout2 = Dropout(0.4, name='dropout2')(fc1_output)

    output_layer = Dense(1427, activation='softmax', name='Output_layer')
    output = output_layer(dropout2)

    encoder_decoder = Model(inputs = [input1, input2], outputs = output)

    encoder_decoder.load_weights("encoder_decoder_epoch_5.h5")
    # encoder
  
    encoder_input = encoder_decoder.input[0]
    encoder_output = encoder_decoder.get_layer('dense_encoder').output
    encoder_model = Model(encoder_input, encoder_output)

    # decoder#
    text_input = encoder_decoder.input[1]
    enc_output = Input(shape=(256,), name='Enc_Output')
    text_output = encoder_decoder.get_layer('LSTM2').output
    add1 = tf.keras.layers.Add()([text_output, enc_output])
    fc_1 = fc1(add1)
    decoder_output = output_layer(fc_1)

    decoder_model = Model(inputs = [text_input, enc_output], outputs = decoder_output)

    return encoder_model,decoder_model


def greedysearch(image):
    # Open the pickle file for reading
    train_data = pd.read_csv('Final_Train_Data.csv')
    y_train = train_data['Report']
    encoder_model, decoder_model = getModel()
    input_ = 'startseq'
    image_features = encoder_model.predict(image)
    result = []
    tokenizer = Tokenizer(filters='!"#$%&()*+,-/:;<=>?@[\]^_`{|}~\t\n')
    tokenizer.fit_on_texts(y_train.values)
    for i in range(153):
        input_tok = [tokenizer.word_index[w] for w in input_.split()]
        input_padded = pad_sequences([input_tok], 153, padding='post')
        predictions = decoder_model.predict([input_padded, image_features])
        arg = np.argmax(predictions)
        if arg != 7:   # endseq
            result.append(tokenizer.index_word[arg])
            input_ = input_ + ' ' + tokenizer.index_word[arg]
        else:
            break
    rep = ' '.join(e for e in result)
    return rep


def get_result(img):
    pre_Report = greedysearch(img)
    print('------------------------------------------------------------------------------------------------------')
    print("Predicted Report : ",pre_Report)
    print('------------------------------------------------------------------------------------------------------')
    return pre_Report


# with open('/content/Image_features_ecoder_decoder.pickle', 'rb') as f:
#       Xnet_features = pickle.load(f)

# image = Xnet_features["/content/drive/MyDrive/cnn-rnn/NLMCXR_png/CXR545_IM-2149_0"]
# get_result(image)