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Image_features_ecoder_decoder.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1770e1e63a3c67820b988ce259774f255a3e96e599d41e09fa9d5219a9bc51fe
+size 30734447

cnn-rnn.py

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+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():
+    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("/content/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
+    with open('/content/Image_features_ecoder_decoder.pickle', 'rb') as f:
+        Xnet_features = pickle.load(f)
+
+    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
+
+# get_result("/content/drive/MyDrive/cnn-rnn/NLMCXR_png/CXR192_IM-0598-1001")
+
+
+

encoder_decoder_epoch_5.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ebfd374f6b911ee367b3ebec6f32333e748e2434907173b9bcbf4e725073813c
+size 9953276

my_embedding_matrix.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:91e0fb9ea63d6d1c851f90b379dac110c5d7418334f13a36cc214fde62121575
+size 2572928