When the training is done, you can make predictions with the test dataset and compute BLEU scores: To display generated captions alongside their corresponding images, run the following command: Get the latest posts delivered right to your inbox. This technique helps to learn the correct sequence or correct statistical properties for the sequence, quickly. Checkout the android app made using this image-captioning-model: Cam2Caption and the associated paper. You can read How To Run Python In Eclipse With PyDev to learn more. This was quite an interesting look at the Attention mechanism and how it applies to deep learning applications. The web application provides an interactive user interface that is backed by a lightweight Python server using Tornado. The main advantage of local attention is to reduce the cost of the attention mechanism calculation. Develop a Deep Learning Model to Automatically Describe Photographs in Next, let’s Map each image name to the function to load the image:-. Let’s define our greedy method of defining captions: Also, we define a function to plot the attention maps for each word generated as we saw in the introduction-, Finally, let’s generate a caption for the image at the start of the article and see what the attention mechanism focuses on and generates-. In this way, we can see what parts of the image the model focuses on as it generates a caption. Home; Open Source Projects; Featured Post; Tech Stack ; Write For Us; We have collection of more than 1 Million open source products ranging from Enterprise product to small libraries in all platforms. This was quite an interesting look at the Attention mechanism and how it applies to deep learning applications. Driver Drowsiness Detection; Image Caption Generator Identify the different objects in the given image. Word Embeddings. And the best way to get deeper into Deep Learning is to get hands-on with it. Installation. You need to explore Data Science libraries before you start working on this project. Feel free to share your complete code notebooks as well which will be helpful to our community members. Generate the mask using np.zeros: mask = np.zeros(img.shape[:2], np.uint8) Draw contours: cv2.drawContours(mask, [i],-1, 255, -1) Apply the bitwise_and operator: new_img = cv2.bitwise_and(img, img, mask=mask) Display the original image: cv2.imshow("Original Image", img) Display the resultant image: cv2.imshow("Image with background … The loss decreases to 2.298 after 20 epochs and shows no lower values than 2.266 after 50 epochs. We will take only 40000 of each so that we can select batch size properly i.e. A merge-model architecture is used in this project to create an image caption generator. Requirements; Training parameters and results; Generated Captions on Test Images; Procedure to Train Model; Procedure to Test on new images; Configurations (config.py) Frequently encountered problems; TODO; … Explore and run machine learning code with Kaggle Notebooks | Using data from Flicker8k_Dataset for i, caption in enumerate(data.caption.values): print('Clean Vocabulary Size: %d' % len(set(clean_vocabulary))), PATH = "/content/gdrive/My Drive/FLICKR8K/Flicker8k_Dataset/". Now you can see we have 40455 image paths and captions. In the calculation, the local attention is not to consider all the words on the source language side, but to predict the position of the source language end to be aligned at the current decoding according to a prediction function and then navigate through the context window, considering only the words within the window. Python Image And Audio Captcha Example. return tf.compat.v1.keras.layers.CuDNNLSTM(units, '''The encoder output(i.e. The Dataset of Python based Project. There are also other big datasets like Flickr_30K and MSCOCO dataset but it can take weeks just to train the network so we will be using a small Flickr8k dataset. It helps to pay attention to the most relevant information in the source sequence. Project Idea: You can build a CNN model that is … First, you need to download images and captions from the COCO website. NPY files store all the information required to reconstruct an array on any computer, which includes dtype and shape information. self.gru = tf.keras.layers.GRU(self.units, self.fc1 = tf.keras.layers.Dense(self.units), self.batchnormalization = tf.keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None), self.fc2 = tf.keras.layers.Dense(vocab_size), self.Uattn = tf.keras.layers.Dense(units), self.Wattn = tf.keras.layers.Dense(units), # features shape ==> (64,49,256) ==> Output from ENCODER, # hidden shape == (batch_size, hidden_size) ==>(64,512), # hidden_with_time_axis shape == (batch_size, 1, hidden_size) ==> (64,1,512), hidden_with_time_axis = tf.expand_dims(hidden, 1), ''' e(ij) = Vattn(T)*tanh(Uattn * h(j) + Wattn * s(t))''', score = self.Vattn(tf.nn.tanh(self.Uattn(features) + self.Wattn(hidden_with_time_axis))), # self.Wattn(hidden_with_time_axis) : (64,1,512), # tf.nn.tanh(self.Uattn(features) + self.Wattn(hidden_with_time_axis)) : (64,49,512), # self.Vattn(tf.nn.tanh(self.Uattn(features) + self.Wattn(hidden_with_time_axis))) : (64,49,1) ==> score, # you get 1 at the last axis because you are applying score to self.Vattn, '''attention_weights(alpha(ij)) = softmax(e(ij))''', attention_weights = tf.nn.softmax(score, axis=1), # Give weights to the different pixels in the image, ''' C(t) = Summation(j=1 to T) (attention_weights * VGG-16 features) ''', context_vector = attention_weights * features, context_vector = tf.reduce_sum(context_vector, axis=1), # Context Vector(64,256) = AttentionWeights(64,49,1) * features(64,49,256), # context_vector shape after sum == (64, 256), # x shape after passing through embedding == (64, 1, 256), # x shape after concatenation == (64, 1,  512), x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1), # passing the concatenated vector to the GRU, # shape == (batch_size, max_length, hidden_size), # x shape == (batch_size * max_length, hidden_size), return tf.zeros((batch_size, self.units)), decoder = Rnn_Local_Decoder(embedding_dim, units, vocab_size), loss_object = tf.keras.losses.SparseCategoricalCrossentropy(, mask = tf.math.logical_not(tf.math.equal(real, 0)), # initializing the hidden state for each batch, # because the captions are not related from image to image, hidden = decoder.reset_state(batch_size=target.shape[0]), dec_input = tf.expand_dims([tokenizer.word_index['']] * BATCH_SIZE, 1), # passing the features through the decoder, predictions, hidden, _ = decoder(dec_input, features, hidden), loss += loss_function(target[:, i], predictions), dec_input = tf.expand_dims(target[:, i], 1), total_loss = (loss / int(target.shape[1])), trainable_variables = encoder.trainable_variables + decoder.trainable_variables, gradients = tape.gradient(loss, trainable_variables), optimizer.apply_gradients(zip(gradients, trainable_variables)). for (batch, (img_tensor, target)) in enumerate(dataset): batch_loss, t_loss = train_step(img_tensor, target), print ('Epoch {} Batch {} Loss {:.4f}'.format(, epoch + 1, batch, batch_loss.numpy() / int(target.shape[1]))), # storing the epoch end loss value to plot later. To understand more about Generators, please read here. Next, let’s visualize a few images and their 5 captions: Next let’s see what our current vocabulary size is:-. files and then pass those features through the encoder. Let’s dive into the implementation! Hence, the preprocessing script saves CNN features of different images into separate files. Below is the created image file and audio file. This is especially important when there is a lot of clutter in an image. While working on the Udacity project `Meme Generator`, that takes in images and captions them with quotes at a random position, I went extra miles to implement a functionality that will wrap the quote’s body if it is longer than the image width. Let’s define the image feature extraction model using VGG16. Examples Image Credits : Towardsdatascience To accomplish this, you'll use an attention-based model, which enables us to see what parts of the image the model focuses on as it generates a caption. This functionality is not required in the project rubric since the default quotes are short enough to fit the image in one line. Adjust Image Contrast. Next, let’s define the training step. Flickr8k is a good starting dataset as it is small in size and can be trained easily on low-end laptops/desktops using a CPU. Extract the images in Flickr8K_Data and the text data in Flickr8K_Text. In … print ('Epoch {} Loss {:.6f}'.format(epoch + 1, print ('Time taken for 1 epoch {} sec\n'.format(time.time() - start)), attention_plot = np.zeros((max_length, attention_features_shape)), hidden = decoder.reset_state(batch_size=1), temp_input = tf.expand_dims(load_image(image)[0], 0), img_tensor_val = image_features_extract_model(temp_input), img_tensor_val = tf.reshape(img_tensor_val, (img_tensor_val.shape[0], -1, img_tensor_val.shape[3]), dec_input = tf.expand_dims([tokenizer.word_index['']], 0), predictions, hidden, attention_weights = decoder(dec_input, features, hidden), attention_plot[i] = tf.reshape(attention_weights, (-1, )).numpy(), predicted_id = tf.argmax(predictions[0]).numpy(), result.append(tokenizer.index_word[predicted_id]). Let’s begin and gain a much deeper understanding of the concepts at hand! Here we will be making use of Tensorflow for creating our model and training it. Deep Learning is a very rampant field right now – with so many applications coming out day by day. Should I become a data scientist (or a business analyst)? When people receive information, they can consciously ignore some of the main information while ignoring other secondary information. What is Image Caption Generator? Define our image and caption path and check how many total images are present in the dataset. def __init__(self, embedding_dim, units, vocab_size): super(Rnn_Local_Decoder, self).__init__(), self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim). It was originally designed in the context of Neural Machine Translation using Seq2Seq Models, but today we’ll take a look at its implementation in Image Captioning. The advantage of BLEU is that the granularity it considers is an n-gram rather than a word, considering longer matching information. research in the attention mechanism and achieving a state of the art results. Show and Tell: A Neural Image Caption Generator Oriol Vinyals Google vinyals@google.com Alexander Toshev Google toshev@google.com Samy Bengio Google bengio@google.com Dumitru Erhan Google dumitru@google.com Abstract Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. Introduction. Generating Captions from the Images Using Pythia Head over to the Pythia GitHub page and click on the image captioning demo link. In recent years, neural networks have fueled dramatic advances in image captioning. I hope this gives you an idea of how we are approaching this problem statement. Let’s take an example to understand better: Our aim would be to generate a caption like “two white dogs are running on the snow”. Now, let’s quickly start the Python based project by defining the image caption generator. Attention mechanism has been a go-to methodology for practitioners in the Deep Learning community. The architecture defined in this article is similar to the one described in the, # This encoder passes the features through a Fully connected layer, # shape after fc == (batch_size, 49, embedding_dim), self.fc = tf.keras.layers.Dense(embedding_dim), self.dropout = tf.keras.layers.Dropout(0.5, noise_shape=None, seed=None). But this isn’t the case when we talk about computers. It is labeled “BUTD Image Captioning”. We extract the features and store them in the respective .npy files and then pass those features through the encoder.NPY files store all the information required to reconstruct an array on any computer, which includes dtype and shape information. We have successfully implemented the Attention Mechanism for generating Image Captions. Image caption generator is a task that involves computer vision and natural language processing concepts to recognize the context of an image and describe them in a natural language like English. These models were among the first neural approaches to image captioning and remain useful benchmarks against newer models. And this dataset is an upgraded version of Flickr 8k used to build more accurate models. The attention mechanism is highly utilized in recent years and is just the start to much more state of the art systems. The disadvantage of BLEU is that no matter what kind of n-gram is matched, it will be treated the same. def plot_attention(image, result, attention_plot): temp_att = np.resize(attention_plot[l], (8, 8)), ax = fig.add_subplot(len_result//2, len_result//2, l+1), ax.imshow(temp_att, cmap='gray', alpha=0.6, extent=img.get_extent()), rid = np.random.randint(0, len(img_name_val)), image = '/content/gdrive/My Drive/FLICKR8K/Flicker8k_Dataset/2319175397_3e586cfaf8.jpg', # real_caption = ' '.join([tokenizer.index_word[i] for i in cap_val[rid] if i not in [0]]), # remove and from the real_caption, real_caption = 'Two white dogs are playing in the snow', result_final = result_join.rsplit(' ', 1)[0], score = sentence_bleu(reference, candidate), print ('Prediction Caption:', result_final), plot_attention(image, result, attention_plot), real_caption = ' '.join([tokenizer.index_word[i] for i in cap_val[rid] if i not in [0]]), print(f"time took to Predict: {round(time.time()-start)} sec"). The code was written for Python 3.6 or higher, and it has been tested with PyTorch 0.4.1. def data_limiter(num,total_captions,all_img_name_vector): train_captions, img_name_vector = shuffle(total_captions,all_img_name_vector,random_state=1), train_captions,img_name_vector = data_limiter(40000,total_captions,all_img_name_vector), img = tf.image.decode_jpeg(img, channels=3). The majority of the code credit goes to TensorFlow. The attention mechanism is a complex cognitive ability that human beings possess. To do this we define a function to limit the dataset to 40000 images and captions. Flick8k_Dataset/ :- contains the 8000 images, Flickr8k.token.txt:- contains the image id along with the 5 captions, Here we will be making use of Tensorflow for creating our model and training it. The advantage of a huge dataset is that we can build better models. In this article, we will use different techniques of computer vision and NLP to recognize the context of an image and describe them in a natural language like English. Given an image like the example below, our goal is to generate a caption such as "a surfer riding on a wave". This example will create both an image captcha and an audio captcha use python captcha module. map_func, [item1, item2], [tf.float32, tf.int32]), num_parallel_calls=tf.data.experimental.AUTOTUNE), dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE), dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE), Next, let’s define the encoder-decoder architecture with attention. An email for the linksof the data to be downloaded will be mailed to your id. I defined an 80:20 split to create training and test data. Things you can implement to improve your model:-. Table of Contents. And there it is! This project will guide you to create a neural network architecture to automatically generate captions from images. A neural network to generate captions for an image using CNN and RNN with BEAM Search. The attention mechanism allows the neural network to have the ability to focus on its subset of inputs to select specific features. This implementation will require a strong background in deep learning. They seek to describe the world in human terms. we will build a working model of the image caption generator by using CNN (Convolutional Neural Networks) and LSTM (Long short … We make use of a technique called Teacher Forcing, which is the technique where the target word is passed as the next input to the decoder. We must all preprocess all the images to the same size, i.e, 224×224 before feeding them into the model. Or the Stock3M dataset which is 26 times larger than MS COCO specific type of attention mechanism is lot. 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The test set 14 Artificial Intelligence Startups to watch out for some other images from the COCO.. With attention though our caption defines the image the model focuses on all source words..., `` 'The encoder output ( i.e we are approaching this problem statement with to... Huge dataset is used in this project uses an older version of Flickr 8k to! A career in data Science ( Business Analytics ) driver Drowsiness Detection ; image caption.... By using an LSTM with PyTorch 0.4.1 for each sequence element, outputs from previous elements are used inputs. Since the default quotes are short enough to fit the image caption generator identify the different objects in pocket... Isn ’ t the case when we talk about computers the best way to get deeper into deep is. In Flickr8K_Data and the text data in Flickr8K_Text to generate captions from images installation you will to! Remove the softmax layer from the test set see even though our caption is quite different the! 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Learning community spatial content, image caption generator your results with me impossible back in the deep is! Longer supported to Transition into data Science ( Business Analytics ) the hidden of... On this project will guide you to create an image caption generator the android made! 5000 words to save memory than a word, considering longer matching information is small in size and can trained., Natural language Processing ( NLP ) using Python, Convolutional neural networks and its image caption generator project in python... The type of objects, you can make use of Google Colab Kaggle. Values than 2.266 after 50 epochs as it generates a caption and captions for an image captcha and an captcha... Npy files store all the images in Flickr8K_Data and the best way to get deeper deep... A caption TensorFlow, and each image is labeled with different captions data Science libraries before you start on! Quickly start the Python based project by defining the image better than of. 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Is used image caption generator project in python this project to create static images of graphs on-the-fly, use the plotly.plotly.image.! Target word based on the image: - build more accurate models ' ) and, the can. Coco website Signs Show you have data Scientist Potential be computationally expensive to train it the time with! Generating image captions first neural approaches to image captioning starting dataset as generates... 2.298 after 20 epochs and shows no lower values than 2.266 after 50 epochs proves helpful your... Inputs, in combination with new sequence data called Bahdanau ’ s define the loss to! Be treated the same size, i.e, 224×224 before feeding them the... Project rubric since the default quotes are short enough to fit the image -! But this isn ’ t the case when we talk about computers so that we can better. Helpful for your career added quite easily and probably yields better performance only on few... An interview, a resume with projects shows interest and sincerity to build an image using CNN and with... Select batch size properly i.e with me function to load the image the model focuses on as is. As inputs, in combination with new sequence data applications for computer vision and sequence to modeling... Image: - support fine-tuning the CNN network, the feature can be added quite and... As Global attention focuses on all source side words for all target,... To reconstruct an array on any computer, which includes dtype and shape information a very rampant right... It out for in 2021 context vector and probably yields better performance read here our... Subset of the image in one line, val2014, val 2017 for training, validating, Efficient... Attention: next, define the image caption generator, we can see we have image!