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License Plate Recognition with OpenCV and Tesseract OCR

Image Segmentation Using TensorFlow

Last Updated : 01 Aug, 2022

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Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars, satellite image analysis, etc. There are different types of image segmentation techniques like semantic segmentation, instance segmentation, etc. To summarize the key goal of image segmentation is to recognize and understand what's in an image at the pixel level.

For the image segmentation task, we will use "The Oxford-IIIT Pet Dataset" which is free to use dataset. They have 37 category pet dataset with roughly 200 images for each class. The images have large variations in scale, pose and lighting. All images have an associated ground truth annotation of breed, head ROI, and pixel-level trimap segmentation. Each pixel is classified into one of the three categories:

Pixel belonging to the pet
Pixel bordering the pet
Pixel belongs neither in class 1 nor in class 2

Imports

Let's first import all the required dependencies and packages required to run our program.

Python3

import pandas as pd import numpy as np import matplotlib.pyplot as plt import os import seaborn as sns from tensorflow import keras import tensorflow as tf import tensorflow_datasets as tfds import cv2 import PIL from IPython.display import clear_output

Downloading dataset

The given dataset is readily available in the TensorFlow datasets. Just download it from there using the tfds.load() function.

Python3

dataset, info = tfds.load('oxford_iiit_pet:3.*.*', with_info=True)

Data Processing

First, we will normalize the pixel values in the range of [0,1]. For this, we will divide each pixel value by 255. The dataset included from the TensorFlow is already divided into train and test split.

Python3

def normalize(input_image, input_mask):        # Normalize the pixel range values between [0:1]     img = tf.cast(input_image, dtype=tf.float32) / 255.0     input_mask -= 1     return img, input_mask  @tf.function def load_train_ds(dataset):     img = tf.image.resize(dataset['image'],                            size=(width, height))     mask = tf.image.resize(dataset['segmentation_mask'],                            size=(width, height))      if tf.random.uniform(()) > 0.5:         img = tf.image.flip_left_right(img)         mask = tf.image.flip_left_right(mask)      img, mask = normalize(img, mask)     return img, mask  @tf.function def load_test_ds(dataset):     img = tf.image.resize(dataset['image'],                            size=(width, height))     mask = tf.image.resize(dataset['segmentation_mask'],                             size=(width, height))      img, mask = normalize(img, mask)     return img, mask

Now we will set some constant values such as Buffer size, input height and width, etc.

Python3

TRAIN_LENGTH = info.splits['train'].num_examples  # Batch size is the number of examples used in one training example. # It is mostly a power of 2 BATCH_SIZE = 64 BUFFER_SIZE = 1000 STEPS_PER_EPOCH = TRAIN_LENGTH // BATCH_SIZE  # For VGG16 this is the input size width, height = 224, 224

Now, let's load the train and test data into different variables and perform the data augmentation after batching is done.

Python3

train = dataset['train'].map(     load_train_ds, num_parallel_calls=tf.data.AUTOTUNE) test = dataset['test'].map(load_test_ds)  train_ds = train.cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE).repeat() train_ds = train_ds.prefetch(buffer_size=tf.data.AUTOTUNE) test_ds = test.batch(BATCH_SIZE)

Visualizing Data

Visualize an image example and its corresponding mask from the dataset.

Python3

def display_images(display_list):     plt.figure(figsize=(15, 15))     title = ['Input Image', 'True Mask',               'Predicted Mask']      for i in range(len(display_list)):         plt.subplot(1, len(display_list), i+1)         plt.title(title[i])         plt.imshow(keras.preprocessing.image.array_to_img(display_list[i]))         plt.axis('off')      plt.show()   for img, mask in train.take(1):     sample_image, sample_mask = img, mask     display_list = sample_image, sample_mask  display_images(display_list)

Output:

U-Net

U-Net is a CNN architecture used for most of the segmentation tasks. It consists of a contraction and expansion path which gives it the name UNet. The contraction path consists of a convolution layer, followed by ReLu followed by max-pooling layers. Along the contraction path, the features get extracted and the spatial information is reduced. Along the expansion path, a series of up convolutions along with concatenation of nigh resolution features are done from the contraction path.

For this project, we will use the encoder of the VGG16 model as it is already trained on the ImageNet dataset and has learned some features. If the original UNet encoder is used it will learn everything from scratch and will take more time.

Python3

base_model = keras.applications.vgg16.VGG16(     include_top=False, input_shape=(width, height, 3))  layer_names = [     'block1_pool',     'block2_pool',     'block3_pool',     'block4_pool',     'block5_pool', ] base_model_outputs = [base_model.get_layer(     name).output for name in layer_names] base_model.trainable = False  VGG_16 = tf.keras.models.Model(base_model.input,                                base_model_outputs)

Now define the decoder

Python3

def fcn8_decoder(convs, n_classes):     f1, f2, f3, f4, p5 = convs      n = 4096     c6 = tf.keras.layers.Conv2D(         n, (7, 7), activation='relu', padding='same',        name="conv6")(p5)     c7 = tf.keras.layers.Conv2D(         n, (1, 1), activation='relu', padding='same',        name="conv7")(c6)      f5 = c7      # upsample the output of the encoder     # then crop extra pixels that were introduced     o = tf.keras.layers.Conv2DTranspose(n_classes, kernel_size=(         4, 4),  strides=(2, 2), use_bias=False)(f5)     o = tf.keras.layers.Cropping2D(cropping=(1, 1))(o)      # load the pool 4 prediction and do a 1x1     # convolution to reshape it to the same shape of `o` above     o2 = f4     o2 = (tf.keras.layers.Conv2D(n_classes, (1, 1),                                  activation='relu',                                   padding='same'))(o2)      # add the results of the upsampling and pool 4 prediction     o = tf.keras.layers.Add()([o, o2])      # upsample the resulting tensor of the operation you just did     o = (tf.keras.layers.Conv2DTranspose(         n_classes, kernel_size=(4, 4),  strides=(2, 2),        use_bias=False))(o)     o = tf.keras.layers.Cropping2D(cropping=(1, 1))(o)      # load the pool 3 prediction and do a 1x1     # convolution to reshape it to the same shape of `o` above     o2 = f3     o2 = (tf.keras.layers.Conv2D(n_classes, (1, 1),                                  activation='relu',                                   padding='same'))(o2)      # add the results of the upsampling and pool 3 prediction     o = tf.keras.layers.Add()([o, o2])      # upsample up to the size of the original image     o = tf.keras.layers.Conv2DTranspose(         n_classes, kernel_size=(8, 8),  strides=(8, 8),       use_bias=False)(o)      # append a softmax to get the class probabilities     o = tf.keras.layers.Activation('softmax')(o)     return o

Combining everything and creating a final model and compiling it

Python3

def segmentation_model():      inputs = keras.layers.Input(shape=(width, height, 3))     convs = VGG_16(inputs)     outputs = fcn8_decoder(convs, 3)     model = tf.keras.Model(inputs=inputs, outputs=outputs)      return model   opt = keras.optimizers.Adam()  model = segmentation_model() model.compile(optimizer=opt,               loss=tf.keras.losses.SparseCategoricalCrossentropy(                   from_logits=True),               metrics=['accuracy'])

Creating prediction mask utility

We will generate a function which will show us the real image its true mask and the predicted mask in a single row. It is more of a visualization utility function

Python3

def create_mask(pred_mask):     pred_mask = tf.argmax(pred_mask, axis=-1)     pred_mask = pred_mask[..., tf.newaxis]     return pred_mask[0]   def show_predictions(dataset=None, num=1):     if dataset:         for image, mask in dataset.take(num):             pred_mask = model.predict(image)             display_images([image[0], mask[0], create_mask(pred_mask)])     else:         display_images([sample_image, sample_mask,                         create_mask(model.predict(sample_image[tf.newaxis, ...]))])   show_predictions()

Output:

Training

As all the required functions along with the model are created now we will train the model. We will train the model for 20 epochs and perform a validation split of 5.

Python3

EPOCHS = 20 VAL_SUBSPLITS = 5 VALIDATION_STEPS = info.splits['test'].num_examples//BATCH_SIZE//VAL_SUBSPLITS  model_history = model.fit(train_ds, epochs=EPOCHS,                           steps_per_epoch=STEPS_PER_EPOCH,                           validation_steps=VALIDATION_STEPS,                           validation_data=test_ds)

Output:

Prediction after 20 epochs

Metrics

For the segmentation task, 2 types of metrics are considered. The first one is the Intersection over Union(IOU) and dice score. The formulas for each of them are as follows:

IOU = \frac{Area of overlap}{Are of Union} and DiceScore = 2*\frac{Area of Overlap}{Combined Area}

IoU is the area of overlap between predicted segmentation and real mask divided by the union of the two.

Python3

def compute_metrics(y_true, y_pred):     '''     Computes IOU and Dice Score.      Args:       y_true (tensor) - ground truth label map       y_pred (tensor) - predicted label map     '''      class_wise_iou = []     class_wise_dice_score = []      smoothening_factor = 0.00001      for i in range(3):         intersection = np.sum((y_pred == i) * (y_true == i))         y_true_area = np.sum((y_true == i))         y_pred_area = np.sum((y_pred == i))         combined_area = y_true_area + y_pred_area          iou = (intersection + smoothening_factor) / \             (combined_area - intersection + smoothening_factor)         class_wise_iou.append(iou)          dice_score = 2 * ((intersection + smoothening_factor) /                           (combined_area + smoothening_factor))         class_wise_dice_score.append(dice_score)      return class_wise_iou, class_wise_dice_score

Model Prediction with Metrics

Python3

def get_test_image_and_annotation_arrays():     '''     Unpacks the test dataset and returns     the input images and segmentation masks     '''      ds = test_ds.unbatch()     ds = ds.batch(info.splits['test'].num_examples)      images = []     y_true_segments = []      for image, annotation in ds.take(1):         y_true_segments = annotation.numpy()         images = image.numpy()      y_true_segments = y_true_segments[:(         info.splits['test'].num_examples - (info.splits['test']                                             .num_examples % BATCH_SIZE))]     images = images[:(info.splits['test'].num_examples -                       (info.splits['test'].num_examples % BATCH_SIZE))]      return images, y_true_segments   y_true_images, y_true_segments = get_test_image_and_annotation_arrays()  integer_slider = 2574 img = np.reshape(y_true_images[integer_slider], (1, width, height, 3)) y_pred_mask = model.predict(img) y_pred_mask = create_mask(y_pred_mask) y_pred_mask.shape   def display_prediction(display_list, display_string):     plt.figure(figsize=(15, 15))     title = ['Input Image', 'True Mask', 'Predicted Mask']      for i in range(len(display_list)):         plt.subplot(1, len(display_list), i+1)         plt.title(title[i])         plt.xticks([])         plt.yticks([])         if i == 1:             plt.xlabel(display_string, fontsize=12)         plt.imshow(keras.preprocessing.image.array_to_img(display_list[i]))     plt.show()   iou, dice_score = compute_metrics(     y_true_segments[integer_slider], y_pred_mask.numpy()) display_list = [y_true_images[integer_slider],                 y_true_segments[integer_slider], y_pred_mask]  display_string_list = ["{}: IOU: {} Dice Score: {}".format(class_names[idx],                                                            i, dc) for idx, (i, dc) in                        enumerate(zip(np.round(iou, 4), np.round(dice_score, 4)))] display_string = "\n\n".join(display_string_list)   # showing predictions with metrics display_prediction(display_list, display_string)

Output:

Hence, we have finally performed Image segmentation using TensorFlow with the Oxford IIIT pet dataset.

License Plate Recognition with OpenCV and Tesseract OCR

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sahilgangurde08

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Article Tags :

Deep Learning
AI-ML-DS
Tensorflow

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Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed for processing images. Unlike traditional neural networks CNNs uses convolutional layers to automatically and efficiently extract features such as edges, textures and patterns from images. This makes them hi

Traffic Signs Recognition using CNN and Keras in Python

We always come across incidents of accidents where drivers' Overspeed or lack of vision leads to major accidents. In winter, the risk of road accidents has a 40-50% increase because of the traffic signs' lack of visibility. So here in this article, we will be implementing Traffic Sign recognition us

Lung Cancer Detection using Convolutional Neural Network (CNN)

Computer Vision is one of the applications of deep neural networks and one such use case is in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using Convolution Neural Network which can classify normal lung tissues from cancerous tissues.The follo

Lung Cancer Detection Using Transfer Learning

Computer Vision is one of the applications of deep neural networks that enables us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells.In this article, we will learn how to build a classifier using the Transfer Learning technique

Pneumonia Detection using Deep Learning

In this article, we will discuss solving a medical problem i.e. Pneumonia which is a dangerous disease that may occur in one or both lungs usually caused by viruses, fungi or bacteria. We will detect this lung disease based on the x-rays we have. Chest X-rays dataset is taken from Kaggle which conta

Detecting Covid-19 with Chest X-ray

COVID-19 pandemic is one of the biggest challenges for the healthcare system right now. It is a respiratory disease that affects our lungs and can cause lasting damage to the lungs that led to symptoms such as difficulty in breathing and in some cases pneumonia and respiratory failure. In this artic

Skin Cancer Detection using TensorFlow

In this article, we will learn how to implement a Skin Cancer Detection model using Tensorflow. We will use a dataset that contains images for the two categories that are malignant or benign. We will use the transfer learning technique to achieve better results in less amount of training. We will us

Age Detection using Deep Learning in OpenCV

The task of age prediction might sound simple at first but it's quite challenging in real-world applications. While predicting age is typically seen as a regression problem this approach faces many uncertainties like camera quality, brightness, climate condition, background, etc. In this article we'

Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV

In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions to detect all the face and hand landmarks. We will be also seeing how we can access different landmarks of the face and hands which can be used for diff

Detecting COVID-19 From Chest X-Ray Images using CNN

A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. T

Image Segmentation Using TensorFlow

Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars,

License Plate Recognition with OpenCV and Tesseract OCR

License Plate Recognition is widely used for automated identification of vehicle registration plates for security purpose and law enforcement. By combining computer vision techniques with Optical Character Recognition (OCR) we can extract license plate numbers from images enabling applications in ar

Detect and Recognize Car License Plate from a video in real time

Recognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro

Residual Networks (ResNet) - Deep Learning

After the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble

Natural Language Processing Projects

Twitter Sentiment Analysis using Python

This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. Itâ€™s also known as opinion mini

Facebook Sentiment Analysis using python

This article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w

Next Sentence Prediction using BERT

Next Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows

Hate Speech Detection using Deep Learning

There must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat

Image Caption Generator using Deep Learning on Flickr8K dataset

Generating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image

Movie recommendation based on emotion in Python

Movies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their

Speech Recognition in Python using Google Speech API

Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, youâ€™ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require

Voice Assistant using python

Speech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide weâ€™ll create a basic voice assistant using Python.Step 1: Install Required

Human Activity Recognition - Using Deep Learning Model

Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque

Fine-tuning BERT model for Sentiment Analysis

Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train

Sentiment Classification Using BERT

BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec

Sentiment Analysis with an Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews

Building an Autocorrector Using NLP in Python

Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N

Python | NLP analysis of Restaurant reviews

Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach

Restaurant Review Analysis Using NLP and SQLite

Normally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei

Twitter Sentiment Analysis using Python

This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. Itâ€™s also known as opinion mini

Clustering Projects

Customer Segmentation using Unsupervised Machine Learning in Python

Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning

Music Recommendation System Using Machine Learning

When did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona

K means Clustering â€“ Introduction

K-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ

Image Segmentation using K Means Clustering

Image segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image.Â Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im

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