In the previous tutorial we learned how the DNN module in OpenCV works, we went into a lot of details regarding different aspects of the module including where to get the models, how to configure them, etc.
This Tutorial will build on top of the previous one so if you haven’t read the previous post then you can read that here.
Today’s post is the second tutorial in our brand new 3 part Deep Learning with OpenCV series. All three posts are titled as:
In this post, we will train a custom image classifier with Tensorflow’s Keras API. So if you want to learn how to get started creating a Convolutional Neural Network using Tensorflow, then this post is for you, and not only that but afterward, we will also convert our trained .h5 model to ONNX format and then use it with OpenCV DNN module.
Converting your model to onnx will give you more than a 3x reduction in model size.
This whole process shows you how to train models in Tensorflow and then deploy them directly in OpenCV.
What’s the advantage of using the trained model in OpenCV vs using it in Tensorflow?
So here are some points you may want to consider.
By using OpenCV’s DNN module, the final code is a lot compact and simpler.
Someone who’s not familiar with the training framework like TensorFlow can also use this model.
Besides supporting CUDA based NVIDIA’s GPU, OpenCV’s DNN module also supports OpenCL based Intel GPUs.
Most Importantly by getting rid of the training framework (Tensorflow) not only makes the code simpler but it ultimately gets rid of a whole framework, this means you don’t have to build your final application with a heavy framework like TensorFlow. This is a huge advantage when you’re trying to deploy on a resource-constrained edge device, e.g. a Raspberry pie
So this way you’re getting the best of both worlds, a framework like Tensorflow for training and OpenCV DNN for faster inference during deployment.
This tutorial can be split into 3 parts.
Training a Custom Image Classifier in OpenCV with Tensorflow
Converting Our Classifier to ONNX format.
Using the ONNX model directly in the OpenCV DNN module.
Let’s start with the Code
Download Code
Part 1: Training a Custom Image Classifier with Tensorflow:
For this tutorial you need OpenCV4.0.0.21 and Tensorflow 2.2
So you should do:
pip install opencv-contrib-python==4.0.0.21 (Or install from Source, Make sure to change the version)
Note: The reason I’m asking you to install version 4.0 instead of the latest 4.3 version of OpenCV is that later on, we’ll be using a function called readNetFromONNX() now with our model this function was giving an error in 4.3 and 4.2, possibly due to some bug in those versions. This does not mean that you can’t use custom models with those versions but that for my specific case there was an issue. Converting models only takes 2-3 lines of code but sometimes you get ambiguous errors that are hard to diagnose, but it can be done.
Hopefully, the conversion process will get better in the future.
One thing you can do is create a custom environment (with Anaconda or virtualenv) in which you can install version 4.0 without affecting your root environment and if you’re using google colab for this tutorial then you don’t need to worry about that.
You can go ahead and download the source code from the download code section. After downloading the zip folder, unzip it and you will have the following directory structure.
Let’s see how you would go about training a basic Convolutional Network in Tensorflow. I assume you know some basics of deep learning. Also in this tutorial, I will be teaching how to construct and train a classifier using a real-world dataset, not a toy one, I will not go in-depth and explain the theory behind neural networks. If you want to start learning deep learning then you can take a look at Andrew Ng’s Deep Learning specialization, although this specialization is basic and covers mostly foundational things now if your end goal is to specialize in computer Vision then I would strongly recommend that you first learn Image Processing and Classical Computer Vision techniques from my 3-month comprehensive course here.
The Dataset we’re going to use here is a dataset of 5 different flowers, namely rose, tulips, sunflower, daisy, and dandelion. I avoided the usual cats and dogs dataset.
You can download the dataset from a URL, you just have to run this cell
After downloading the dataset you’ll have to unzip it, you can also do this manually.
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filename="flower_photos.tgz"
tf=tarfile.open(filename)
tf.extractall()
After extracting you can check the folder named flower_photos in your current directory which will contain these 5 subfolders.
You can check the number of images in each class using the code below.
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# This is the directory path where all the class folders are
dir_path='flower_photos'
# Initialize classes list, this list will contain the names of our classes.
classes=[]
# Iterate over the names of each class
forclass_name inos.listdir(dir_path):
# Get the full path of each class
class_path=os.path.join(dir_path,class_name)
# Check if the class is a directory/folder
ifos.path.isdir(class_path):
# Get the number of images in each class and print them
No_of_images=len(os.listdir(class_path))
print("Found {} images of {}".format(No_of_images,class_name))
# Also store the name of each class
classes.append(class_name)
# Sort the list in alphabatical order and print it
classes.sort()
print(classes)
Found 699 images of sunflowers Found 898 images of dandelion Found 633 images of daisy Found 799 images of tulips Found 641 images of roses [‘daisy’, ‘dandelion’, ‘roses’, ‘sunflowers’, ‘tulips’]
Generate Images:
Now it’s time to load up the data, now since the data is approx 218 MB, we can actually load it in RAM but most real datasets are large several GBs in size, and will fit in your RAM. In those scenarios, you use data generators to fetch batches of data and feed it to the neural network during training, so today we’ll also be using a data generator to load the data.
Before we can pass the images to a deep learning model, we need to do some preprocessing, like resizing the image in the required shape, converting them to floating-point tensors, rescale the pixel values from 0-255 to 0-1 range as this helps in training.
Fortunately, all of this can be done by the ImageDataGenerator class in tf.keras. Not only that but the ImageDataGenerator Class can also perform data augmentation. Data augmentation means that the generator takes your image and performs random transformations like randomly rotating, zooming, translating, and performing other such operations to the image. This is really effective when you don’t have much data as this increases your dataset size on the fly and your dataset contains more variation which helps in generalization.
As you’ve already seen that each flower class has less than 1000 examples, so in our case data augmentation will help a lot. It will expand our dataset.
When training a Neural Network, we normally use 2 datasets, a training dataset, and a validation dataset. The neural network tunes its parameters using the training dataset and the validation dataset is used for the evaluation of the Network’s performance.
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# Set the batch size, width, height and the percentage of the validation split.
batch_size=60
IMG_HEIGHT=224
IMG_WIDTH=224
split=0.2
# Setup the ImagedataGenerator for training, pass in any supported augmentation schemes, notice that we're also splitting the data with split argument.
# Setup the ImagedataGenerator for validation, no augmentation is done, only rescaling is done, notice that we're also splitting the data with split argument.
# Data Generator for validation images with the same seed to make sure there is no data overlap, notice that we are specifying the subset as 'validation'
# The "subset" variable tells the Imagedatagerator class which generator gets 80% and which gets 20% of the data
Found 2939 images belonging to 5 classes. Found 731 images belonging to 5 classes.
Note: Usually when using an ImageDataGenerator to read from a directory with data augmentation we usually have two folders for each class because data augmentation is done only to the training dataset, not the validation set as this set is only used for evaluation. So I’ve actually created two data generators instances for the same directory with a validation split of 20% and used a constant random seed on both generators so there is no data overlap.
I’ve rarely seen people split with augmentation this way but this approach actually works and saves us the time of splitting data between two directories.
Visualize Images:
It’s always a good idea to see what images look like in your dataset, so here’s a function that will plot new images from the dataset each time you run it.
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# Here we are creating a function for displaying images of flowers from the data generators
# By default we're displaying 15 images, you can show more examples
total_samples=sample_training_images[:no]
cols=5
rows=np.floor(len(total_samples)/cols)
fori,img inenumerate(total_samples,1):
plt.subplot(rows,cols,i);plt.imshow(img);
# Converting One hot encoding labels to string labels and displaying it.
class_name=classes[np.argmax(labels[i-1])]
plt.title(class_name);plt.axis('off');
Alright, now we’ll use the above function to first display a few of the original images using the validation generator.
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# Display Original Images
display_images(vald_data_generator)
Now we will generate some Augmented images using the trained generator. Notice how images are rotated, zoomed, etc.
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# Display Augmented Images
display_images(train_data_generator)
Create the Model
Since we’re using Tensorflow 2 (TF2) and in TF2the most popular way to go about creating neural networks is by using the Keras API. Previously Keras used to be a separate framework (it still is) but not so long ago because of Keras’ popularity in the community it was included in TensorFlow as the default high-level API. This abstraction allows developers to use TensorFlow’s low-level functionality with high-level Keras code.
This way you can design powerful neural networks in just a few lines of code. E.g. take a look at how we have created effective Convolutional Networks.
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# First Reset the generators, since we used the first batch to display the images.
vald_data_generator.reset()
train_data_generator.reset()
# Here we are creating Sequential model also defing its layers
A typical neural network has a bunch of layers, in a Convolutional network, you’ll see convolutional layers. These layers are created with the Conv2d function. Take a look at the first layer:
The number 16 refers to the number of filters in that layer, normally we increase the number of filters as you add more layers. You should notice that I double the number of filters in each subsequent convolutional layer i.e. 16, 32, 64 …, this is common practice. In the first layer, you also specify a fixed input shape that the model will accept, which we have already set as 200x200
Another thing you’ll see is that typically a convolutional layer is followed by a pooling layer. So the Conv layer outputs a number offeature maps and the pooling layer reduces the spatial size (width and height) of these feature maps which effectively reduces the number of parameters in the network thus reducing computation.
So you’ll commonly have a convolutional layer followed by a pooling layer, this is normally repeated several times, at each stage the size is reduced and the no of filters is increased. We are using a MaxPooling layer there are other pooling types too e.g. AveragePooling.
The Dropout layer randomly drops x% percentage of parameters from the network, this allows the network to learn robust features. In the network above I’m using dropout twice and so in those stages I’m dropping 10% of the parameters. The whole purpose of the Dropout layer is to reduce overfitting.
Now before we add the final layer we need to flatten the output in a single-dimensional vector, this can be done by the flatten layer but a better method is using the GlobalAveragePooling2D Layer, which flattens the output while reducing the parameters.
Finally, before our last layer, we also use a Dense layer (A fully connected layer) with 1024 units. The final layer contains the number of units equal to the number of classes. The activation function here is softmax as I want the network to produce class probabilities at the end.
Compile the model
Before we can start training the network we need to compile it, this is the step where we define our loss function, optimizer, and metrics.
For this example, we are using the ADAM optimizer and a categorical cross-entropy loss function as we’re dealing with a multi-class classification problem. The only metric we care about right now is the accuracy of the model.
By using the built-in method called summary() we can see the whole architecture of the model that we just created. You can see the total parameter count and the number of params in each layer.
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model.summary()
Notice how the number of params is 0 in all layers except the Conv and Dense layers, this is because these are the only two types of layers here which are actually involved in learning.
Training the Model:
You can start training the model using the model.fit() method but first, specify the number of epochs, and the steps per epoch.
Epoch: A single epoch means 1 pass of the whole data meaning an epoch is considered done when the model goes over all the images in the training data and uses it for gradient calculation and optimizations. So this number decides how many times the model will go over your whole data.
Steps per epoch: A single step means the model goes over a single batch of the data, so steps per epoch tells, after how many steps should an epoch be considered done. This should be set to dataset_size / batch_size which is the number of steps required to go over the whole data once.
# Use model.fit_generator() if using TF version < 2.2
………………………………….. …………………………………..
You can see in the last epoch that our validation loss is low and accuracy is high so our model has successfully converged, we can further verify this by plotting the loss and accuracy graphs.
After you’re done training it’s a good practice to plot accuracy and loss graphs.
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# Plot the accuracy and loss curves for both training and validation
The model has slightly overfitted at the end but that is okay considering the number of images we used and our model’s capacity.
You can test out the trained model on a single test image using this code. Make sure to carry out the same preprocessing steps you used before training for e.g. since we trained on normalized images in range 0-1, we will need to divide any new image with 255 before passing it to the model for prediction.
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# Read the rose image
img=cv2.imread('rose.jpg')
# Resize the image to the size you trained on.
imgr=cv2.resize(img,(224,224))
# Convert image BGR TO RGB, since OpenCV works with BGR and tensorflow in RGB.
imgrgb=cv2.cvtColor(imgr,cv2.COLOR_BGR2RGB)
# Normalize the image to be in range 0-1 and then convert to a float array.
# Get the name of the predicted class using the index
label=classes[index]
# Display the image and print the predicted class name with its confidence.
print("Predicted Flowers is : {} {:.2f}%".format(label,prob*100))
plt.imshow(img[:,:,::-1]);plt.axis("off");
Predicted Flowers is : roses, 85.61%
Notice that we are converting our model from BGR to RGB color format. This is because TensorFlow has trained the model using images in RGB format whereas OpenCV reads images in BGR format, so we have to reverse channels before we can perform prediction.
Finally, when you’re satisfied with the model you save it in .h5 format using the model.save function.
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# Saving your model to disk allows you to use it later
model.save('flowers.h5')
# Later on you can load your model this way
#model = load_model('Model/flowers.h5')
Part 2: Converting Our Classifier to ONNX format
Now that we have trained our model, it’s time to convert it to ONNX format.
What is ONNX?
ONNX stands for Open neural network exchange. ONNX is an industry-standard format for changing model frameworks, this means you can train a model in PyTorch or any other common frameworks and then convert to onnx and then convert back to TensorFlow or any other framework.
So ONNX allows developers to move models between different frameworks such as CNTK, Caffe2, Tensorflow, PyTorch, etc.
So why are we converting to ONNX?
Remember our goal is to use the above custom trained model in the DNN module but the issue is the DNN module does not support using the .h5 Keras model directly. So we have to convert our .h5 model to a .onnx model after doing this we will be able to take the onnx model and plug it into the DNN module.
Note: Even if you saved the model in saved_model format then you still can’t use it directly
You need to use keras2onnx module to perform the conversion so you should go ahead and install the keras2onnx module.
pip install keras2onnx
You also need to install onnx so that you can save .onnx models to disk.
pip install onnx
After installing keras2onnx, you can use its convert_keras function to convert the model, we will also serialize the model to disk using keras2onnx.save_model so we can use it later.
tf executing eager_mode: True tf.keras model eager_mode: False The ONNX operator number change on the optimization: 57 -> 25
Now we’re ready to use this model in the DNN module. Check how your ~7.5 MB .h5 model now has reduced to ~2.5 MB .onnx model, a 3x reduction in size. Make sure to check out keras2onnx repo for more details.
Note: You can even use this model with just ONNX using onnxruntimemodule which itself is pretty powerful considering the support of multiple hardware accelerations.
Using the ONNX model in the OpenCV DNN module:
Now we will take this ONNX model and use it directly in our DNN module.
Let’s use this as a test image.
Here’s the code to test the ONNX model on the image.
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# Make sure your version is 4.0.0
importcv2
importnumpy asnp
# Define class names and sort them alphabatically as this is how tf.keras remembers them
Here’s the result of a few images which I took from google, I’m using my custom function classify_flower() to classify these images. You can find this function’s code inside the downloaded Notebook.
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# Initialize the Network
init_classify()
# Classify on image
img=cv2.imread("Images/sunflower.jpg");
classify_flower(img,size=8);
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img=cv2.imread("Images/tulip.jpg");
classify_flower(img,size=2);
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img=cv2.imread("Images/dandelion.jpg");
classify_flower(img,size=3);
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img=cv2.imread("Images/daisy0.jpg");
classify_flower(img,size=2);
If you want to learn about doing image classification using the DNN module in detail then make to read the previous post, Deep learning with OpenCV DNN module. Where I have explained each step in detail.
Summary:
In today’s post we first learned how to train an image classifier with tf.keras, after that we learned how to convert our trained .h5 model to .onnx model.
Finally, we learned to use this onnx model using OpenCV’s DNN module.
Although the model we converted today was quite basic but this same pipeline can be used for converting complex models too.
A word of Caution: I personally have faced some issues while converting some types of models so the whole process is not foolproof yet but it’s still pretty good. Make sure to look at keras2onnx repo and this excellent repo of ONNX conversion tutorials.
You can reach out to me personally for a 1 on 1 consultation session in AI/computer vision regarding your project. Our talented team of vision engineers will help you every step of the way. Get on a call with me directlyhere.
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You can reach out to me personally for a 1 on 1 consultation session in AI/computer vision regarding your project. Our talented team of vision engineers will help you every step of the way. Get on a call with me directlyhere.
In this tutorial we will go over Deep learning using OpenCV’s DNN module in detail, I plan to cover various important details of the DNN module that is never discussed, things that usually trip of people like, selecting preprocessing params correctly and designing pre and postprocessing pipelines for different models.
This post is the first of 3 in our brand new Deep Learning with OpenCV series. All three posts are titled as:
Using a Caffe DenseNet121 model for classification.
Important Details regarding the DNN module, e.g. where to get models, how to configure them, etc.
If you’re just interested in the image classification part then you can skip to the second section or you can even read this great classification with DNN module post by Adrian. However, if you’re interested in getting to know the DNN module in all its glory then keep reading.
Introduction to OpenCV’s DNN module
First let me start by introducing the DNN module for all those people who are new to it, so as you can probably guess, the DNN module stands for Deep Neural Network module. This is the module in OpenCV which is responsible for all things deep learning related.
It was introduced in OpenCV version 3 and now in version 4.3, it has evolved a lot. This module lets you use pre-trained neural networks from popular frameworks like TensorFlow, PyTorch etc, and use those models directly in OpenCV.
This means you can train models using a popular framework like Tensorflow and then do inference/prediction with just OpenCV.
So what are the benefits here?
Here are some advantages you might want to consider when using OpenCV for inference.
By using OpenCV’s DNN module for inference the final code is a lot compact and simpler.
Someone who’s not familiar with the training framework can also use the model.
There are cases where using OpenCV’s DNN module will give you faster inference results for the CPU. See these results by Satya.
Beside supporting CUDA based NVIDIA’s GPU, OpenCV’s DNN module also supports OpenCL based Intel GPUs.
Most Importantly by getting rid of the training framework not only makes the code simpler but it ultimately gets rid of a whole framework, this means you don’t have to build your final application with a heavy framework like TensorFlow. This is a huge advantage when you’re trying to deploy on a resource-constrained edge device, e.g. a Raspberry pie.
One thing that might put you off is the fact that OpenCV can’t be used for training deep learning networks. This might sound like a bummer but fret not, for training neural networks you shouldn’t use OpenCV there are other specialized libraries like Tensorflow, PyTorch, etc for that task.
So which frameworks can you use to train Neural Networks:
These are the frameworks that are currently supported with the DNN module.
Now there are many interesting pre-trained models already available in the OpenCV Model Zoo that you can use, to keep things simple for this tutorial, I will be using an image classification network to do classification.
Details regarding other types of models are discussed in the 3rd section. By the way, I actually go over 13-14 different types of models in our Computer Vision and Image processing Course. These contain notebooks tutorials and video walk-throughs.
Image Classification pipeline with OpenCV DNN
Now we will be using a DenseNet121 model, which is a Caffe model trained on 1000 classes of ImageNet. The model is from the paper Densely Connected Convolutional Networks by Gap Huang et al.
Generally, there are 4 steps you need to perform when doing deep learning with the DNN module.
Read the image and the target classes.
Initialize the DNN module with an architecture and model parameters.
Perform the forward pass on the image with the module
Post-process the results.
The pre and post-processing steps are different for different tasks.
Let’s start with the code
Download Code
You can go ahead and download the source code from the download code section. After downloading the zip folder, unzip it and you will have the following directory structure.
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├──Image Classification withDenseNet121.ipynb
│
├───images
│cat.jpeg
│dog.jpeg
│green_lizard.jpg
│jemma2.jpg
│spcar.JPG
│
└───model
DenseNet_121.caffemodel
DenseNet_121.prototxt.txt
synset_words.txt
Now run the Image Classification with DenseNet121.ipynb notebook, and start executing the cells.
Import Libraries
First, we will import the required libraries.
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# Importing Required libraries
importnumpy asnp
importtime
importcv2
importmatplotlib.pyplot asplt
importos
importsys
Loading Class Labels
Now we’ll start by loading class names, In this notebook, we are going to classify among 1000 classes defined in ImageNet.
All these classes are in the text file named synset_words.txt. In this text file, each class is in on a new line with its unique id, Also each class has multiple labels for e.g look at the first 3 lines in the text file:
‘n01440764 tench, Tinca tinca’
‘n01443537 goldfish, Carassius auratus’
‘n01484850 great white shark, white shark
So for each line, we have the Class ID, then there are multiple class names, they all are valid names for that class and we’ll just use the first one. So in order to do that we’ll have to extract the second word from each line and create a new list, this will be our labels list.
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# Split all the classes by a new line and store it in variable called rows.
prototxt: Path to the .prototxt file, this is the text description of the architecture of the model.
caffeModel: path to the .caffemodel file, this is your actual trained neural network model, it contains all the weights/parameters of the model. This is usually several MBs in size.
Note: If you load the model and proto file via readNetFromTensorFlow then the order of architecture and model inputs are reversed.
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# Load the Model Weights.
weights='model/DenseNet_121.caffemodel'
# Load the Model Architecture.
architecture='model/DenseNet_121.prototxt.txt'
# Here we are reading pre-trained caffe model with its architecture
Let’s read an example image and display it with matplotlib imshow
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# Load the input image
image=cv2.imread('images/jemma2.jpg')
# Display the image
plt.figure(figsize=(10,10))
plt.imshow(image[:,:,::-1]);plt.axis("off");
Pre-processing the image
Now before you pass an image in the network you need to preprocess it, this means resizing the image to the size it was trained on, for many networks, this is 224×224, in pre-processing step you also do other things like Normalize the image (make the range of intensity values between 0-1) and mean subtraction, etc. These are all the steps the authors did on the images that were used during model training.
Fortunately, In OpenCV, you have a function called cv2.dnn.blobFromImage() which most of the time takes care of all the pre-processing for you.
Scalefactor Used to normalize the image. This value is multiplied by the image, value of 1 means no scaling is done.
Size The size to which the image will be resized to, this depends upon the each model.
Mean These are mean R,G,B Channel values from the whole dataset and these are subtracted from the image’s R,G,B respectively, this gives illumination invariance to the model.
swapRB Boolean flag (false by default) this indicates weather swap first and last channels in 3-channel image is necessary.
crop flag which indicates whether the image will be cropped after resize or not. If crop is true, input image is resized so one side after resize is equal to the corresponding dimension in size and another one is equal or larger. Then, a crop from the center is performed. If crop is false, direct resize without cropping and preserving aspect ratio is performed.
So After this function, we get a 4d blob, this is what we’ll pass to the network.
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print(blob.shape)
(1, 3, 224, 224)
Note: There is also blobFromImages() which does the same thing but with multiple images.
Input the Blob Image to the Network
Here you’re setting up the blob image as the input to the network.
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# Passing the blob as input through the network
net.setInput(blob)
Forward Pass
Here the actual computation will take place, Most of the time in your whole pipeline will be taken here. Here your image will go through all the model parameters and in the end, you will get the output of the classifier.
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%%time
Output=net.forward()
Wall time: 166 ms
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# Length of the number of predictions
print("Total Number of Predictions are: {}".format(len(Output[0])))
By looking at the output, you can tell that the model has returned a set of scores for each class but we need Probabilities between 0-1 for each class. We can get them by applying a softmax function on the scores.
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# Reshape the Output so its a single dimensional vector
new_Output=Output.reshape(len(Output[0][:]))
# Convert the scores to class probabilities between 0-1 by applying softmax
Now that we have understood step by step how to create the pipeline for classification using OpenCV’s DNN module, we’ll now create functions that do all the above in a single step. In short, we will be creating the following two functions.
Initialization Function: This function will contain parts of the network that will be set once, like loading the model.
Main Function: This function will contain all the rest of the code from preprocessing to postprocessing, it will also have the option to either return the image or display it with matplotlib.
Initialization Function
This method will be run once and it will initialize the network with the required files.
# Release the camera and destroy all opened windows
cap.release()
cv2.destroyAllWindows()
Important Details Regarding the DNN module
Let’s discuss some interesting details and some tips to fully utilize the DNN module.
Where to get the pre-trained Models:
Earlier I mentioned that you can get other pre-trained models, so where are they?
The best place to get pre-trained models is here. This page is a wiki for Deep learning with OpenCV, you will find models that have been tested by the OpenCV team.
There are a variety of models present here, for things like Classification, Pose Detection, Colorization, Segmentation, Face recognition, text detection, style transfer, and more. You can take models from any of the above 5 frameworks.
Just click on the models to go to their repo and download them from there. Note: The models listed on the page above are only the tested models, in theory, you can almost take any pre-trained model and use it in OpenCV.
A faster and easier way to download models is to go here. Now, this is a python script that will let you download not only the most commonly used models but also some State of the Art ones like Yolo v4 etc. You can download this script and then run from the command line. Alternatively, if you’re in a rush and just one specific model then you can take the downloadable URL of any model and download it.
After downloading the model, you will need a couple of more things before you can actually use the model in the OpenCV dnn module.
You’re now probably familiar with those things, so yeah you will need the model configuration file like the prototxt file we just used with our Caffe model above. You will also need class labels, now for classification problems, models are usually trained on the ImageNet dataset so we needed synset_word.txt file, for Object detection you will find models trained on COCO or Pascal VOC dataset. And similarly, other tasks may require other files.
So where are all these files present?
You will find most of these configuration files present here and the class names here. If the configuration file you’re looking for is not present in the above links then I would recommend that you look at the GitHub repo of the model, the files would be present there. Otherwise, you have to create it yourself. (More on this later)
After getting the configuration files, the only thing you need is the pre-processing parameters that go in blobFromImage. E.g. the mean subtraction values, scaling params, etc.
You can get that information from here. Now, this script only contains parameter details for a few popular models.
So how do you get the details for other models?
For that you would need to go to the repo of the model and look in the ReadMe section, the authors usually put that information there.
For e.g. If I visit the GitHub repo of the Human Pose Estimation model using this link which I got from the model downloading script.
By scrolling down the readme I can find these details here:
Note: These details are not always present in the Readme and sometimes you have to do quite some digging before you can find these parameters.
What to do if there is no GitHub repo link with the model, for e.g. this shuffleNet model does not have a GitHub link, in that case, I can see that the framework is ONNX.
So now I will visit the ONNX model zoo repo and find that model.
After clicking on the model I will find its readme and then its preprocessing steps.
Notice that this model contains some preprocessing steps that are not supported by blobfromImage function. So this could happen and at times you would need to write custom preprocessing steps without using blobfromImage function, for e.g. in our Super Resolution post, I had to write a custom pre-processing pipeline for the network.
How to use our own Custom Trained Networks
Now that we have learned to use different models, you might wonder exactly how can we use our own custom-trained models. So the thing is you can’t directly plug a trained network in a DNN module but you need to perform some operations to get a configuration file, which is why we needed a prototxt file along with the model.
Fortunately, In the next two blog posts, I plan to cover exactly this topic and show you how to use a custom trained classifier and a custom trained Detection network.
For now, you can take a look at this page which briefly describes how you can use models trained with Tensorflow Object Detection API in OpenCV.
One thing to note is that not all networks are supported by the DNN module, this is because the DNN module supports some 30+ layer types, these layer names can be found at the wiki here. So if a model contains layers that are not among the supported layers then it won’t run, this is not a major issue as most common layers used in deep learning models are supported.
Using GPU’s and Faster Backends to speed up OpenCV DNN Module
By default OpenCV’s DNN deep learning module runs on the default C++ implementation which itself is pretty fast but OpenCV further allows you to change this backend to increase the speed even more.
Option 1: Use NVIDIA GPU with CUDA backend in the DNN module:
If you have an Nvidia GPU present then great, you can use that with the DNN module, you can follow my OpenCV source installation guide to configure your NVIDIA GPU for OpenCV and learn how to use it. This will make your networks run several times faster.
Option 2: Use OpenCL based INTEL GPU’s:
If you have an OpenCL-based GPU then you can use that as a backend, although this increases speed but in my experience, I’ve seen speed gains only in 32 bit systems. To use the OpenCL as a backend you can see the last section of my OpenCV source installation section linked above.
Option 3: Use Halide Backend:
As described in this post from learnOpenCV.com, for some time in the past using the halide backend increased the speed but then OpenCV engineer’s optimized the default C++ implementation so much that the default implementation actually got faster. So I don’t see a reason to use this backend now, Still here’s how you configure halide as a backend.
Option 4: Use Intel’s Deep Learning Inference Engine backend:
Intel’s Deep Learning Inference Engine backend is part of the OpenVINO toolkit, OpenVINO stands for Open Visual Inferencing and Neural Network Optimization. OpenVINO is designed by Intel to speed up inference with neural networks, especially for tasks like classification, detection, etc. OpenVINO speeds up by optimizing the model in a hardware-agnostic way. You can learn to install OpenVINO here and here’s a nice tutorial for it.
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In today’s tutorial, we went over a number of things regarding OpenCV’s DNN module. From using pre-trained models to Optimizing for faster inference speed.
We also learned to perform a classification pipeline using densenet121.
This post should serve as an excellent guide for anyone trying to get started in Deep learning using OpenCV’s DNN module.
Finally, OpenCV’s DNN repo contains an example python script to run common networks like classification, text, object detection, and more. You can start utilizing the DNN module by using these scripts and here are a few DNN Tutorials by OpenCV.
The main contributor for the DNN module in OpenCV is Dmitry Kurtaev and formerly it was Aleksandr Rybnikov, so big thanks to them and the rest of the contributors for making such a great module.
I hope you enjoyed today’s tutorial, feel free to comment and ask questions.
You can reach out to me personally for a 1 on 1 consultation session in AI/computer vision regarding your project. Our talented team of vision engineers will help you every step of the way. Get on a call with me directlyhere.
Ready to seriously dive into State of the Art AI & Computer Vision? Then Sign up for these premium Courses by Bleed AI
You can reach out to me personally for a 1 on 1 consultation session in AI/computer vision regarding your project. Our talented team of vision engineers will help you every step of the way. Get on a call with me directlyhere.
This tutorial will serve as a crash course to dlib library. Dlib library is another powerful computer vision library out there. It is not as extensive as OpenCV but still, there is a lot you can do with it.
This crash course assumes you’re somewhat familiar with OpenCV, if not then I’ve also published a crash course on OpenCV too. Make sure to download Dlib Resource Guide above which includes all important links in this post.
Side Note: I missed publishing a tutorial last week as I tested covid positive and was ill, still not 100% but getting better 🙂
The Dlib Library is created and maintained by Davis King, It’s a C++ toolkit containing machine learning & Computer Vision algorithms for a number of important tasks including, Facial Landmark detection, Deep Metric Learning, Object tracking, and more. It also has a python API.
Note: It’s worth noting that the main power of dlib library is in numerical optimization but today I’m only going to focus on applications, you can look at optimization examples here.
It’s a popular library that is used by people in both industry and academia in a wide range of domains including robotics, embedded devices, and other areas.
I plan to cover most of the prominent features and algorithms present in dlib library so this blog post alone can give you the best overview of dlib library and its functionality. Now, this is a big statement, If I had to explain most of dlib features in a single place then I would probably be writing a book or making a course on it but rather I plan to explain it all in this post.
So how am I going to accomplish that?
So here’s the thing I’m not going to write and explain the code for each algorithm with dlib library, because I don’t want to write several thousand’s of words worth of a blog post and also because almost all of the features of dlib library have been explained pretty well in several posts on the internet.
So if everything is out there then why the heck am I trying to make a crash course out of it ?
So here’s the real added value of this crash course:
In this post, I will connect all the best and the most important tutorials on different aspects of dlib library out there in a nice hierarchical order. This will not only serve as a golden Dlib library 101 to Mastery post for people just starting out with dlib but will also serve as a well-structured reference guide for dlib library users.
The post is split into various sections, in each section, I will briefly explain a useful algorithm or technique present in dlib library. If that explanation intrigues you and you feel that you need to explore that particular algorithm further then in each section I provide links to high-quality tutorials that goes in-depth about that function, the links would mostly be from Pyimagesearch, LearnOpenCV as these are golden sites when it comes to Computer Vision Tutorials.
When learning some topic, ideally we prefer these two things:
A Collection of all the useful material regarding the topic presented at one place in a nice and neat hierarchical order.
Each material presented and delivered in a high-quality format preferably by an author who knows how to teach it the right way.
In this post, I’ve made sure both of these points are true, all the information is presented in a nice order and the posts that I link to will be of high quality. Other than that I will also try to include other extra resources where I feel necessary.
This will only work if you have Visual Studio (i.e. you need a C++ compiler) and CMake installed as dlib will build and compile first before installing. If you don’t have these then you can use my OpenCV’s source installation tutorial to install these two things.
If you don’t want to bother installing these then here’s what you can do, if you have a python version greater then 3.6 then create a virtual environment for python 3.6 using Anaconda or virtualenv.
After creating a python 3.6 environment you can do:
pip install dlib==19.8.1
This will let you directly install pre-built binaries of dlib but this currently only works with python 3.6 and below.
Now that we have installed dlib, let’s start with face detection.
Why face detection ?
Well, most of the interesting use cases in dlib for computer vision is with faces, like facial landmark detection, face recognition, etc so before we can detect facial landmarks, we need to detect faces in the image.
Dlib not only comes with a face detector but it actually comes with 2 of them. If you’re a computer vision practitioner then you would most likely be familiar with the old Haar cascade based face detector. Although this face detector is a lot popular, it’s almost 2 decades old and not very effective when it comes to different orientations of the faces.
Dlib comes with 2 face detection algorithms that are way more effective than the haar cascade based detectors.
These 2 detectors are:
HOG (histogram of oriented gradients) based detection: This detector uses HOG and Support vector machines, its slower than haar cascades but its more accurate and able to handle different orientations CNN Based Detector: This is a really accurate deep learning based detector but its extremely slow on a CPU, you should only use this if you’ve compiled dlib with GPU.
You can learn more about these detectors here. Other than that I published a library called bleedfacedetector which lets you use these 2 detectors using just a few lines of the same code, and the library also has 2 other face detectors including the haar cascade one. You can look at bleedfacedetector here.
Now that we have learned how to detect faces in images, we will now learn the most common use case of dlib library which is facial landmark detection, with this method you will be able to detect key landmarks/features of the face like eyes, lips, etc.
The detection of these features will allow you to do a lot of things like track the movement of eyes, lips to determine the facial expression of a person, control a virtual Avatar with your facial expressions, understand 3d facial pose of a person, virtual makeover, face swapping, morphing, etc.
Remember those smart Snapchat overlays which trigger based on the facial movement, like that tongue that pops out when you open your mouth, well you can also make that using facial landmarks.
So its suffice to say that Facial landmark detection has a lot of interesting applications.
After reading the above tutorial the next step is to learn to manipulate the ROI of these landmarks so, you can modify or extract the individual features like the eyes, nose lips, etc. You can learn that by reading this Tutorial.
After you have gone through both of the above tutorials then you’re ready for running the landmark detector in real time but if you’re still confused about the exact process then take a look at this tutorial.
After you’re fully comfortable working with facial landmarks that’s when the fun starts. Now you’re ready to make some exciting applications, you can start by making a blink detection system by going through the tutorial here.
The main idea for a blink detection system is really simple, you just look at 2 vertical landmark points of the eyes and take the distance between these points, if the distance is too small (below some threshold) then that means the eyes are closed.
Of course, for a robust estimate, you won’t just settle for the distance between two points but rather you will take a smart average of several distances. One smart approach is to calculate a metric called Eye aspect ratio (EAR) for each eye. This metric was introduced in a paper called “ Real-Time Eye Blink Detection using Facial Landmarks”
This will allow you to utilize all 6 x,y landmark points of the eyes returned by dlib, and this way you can accurately tell if there was a blink or not.
Here’s the equation to calculate the EAR.
The full implementation details are explained in the tutorial linked above.
You can also easily extend the above method to create a drowsiness detector that alerts drivers if they feel drowsy, this can be done by monitoring how long the eyes are closed for. This is a really simple extension of the above and have real-world applications and could be used to save lives. Here’s a tutorial that explains how to build a step by step drowsiness detection system.
Interestingly you can take the same blink detection approach above and apply it to lips instead of the eyes, and create a smile detector. Yeah, the only thing you would need to change would be the x,y point coordinates (replace eye points with lip points), the EAR equation (use trial and error or intuition to change this), and the threshold.
Few years back I created this smile camera application with only a few lines of code, it takes a picture when you smile. You can easily create that by modifying the above tutorial.
What more can you create with this ?
How about a yawn detector, or a detector that tells if the user’s mouth is opened or not. You can do this by slightly modifying the above approach, you will be using the same lips x,y landmark points, the only difference would be how you’re calculating the distance between points.
Here’s a cool application I built a while back, its the infamous google dino game that’s controlled by me opening and closing the mouth.
The only drawback of the above application is that I can’t munch food while playing this game.
Taking the same concepts above you can create interesting snapchat overlay triggers.
Here’s an eye bulge and fire throw filter I created that triggers when I glare or open my mouth.
Similarly you can create lots of cool things using the facial landmarks.
Facial Alignment & Filter Orientation Correction:
Doing a bit of math with the facial landmarks will allow you to do facial alignment correction. Facial alignment allows you to correctly orient a rotated face.
Why is facial alignment important?
One of the most important use case for facial alignment is in face recognition, there are many classical face recognition algorithms that will perform better if the face is oriented correctly before performing inference on them.
One other useful thing concerning facial alignment is that you can actually extract the angle of the rotated face, this is pretty useful when you’re working with an augmented reality filter application as this will allow you to rotate the filters according to the orientation of the face.
Here’s an application I built that does that.
Head Pose Estimation:
A problem similar to facial alignment correction could be head pose estimation. In this technique instead of determining the 2d head rotation, you will learn to extract the full 3d head pose orientation. This is particularly useful when you’re working with an augmented reality application like overlaying a 3d mask on the face. You will only be able to correctly render the 3d object on the face if you know the face’s 3d orientation.
Landmark detection is not all dlib has to offer, there are other useful techniques like a correlation tracking algorithm for Object Tracking that comes packed with dlib.
This tracker works well with changes in translation and scale and it works in real time.
Object Detection VS Object Tracking:
If you’re just starting out in your computer vision journey and have some confusion regarding object detection vs tracking then understand that in Object Detection, you try to find an instance of the target object in the whole image. And you perform this detection in each frame of the video. There can be multiple instances of the same object and you’ll detect all of them with no differentiation between those object instances.
What I’m trying to say above is that a single image or frame of a video can contain multiple objects of the same class for e.g. multiple cats can be present on the same image and the object detector will see it as the same thing CAT with no difference between the individual cats throughout the video.
Whereas an Object Tracking algorithm will track each cat separately in each frame and will recognize each cat by a unique ID throughout the video.
Here’s a series of cool facial manipulations you can do by utilizing facial landmarks and some other techniques.
Face Morphing:
What you see in the above video is called facial morphing. I’m sure you have seen such effects in other apps and movies. This effect is a lot more than a simple image pixel blending or transition.
To have a morph effect like the above, you need to do image alignment, establish pixel correspondences using facial landmark detection and more.
By understanding and utilizing facial morphing techniques you can even do morphing between dissimilar objects like a face to a lion.
Face Swapping:
After you’ve understood face morphing then another really interesting you can do is face swapping, where you take a source face and put it over a destination face. Like putting Modi’s face over Musharaf’s above.
The techniques underlying face swapping is pretty similar to the one used in face morphing so there is not much new here.
The way this swapping is done makes the results look real and freakishly weird. See how everything from lightning to skin tone is matched.
Tip: If you want to make the above code work in real-time then you would need to replace the seamless cloning function with some other faster cloning method, the results won’t be as good but it’ll work in real-time.
Note: It should be noted this technique although gives excellent results but the state of the art in face swapping is achieved by deep learning based methods (deepfakes, FaceApp etc).
Face Averaging:
Average face of: Aiman Khan, Ayeza Khan, Mahira Khan, Mehwish Hayat, Saba Qamar & Syra Yousuf
Similar to above methods there’s also Face averaging where you smartly average several faces together utilizing facial landmarks.
The face image you see above is the average face I created using 6 different Pakistani female celebrities.
It should not come as a surprise that dlib also has a face recognition pipeline, not only that but the Face recognition implementation is really robust one and is a modified version of ResNet-34, based on the paper “ Deep Residual Learning for Image Recognition paper by He et al.”, it has an accuracy of 99.38% on the Labeled Faces in the Wild (LFW) dataset. This dataset contains ~3 million images.
The model was trained using deep metric learning and for each face, it learned to output a 128-dimensional vector. This vector encodes all the important information about the face. This vector is also called a face embedding.
First, you will store some face embeddings of target faces and then you will test on different new face images. Meaning you will extract embedding from test images and compare it with the saved embeddings of the target faces.
If two vectors are similar (i.e. the euclidean distance between them is small) then it’s said to be a match. This way you can make thousands of matches pretty fast. The approach is really accurate and works in real-time.
Dlib’s Implementation of face recognition can be found here. But I would recommend that you use the face_recognition library to do face recognition.This library uses dlib internally and makes the code a lot simpler.
Consider this, you went to a museum with a number of friends, all of them asked you to take their pictures behind several monuments/statues such that each of your friend had several images of them taken by you.
Now after the trip, all your friends ask for their pictures, now you don’t want to send each of them your whole folder. So what can you do here?
Fortunately, face clustering can help you out here, this method will allow you to make clusters of images of each unique individual.
Consider another use case: You want to quickly build a face recognition dataset for 10 office people that reside in a single room. Instead of taking manual face samples of each person, you instead record a short video of everyone together in the room, you then use a face detector to extract all the faces in each frame, and then you can use a face clustering algorithm to sort all those faces into clusters/folders. Later on, you just need to name these folders and your dataset is ready.
Clustering is a useful unsupervised problem and has many more use cases. Face clustering is built on top of face recognition so once you’ve understood the recognition part this is easy.
Just like the Dlib’s Facial Landmark detector, you can train your own custom landmark detector. This detector is also called a shape predictor. Now you aren’t restricted to only facial landmarks but you can go ahead and train a landmark detector for almost anything, body joints of a person, some key points of a particular object, etc.
As long as you can get sufficient annotated data for the key points, you can use dlib to train a landmark detector on it.
Just like a custom landmark detector, you can train a custom Object detector with dlib. Dlib uses Histogram of Oriented Gradients (HOG) as features and a Support Vector Machine (SVM) Classifier. This combined with sliding windows and image pyramids, you’ve got yourself an Object detector. The only limitation is that you can train it to detect a single object at a time.
The Object detection approach in dlib is based on the same series of steps used in the sliding window based object detector first published by Dalal and Triggs in 2005 in the Histograms of Oriented Gradients for Human Detection.
HOG + SVM based detector are the strongest non Deep learning based approach for object detection, Here’s a hand detector I built using this approach a few years back.
I didn’t even annotated nor collected training data for my hands but instead made a sliding window application that automatically collected my hand pictures as it moved on the screen and I placed my hands in the bounding box.
Afterward, I took this hand detector created a Video car game controller, so now I was steering the Video game car with my hands literally. To be honest, that wasn’t a pleasant experience, my hand was sore afterwards. Making something cool is not hard but it would take a whole lot effort to make a practical VR or AR-based application.
Dlib Optimizations For Faster & Better Performance:
Here’s a bunch of techniques and tutorials that will help you get the most out of dlib’s landmark detection.
Using A Faster Landmark Detector:
Beside’s the 68 point landmark detector, dlib also has 5 point landmark detector that is 10 times smaller and faster (about 10%) than the 68 point one. If you need more speed and the 5 landmark points as visualized above is all you need then you should opt for this detector. Also from what I’ve seen its also somewhat more efficient than the 68 point detector.
There are a bunch of tips and techniques that you can use to get a faster detection speed, now a landmark detector itself is really fast, the rest of the pipeline takes up a lot of time. Some tricks you can do to increase speed are:
Skip Frames:
If you’re reading from a high fps camera then it won’t hurt to perform detection on every other frame, this will effectively double your speed.
Reduce image Size:
If you’re using Hog + Sliding window based detection or a haar cascade + Sliding window based one then the face detection speed depends upon the size of the image. So one smart thing you can do is reduce the image size before face detection and then rescale the detected coordinates for the original image later.
Tip:The biggest bottleneck you’ll face in the landmark detection pipeline is the HOG based face detector in dlib which is pretty slow. You can replace this with haar cascades or the SSD based face detector for faster performance.
Summary:
Let’s wrap up, in this tutorial we went over a number of algorithms and techniques in dlib.
We started with installation, moved on to face detection and landmark prediction, and learned to build a number of applications using landmark detection. We also looked at other techniques like correlation tracking and facial recognition.
We also learned that you can train your own landmark detectors and object detectors with dlib.
At the end we learned some nice optimizations that we can do with our landmark predictor.
Final Tip: I know most of you won’t be able to go over all the tutorials linked here in a single day so I would recommend that you save and bookmark this page and tackle a single problem at a time. Only when you’ve understood a certain technique move on to the next.
It goes without saying that Dlib is a must learn tool for serious computer vision practitioners out there.
I hope you enjoyed this tutorial and found it useful. If you have any questions feel free to ask them in the comments and I’ll happily address it.
You can reach out to me personally for a 1 on 1 consultation session in AI/computer vision regarding your project. Our talented team of vision engineers will help you every step of the way. Get on a call with me directlyhere.
Ready to seriously dive into State of the Art AI & Computer Vision? Then Sign up for these premium Courses by Bleed AI
I would recommend that you go over that tutorial before reading this one but you can still easily follow along with this tutorial. For those of you who don’t know what Super-resolution is then here is an explanation.
Super Resolution can be defined as the class of Algorithms that upscales an image without losing quality, meaning you take a low-resolution image like an image of size 224×224 and upscale it to a high-resolution version like 1792×1792 (An 8x resolution) without any loss in quality. How cool is that?
Anyways that is Super resolution, so how is this different from the normal resizing you do?
When you normally resize or upscale an image you use Nearest Neighbor Interpolation. This just means you expand the pixels of the original image and then fill the gaps by copying the values of the nearest neighboring pixels.
The result is a pixelated version of the image.
There are better interpolation methods for resizing like bilinear or bicubic interpolation which take weighted average of neighboring pixels instead of just copying them.
Still the results are blurry and not great.
The super resolution methods enhance/enlarge the image without the loss of quality, Again, for more details on the theory of super resolution methods, I would recommend that you read my Super Resolution with OpenCV Tutorial.
In the above tutorial I describe several architectural improvements that happened with SR Networks over the years.
That all changes now, in this tutorial we will work with multiple models, even those that will do 8x resolution.
Today, we won’t be using the DNN module, we could do that but for the super resolution problem OpenCV comes with a special module called dnn_superres which is designed to use 4 different powerful super resolution networks. One of the best things about this module is that It does the required pre and post processing internally, so with only a few lines of code you can do super resolution.
The 4 models we are going to use are:
EDSR: Enhanced Deep Residual Network from the paper Enhanced Deep Residual Networks for Single Image Super-Resolution (CVPR 2017) by Bee Lim et al.
ESPCN: Efficient Subpixel Convolutional Network from the paper Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (CVPR 2016) by Wenzhe Shi et al.
FSRCNN: Fast Super-Resolution Convolutional Neural Networks from the paper Accelerating the Super-Resolution Convolutional Neural Network (ECCV 2016) by Chao Dong et al.
LapSRN: Laplacian Pyramid Super-Resolution Network from the paper Deep Laplacian pyramid networks for fast and accurate super-resolution (CVPR 2017) by Wei-Sheng Lai et al.
Here are the papers for the models and some extra resources.
Make sure to download the zip folder from the download code section above. As you can see by clicking the Download models link that each model has different versions like 3x, 4x etc. This means that the model can perform 3x resolution, 4x resolution of the image, and so on. The download zip that I provide contains only a single version of each of the 4 models above.
You can feel free to test out other models by downloading them. These models should be present in your working directory if you want to use them with the dnn_superres module.
Now the inclusion of this super easy to use dnn_superres module is the result of the work of 2 developers Xavier Weber and Fanny Monori. They developed this module as part of their GSOC (Google summer of code) project. GSOC 2019 also made NVIDIA GPU support possible.
It’s always amazing to see how a summer project for students by google brings forward some great developers making awesome contributions to the largest Computer Vision library out there.
The dnn_superes module in OpenCV was included in version 4.1.2 for C++ but the python wrappers were added in 4.3 version about a month back, so you have to make sure that you have OpenCV version 4.3 installed. And of course, since this module is included in the contrib module so make sure you have also installed OpenCV contrib package.
[UPDATE 7/8/2020, OPENCV 4.3 IS NOW PIP INSTALLABLE]
Note: You can’t install OpenCV 4.3 version by doing pip install as the latest version here open-contrib-python from pipis still version 4.2.0.34.
So the pypi version of OpenCV is maintained by just one guy named: Olli-Pekka Heinisuo by username: skvark and he updates the pypi OpenCV package in his free time. Currently, he’s facing a compiling issue which is why 4.3 version has not come out as of 7-15-2020. But from what I have read, he will be building the .whl files for 4.3 version soon, it may be out this month. If that happens then I’ll update this post.
So right now the only way you will be able to use this module is if you have installed OpenCV 4.3 from Source. If you haven’t done that then you can easily follow my installation tutorial.
I should also take this moment to highlight the fact you should not always rely on OpenCV’s pypi package, no doubt skvark has been doing a tremendous job maintaining OpenCV’s pypi repo but this issue tells you that you can’t rely on a single developer’s free time to update the library for production use cases, learn to install the Official library from source. Still, pip install opencv-contrib-python is a huge blessing for people starting out or in early stages of learning OpenCV, so hats off to skvark.
As you might have noticed among the 4 models above we have already learned to use ESPCNN in the previous tutorial, we will use it again but this time with the dnn_superres module.
Super Resolution with dnn_superres Code
Directory Hierarchy
After downloading the zip folder, unzip it and you will have the following directory structure.
This is how our directory structure looks like, it has a Jupyter notebook, a media folder with images and the model folder containing all 4 models.
In the next few steps, will be using a setModel() function in which we will pass the model’s name and its scale. We could manually do that but all this information is already present in the model’s pathname so we just need to extract the model’s name and scale using simple text processing.
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# Define model path, if you want to use a different model then just change this path.
model_path="models/EDSR_x4.pb"
# Extract model name, get the text between '/' and '_'
Finally we will read the model, this is where all the required weights of the model gets loaded. This is equivalent to DNN module’s readnet function
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# Read the desired model
sr.readModel(model_path)
Setting Model Name & Scale
Here we are setting the name and scale of the model which we extracted above.
Why do we need to do that ?
So remember when I said that this module does not require us to do preprocessing or postprocessing because it does that internally. So in order to initiate the correct pre and post-processing pipelines, the module needs to know which model we will be using and what version meaning what scale 2x, 3x, 4x etc.
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# Set the desired model and scale to get correct pre-processing and post-processing
sr.setModel(model_name,model_scale)
Running the Network
This is where all the magic happens. In this line a forward pass of the network is performed along with required pre and post-processing. We are also making note of the time taken as this information will tell us if the model can be run in real-time or not.
As you can see it takes a lot of time, in fact, EDSR is the most expensive model out of the four in terms of computation.
It should be noted that larger your input image’s resolution is the more time its going to take in this step.
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%%time
# Upscale the image
Final_Img=sr.upsample(image)
Wall time: 45.1 s
Check the Shapes
We’re also checking the shapes of the original image and the super resolution image. As you can see the model upscaled the image by 4 times.
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print('Shape of Original Image: {} , Shape of Super Resolution Image: {}'.format(image.shape,result.shape))
Shape of Original Image: (262, 347, 3) , Shape of Super Resolution Image: (1200, 1200, 3)
Comparing the Original Image & Result
Finally we will display the original image along with its super resolution version. Observe the difference in Quality.
Although you can see the improvement in quality but still you can’t observe the true difference with matplotlib so its recommended that you save the SR image in disk and then look at it.
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# Save the image
cv2.imwrite("outputs/testoutput.png",Final_Img);
Creating Functions
Now that we have seen a step by step implementation of the whole pipeline, we’ll create the 2 following python functions so we can use different models on different images by just calling a function and passing some parameters.
Initialization Function: This function will contain parts of the network that will be set once, like loading the model.
Main Function: This function will contain the rest of the code. it will also have the option to either return the image or display it with matplotlib. We can also use this function to process a real-time video.
Initialization Function
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definit_super(model,base_path='models'):
# Define global variable
globalsr,model_name,model_scale
# Create an SR object
sr=dnn_superres.DnnSuperResImpl_create()
# Define model path
model_path=os.path.join(base_path,model+".pb")
# Extract model name from model path
model_name=model.split('_')[0].lower()
# Extract model scale from model path
model_scale=int(model.split("_")[1][1])
# Read the desired model
sr.readModel(model_path)
sr.setModel(model_name,model_scale)
Main Function
Set returndata = True when you just want the image. This is usually done when I’m working with videos. I’ve also added a few more optional variables to the method.
print_shape: This variable decides if you want to print out the shape of the model’s output.
name: This is the name by which you will save the image in disk.
save_img: This variable decides if you want to save the images in disk or not.
Shape of Original Image: (302, 357, 3) , Shape of Super Resolution Image: (2416, 2856, 3) Wall time: 26 s
Applying Super Resolution on Video
Lastly, I’m also providing the code to run Super-resolution on Videos. Although the example video I’ve used sucks, but that’s the only video I tested on primarily because I’m only interested in doing super resolution on images as this is where most of my use cases lie. Feel free to test out different models for real-time feed.
Tip: You might also want to save the High res video in disk using the VideoWriter Class.
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# Set the fps counter to 0
fps=0
# Initialize the network.
init_super("ESPCN_x4")
# Initialize the videcapture object with the video.
# If the user presses the `q` button then break the loop.
ifk==ord('q'):
break
# Release the camera and destroy all the windows.
cap.release()
cv2.destroyAllWindows()
Conclusion
Here’s a chart for benchmarks using a 768×512 image with 4x resolution on an Intel i7-9700K CPU for all models.
The benchmark shows PSNR (Peak signal to noise ratio) and SSIM (structural similarity index measure) scores, these are the scores which measure how good the supre res network’s output is.
The best performing model is EDSR but it has the slowest inference time, the rest of the models can work in real time.
If you thought upscaling to 8x resolution was cool then take a guess on the scaling ability of the current state of the Art algorithm in super-resolution?
So believe it or not the state of the art in SR can actually do a 64x resolution…yes 64x, that wasn’t a typo.
In fact, the model that does 64x was published just last month, here’s the paper for that model, here’s the GitHub repo and here is a ready to run colab notebook to test out the code. Also here’s a video demo of it. It’s pretty rare that such good stuff is easily accessible for programmers just a month after publication so make sure to check it out.
The model is far too complex to explain in this post but the authors took a totally different approach, instead of using supervised learning they used self-supervised learning. (This seems to be on the rise).
You’ll come across many Computer Vision courses out there, but nothing beats a 1 on 1 video call support from an expert in the field. Plus there is a plethora of subfields and tons of courses on AI and computer vision out there, you need someone to lay out a step-by-step learning path customized to your needs. This is where I come in, whether you need monthly support or just want to have a one-time chat with me, I’ve got you covered. Check all the coaching details and packages here
In today’s tutorial we learned to use 4 different architectures to do Super resolution going from 3x to 8x resolution.
Since the library handles preprocessing and postprocessing, so the code for all the models was almost the same and pretty short.
As I mentioned earlier, I only showed you results of a single version of each model, you should go ahead and try other versions of each model.
These models have been trained using DIV2K BSDS and General100 datasets which contains images of diverse objects but the best results from a super-resolution model is obtained by training them for a domain-specific task, for e.g if you want the SR model to perform best on pedestrians then your dataset should consist mostly of pedestrian images. The best part about training SR networks is that you don’t need to spend hours doing manual annotation, you can just resize them and you’re all set.
Also I would raise a concern regarding these models that we must be careful using SR networks, for e.g. consider this scenario:
You caught an image of a thief stealing your mail on your low res front door cam, the image looks blurry and you can’t make out who’s in the image.
Now you being a Computer Vision enthusiast thought of running a super res network to get a clearer picture. After running the network, you get a much clearer image and you can almost swear that it’s Joe from the next block.
The same Joe that you thought was a friend of yours.
The same Joe that made different poses to help you create a pedestrian datasets for that SR network you’re using right now.
How could Joe do this?
Now you feel betrayed but yet you feel really Smart, you solved a crime with AI right?
You Start STORMING to Joe’s house to confront him with PROOF.
Now hold on! … like really hold on.
Don’t do that, seriously don’t do that.
Why did I go on a rant like that?
Well to be honest back when I initially learned about SR networks that’s almost exactly what I thought I would do. Solve Crimes by AI by doing just that (I know it was a ridiculous idea). But soon I realize that SR networks only learn to hallucinate data based on learned data, they can’t visualize a face with 100% accuracy that they’ve never seen. It’s still pretty useful but you have to use this technology carefully.
I hope you enjoyed this tutorial, feel free to comment below and I’ll gladly reply.
You can reach out to me personally for a 1 on 1 consultation session in AI/computer vision regarding your project. Our talented team of vision engineers will help you every step of the way. Get on a call with me directlyhere.
A few weeks ago we learned how to do Super-Resolution using OpenCV’s DNN module, in today’s post we will perform Facial Expression Recognition AKA Emotion Recognition using the DNN module. Although the term emotion recognition is technically incorrect (I will explain why) for this problem but for the remainder of this post I’ll be using both of these terms, since emotion recognition is short and also good for SEO since people still search for emotion recognition while looking for facial expression recognition xD.
The post is structured in the following way:
First I will define Emotion Recognition & its importance.
Then I will discuss different approaches to tackle this problem.
Finally, we will Implement an Emotion Recognition pipeline using OpenCV’s DNN module.
Emotion Recognition Or Facial Expression Recognition
Now let me start by clarifying what I meant when I said this problem is incorrectly quoted as Emotion recognition. So you see by saying that you’re doing emotion recognition you’re implying that you’re actually finding the emotion of a person whereas in a typical AI-based emotion recognition system you’ll find around and the one that we’re gonna built looks only at a single image of a person’s face to determine the emotion of that person. Now, in reality, our expression may at times exhibit what we feel but not always. People may smile for a picture or someone may have a face that inherently looks gloomy & sad but that doesn’t represent the person’s emotion.
So If we were to build a system that actually recognizes the emotions of a person then we need to do more than look at a simple face image. We would also consider the body language of a person through a series of frames, so the network would be a combination of an LSTM & a CNN network. Also for a more robust system, we may also incorporate a voice tone recognition AI as the tone of a voice, and speech patterns tell a lot about the person’s feelings.
Since today we’ll only be looking at a single face image so it’s better to call our task Facial Expression Recognition rather than Emotion recognition.
Facial Expression Recognition Applications:
Monitoring facial expressions of several people over a period of time provides great insights if used carefully, so for this reason we can use this technology in the following applications.
1: Smart Music players that play music according to your mood:
Think about it, you come home after having a really bad day, you lie down on the bed looking really sad & gloomy and then suddenly just the right music plays to lift up your mood.
2: Student Mood Monitoring System:
Now a system that cleverly averages the expressions of multiple students over a period of time can get an estimate of how a particular topic or teacher is impacting students, does the topic being taught stresses out the students, is a particular session from a teacher a joyful experience for students.
3: Smart Advertisement Banners:
Think about smart advertisement banners that have a camera attached to it, when a commercial airs, it checks real-time facial expressions of people consuming that ad and informing the advertiser if the ad had the desired effect or not. Similarly, companies can get feedback if customers liked their products or not without even asking them.
These are just some of the applications from top of my head, if you start thinking about it you can come up with more use cases. One thing to remember is that you have to be really careful as how you use this technology. Use it as an assistive tool and do not completely rely on it. For e.g don’t deploy on Airport and start interrogating every other black guy who triggers Angry expressions on the system for a couple of frames.
Facial Expression Recognition Approaches:
So let’s talk about the ways we could go about recognizing someone’s facial expressions. We will look at some classical approaches first then move on to deep learning.
Haar Cascades based Recognition:
Perhaps the oldest method that could work are Haar Cascades. So essentially these Haar Cascades also called viola jones Classifier is an outdated Object detection technique by Paul Viola and Michael Jones in 2001. It is a machine learning-based approach where a cascade is trained from a lot of positive and negative images. It is then used to detect objects in images.
The most popular use of these cascades is as a face detector which is still used today, although there are better methods available.
Now instead of using face detection, we could train a cascade to detect expressions. Since you can only train a single class with a cascade so you’ll need multiple cascades. A better way to go about is to first perform face detection then look for different features inside the face ROI, like detecting a smile with this smile detection cascade. You can also train a frown detector and so on.
Truth be told, this method is so weak that I wouldn’t even try experimenting with this in this time and era but since people have used this in the past so I’m just putting it there.
Fisher, Eigen & LBPH based Recognition:
OpenCV’s built-in face_recognition module has 3 different face recognition algorithms, Eigenfaces face recognizer, Fisherfaces face recognizer and Local binary patterns histograms (LBPH) Face Recognizer.
If you’re wondering why am I mentioning face recognition algorithms on a facial expression recognition post, So understand this, these algorithms can extract some really interesting features like principal components and local histograms which you can then feed into an ML classifier like SVM, so in theory, you can repurpose them for emotion recognition, only this time the target classes are not the identities of people but some facial expressions. This will work best if you have a few classes, ideally 2-3. I haven’t seen many people work on emotion like this but take a look at this post in which a guy uses Fisher faces for facial expression recognition.
Again I would mention this is not a robust approach, but would work better than the previous one.
Histogram Oriented Gradients based Recognition (HOG):
Now similar to the above approach instead of using the face_recognizer module to extract features you can extract HOG features of faces, HOG based features are really effective. After extracting HOG features you can train an SVM or any other Machine learning classifier on top of it.
Custom Features with Landmark Detection:
One of the easiest and effective ways to create an emotion recognition system is to use a landmark detector like the one in dlib which allows you to detect 68 important landmarks on the face.
By using this detector you can extract facial features like eyes, eyebrows, mouth, etc. Now you can take custom measurements of these features like measuring the distance between the lip ends to detect if the person is smiling or not. Similarly, you can measure if the eyes are wide open or not, indicating surprise or shock.
Now there are two ways to go about it, either you can send these custom measurements to an ML classifier and let it learn to predict emotions based on these measurements or you use your own heuristics to determine when to call it happy, sad etc based on the measurements.
I do think the former approach is more effective than the latter. But if you’re just determining a singular emotion like if a person is smiling or not then it’s easier to use heuristics.
Deep Learning based Recognizer:
It should not come as a surprise that the State of the Art approach to detect emotions would be a deep learning-based approach. Let me explain how you would create a simple yet effective emotion recognizer system. So what you would simply do is train a Convolutional Neural Network (CNN) on different facial expression images (Ideally thousands of images for each class/emotion) and after the training showed it new samples and if done right it would perform better than all the above approaches I’ve mentioned.
Now that we have discussed different approaches, let’s move on to the coding part of the blog.
Facial Expression Recognition in OpenCV
We will be using a deep learning classifier that will be loaded to the OpenCV DNN module. The authors trained this model using MS Cognitive Toolkit (formerly CNTK) and then converted this model to ONNX (Open neural network exchange ) format.
ONNX format allows developers to move models between different frameworks such as CNTK, Caffe2, Tensorflow, PyTorch etc.
There is also a javascript version of this model (version 1.2) with a live demo which you can check out here. In this post we will be using version 1.3 which has a better performance.
In the paper, the authors demonstrate training a deep CNN using 4 different approaches: majority voting, multi-label learning, probabilistic label drawing, and cross-entropy loss. The model that we are going to use today was trained using cross-entropy loss, which according to the author’s conclusion was one of the best performing models.
The model was trained on FER+ dataset, FER dataset was the standard dataset for emotion recognition task but in FER+ each image has been labeled by 10 crowd-sourced taggers, which provides a better quality of ground truth label for still image emotion than the original FER labels.
More information about the ONNX version of the model can be found here.
The input to our emotion recognition model is a grayscale image of 64×64 resolution. The output is the probabilities of 8 emotion classes: neutral, happiness, surprise, sadness, anger, disgust, fear, and contempt.
Here’s the architecture of the model.
Here are the steps we would need to perform:
Initialize the Dnn module.
Read the image.
Detect faces in the image.
Pre-process all the faces.
Run a forward pass on all the faces.
Get the predicted emotion scores and convert them to probabilities.
Finally get the emotion corresponding to the highest probability
Make sure you have the following Libraries Installed.
OpenCV ( possibly Version 4.0 or above)
Numpy
Matplotlib
bleedfacedetector
Bleedfacedetector is my face detection library which can detect faces using 4 different algorithms. You can read more about the library here.
You can install it by doing:
pip install bleedfacedetector
Before installing bleedfacedetector make sure you have OpenCV & Dlib installed.
pip install opencv-contrib-python
To install dlib you can do:
pip install dlib OR pip install dlib==19.8.1
Directory Hierarchy
You can go ahead and download the source code from the download code section. After downloading the zip folder, unzip it and you will have the following directory structure.
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Emotion Recognition
│Facial Expression Recognition.ipynb
│
├───Media
│emotion1.jpeg
│emotion2.jpg
│emotion3.jpg
│emotion4.jpg
│emotion5.jpeg
│
└───Model
emotion-ferplus-8.onnx
You can now run the Jupyter notebook Facial Expression Recognition.ipynband start executing each cell as follows.
Import Libraries
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# Import required libraries
importcv2
importnumpy asnp
importmatplotlib.pyplot asplt
importos
importbleedfacedetector asfd
Importtime
# This is the magic command to show matplotlib graphs.
%matplotlib inline
Initialize DNN Module
To use Models in ONNX format, you just have to use cv2.dnn.readNetFromONNX(model) and pass the model inside this function.
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# Set model path
model='Model/emotion-ferplus-8.onnx'
# Now read the model
net=cv2.dnn.readNetFromONNX(model)
Read Image
This is our image on which we are going to perform emotion recognition.
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# Read image
image=cv2.imread("Media/surprise.jpeg")
# Display image
plt.figure(figsize=[10,10]);
plt.imshow(image[:,:,::-1]);plt.axis('off');
Line 2: We’re reading the image form disk.
Line 5-6 : We’re setting the figure size and showing the image with matplotlib, [:,:,::-1] means to reverse image channels so we can show OpenCV BGR images properly in matplotlib. OpenCV BGR images.
Define the available classes / labels
Now we will create a list of all 8 available emotions that we need to detect.
Line 4: We’re using an SSD based face detector with 20% filter confidence to detect faces, you can easily swap this detector with any other detector inside bleedfacedetector by just changing this line.
Line 7: We’re extracting the x,y,w,h coordinates from the first face we found in the list of faces.
Line 10-13: We’re padding the face by a value of 3, now this expands the face ROI boundaries, this way the model takes a look at a larger face image when predicting. I’ve seen this improves results in a lot of cases, Although this is not required.
Padded Vs Non Padded Face
Here you can see what the final face ROI looks like when it’s padded and when it’s not padded.
Before you pass the image to a neural network you perform some image processing to get the image in the right format. So the first thing we need to do is convert the face from BGR to Grayscale then we’ll resize the image to be of size 64x64. This is the size that our network requires. After that we’ll reshape the face image into (1, 1, 64, 64), this is the final format which the network will accept.
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# Convert Image into Grayscale
gray=cv2.cvtColor(padded_face,cv2.COLOR_BGR2GRAY)
# Resize into 64x64
resized_face=cv2.resize(gray,(64,64))
# Reshape the image into required format for the model
processed_face=resized_face.reshape(1,1,64,64)
Line 2: Convert the padded face into GrayScale image Line 5: Resize the GrayScale image into 64 x 64 Line 8: Finally we are reshaping the image into the required format for our model
Input the preprocessed Image to the Network
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net.setInput(processed_face)
Forward Pass
Most of the Computations will take place in this step, This is the step where the image goes through the whole neural network.
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Output=net.forward()
Check the output
As you can see, the model outputs scores for each emotion class.
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# The output are the scores for each emotion class
print('Shape of Output: {} n'.format(Output.shape))
# Get the index of the max probability, use that index to get the predicted emotion in the
# emotions list you created above.
predicted_emotion=emotions[prob.argmax()]
# Print the target Emotion
print('Predicted Emotion is: {}'.format(predicted_emotion))
Predicted Emotion is: Surprise
Display Final Result
We already have the correct prediction from the last step but to make it more cleaner we will display the final image with the predicted emotion, we will also draw a bounding box over the detected face.
Now that we have seen a step by step implementation of the network, we’ll create the 2 following python functions.
Initialization Function: This function will contain parts of the network that will be set once, like loading the model.
Main Function: This function will contain all the rest of the code from preprocessing to postprocessing, it will also have the option to either return the image or display it with matplotlib.
Furthermore, the Main Function will be able to predict the emotions of multiple people in a single image, as we will be doing all the operations in a loop.
# Return the the final image if return data is True
returnimg_copy
else:
# Displpay the image
plt.figure(figsize=(10,10))
plt.imshow(img_copy[:,:,::-1]);plt.axis("off");
Initialize the Emotion Recognition
Call the initialization function once.
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init_emotion()
Calling the main function
Now pass in any image to the main function
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image=cv2.imread("Media/emotion1.jpeg")
emotion(image)
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image=cv2.imread("Media/emotion4.jpg")
emotion(image)
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image=cv2.imread("Media/emotion3.jpg")
emotion(image)
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image=cv2.imread("Media/emotion2.jpg")
emotion(image)
Real time emotion recognition on Video:
You can also take the above main function that we created and put it inside a loop and it will start detecting facial expressions on a video, below code detects emotions using your webcam in real time. Make sure to set returndata = True
Here’s the confusion matrix of the model from the author’s paper. As you can see this model is not good at predicting Disgust, Fear & Contempt classes.
You can try running the model on different images and you’ll also agree with the above matrix, that the last three classes are pretty difficult to predict, particularly because It’s also hard for us to differentiate between these many emotions based on just facial expression, a lot of micro expressions overlap between these classes and so it’s understandable why the algorithm would have a hard time differentiating between 8 different emotional expressions.
Improvement Suggestions:
Still, if you really want to detect some expressions that the model seems to fail on then the best way to go about is to train the model yourself on your own data. Ethnicity & color can make a lot of difference.Also, try removing some emotion classes so the model can focus only on those that you care about.
You can also try changing the padding value, this seems to help in some cases.
If you’re working on a live video feed then try to average the results of several frames instead of giving a new result on every new frame.
You’ll come across many Computer Vision courses out there, but nothing beats a 1 on 1 video call support from an expert in the field. Plus there is a plethora of subfields and tons of courses on AI and computer vision out there, you need someone to lay out a step-by-step learning path customized to your needs. This is where I come in, whether you need monthly support or just want to have a one-time chat with me, I’ve got you covered. Check all the coaching details and packages here
In this tutorial we first learned about the Emotion Recognition problem, why it’s important, and what are the different approaches we could take to develop such systems.
Then we learned to perform emotion recognition using OpenCV’s DNN module. After that, we went over some ways on how to improve our results.
I hope you enjoyed this tutorial. If you have any questions regarding this post then please feel free to comment below and I’ll gladly answer them.
You can reach out to me personally for a 1 on 1 consultation session in AI/computer vision regarding your project. Our talented team of vision engineers will help you every step of the way. Get on a call with me directlyhere.
