Computer vision is an exciting field of artificial intelligence that teaches computers to interpret and understand the visual world by using digital images from cameras and videos, and sophisticated deep learning models. By accurately identifying and classifying objects in the visual world, computer vision allows machines to make informed decisions and take appropriate actions based on what they "see".

OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez. The library is cross-platform and free for use under the open-source BSD license.

Here are some projects & their applications which can be build using openCV:-

Use GaussianBlur with a kernel of (91,91) to blur faces. It can be used for Privacy & Identity Protection in public/private areas. Can increase or decrease blur strength by changing the kernel.

Used one image color (source) to change the color of different image (target) using both Color Transfer Algorithm as well as color_transfer modules. It can be used to apply different types of color filters.

convert an image or a video into a sketch.

A simple Python program that can extract faces from an image is a valuable tool for improving the accuracy of face recognition applications. Developers can implement robust algorithms that quickly and accurately detect faces in an image, crop the image to isolate the face, and save it as a separate image file for further analysis or processing. This program can be used to build a wide range of applications, including security systems, and more.

Color detection is the process of detecting the name of any color. Color detection is necessary to recognize objects, it is also used as a tool in various image editing and drawing apps. Some Real-world Applications:-

• In self-driving car, to detect the traffic signals.
• Multiple color detection is used in some industrial robots, to performing pick-and-place task in separating different colored objects.

This code helps you track an object-of-interest to draw colored lines on the screen (just like the Paint application but using the webcam).

Detecting Number plate from image (or webcam) of cars.

Detect all landmarks of face like Eyes, Nose, Lips, Eyebrows. I have used pre-trained HOG + Linear SVM object detector specifically for the task of face detection. Download shape_predictor_68_face_landmarks.dat from here:- https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat

Detecting facial landmarks in an image is a two step process: First i have localize a face(s) in an image. This can be accomplished using a number of different techniques, but normally involve either Haar cascades or HOG + Linear SVM detectors. Apply the shape predictor, specifically a facial landmark detector, to obtain the (x, y)-coordinates of the face regions in the face ROI.

It can used in Face part extraction (i.e., nose, eyes, mouth, jawline, etc.), Facial alignment, Head pose estimation, Face swapping, Blink detection …and much more applications!

Detect if an eye blink or not. We first detect the eyes & then, we detect two lines: an horizontal line and a vertical line crossing the eye. The size of the horizontal line is almost identical in the closed eye and in the open eye while the vertical line is much longer in the open eye in coparison with the closed eye. In the closed eye, the vertical line almost disappears.

I have used pre-trained HOG + Linear SVM object detector specifically for the task of face detection. Download shape_predictor_68_face_landmarks.dat from here:- https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat

Face recognition is a method of identifying or verifying the identity of an individual using their face. Face recognition systems can be used to identify people in photos, video, or in real-time. Law enforcement may also use mobile devices to identify people during police stops.

Building a document scanner with OpenCV can be accomplished in just three simple steps:

• Step 1: Detect edges.
• Step 2: Use the edges in the image to find the contour (outline) representing the piece of paper being scanned.
• Step 3: Apply a perspective transform to obtain the top-down view of the document.

Driver drowsiness detection is a car safety technology which helps prevent accidents caused by the driver getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads.

Some of the current systems learn driver patterns and can detect when a driver is becoming drowsy. I have used pre-trained HOG + Linear SVM object detector specifically for the task of face detection. Download shape_predictor_68_face_landmarks.dat from here:- https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat

Play offline chrome dinosaur game using opencv & eye blink. Just open chrome browser with no internet connection. Drawback of this application is it can be used only for jumping purpose.

I have used pre-trained HOG + Linear SVM object detector specifically for the task of face detection. Download shape_predictor_68_face_landmarks.dat from here:- https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat

Object detection is a computer vision technique that allows us to identify and locate objects in an image or video. With this kind of identification and localization, object detection can be used to count objects in a scene and determine and track their precise locations, all while accurately labeling them.

Created an Angle Finder. In that, I first define two lines using mouse clicks and then find the angle between theses lines using simple mathematics.

Face alignment is the task of identifying the geometric structure of faces in digital images, and attempting to obtain a canonical alignment of the face based on translation, scale, and rotation. Using Face Alignment we can get higher accuracy from our face recognition. As Face Alignment is like data normalization.

I have used pre-trained HOG + Linear SVM object detector specifically for the task of face detection. Download shape_predictor_68_face_landmarks.dat from here:- https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat

Contour detection is a major issue in image processing. For instance, in classification and segmentation, the goal is to split the image into several parts. This problem is strongly related to the detection of the connected contours separating these parts. It is quite easy to detect edges using local image analysis techniques, but the detection of continuous contours is more complicated and needs a global analysis of the image.

Having duplicate images in your dataset creates a problem for two reasons:

• It introduces bias into your dataset, giving your deep neural network additional opportunities to learn patterns specific to the duplicates
• It hurts the ability of your model to generalize to new images outside of what it was trained on

And identifying duplicates in a large dataset manually is very time consuming and error-prone. That's why we want to remove duplicate images from our dataset.

We can use eye blink count detector to check if eyes are blinking regularly or not to avoid the symptoms of Dry Eye. As eye blink is considered to be a suitable indicator for fatigue diagnostics. Drowsy state may be caused by lack of sleep, medication, drugs or driving continuously for long time period.

I have used pre-trained HOG + Linear SVM object detector specifically for the task of face detection. Download shape_predictor_68_face_landmarks.dat from here:- https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat

Simple and realtime object tracking in a video sequence.

Add watermark to images using opencv. Please make sure that watermark has black background.

Simple, multiple and realtime object tracking in a video sequence.

Scanning QRCode & Barcode through camera using opencv and pyzbar.

Semantic segmentation, or image segmentation, is the task of clustering parts of an image together which belong to the same object class. It is a form of pixel-level prediction because each pixel in an image is classified according to a category. It can be used for autonomous driving, robotic navigation, localization, and scene understanding.

Human activity recognition model can recognize over 400 activities with 78.4-94.5% accuracy.

Smoothing is often used to reduce noise within an image or to produce a less pixelated image. Smoothing is also usually based on a single value representing the image, such as the average value of the image or the middle (median) value.

Yawn Detection is all about detecting yawn( open one’s mouth wide and inhale deeply due to tiredness or boredom) using OpenCV and Dlib. It can be used in various major applications like Self Driving Cars, Driver’s Fatigue detection, Driver’s Drowsiness detection, Driver’s consciousness detection etc.

I have used pre-trained HOG + Linear SVM object detector specifically for the task of face detection. Download shape_predictor_68_face_landmarks.dat from here:- https://github.com/italojs/facial-landmarks-recognition/blob/master/shape_predictor_68_face_landmarks.dat

Detect 21 landmarks of a hand using opencv and mediapipe. First, we have to use palm detection then we use hand landmark detector. Hand landmarks can be used in gesture control.

Building a Volume Controller with OpenCV can be accomplished in just 3 simple steps:

• Step 1. Detect Hand landmarks
• Step 2. Calculate the distance between thumb tip and index finger tip.
• Step 3. Map the distance of thumb tip and index finger tip with volume range. For my case, distance between thumb tip and index finger tip was within the range of 15 - 220 and the volume range was from -63.5 - 0.0.

Building a Brightness Controller with OpenCV can be accomplished in just 3 simple steps:

• Step 1. Detect Hand landmarks
• Step 2. Calculate the distance between thumb tip and index finger tip.
• Step 3. Map the distance of thumb tip and index finger tip with volume range. For my case, distance between thumb tip and index finger tip was within the range of 15 - 220 and the volume range was from 0 - 100.

Check whether the given hand in an image is left or right hand. It can be used to advance hand gestures control.

It's same as Volume Control Using Hand Detection & Brightness Control Using Hand Detection. Only difference is now we can manage brightness with left hand & volume with right hand simultaneous.

Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time. It can be used as facial landmarks for detecting eyes, mouth, jawline, nose & eyebrows. Face mesh is mainly useful for real-time augmented reality (AR) applications.

So far I have used haar cascade & dlib for face detection. The main disadvantage of using this model was poor accuracy in terms of lightning, detecting background as face sometimes & much more. In order to overcome this, I have use the resnet caffe pretrained model.

Detect 33 3D landmarks on the whole body (or 25 upper-body landmarks) using opencv and mediapipe. It can be used in various applications such as quantifying physical exercises, sign language recognition, and full-body gesture control. For example, it can form the basis for yoga, dance, and fitness applications. It can also enable the overlay of digital content and information on top of the physical world in augmented reality.

Live perception of simultaneous human pose, face landmarks, and hand tracking in real-time can enable various modern life applications: fitness and sport analysis, gesture control and sign language recognition, augmented reality try-on and effects. It generate a total of 543 landmarks (33 pose landmarks, 468 face landmarks, and 21 hand landmarks per hand).

Often, we have to capture live stream with camera. OpenCV provides a very simple interface to this.

Create an animated GIF in real-time.

Objectron is a real-time 3D object detection solution for everyday objects. It detects objects in 2D images, and estimates their poses through a machine learning (ML) model. Currently supports Shoe, Chair, Cup & Camera.

Show Timer on webcam.

Converting a video into frames using OpenCV and Python can be useful in a variety of applications. Here are a few use cases:

• Video analysis
• Machine learning
• Video editing
• Data compression
• Video indexing and search