Tag: OpenCV

What will we cover in this tutorial?

How do you detect movements in a webcam stream? Also, how do you insert objects in a live webcam stream? Further, how do you change the position of the object based on the movements?

We will learn all that in this tutorial. The end result can be seen in the video below.

Step 1: Understand the flow of webcam processing

A webcam stream is processed frame-by-frame.

As the above illustration shows, when the webcam captures the next frame, the actual processing often happens on a copy of the original frame. When all the updates and calculations are done, they are inserted in the original frame.

This is interesting. To extract information from the webcam frame we need to work with the frame and find the features we are looking for.

In our example, we need to find movement and based on that see if that movement is touching our object.

A simple flow without any processing would look like this.

import cv2


# Get the webcam (default webcam is 0)
cap = cv2.VideoCapture(0)
# If your webcam does not support 640 x 480, this will find another resolution
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

# To detect movement (to get the background)
background_subtractor = cv2.createBackgroundSubtractorMOG2()

# This will create an object
obj = Object()
# Loop forever (or until break)
while True:
    # Read the a frame from webcam
    _, frame = cap.read()
    # Flip the frame
    frame = cv2.flip(frame, 1)

    # Show the frame in a window
    cv2.imshow('WebCam', frame)

    # Check if q has been pressed to quit
    if cv2.waitKey(1) == ord('q'):
        break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()

The above code will create a direct stream from your webcam to a window.

Step 2: Insert a logo – do it with a class that we will extend later

Here we want to insert a logo in a fixed position in our webcam stream. This can be achieved be the following code. The main difference is the new object Object defined and created.

The object briefly explained

• The object will represent the logo we want to insert.
• It will keep the current position (which is static so far)
• The logo itself.
• The mask used to insert it later (when insert_object is called).
• The constructor (__init__(…)) does the stuff only needed once. Read the logo (it assumes you have a file named logo.png in the same folder), resize it, creating a mask (by gray scaling and thresholding), setting the initial positions of the logo.

Before the while-loop the object obj is created. All that is needed at this stage is to insert the logo in each frame.

import cv2
import numpy as np


# Object class to insert logo
class Object:
    def __init__(self, start_x=100, start_y=100, size=50):
        self.logo_org = cv2.imread('logo.png')
        self.size = size
        self.logo = cv2.resize(self.logo_org, (size, size))
        img2gray = cv2.cvtColor(self.logo, cv2.COLOR_BGR2GRAY)
        _, logo_mask = cv2.threshold(img2gray, 1, 255, cv2.THRESH_BINARY)
        self.logo_mask = logo_mask
        self.x = start_x
        self.y = start_y

    def insert_object(self, frame):
        roi = frame[self.y:self.y + self.size, self.x:self.x + self.size]
        roi[np.where(self.logo_mask)] = 0
        roi += self.logo


# Get the webcam (default webcam is 0)
cap = cv2.VideoCapture(0)
# If your webcam does not support 640 x 480, this will find another resolution
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

# This will create an object
obj = Object()
# Loop forever (or until break)
while True:
    # Read the a frame from webcam
    _, frame = cap.read()
    # Flip the frame
    frame = cv2.flip(frame, 1)

    # Insert the object into the frame
    obj.insert_object(frame)

    # Show the frame in a window
    cv2.imshow('WebCam', frame)

    # Check if q has been pressed to quit
    if cv2.waitKey(1) == ord('q'):
        break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()

This will result in the following output (when you put me in front of the webcam – that said, if you do it, expect that you sit in the picture and not me (just want to avoid any uncomfortable surprises for you when you show up in the window)).

For more details on how to insert a logo in a live webcam stream, you can read this tutorial.

Step 3: Detect movement in the frame

Detecting movement is not a simple task. Depending on your needs, it can be solved quite simple. In this tutorial we only need to detect simple movement. That is, if you are in the frame and sit still, we do not care to detect it. We only care to detect the actual movement.

We can solve that problem by using the library function createBackgroundSubtractorMOG2(), which can “remove” the background from your frame. It is far from a perfect solution, but it is sufficient for what we want to achieve.

As we only want to see if there is movement or not, and not how much the difference is from previous detected background, we will use a threshold function to make the image black and white based on that. We set the threshold quite high, as it will also remove noise from the image.

It might happen that in your settings (lightening etc.) you need to adjust that value. See the comments in the code how to do that.

import cv2
import numpy as np


# Object class to insert logo
class Object:
    def __init__(self, start_x=100, start_y=100, size=50):
        self.logo_org = cv2.imread('logo.png')
        self.size = size
        self.logo = cv2.resize(self.logo_org, (size, size))
        img2gray = cv2.cvtColor(self.logo, cv2.COLOR_BGR2GRAY)
        _, logo_mask = cv2.threshold(img2gray, 1, 255, cv2.THRESH_BINARY)
        self.logo_mask = logo_mask
        self.x = start_x
        self.y = start_y

    def insert_object(self, frame):
        roi = frame[self.y:self.y + self.size, self.x:self.x + self.size]
        roi[np.where(self.logo_mask)] = 0
        roi += self.logo


# Get the webcam (default webcam is 0)
cap = cv2.VideoCapture(0)
# If your webcam does not support 640 x 480, this will find another resolution
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

# To detect movement (to get the background)
background_subtractor = cv2.createBackgroundSubtractorMOG2()

# This will create an object
obj = Object()
# Loop forever (or until break)
while True:
    # Read the a frame from webcam
    _, frame = cap.read()
    # Flip the frame
    frame = cv2.flip(frame, 1)

    # Get the foreground mask (it is gray scale)
    fg_mask = background_subtractor.apply(frame)
    # Convert the gray scale to black and white with a threshold
    # Change the 250 threshold fitting your webcam and needs
    # - Setting it lower will make it more sensitive (also to noise)
    _, fg_mask = cv2.threshold(fg_mask, 250, 255, cv2.THRESH_BINARY)

    # Insert the object into the frame
    obj.insert_object(frame)

    # Show the frame in a window
    cv2.imshow('WebCam', frame)
    # To see the foreground mask
    cv2.imshow('fg_mask', fg_mask)

    # Check if q has been pressed to quit
    if cv2.waitKey(1) == ord('q'):
        break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()

This results in the following output.

As you see, it does a decent job to detect movement. Sometimes it happens that you create a shadow after your movements. Hence, it is not perfect.

Step 4: Detecting movement where the object is and move it accordingly

This is the tricky part. But let’s break it down simple.

• We need to detect if the mask, we created in previous step, is overlapping with the object (logo).
• If so, we want to move the object (logo).

That is what we want to achieve.

How do we do that?

• Detect if there is an overlap by using the same mask we create for the logo and see if it overlaps with any points on the mask of the movement.
• If so, we move the object by choosing a random movement. Measure how much overlap is. Then choose another random movement. See if the overlap is less.
• Continue this a few times and chose the random movement with the least overlap.

This turns out to by chance to move away from the overlapping areas. This is the power of introducing some randomness, which simplifies the algorithm a lot.

A more precise approach would be to calculate in which direction the least mask is close to the object (logo). This becomes quite complicated and needs a lot of calculations. Hence, we chose to have this simple approach, which has both a speed element and direction element that works fairly well.

All we need to do, is to add a update_position function to our class and call it before we insert the logo.

import cv2
import numpy as np


# Object class to insert logo
class Object:
    def __init__(self, start_x=100, start_y=100, size=50):
        self.logo_org = cv2.imread('logo.png')
        self.size = size
        self.logo = cv2.resize(self.logo_org, (size, size))
        img2gray = cv2.cvtColor(self.logo, cv2.COLOR_BGR2GRAY)
        _, logo_mask = cv2.threshold(img2gray, 1, 255, cv2.THRESH_BINARY)
        self.logo_mask = logo_mask
        self.x = start_x
        self.y = start_y
        self.on_mask = False

    def insert_object(self, frame):
        roi = frame[self.y:self.y + self.size, self.x:self.x + self.size]
        roi[np.where(self.logo_mask)] = 0
        roi += self.logo

    def update_position(self, mask):
        height, width = mask.shape

        # Check if object is overlapping with moving parts
        roi = mask[self.y:self.y + self.size, self.x:self.x + self.size]
        check = np.any(roi[np.where(self.logo_mask)])

        # If object has moving parts, then find new position
        if check:
            # To save the best possible movement
            best_delta_x = 0
            best_delta_y = 0
            best_fit = np.inf
            # Try 8 different positions
            for _ in range(8):
                # Pick a random position
                delta_x = np.random.randint(-15, 15)
                delta_y = np.random.randint(-15, 15)

                # Ensure we are inside the frame, if outside, skip and continue
                if self.y + self.size + delta_y > height or self.y + delta_y < 0 or \
                        self.x + self.size + delta_x > width or self.x + delta_x < 0:
                    continue

                # Calculate how much overlap
                roi = mask[self.y + delta_y:self.y + delta_y + self.size, self.x + delta_x:self.x + delta_x + self.size]
                check = np.count_nonzero(roi[np.where(self.logo_mask)])
                # If perfect fit (no overlap), just return
                if check == 0:
                    self.x += delta_x
                    self.y += delta_y
                    return
                # If a better fit found, save it
                elif check < best_fit:
                    best_fit = check
                    best_delta_x = delta_x
                    best_delta_y = delta_y

            # After for-loop, update to best fit (if any found)
            if best_fit < np.inf:
                self.x += best_delta_x
                self.y += best_delta_y
                return


# Get the webcam (default webcam is 0)
cap = cv2.VideoCapture(0)
# If your webcam does not support 640 x 480, this will find another resolution
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

# To detect movement (to get the background)
background_subtractor = cv2.createBackgroundSubtractorMOG2()

# This will create an object
obj = Object()
# Loop forever (or until break)
while True:
    # Read the a frame from webcam
    _, frame = cap.read()
    # Flip the frame
    frame = cv2.flip(frame, 1)
    # Get the foreground mask (it is gray scale)
    fg_mask = background_subtractor.apply(frame)
    # Convert the gray scale to black and white with a threshold
    # Change the 250 threshold fitting your webcam and needs
    # - Setting it lower will make it more sensitive (also to noise)
    _, fg_mask = cv2.threshold(fg_mask, 250, 255, cv2.THRESH_BINARY)

    # Find a new position for object (logo)
    # - fg_mask contains all moving parts
    # - updated position will be the one with least moving parts
    obj.update_position(fg_mask)
    # Insert the object into the frame
    obj.insert_object(frame)

    # Show the frame in a window
    cv2.imshow('WebCam', frame)
    # To see the fg_mask uncomment the line below
    # cv2.imshow('fg_mask', fg_mask)

    # Check if q has been pressed to quit
    if cv2.waitKey(1) == ord('q'):
        break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()

Step 5: Test it

Well, this is the fun part. See a live demo in the video below.

What is next step?

I would be happy to hear any suggestions from you. I see a lot of potential improvements, but the conceptual idea is explained and showed in this tutorial.

What will we cover in this tutorial?

How to create this effect.

The idea behind the code

The idea behind the above effect is simple. We will use a background subtractor, which will get the background of an image and make a mask of the foreground.

The it simple follows this structure.

1. Capture a frame from the webcam.
2. Get the foreground mask fg_mask.
3. To get greater effect dilate the fg_mask.
4. From the original frame, create a cartoon frame.
5. Use the zero entries of fg_mask as index to copy the cartoon frame into frame. That is, it overwrites all pixel corresponding to a zero (black) value in fg_mask to the values of the cartoon in the original frame. That results in that we only get cartoon effect in the background and not on the objects.
6. Show the frame with background cartoon effect.

The code you need to create the above effect

This is all done by using OpenCV. If you need help to install OpenCV I suggest you read this tutorial. Otherwise the code follows the above steps.

import cv2

backSub = cv2.createBackgroundSubtractorKNN(history=200)

cap = cv2.VideoCapture(0)

while True:
    _, frame = cap.read()

    fg_mask = backSub.apply(frame)
    fg_mask = cv2.dilate(fg_mask, None, iterations=2)

    _, cartoon = cv2.pencilSketch(frame, sigma_s=50, sigma_r=0.3, shade_factor=0.02)

    idx = (fg_mask < 1)
    frame[idx] = cartoon[idx]
    cv2.imshow('Frame', frame)
    cv2.imshow('FG Mask', fg_mask)

    keyboard = cv2.waitKey(1)
    if keyboard == ord('q'):
        break

What will we cover in this tutorial?

In this tutorial we will look into how you can track an object with a specific color and replace it with a new object. The inserted new object will be scaled to the size of the object tracked. This will be done on a live stream from the webcam.

Understand the process from webcam and feeding it to a window

First thing to understand is that when processing a live stream from a webcam you are actually processing it frame by frame.

Hence, the base code is as follows.

import cv2

# Get the webcam
cap = cv2.VideoCapture(0)

while True:
    # Step 1: Capture the frame
    _, frame = cap.read()

    # Step 2: Show the frame with blurred background
    cv2.imshow("Webcam", frame)
    # If q is pressed terminate
    if cv2.waitKey(1) == ord('q'):
        break

# Release and destroy all windows
cap.release()
cv2.destroyAllWindows()

First we import the OpenCV library cv2. If you need help to install it read this tutorial. Then you capture the webcam by calling the cv2.VideoCapture(0), where we assume you have 1 webcam and it is the first one (0).

The the while-loop where you capture the video stream frame by frame. It is done calling the cap.read(), which returns a return code and the frame (we ignore the return code _).

To show the frame we read from the webcam, we call the cv2.imshow(“Webcam”, frame), which will create a window with the frame (image from your webcam).

The final part of the while-loop is checking if the key q has been pressed, if so, break out of the while-loop and release webcam and destroy all windows.

That is how processing works for webcam flow. The processing will be between step 1 and step 2 in the above code. Pro-processing and setup is most often done before the while-loop.

The process flow to identify and track object to insert scaled logo

In the last section we looked at how a webcam stream is processed. Then in this section we will explain the process for how to identify a object by color, scale the object we want to insert, and how to insert it into the frame.

The process is depicted in the image below followed by an explanation of all the steps.

The steps are described here.

1. This is the step where we capture the raw frame from the webcam.
2. To easier identify a specific color object in the frame, we convert the image to the HSV color model. It contains of Hue, Saturation, and Volume.
3. Make a mask with all object of the specific color. This is where the HSV color model makes it easy.
4. To make it more visible and easier for detection, we dilate the mask.
5. Then we find all the contours in the mask.
6. We loop over all the contours found. Ideally we only find one, but there might be small objects, which we will discard.
7. Based on the contour found, get the size of it, which we use to scale (resize) the logo we want to insert.
8. Resize the logo to fit the size of the contour.
9. As the logo is not square, we need to create a mask to insert it.
10. To insert it easily, we create a RIO (region of image) where the contour is. This is nothing needed, just makes it easier to avoid a lot of extra calculations. If you know NumPy, it is a view into it.
11. Then we insert the logo using the mask.
12. Finally, time to show the frame.

The implementation

The code following the steps described in the previous section is found here.

import cv2
import time
import imutils
import numpy as np

# Get the webcam
cap = cv2.VideoCapture(0)
# Setup the width and the height (your cam might not support these settings)
width = 640
height = 480
cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)

# Read the logo to use later
logo_org = cv2.imread('logo.png')

# Time is just used to get the Frames Per Second (FPS)
last_time = time.time()
while True:
    # Step 1: Capture the frame
    _, frame = cap.read()

    # Step 2: Convert to the HSV color space
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    # Step 3: Create a mask based on medium to high Saturation and Value
    # - Hue 8-10 is about orange, which we will use
    # - These values can be changed (the lower ones) to fit your environment
    mask = cv2.inRange(hsv, (8, 180, 180), (10, 255, 255))
    # Step 4: This dilates with two iterations (makes it more visible)
    thresh = cv2.dilate(mask, None, iterations=2)
    # Step 5: Finds contours and converts it to a list
    contours = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    contours = imutils.grab_contours(contours)

    # Step 6: Loops over all objects found
    for contour in contours:
        # Skip if contour is small (can be adjusted)
        if cv2.contourArea(contour) < 750:
            continue

        # Step 7: Get the box boundaries
        (x, y, w, h) = cv2.boundingRect(contour)
        # Compute size
        size = (h + w)//2

        # Check if logo will be inside frame
        if y + size < height and x + size < width:
            # Step 8: Resize logo
            logo = cv2.resize(logo_org, (size, size))
            # Step 9: Create a mask of logo
            img2gray = cv2.cvtColor(logo, cv2.COLOR_BGR2GRAY)
            _, logo_mask = cv2.threshold(img2gray, 1, 255, cv2.THRESH_BINARY)

            # Step 10: Region of Image (ROI), where we want to insert logo
            roi = frame[y:y+size, x:x+size]

            # Step 11: Mask out logo region and insert
            roi[np.where(logo_mask)] = 0
            roi += logo

    # (Extra) Add a FPS label to image
    text = f"FPS: {int(1 / (time.time() - last_time))}"
    last_time = time.time()
    cv2.putText(frame, text, (10, 20), cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 0), 2)

    # Step 12: Show the frame
    cv2.imshow("Webcam", frame)
    # If q is pressed terminate
    if cv2.waitKey(1) == ord('q'):
        break

# Release and destroy all windows
cap.release()
cv2.destroyAllWindows()

Time to test it.

Testing the code

When using your webcam, you might need to change the colors. I used the following setting for the blue marker in my video.

    mask = cv2.inRange(hsv, (110, 120, 120), (130, 255, 255))

The two 3-tuples are HSV color space representation. The item of the tuples is setting the Hue. Here is 110 and 130. That means the color range we want to mask out is from 110-130, which you can see is in the blue range (image below). The other two are Saturation from 120-255 and Value from 120-255. To fit your camera and light settings, you need to change that range.

Where you can see the HSV color specter here.

You might need to choose different values.