Object Tracking

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Banish J

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Core Concept

This code captures video from the webcam, processes each frame to detect objects of a specified color (defined by HSV color ranges), highlights the detected objects, and displays the results in a window. The program prints directions based on the position of the detected object in the frame and can be terminated by pressing the 'q' key.

Importing Necessary Libraries

import imutils
import cv2

• imutils: A convenience library for basic image processing functions, making it easier to work with OpenCV.
• cv2: The OpenCV library for computer vision tasks.

Defining Color Ranges for Detection

####for Phone in blue
##redLower = (101,101,90)         
##redUpper = (121,203,191)

####for a flower box
##redLower = (124,49,79)       
##redUpper = (179,255,255)

###for (green balm)
##redLower = (68,41,75)
##redUpper = (84,188,139)

###for face
##redLower = (0,23,38)
##redUpper = (61,214,146)

###for Hit
##redLower = (124,49,79)       
##redUpper = (179,255,255)

###for Green T-shirt
##redLower = (58,206,0)       
##redUpper = (100,255,255)

#for Light source
redLower = (0,0,255)       
redUpper = (0,0,255)

• These are predefined HSV color ranges for detecting various objects. Uncomment the desired range for different objects.
• Currently, the color range for a light source is being used.

Initializing the Webcam

camera = cv2.VideoCapture(0)

• cv2.VideoCapture(0): Initializes the webcam for video capture. The argument 0 typically refers to the default webcam on the computer.

Real-Time Object Detection Loop

while True:
    (grabbed, frame) = camera.read()
    frame = imutils.resize(frame, width=1000)
    blurred = cv2.GaussianBlur(frame, (11, 11), 0)
    hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)

• camera.read(): Captures a frame from the webcam. grabbed indicates if the frame was read correctly, and frame stores the captured image.
• imutils.resize(frame, width=1000): Resizes the frame to a width of 1000 pixels while maintaining the aspect ratio.
• cv2.GaussianBlur(frame, (11, 11), 0): Applies a Gaussian blur to the image to reduce noise and smooth the image.
• cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV): Converts the blurred image from BGR color space to HSV color space.

Creating a Mask and Finding Contours

    mask = cv2.inRange(hsv, redLower, redUpper)
    mask = cv2.erode(mask, None, iterations=2)
    mask = cv2.dilate(mask, None, iterations=2)
    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
    center = None

• cv2.inRange(hsv, redLower, redUpper): Creates a binary mask where the white pixels represent the colors within the specified range.
• cv2.erode(mask, None, iterations=2): Erodes the mask to remove small white noise.
• cv2.dilate(mask, None, iterations=2): Dilates the mask to restore the eroded parts of the object.
• cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]: Finds contours in the mask. The [-2] index is used to grab the correct contours list based on the version of OpenCV.
• center = None: Initializes the center variable.

Detecting and Highlighting the Object

    if len(cnts) > 0:
        c = max(cnts, key=cv2.contourArea)
        ((x, y), radius) = cv2.minEnclosingCircle(c)
        M = cv2.moments(c)
        center = (int(M["m10"] / M["m00"]), int(M["m10"] / M["m00"]))
        if radius > 10:
            cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), -1)
            print(center, radius)
            if radius > 250:
                 print("stop")
            else:
                if(center[0] < 150):
                    print("Right")
                elif(center[0] > 450):
                    print("Left")
                elif(radius < 250):
                    print("Front")
                else:
                    print("Stop")

• if len(cnts) > 0: Checks if any contours are found.
• c = max(cnts, key=cv2.contourArea): Selects the largest contour based on area.
• ((x, y), radius) = cv2.minEnclosingCircle(c): Finds the smallest circle that can enclose the contour.
• M = cv2.moments(c): Calculates image moments, which help in finding the center of the contour.
• center = (int(M["m10"] / M["m00"]), int(M["m10"] / M["m00"])): Computes the center of the contour.
• if radius > 10: Filters out small objects based on the radius.
  • cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), -1): Draws a circle around the detected object.
  • print(center, radius): Prints the center coordinates and radius of the detected object.
  • if radius > 250: Prints "stop" if the object is too close.
  • if(center[0] < 150): Prints "Right" if the object is on the left side.
  • if(center[0] > 450): Prints "Left" if the object is on the right side.
  • if(radius < 250): Prints "Front" if the object is within the range but not too close.

Displaying the Result

    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1)
    if key == ord("q"):
        break

• cv2.imshow("Frame", frame): Displays the frame with the detected object.
• key = cv2.waitKey(1): Waits for 1 millisecond for a key press.
• if key == ord("q"): Checks if the 'q' key is pressed to break the loop and stop the program.

Releasing Resources

camera.release()
cv2.destroyAllWindows()

• camera.release(): Releases the webcam resource.
• cv2.destroyAllWindows(): Closes all OpenCV windows.