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Giving computer the ability to understand the real world.
A young aspiring Data Scientist, who love to build AI and ML applications, that can contribute as a solution in real world problems. I started my coding journey in grade 8, since then I have developed solutions to many organizations.
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High school Maths to Machine Learning
Computer vision fascinates me a lot, these days I am exploring the world of Computer Vision. While working I realised that we have to write hundreds of lines of code just for basic computer vision techniques like face detection, hand detection, pose classification etc. I explored opencv and mediapipe library which helped me gain a lot of insight into computer vision. I have developed some packages that might help the developer community to focus more on the implementation rather than on coding from scratch. Here are three packages that I developed recently.
faceDetector(image, draw=False)
image (a single frame) as input and a variable draw with default value False. You can change the value of parameter draw to True if you want to draw the rectangular box over the face on the image frame.frame and a list of [x,y,w,h]. x is the minimum x co-ordinates of the face, y is the minimum y co-ordinate of face, w is the width and h is the height of the face. NOTE that the frame will have rectangular box over the face if value of draw is set to True in the function.faceDetector(image, draw=False)
With detection over the face directly through function
from lkfacedetection import faceDetector
import cv2
cap = cv2.VideoCapture(0)
while True:
success, image = cap.read()
functionValues = faceDetector(image,draw=True) #draw over the frame from function
frame = functionValues[0]
cv2.imshow('Face', frame)
cv2.waitKey(1)
cap.release()
With detection externally using the values from function
from lkfacedetection import faceDetector
import cv2
cap = cv2.VideoCapture(0)
while True:
success, image = cap.read()
functionValues = faceDetector(image) #doesn't draw over the frame
frame = functionValues[0]
x,y,w,h = functionValues[1]
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) #Draws a rectangle over the face
cv2.imshow('Face', frame)
cv2.waitKey(1)
cap.release()
handTracker(image,draw=True)
image (a single frame) as input and a variable draw with default value False. You can change the value of parameter draw to True if you want to the mapping of hands over the image frame.frame and a list of handLandmarks. Know more about these handLandmarks on this link. NOTE that the function will return [0] at index 1 in the list if no hands are detected.handTracker(image,draw=False)
Mapping over the hands directly from function.
from lkhandtracking import handTracker
import cv2
cap = cv2.VideoCapture(0)
while True:
success, image = cap.read()
functionValues = handTracker(image,draw=True)
image = functionValues[0]
handLms = functionValues[1]
print(handLms)
cv2.imshow('Hands', image)
cv2.waitKey(1)
cap.release()
Track hands without mapping.
from lkhandtracking import handTracker
import cv2
cap = cv2.VideoCapture(0)
while True:
success, image = cap.read()
functionValues = handTracker(image,draw=False)
image = functionValues[0]
handLms = functionValues[1]
print(handLms)
cv2.imshow('Hands', image)
cv2.waitKey(1)
cap.release()
bodySegmentation(orignalImg,backgroundImg=(255,255,255),threshold=0.3)
orignalImg i.e. the image on which the segmentation has to happen. Other parameters i.e. backgroundImg sets the background color, by default it is white, but it can be changed as per user's need. threshold defines the level at which background has to be removed.bodySegmentation(img)
Default Segmentation (White Background)
from lkbodysegmentation import bodySegmentation
import cv2
cap = cv2.VideoCapture(0)
while True:
success, img = cap.read()
img = bodySegmentation(img)
cv2.imshow('Image',img)
cv2.waitKey(1)
bodySegmentation(img, backgroundColor, threshold)
Customized Segmentation
from lkbodysegmentation import bodySegmentation
import cv2
cap = cv2.VideoCapture(0)
while True:
success, img = cap.read()
backgroundColor = (255,0,255) #You can replace with an image too
threshold = 0.45 #Level of background to be erased
img = bodySegmentation(img,backgroundColor,threshold)
cv2.imshow('Image',img)
cv2.waitKey(1)
This package is developed by Lakshay Kumar an enthusiastic AI Researcher. This is developed keeping in mind the pain to write lengthy lines of code just to detect faces. This will enable other developers to focus more on implementation part rather than spending time on coding the face detection module.
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