datacollection.py

import cv2
from cvzone.HandTrackingModule import HandDetector
import numpy as np
import math
import time

cap = cv2.VideoCapture(0) #0 for device cam, 1 for web cam
detector = HandDetector(maxHands=1)
offset = 20
imgSize = 300
counter = 0

folder = "/home/raunak/Desktop/SignWave/Data/Yes"

while True:
    success, img = cap.read()
    if not success:
        break

    hands, img = detector.findHands(img)
    if hands:
        hand = hands[0]
        x, y, w, h = hand['bbox']

        imgWhite = np.ones((imgSize, imgSize, 3), np.uint8) * 255

        # Ensure the coordinates are within image bounds
        y1 = max(0, y - offset)
        y2 = min(img.shape[0], y + h + offset)
        x1 = max(0, x - offset)
        x2 = min(img.shape[1], x + w + offset)

        imgCrop = img[y1:y2, x1:x2]
        imgCropShape = imgCrop.shape

        if imgCropShape[0] > 0 and imgCropShape[1] > 0:  # Proceed only if the crop is valid
            aspectRatio = h / w

            if aspectRatio > 1:
                k = imgSize / h
                wCal = math.ceil(k * w)
                imgResize = cv2.resize(imgCrop, (wCal, imgSize))
                wGap = math.ceil((imgSize - wCal) / 2)
                imgWhite[:, wGap:wCal + wGap] = imgResize

            else:
                k = imgSize / w
                hCal = math.ceil(k * h)
                imgResize = cv2.resize(imgCrop, (imgSize, hCal))
                hGap = math.ceil((imgSize - hCal) / 2)
                imgWhite[hGap:hCal + hGap, :] = imgResize

            cv2.imshow('ImageCrop', imgCrop)
            cv2.imshow('ImageWhite', imgWhite)

    cv2.imshow('Image', img)
    key = cv2.waitKey(1)
    if key == ord("s"):
        counter += 1
        cv2.imwrite(f'{folder}/Image_{time.time()}.jpg', imgWhite)
        print(counter)