dataCollection.py

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

cap = cv2.VideoCapture(0)
detector = HandDetector(maxHands=1, detectionCon=0.8)

offset = 20
imgSize = 300

folder = "Data/1"  
counter = 0
while True:
    success, img = cap.read()
    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

        # --- Safe Crop with Bounds Check ---
        y1 = max(0, y - offset)
        y2 = min(y + h + offset, img.shape[0])
        x1 = max(0, x - offset)
        x2 = min(x + w + offset, img.shape[1])

        imgCrop = img[y1:y2, x1:x2]

        if imgCrop.size != 0:  # Prevent empty image resize
            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: wGap + wCal] = imgResize
            else:
                k = imgSize / w
                hCal = math.ceil(k * h)
                imgResize = cv2.resize(imgCrop, (imgSize, hCal))
                hGap = math.ceil((imgSize - hCal) / 2)
                imgWhite[hGap: hGap + hCal, :] = imgResize

            cv2.imshow("Cropped Image", 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)