test.py

import cv2
from cvzone.HandTrackingModule import HandDetector
from cvzone.ClassificationModule import Classifier
import numpy as np
import math
cap=cv2.VideoCapture(0)
detector=HandDetector(maxHands=1)
classifier=Classifier("Model/keras_model.h5", "Model/labels.txt")
offset=20
imgSize=300
counter=0
labels=["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"]
while True:
    success, img=cap.read()
    imgOutput=img.copy()
    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
        imgCrop=img[y-offset:y+h+offset, x-offset:x+w+offset]
        imgCropShape=imgCrop.shape
        aspectRatio=h/w
        if aspectRatio>1:
            k=imgSize/h
            wCal=math.ceil(k*w)
            imgResize=cv2.resize(imgCrop, (wCal, imgSize))
            imgResizeShape=imgResize.shape
            wGap=math.ceil((imgSize-wCal)/2)
            imgWhite[:, wGap:wCal+wGap]=imgResize
            prediction, index=classifier.getPrediction(imgWhite)
        else:
            k=imgSize/w
            hCal=math.ceil(k*h)
            imgResize=cv2.resize(imgCrop, (imgSize, hCal))
            imgResizeShape=imgResize.shape
            hGap=math.ceil((imgSize-hCal)/2)
            imgWhite[hGap:hCal+hGap, :]=imgResize
            prediction, index=classifier.getPrediction(imgWhite)
        cv2.rectangle(imgOutput, (x-offset, y-offset-50), (x-offset+90, y-offset), (255, 0, 255), cv2.FILLED)
        cv2.putText(imgOutput, labels[index], (x, y-26), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 255, 255), 2)
        cv2.rectangle(imgOutput, (x-offset, y-offset), (x+w+offset, y+h+offset), (255, 0, 255), 4)
        cv2.imshow("ImageCrop", imgCrop)
        cv2.imshow("ImageWhite", imgWhite)
    cv2.imshow("Image", imgOutput)
    cv2.waitKey(1)