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


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()
    impOutput = 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((300-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((300 - hCal) / 2)
            imgWhite[hGap:hCal + hGap, :] = imgResize
            prediction, index = classifier.getPrediction(imgWhite)


        cv2.putText(impOutput,labels[index],(x,y-20),cv2.FONT_HERSHEY_COMPLEX,2, (255,0,255),2)
        # cv2.imshow('imgcrop', imgCrop)
        # cv2.imshow('imgWhite', imgWhite)
        result_out = labels[index]
    cv2.imshow("frame",impOutput)
    key = cv2.waitKey(1)