camera.py

import cv2
import os
import numpy as np
import onnxruntime as ort

def center_crop(frame):
    h , w , _ = frame.shape
    start = abs(h - w) // 2
    if h > w:
        return frame[start:start+w]
    return frame[:,start:start+h]

def main():

    #constants
    index_to_letter = list('ABCDEFGHIKLMNOPQRSTUVWXY')
    mean = 0.485 * 255.
    std = 0.229 * 255.

    #create a runnable session with exported model
    fname = "signlanguage.onnx"
    ort_session = ort.InferenceSession(fname)

    execution_path = os.getcwd()
    cap = cv2.VideoCapture(0)
    

    while True:

        #create frame by frame
        ret , frame = cap.read()

        #preprocess data
        frame = center_crop(frame)
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
        x = cv2.resize(frame,(28,28))
        

        x = x.reshape(1,1,28,28).astype(np.float32)
        y = ort_session.run(None,{'input':x})[0]

        index = np.argmax(y,axis=1)
        letter = index_to_letter[int(index)]

        cv2.putText(frame,letter,(100,100),cv2.FONT_HERSHEY_SIMPLEX, 2.0, (0, 255, 0), thickness=2)
        cv2.imshow("Sign Language Translator",frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()