import cv2

from predictions.k_nearest import KNearestNeighbours
from predictions.predictor import Predictor
from predictions.svm_model import SVM

if __name__ == '__main__':
    buffer = []
    # open webcam stream
    cap = cv2.VideoCapture(0)

    type_predictor = "svm"
    if type_predictor == "knn":
        k = 10
        predictor_type = KNearestNeighbours(k)
    elif type_predictor == "svm":
        predictor_type = SVM()
    else:
        predictor_type = KNearestNeighbours(1)



    # embeddings_path = 'embeddings/basic-signs/embeddings.csv'
    embeddings_path = 'embeddings/fingerspelling/embeddings.csv'

    predictor = Predictor(embeddings_path, predictor_type)

    index = 0

    while cap.isOpened():
        # Wait for key press to exit
        if cv2.waitKey(5) & 0xFF == 27:
            break

        ret, frame = cap.read()
        pose = predictor.extract_keypoints(frame)

        if pose is None:
            cv2.imshow('MediaPipe Hands', frame)
            continue

        buffer.append(pose)
        if len(buffer) > 15:
            buffer.pop(0)

        if len(buffer) == 15:
            label, score = predictor.make_prediction(buffer)

            # draw label
            cv2.putText(frame, str(label), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
            cv2.putText(frame, str(score), (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)

        # Show the frame
        cv2.imshow('MediaPipe Hands', frame)