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import cv2
from cvzone.HandTrackingModule import HandDetector
from cvzone.ClassificationModule import Classifier
import numpy as np
import math
import pyttsx3
from collections import deque
# Initialize the text-to-speech engine
engine = pyttsx3.init()
voices = engine.getProperty('voices')
engine.setProperty('voice', voices[1].id)
rate = engine.getProperty('rate')
engine.setProperty('rate', 125)
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"]
# Deque to store the last few predictions
prediction_history = deque(maxlen=3)
last_prediction = None
while True:
try:
success, img = cap.read()
if not success:
print("Failed to capture image")
continue
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
try:
imgCrop = img[y - offset:y + h + offset, x - offset:x + w + offset]
if imgCrop.size == 0:
raise ValueError("Empty image crop detected")
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
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
try:
prediction, index = classifier.getPrediction(imgWhite, draw=False)
print(prediction, index)
prediction_history.append(labels[index])
if len(prediction_history) == 3 and len(set(prediction_history)) == 1 and prediction_history[1]!=last_prediction:
last_prediction=labels[index]
engine.say(labels[index])
engine.runAndWait()
prediction_history = deque(maxlen=3)
except Exception as e:
print("Error in classifier prediction:", e)
except cv2.error as e:
print("OpenCV error:", e)
except ValueError as e:
print(e)
cv2.rectangle(imgOutput, (x - offset, y - offset - 50),
(x - offset + 90, y - offset - 50 + 50), (255, 0, 255), cv2.FILLED)
cv2.putText(imgOutput, labels[index], (x, y - 26), cv2.FONT_HERSHEY_COMPLEX, 1.7, (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)
except Exception as e:
print("Unexpected error:", e)
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