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Test.py
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Test.py
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import cv2
from cvzone.HandTrackingModule import HandDetector
from cvzone.ClassificationModule import Classifier
import numpy as np
import math
import time
cap = cv2.VideoCapture(0)
detector = HandDetector(maxHands=1)
classifier = Classifier("Data/Model/keras_model.h5", "Data/Model/labels.txt")
offset = 20
imgSize = 300
folder = "Data/L"
counter = 0
labels = ["Hello", "I'm", "C", "A", "R", "O", "L"]
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, draw=False)
print(prediction, index)
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, draw=False)
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, 1.0, (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)