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testing.py
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testing.py
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# dependencies
import cv2
import mediapipe as mp
import tensorflow as tf
from cvzone.HandTrackingModule import HandDetector
from cvzone.ClassificationModule import Classifier
import numpy as np
import math
import time
# defined variables
cap = cv2.VideoCapture(0)
detector = HandDetector(maxHands=1)
offset = 20
imgSize = 300
labels = ["A", "B", "C", "Hello!", "Love You!", "No!", "Yes!"]
# data trained using Teachable Machine by withGoogle
model_path = 'Model/keras_model.h5'
labels_file_path = 'Model/labels.txt'
classifier = Classifier(model_path, labels_file_path)
#function
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']
imgCrop = img[y-offset:y+h+offset, x-offset:x+w+offset]
imgWhite = np.ones((imgSize,imgSize,3),np.uint8)*255
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)
print(prediction, index)
cv2.putText(imgOutput, labels[index], (x+127, y-27), cv2.FONT_HERSHEY_COMPLEX, -1.7, (255,255,255), 2, 1, True)
cv2.rectangle(imgOutput, (x-offset,y-offset), (x+w+offset, y+h+offset), (0,128,0),2)
#cv2.imshow("Cropped Image", imgCrop)
imgWh = cv2.flip(imgWhite, 1)
cv2.imshow("Gesture Mapping", imgWh)
imgOut = cv2.flip(imgOutput, 1)
cv2.imshow("My Camera", imgOut)
cv2.waitKey(1)