Create app.py

app.py

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+import math
+import time
+import cv2
+import numpy as np
+from cvzone.HandTrackingModule import HandDetector
+from cvzone.ClassificationModule import Classifier
+
+classifier = Classifier("model/keras_model.h5","model/labels.txt")
+
+labels = ["HELLO","HOW ARE YOU","WHERE ARE YOU"]
+
+cap = cv2.VideoCapture(0)
+detector = HandDetector(maxHands=2,)
+offset = 20
+imgSize = 300
+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
+        ascpectRatio = h/w
+        if ascpectRatio>1:
+            k = imgSize/h
+            wcal = math.ceil(k*w)
+            imgResize = cv2.resize(imgCrop,(wcal,imgSize))
+            imageResizeshape = 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))
+            imageResizeshape = 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, y - 26), cv2.FONT_HERSHEY_COMPLEX, 1.7, (98, 200, 55))
+
+        #cv2.imshow("ImageCrop",imgCrop)
+        #cv2.imshow("ImageWhite",imgWhite)
+    cv2.imshow("image",imgoutput)
+    cv2.waitKey(1)
+