System Initialization Code Done

test.py

+import cv2
+from cvzone.HandTrackingModule import HandDetector
+from cvzone.ClassificationModule import Classifier
+import numpy as np
+import math
+from collections import Counter
+import time
+
+cap = cv2.VideoCapture(0)
+detector = HandDetector(maxHands=1)
+classifier = Classifier("Model/keras_model.h5", "Model/labels.txt")
+
+offset = 20
+imgSize = 300
+
+folder = "Data/Yes"
+counter = 0
+
+labels = ["Fine", "Home", "Later", "Law", "Me", "No", "See", "Study", "Thanks", "Visit", "Yes"]
+
+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 + 100, y - offset - 50 + 50), (255, 0, 255), cv2.FILLED)
+        cv2.putText(imgOutput, labels[index], (x, y-30), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 255, 255), 2)
+        cv2.rectangle(imgOutput, (x - offset, y - offset),
+                      (x + w + offset, y + h + offset), (255, 0, 255), 4)
+
+        f = open("test.txt", "a")
+        new = (labels[index])
+        f.write(new)
+        f.close()
+
+        array = []
+
+        def word_count(filename):
+            with open(filename) as f:
+                return Counter(f.read().split())
+
+
+        file = "D:/RealTimeSignDetection/test.txt"
+        counter = word_count('D:/RealTimeSignDetection/test.txt')
+        for i in counter:
+            array.append(i)
+            sentence = ' '.join(array)
+        print(array)
+        print(sentence)
+        ThisFile = open(file, "w")
+        ThisFile.write(str(sentence) + str("\n"))
+
+        cv2.imshow("ImageCrop", imgCrop)
+        cv2.imshow("ImageWhite", imgWhite)
+
+    cv2.imshow("Image", imgOutput)
+    cv2.waitKey(1)
+
+
+