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app.py

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+import cv2
+from cvzone.HandTrackingModule import HandDetector
+from cvzone.ClassificationModule import Classifier
+import numpy as np
+import math
+
+cap = cv2.VideoCapture(0)
+detector = HandDetector(maxHands=1)
+classifier = Classifier("keras_model.h5", "labels.txt")
+offset = 20
+imgSize = 300
+counter = 0
+
+labels = ["iam",  "ok", "going", "no", "yes" , "hi",]
+
+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]
+
+        # Add a check to ensure imgCrop is not empty
+        if imgCrop.size == 0:
+            continue
+
+        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 - 70), (x - offset + 400, y - offset + 60 - 50), (0, 255, 0),
+                      cv2.FILLED)
+        cv2.putText(imgOutput, labels[index], (x, y - 30), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2)
+        cv2.rectangle(imgOutput, (x - offset, y - offset), (x + w + offset, y + h + offset), (0, 255, 0), 4)
+
+        cv2.imshow('ImageCrop', imgCrop)
+        cv2.imshow('ImageWhite', imgWhite)
+
+    cv2.imshow('Image', imgOutput)
+    cv2.waitKey(1)

keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b639c6b7cfb968077c7539ac921341b5dd3c8aabd8cbddd8357f356a30f43d46
+size 2457008

labels.txt

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+0 iam
+1 ok
+2 going
+3 no
+4 yes
+5 hi