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import cv2
import gradio as gr
import math
import numpy as np
from cvzone.ClassificationModule import Classifier
from cvzone.HandTrackingModule import HandDetector
bgSize = 96
classifier = Classifier("keras_model.h5", "labels.txt")
detector = HandDetector(maxHands=1)
labels = ["Look", "Drink", "Eat", "Ok"]
offset = 20
def segment(image):
hands, frame = detector.findHands(image)
try:
if hands:
hand = hands[0]
x, y, w, h = hand['bbox']
croppedHand = np.ones((bgSize, bgSize, 3), np.uint8) * 12
imgCrop = frame[y - offset:y + h +
offset, x - offset:x + w + offset]
aspectRatio = h / w
if aspectRatio > 1:
constant = bgSize / h
wComputed = math.floor(constant * w)
bgResize = cv2.resize(imgCrop, (wComputed, bgSize))
bgResizeShape = bgResize.shape
wGap = math.floor((bgSize-wComputed)/2)
croppedHand[:bgResizeShape[0],
wGap:wGap + wComputed] = bgResize
else:
constant = bgSize / w
hComputed = math.floor(constant * h)
bgResize = cv2.resize(imgCrop, (bgSize, hComputed))
bgResizeShape = bgResize.shape
hGap = math.floor((bgSize - hComputed) / 2)
croppedHand[hGap: hComputed + hGap, :] = bgResize
_, index = classifier.getPrediction(croppedHand, draw=False)
return labels[index]
except Exception as e:
print(e)
return 'No sign detected'
gr.interface.Interface(fn=segment, live=True, inputs=gr.Image(source='webcam', streaming=True), outputs="text").launch()
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