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import cv2 |
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from cvzone.HandTrackingModule import HandDetector |
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from cvzone.ClassificationModule import Classifier |
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import numpy as np |
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import math |
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cap = cv2.VideoCapture(0) |
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detector = HandDetector(maxHands=1) |
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classifier = Classifier("keras_model.h5", "labels.txt") |
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offset = 20 |
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imgSize = 300 |
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counter = 0 |
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labels = ["iam", "ok", "going", "no", "yes" , "hi",] |
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while True: |
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success, img = cap.read() |
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imgOutput = img.copy() |
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hands, img = detector.findHands(img) |
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if hands: |
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hand = hands[0] |
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x, y, w, h = hand['bbox'] |
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imgWhite = np.ones((imgSize, imgSize, 3), np.uint8) * 255 |
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imgCrop = img[y - offset:y + h + offset, x - offset:x + w + offset] |
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if imgCrop.size == 0: |
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continue |
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imgCropShape = imgCrop.shape |
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aspectRatio = h / w |
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if aspectRatio > 1: |
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k = imgSize / h |
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wCal = math.ceil(k * w) |
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imgResize = cv2.resize(imgCrop, (wCal, imgSize)) |
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imgResizeShape = imgResize.shape |
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wGap = math.ceil((imgSize - wCal) / 2) |
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imgWhite[:, wGap: wCal + wGap] = imgResize |
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prediction, index = classifier.getPrediction(imgWhite, draw=False) |
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print(prediction, index) |
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else: |
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k = imgSize / w |
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hCal = math.ceil(k * h) |
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imgResize = cv2.resize(imgCrop, (imgSize, hCal)) |
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imgResizeShape = imgResize.shape |
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hGap = math.ceil((imgSize - hCal) / 2) |
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imgWhite[hGap: hCal + hGap, :] = imgResize |
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prediction, index = classifier.getPrediction(imgWhite, draw=False) |
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cv2.rectangle(imgOutput, (x - offset, y - offset - 70), (x - offset + 400, y - offset + 60 - 50), (0, 255, 0), |
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cv2.FILLED) |
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cv2.putText(imgOutput, labels[index], (x, y - 30), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2) |
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cv2.rectangle(imgOutput, (x - offset, y - offset), (x + w + offset, y + h + offset), (0, 255, 0), 4) |
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cv2.imshow('ImageCrop', imgCrop) |
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cv2.imshow('ImageWhite', imgWhite) |
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cv2.imshow('Image', imgOutput) |
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cv2.waitKey(1) |
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