| 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] |
|
|
| |
| 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) |
|
|
