ASL.py

import cv2
import numpy as np
import util as ut
import svm_train as st
import re

model = st.trainSVM(17)
# create and train SVM model each time coz bug in opencv 3.1.0 svm.load() https://github.com/Itseez/opencv/issues/4969
cam = int(raw_input("Enter Camera number: "))
cap = cv2.VideoCapture(cam)
font = cv2.FONT_HERSHEY_SIMPLEX


def nothing(x):
    pass


text = " "

temp = 0
previouslabel = None
previousText = " "
label = None
while (cap.isOpened()):

    if cap.read():
        _, img = cap.read()
        cv2.rectangle(img, (350, 128), (600, 400), (255, 0, 0),
                      3)  # (0,0),(511,511),(255,0,0),5 bounding box which captures ASL sign to be detected by the system
        img1 = img[128:400, 350:600]
        img_ycrcb = cv2.cvtColor(img1, cv2.COLOR_BGR2YCR_CB)
        blur = cv2.GaussianBlur(img_ycrcb, (11, 11), 0)
        skin_ycrcb_min = np.array((0, 138, 67))  # np.array((0, 138, 67))
        skin_ycrcb_max = np.array((255, 173, 133))  # np.array((255, 173, 133))
        mask = cv2.inRange(blur, skin_ycrcb_min,
                           skin_ycrcb_max)  # detecting the hand in the bounding box using skin detection
        contours, hierarchy = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, 2)
        cnt = ut.getMaxContour(contours,3000)  # using contours to capture the skin filtered image of the hand
        forloop=0
        try:
            forloop=len(cnt)
        except:
            forloop=0

        if  forloop>0:#cnt.any() != None:
            gesture, label = ut.getGestureImg(cnt, img1, mask,
                                              model)  # passing the trained model for prediction and fetching the result
            if (label != None):
                if (temp == 0):
                    previouslabel = label
            if previouslabel == label:
                previouslabel = label
                temp += 1
            else:
                temp = 0
            if (temp == 40):
                if (label == 'P'):
                    label = " "
                text = text + label
                if (label == 'Q'):
                    words = re.split(" +", text)
                    words.pop()
                    text = " ".join(words)
                # text=previousText
                print text

            cv2.imshow('PredictedGesture', gesture)  # showing the best match or prediction
            cv2.putText(img, label, (50, 150), font, 3, (0, 0, 255),
                        2)  # displaying the predicted letter on the main screen
            # cv2.putText(img, text, (50, 450), font, 3, (0, 0, 255), 2)

        cv2.imshow('Frame', img)
        cv2.imshow('Mask', mask)
        k = 0xFF & cv2.waitKey(10)
        if k == 27:
            break

cap.release()
cv2.destroyAllWindows()