backgroundClass = 3

predictionArray = []

centroidArray = []

# Convert the image into a grayscale image

grayScaleInput = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

# Apply meanshift on the RGB image

meanShiftResult = prePro.meanShift(frame)

# Convert the result of the Mean shifted image into Grayscale

meanShiftGray = cv2.cvtColor(meanShiftResult, cv2.COLOR_BGR2GRAY)

# Apply adaptive thresholding on the resulting greyscale image

meanShiftAdapResult = prePro.adapThresh(meanShiftGray)

# Draw Contours on the Input image with results from the meanshift

contourPlot = prePro.contourDraw(frame, meanShiftAdapResult)

#cv2.imwrite('contourPlot.png',contourPlot)

# Find the contours on the mean shifted image

contours, hierarchy = prePro.contourFindFull(meanShiftAdapResult)

# Find the contours on the mean shifted image

boundBoxContour = grayScaleInput.copy()

frameBoundingBox = frame.copy()

# For each contour

for cnt in contours:

# If the area covered by the contour is greater than 500 pixels

if cv2.contourArea(cnt)>20:

# Get the bounding box of the contour

[x, y, w, h] = cv2.boundingRect(cnt)

# Get the moments of the each contour for computing the centroid of the contour

moments = cv2.moments(cnt)

if moments['m00']!=0:

cx = int(moments['m10']/moments['m00']) # cx = M10/M00

cy = int(moments['m01']/moments['m00']) # cy = M01/M00

centroid = (cx,cy)

# cx,cy are the centroid of the contour

extendBBox = 10

# Extend it by 10 pixels to avoid missing the key points on the edges

roiImage = boundBoxContour[y-extendBBox:y+h+extendBBox, x-extendBBox:x+w+extendBBox]

roiImageFiltered = roiImage

# Detect the corner key points

kp, roiKeyPointImage = detDes.featureDetectCorner(roiImageFiltered)

# Use the ORB feature detector and descriptor on the contour

kp, des, roiKeyPointImage = detDes.featureDescriptorORB(roiImageFiltered, kp)

if np.size(kp)>0:

histPoints = tH.histogramContour(des)

prediction = tC.classify(np.float32(histPoints))

## If the predicted class is not the background class then add the prediction to the prediction array and get its centroid

if prediction != backgroundClass:

cv2.rectangle(frameBoundingBox,(x,y),(x+w,y+h),(0,255,0),2)