def processAndClassify(frame): backgroundClass = 3 predictionArray = [] centroidArray = [] grayScaleInput = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) meanShiftAdapResult = prePro.adapThresh(grayScaleInput) contours, hierarchy = prePro.contourFind(meanShiftAdapResult) for cnt in contours: if cv2.contourArea(cnt)>500: [x, y, w, h] = cv2.boundingRect(cnt) extendBBox = 5 roiImage = grayScaleInput[y-extendBBox:y+h+extendBBox, x-extendBBox:x+w+extendBBox] kp, des, roiImageKeyPoints = detDes.featureDetectDesORB(roiImage) if np.size(kp)>0:
for fileName in fileList:
image = cv2.imread(fileName)
grayScaleInput = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Apply meanshift on the RGB image
meanShiftResult = prePro.meanShift(image)
# 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(image, meanShiftAdapResult)
cv2.imshow('contourplot', contourPlot)
# Find the contours on the mean shifted image
contours, hierarchy = prePro.contourFind(meanShiftAdapResult)
## Use Histogram equalization
boundBoxContour = grayScaleInput.copy()
count = 0
# For each contour
for cnt in contours:
# If the area covered by the contour is greater than 500 pixels
if cv2.contourArea(cnt) > 500:
# 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
for fileName in fileList:
image = cv2.imread(fileName)
grayScaleInput = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Apply meanshift on the RGB image
meanShiftResult = prePro.meanShift(image)
# 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(image, meanShiftAdapResult)
cv2.imshow('contourplot',contourPlot)
# Find the contours on the mean shifted image
contours, hierarchy = prePro.contourFind(meanShiftAdapResult)
## Use Histogram equalization
boundBoxContour = grayScaleInput.copy()
count = 0
# For each contour
for cnt in contours:
# If the area covered by the contour is greater than 500 pixels
if cv2.contourArea(cnt)>500:
# 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