def processAndClassify(frame):
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)
# 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)
return contourPlot
def processAndClassify(frame):
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)
return predictionArray, centr
formatName = '*.jpg'
fileList = glob.glob('dataset/' + formatName)
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)
print ("Current Directory Name:" + rootInputName)
count = 0
for files in fileList:
inputImage=cv2.imread(fileList[count])
fileName = os.path.basename(fileList[count])
print ("Processing Image " + fileList[count])
grayScaleInput = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)
meanShiftResult = prePro.meanShift(inputImage)
cv2.imwrite(rootMeanName + fileName, meanShiftResult)
meanShiftGray = cv2.cvtColor(meanShiftResult, cv2.COLOR_BGR2GRAY)
meanShiftAdapResult = prePro.adapThresh(meanShiftGray)
cv2.imwrite(rootAdapThreshold + fileName, meanShiftAdapResult)
contourPlot = prePro.contourDraw(inputImage, meanShiftAdapResult)
cv2.imwrite(rootContourDraw + fileName, contourPlot)
print ("Preprocessing Results Written.")
contours, hierarchy = prePro.contourFindFull(meanShiftAdapResult)
boundBoxContour = grayScaleInput.copy()
counter = 0
for cnt in contours:
if cv2.contourArea(cnt)>20:
print("Processing Contour no.", str(counter))
[x, y, w, h] = cv2.boundingRect(cnt)
extendBBox = 5
roiImage = boundBoxContour[y-extendBBox:y+h+extendBBox, x-extendBBox:x+w+extendBBox]
roiImageFiltered = roiImage #cv2.medianBlur(roiImage, 3)
kp, roiKeyPointImage = detDes.featureDetectCorner(roiImageFiltered)
# kernel = np.ones((5,5),np.uint8)
# opening = cv2.dilate(meanShiftAdapResult,kernel,iterations=1)
# # Draw Contours on the Input image with results from the meanshift
# contourPlot = prePro.contourDraw(frame, opening)
# cv2.imwrite('contourplot.png',contourPlot)
# # Find the contours on the mean shifted image
# contours, hierarchy = prePro.contourFindFull(opening)
# # Draw Contours on the Input image with results from the meanshift
'''##############################################Morphology ends ##########################################################################
Comment the following lines to add morphology
Uncomment them if no morphology needs to be used
######################################### No Morphology begins ########################################################################'''
contourPlot = prePro.contourDraw(frame, meanShiftAdapResult)
#cv2.imshow('contourplot',contourPlot)
# Find the contours on the mean shifted image
contours, hierarchy = prePro.contourFindFull(meanShiftAdapResult)
'''######################################## No Morphology ends ########################################################################'''
## Use Histogram equalization
boundBoxContour = grayScaleInput.copy()
counter = 0
for cnt in contours:
if cv2.contourArea(cnt) > 500:
print("Processing Contour no.", str(counter))
[x, y, w, h] = cv2.boundingRect(cnt)
extendBBox20 = 20
extendBBox10 = 10
left = 0
meanShiftOtsuContours = 'meanShiftOtsuContours/' meanShiftAda = 'meanShiftAda/' meanShiftAdaContours = 'meanShiftAdaContours/' cannyEdges = 'cannyEdges/' cannyEdgesContours = 'cannyEdgesContours/' meanShiftCanny = 'meanShiftCanny/' meanShiftCannyContours = 'meanShiftCannyContours/' inputFileName = 'yellowdice.png' inputImage = cv2.imread(inputFileName) grayScaleInput = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY) # # # Otsu Result otsuResult = prePro.otsuBin(grayScaleInput) otsuContourPlot = prePro.contourDraw(grayScaleInput, otsuResult) # meanShiftOtsu Result meanShiftResult = prePro.meanShift(inputImage) meanShiftGray = cv2.cvtColor(meanShiftResult, cv2.COLOR_BGR2GRAY) meanShiftOtsuResult = prePro.otsuBin(meanShiftGray) mOtsuContourPlot = prePro.contourDraw(grayScaleInput, meanShiftOtsuResult) # Adaptive Threshold meanShiftAdaResult = prePro.adapThresh(meanShiftGray) meanShiftAdaContourPlot = prePro.contourDraw(grayScaleInput, meanShiftAdaResult) # Canny Edges Result cannyEdgesResult = prePro.cannyEdge(grayScaleInput) cannyEdgesContourPlot = prePro.contourDraw(grayScaleInput, cannyEdgesResult)
meanShiftOtsuContours = 'meanShiftOtsuContours/'
meanShiftAda = 'meanShiftAda/'
meanShiftAdaContours = 'meanShiftAdaContours/'
cannyEdges = 'cannyEdges/'
cannyEdgesContours = 'cannyEdgesContours/'
meanShiftCanny = 'meanShiftCanny/'
meanShiftCannyContours = 'meanShiftCannyContours/'
inputFileName = 'yellowdice.png'
inputImage = cv2.imread(inputFileName)
grayScaleInput = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)
#
#
# Otsu Result
otsuResult = prePro.otsuBin(grayScaleInput)
otsuContourPlot = prePro.contourDraw(grayScaleInput, otsuResult)
# meanShiftOtsu Result
meanShiftResult = prePro.meanShift(inputImage)
meanShiftGray = cv2.cvtColor(meanShiftResult, cv2.COLOR_BGR2GRAY)
meanShiftOtsuResult = prePro.otsuBin(meanShiftGray)
mOtsuContourPlot = prePro.contourDraw(grayScaleInput, meanShiftOtsuResult)
# Adaptive Threshold
meanShiftAdaResult = prePro.adapThresh(meanShiftGray)
meanShiftAdaContourPlot = prePro.contourDraw(grayScaleInput,
meanShiftAdaResult)
# Canny Edges Result
cannyEdgesResult = prePro.cannyEdge(grayScaleInput)
cannyEdgesContourPlot = prePro.contourDraw(grayScaleInput, cannyEdgesResult)
# # Draw Contours on the Input image with results from the meanshift
# contourPlot = prePro.contourDraw(frame, opening)
# cv2.imwrite('contourplot.png',contourPlot)
# # Find the contours on the mean shifted image
# contours, hierarchy = prePro.contourFindFull(opening)
# # Draw Contours on the Input image with results from the meanshift
'''##############################################Morphology ends ##########################################################################
Comment the following lines to add morphology
Uncomment them if no morphology needs to be used
######################################### No Morphology begins ########################################################################'''
contourPlot = prePro.contourDraw(frame, meanShiftAdapResult)
#cv2.imshow('contourplot',contourPlot)
# Find the contours on the mean shifted image
contours, hierarchy = prePro.contourFindFull(meanShiftAdapResult)
'''######################################## No Morphology ends ########################################################################'''
## Use Histogram equalization
boundBoxContour = grayScaleInput.copy()
counter = 0
for cnt in contours:
if cv2.contourArea(cnt)>500:
print("Processing Contour no.", str(counter))
[x, y, w, h] = cv2.boundingRect(cnt)
extendBBox20 = 20
extendBBox10 = 10
