def DetectObjectFromImage(beforeImage, afterImage, beforeGrayImage,
afterGrayImage):
resizeRate = GetContour.SquareDetectAndReturnRateAsSquare(beforeGrayImage)
beforeImage = CustomOpenCV.ResizeImageAsRate(beforeImage, resizeRate)
beforeGrayImage = CustomOpenCV.ResizeImageAsRate(beforeGrayImage,
resizeRate)
afterImage = CustomOpenCV.ResizeImageAsRate(afterImage, resizeRate)
afterGrayImage = CustomOpenCV.ResizeImageAsRate(afterGrayImage, resizeRate)
squareContourData = DetectBackgroundSquare.DetectBackgroundSquareFromImage(
beforeImage) #형광색 인식으로 점 4개 찾는 함수
#squareContourData = DetectBlackBoardContourFromOriginImage(beforeGrayImage)
# 굴곡진 큰 사각형 정사각형으로 보정
perspectiveUpdatedBeforeImage = ImageMatrixMove.ImageMatrixMove(
beforeImage, squareContourData)
perspectiveUpdatedAfterImage = ImageMatrixMove.ImageMatrixMove(
afterImage, squareContourData)
perspectiveUpdatedBeforeImage = CustomOpenCV.ResizeImageAsWidth(
perspectiveUpdatedBeforeImage, DefineManager.IMAGE_WIDTH)
perspectiveUpdatedAfterImage = CustomOpenCV.ResizeImageAsWidth(
perspectiveUpdatedAfterImage, DefineManager.IMAGE_WIDTH)
# Resize image as shape [ rateHeight, DefineManager.IMAGE_WIDTH ]
#CustomOpenCV.ShowImagesWithName([perspectiveUpdatedBeforeImage, perspectiveUpdatedAfterImage],
# ["perspectiveUpdatedBeforeImage", "perspectiveUpdatedAfterImage"])
perspectiveUpdatedBeforeGrayImage = cv2.cvtColor(
perspectiveUpdatedBeforeImage, cv2.COLOR_BGR2GRAY)
perspectiveUpdatedAfterGrayImage = cv2.cvtColor(
perspectiveUpdatedAfterImage, cv2.COLOR_BGR2GRAY)
morphologyKernel = np.ones(
(Setting.DefineManager.MORPHOLOGY_MASK_SIZE + 1,
Setting.DefineManager.MORPHOLOGY_MASK_SIZE + 1), np.uint8)
perspectiveUpdatedBeforeMorphologyGrayImage = cv2.morphologyEx(
perspectiveUpdatedBeforeGrayImage, cv2.MORPH_OPEN, morphologyKernel)
perspectiveUpdatedAfterMorphologyGrayImage = cv2.morphologyEx(
perspectiveUpdatedAfterGrayImage, cv2.MORPH_OPEN, morphologyKernel)
# Reduce image noise
beforeThresholdedBlackBoardImage = cv2.adaptiveThreshold(
perspectiveUpdatedBeforeMorphologyGrayImage,
Setting.DefineManager.SET_IMAGE_WHITE_COLOR,
cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,
Setting.DefineManager.NEIGHBORHOOD_MASK_SIZE, 10)
afterThresholdedBlackBoardImage = cv2.adaptiveThreshold(
perspectiveUpdatedAfterMorphologyGrayImage,
Setting.DefineManager.SET_IMAGE_WHITE_COLOR,
cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,
Setting.DefineManager.NEIGHBORHOOD_MASK_SIZE, 10)
# Adaptive Threshold Image
#CustomOpenCV.ShowImagesWithName([beforeThresholdedBlackBoardImage, afterThresholdedBlackBoardImage], ['beforeThresholdedBlackBoardImage', 'afterThresholdedBlackBoardImage'])
differenceBasedOnThreshImage = cv2.absdiff(
beforeThresholdedBlackBoardImage, afterThresholdedBlackBoardImage)
differenceBasedOnThreshImage[
differenceBasedOnThreshImage > Setting.DefineManager.
EACH_IMAGE_DIFFERENCE_THRESHOLD] = Setting.DefineManager.SET_IMAGE_WHITE_COLOR
# Detect each image difference from Threshold Image
#CustomOpenCV.ShowImagesWithName([differenceBasedOnThreshImage], ["differenceBasedOnThreshImage"])
objectFoundedImage = GetContour.GetObjectImage(
perspectiveUpdatedBeforeImage, perspectiveUpdatedAfterImage)
humanDetectedContour, contourLineDrawImage = GetContour.GetContour(
objectFoundedImage, perspectiveUpdatedAfterImage)
GetContour.FindNavel(humanDetectedContour, contourLineDrawImage)
importantPoint = GetContour.AngleAsDealWithPointFromContours(
humanDetectedContour, contourLineDrawImage)
return [
beforeThresholdedBlackBoardImage, afterThresholdedBlackBoardImage,
differenceBasedOnThreshImage, humanDetectedContour
]
def DetectObjectFromImage(beforeImage, afterImage, beforeGrayImage,
afterGrayImage):
resizeRate = GetContour.SquareDetectAndReturnRateAsSquare(beforeGrayImage)
beforeImage = CustomOpenCV.ResizeImageAsRate(beforeImage, resizeRate)
beforeGrayImage = CustomOpenCV.ResizeImageAsRate(beforeGrayImage,
resizeRate)
afterImage = CustomOpenCV.ResizeImageAsRate(afterImage, resizeRate)
afterGrayImage = CustomOpenCV.ResizeImageAsRate(afterGrayImage, resizeRate)
#squareContourData = DetectBackgroundSquare.DetectBackgroundSquareFromImage(beforeImage) #형광색 인식으로 점 4개 찾는 함수
# in mac
# this function is not working and falling loop.
squareContourData = DetectBlackBoardContourFromOriginImage(beforeGrayImage)
# 굴곡진 큰 사각형 정사각형으로 보정
perspectiveUpdatedBeforeImage = ImageMatrixMove.ImageMatrixMove(
beforeImage, squareContourData)
perspectiveUpdatedAfterImage = ImageMatrixMove.ImageMatrixMove(
afterImage, squareContourData)
perspectiveUpdatedBeforeImage = CustomOpenCV.ResizeImageAsWidth(
perspectiveUpdatedBeforeImage, DefineManager.IMAGE_WIDTH)
perspectiveUpdatedAfterImage = CustomOpenCV.ResizeImageAsWidth(
perspectiveUpdatedAfterImage, DefineManager.IMAGE_WIDTH)
# Resize image as shape [ rateHeight, DefineManager.IMAGE_WIDTH ]
#CustomOpenCV.ShowImagesWithName([perspectiveUpdatedBeforeImage, perspectiveUpdatedAfterImage],
# ["perspectiveUpdatedBeforeImage", "perspectiveUpdatedAfterImage"])
perspectiveUpdatedBeforeGrayImage = cv2.cvtColor(
perspectiveUpdatedBeforeImage, cv2.COLOR_BGR2GRAY)
perspectiveUpdatedAfterGrayImage = cv2.cvtColor(
perspectiveUpdatedAfterImage, cv2.COLOR_BGR2GRAY)
morphologyKernel = np.ones(
(Setting.DefineManager.MORPHOLOGY_MASK_SIZE + 1,
Setting.DefineManager.MORPHOLOGY_MASK_SIZE + 1), np.uint8)
perspectiveUpdatedBeforeMorphologyGrayImage = cv2.morphologyEx(
perspectiveUpdatedBeforeGrayImage, cv2.MORPH_OPEN, morphologyKernel)
perspectiveUpdatedAfterMorphologyGrayImage = cv2.morphologyEx(
perspectiveUpdatedAfterGrayImage, cv2.MORPH_OPEN, morphologyKernel)
# Reduce image noise
beforeThresholdedBlackBoardImage = cv2.adaptiveThreshold(
perspectiveUpdatedBeforeMorphologyGrayImage,
Setting.DefineManager.SET_IMAGE_WHITE_COLOR,
cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,
Setting.DefineManager.NEIGHBORHOOD_MASK_SIZE, 10)
afterThresholdedBlackBoardImage = cv2.adaptiveThreshold(
perspectiveUpdatedAfterMorphologyGrayImage,
Setting.DefineManager.SET_IMAGE_WHITE_COLOR,
cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,
Setting.DefineManager.NEIGHBORHOOD_MASK_SIZE, 10)
# Adaptive Threshold Image
#CustomOpenCV.ShowImagesWithName([beforeThresholdedBlackBoardImage, afterThresholdedBlackBoardImage], ['beforeThresholdedBlackBoardImage', 'afterThresholdedBlackBoardImage'])
differenceBasedOnThreshImage = cv2.absdiff(
beforeThresholdedBlackBoardImage, afterThresholdedBlackBoardImage)
differenceBasedOnThreshImage[
differenceBasedOnThreshImage > Setting.DefineManager.
EACH_IMAGE_DIFFERENCE_THRESHOLD] = Setting.DefineManager.SET_IMAGE_WHITE_COLOR
# Detect each image difference from Threshold Image
#CustomOpenCV.ShowImagesWithName([differenceBasedOnThreshImage], ["differenceBasedOnThreshImage"])
objectFoundedImage = GetContour.GetObjectImage(
perspectiveUpdatedBeforeImage, perspectiveUpdatedAfterImage)
humanDetectedContour, contourLineDrawImage = GetContour.GetContour(
objectFoundedImage, perspectiveUpdatedAfterImage)
faceMinY, faceMaxY = GetContour.DetectFaceAndGetY(
perspectiveUpdatedAfterImage)
navelPoint, faceRate, maxY, minY = GetContour.FindNavel(
humanDetectedContour, faceMaxY, contourLineDrawImage)
height = maxY - minY
importantPoint = GetContour.AngleAsDealWithPointFromContours(
humanDetectedContour, contourLineDrawImage)
beforeDrawImage = np.copy(perspectiveUpdatedBeforeImage)
afterDrawImage = np.copy(perspectiveUpdatedAfterImage)
functionParameter = []
for index in range(len(importantPoint)):
xArray = []
yArray = []
for point in importantPoint[index]:
x, y = point.ravel()
xArray.append(x)
yArray.append(y)
xArray = np.asarray(xArray)
yArray = np.asarray(yArray)
if xArray.shape[0] > 0:
# ax + b = y (a, b 를 받아옴)
functionCharacteristic = sp.polyfit(
xArray, yArray, DefineManager.FUNCTION_DIMENSION)
functionParameter.append(functionCharacteristic)
yRegressionArray = sp.polyval(functionCharacteristic, xArray)
err = np.sqrt(
sum((yArray - yRegressionArray)**2) / yArray.shape[0])
pointA, pointB = GetContour.GetStartAndEndPointsFromLine(
functionCharacteristic, xArray)
cv2.line(beforeDrawImage, pointA, pointB,
DefineManager.RGB_COLOR_GREEN, 1)
cv2.line(afterDrawImage, pointA, pointB,
DefineManager.RGB_COLOR_GREEN, 1)
CustomOpenCV.ShowImagesWithName([beforeDrawImage, afterDrawImage])
return [
perspectiveUpdatedBeforeImage, perspectiveUpdatedAfterImage, height,
navelPoint, humanDetectedContour, functionParameter, beforeDrawImage,
faceRate
]