for y in range(0, TextImage.size[1]):
# discount everything that's completely white
if TextImagePixels[x, y] != 255:
ImageSlice.append(TextImagePixels[x, y])
Difference = 0
SecondDifference = 0
if len(ImageSlice) > 2:
Difference = sorted(ImageSlice,
reverse=True)[0] - sorted(ImageSlice)[0]
DifferenceArray.append(Difference)
# find all of the local mins
LocalMins = Array.FindAllLocalMins(DifferenceArray)
Slope = Array.FindSlope(DifferenceArray)
# make sure that clusters of data are treated as one individual point
UnfilteredCropPoints = []
# add the first point because it automatically counts
UnfilteredCropPoints.append(LocalMins[0])
for i in range(1, len(LocalMins)):
# the current crop point is set here
CurrentCropPoint = UnfilteredCropPoints[len(UnfilteredCropPoints) - 1]
# create an array of difference values between the two crop points
ValueOffsetArray = DifferenceArray[CurrentCropPoint:LocalMins[i]]
# compute the
TempArray = []
def CalculateCropPoints(LineHeight, ShowGraphs=False):
global FileName
# do the gaussian smoothing that enables us to find the crop points
# blur the image
img = cv2.imread("Processed/" + FileName)
# compute the raw array
RawArray = Array.VerticalArrayFromImage(Image.fromarray(img))
# calculate filter and smooth sizes based on how big the image is
YFilterSize = Statistics.RoundToOdd(len(img) / 20)
XFilterSize = Statistics.RoundToOdd(len(img[0]) / 20)
SmoothSize = Statistics.RoundToEven(len(img) / 20)
# use the line height, and make the smoothing size 1/2 of that
SmoothSize = Statistics.RoundToEven(LineHeight / 2.0)
# use the recomended standard deviation
blur = cv2.GaussianBlur(img, (XFilterSize, YFilterSize), 0)
BlurredImageArray = Array.VerticalArrayFromImage(Image.fromarray(blur),
BandPercentage=.5)
MeanArray = Array.MeanArray(BlurredImageArray, SmoothSize)
MedianArray = Array.MedianArray(BlurredImageArray, SmoothSize)
# get the crop points by finding local maxes
CropPoints = Array.FindAllLocalMaxes(MeanArray)
# check to see if there should be any extra crop points added
SlopeArray = Array.FindSlope(MeanArray)
# if the initial slope is less than 0
if SlopeArray[0] < 0:
# insert 0 at position 0
CropPoints.insert(0, 0)
# if the final slope is more than 0 or 0
if SlopeArray[len(SlopeArray) - 1] >= 0:
CropPoints.append(len(MeanArray) - 1)
if ShowGraphs:
# graph / visualize some stuff
TempArray = []
for i in range(0, len(MeanArray)):
if i in CropPoints:
TempArray.append(255)
else:
TempArray.append(150)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,
2,
sharex=False,
sharey=False)
ax1.set_title("Blurred then Mean")
ax1.plot(MeanArray)
ax1.plot(RawArray)
ax2.plot(BlurredImageArray)
ax2.plot(RawArray)
ax2.set_title("Blurred")
ax3.imshow(img)
ax3.set_title("Image")
ax4.imshow(blur)
ax4.set_title("Blur Image")
plt.show()
return CropPoints
