def CropImageAroundEdges(InputImage):
# convert the input image to black
InputImage = InputImage.convert('L')
# extract the edges of the letters
RawImage = PILToCV2(InputImage)
GrayImage = cv2.cvtColor(RawImage, cv2.COLOR_BGR2GRAY)
# convert back to a PIL image
ImageEdges = Image.fromarray(InvertColor(cv2.Canny(GrayImage, 50, 150, apertureSize = 3)))
CharacterHorizontalArray = Array.HorizontalArrayFromImage(ImageEdges)
CharacterVerticalArray = Array.VerticalArrayFromImage(ImageEdges)
YMin = 0
YMax = 0
XMin = 0
XMax = 0
# find the min and max for y
for i in range(0, len(CharacterVerticalArray)):
if CharacterVerticalArray[i] != 255:
YMin = i
break
for i in range(ImageEdges.size[1] - 1, -1, -1):
if CharacterVerticalArray[i] != 255:
YMax = i
break
# find the min and max for x
for i in range(0, len(CharacterHorizontalArray)):
if CharacterHorizontalArray[i] != 255:
XMin = i
break
for i in range(ImageEdges.size[0] - 1, -1, -1):
if CharacterHorizontalArray[i] != 255:
XMax = i
break
# +1's are to compensate for how crop function works
InputImage = InputImage.crop((XMin, YMin, XMax + 1, YMax + 1))
return InputImage
YFilterSize = Statistics.RoundToOdd(len(img) / 40)
XFilterSize = Statistics.RoundToOdd(len(img[0]) / 40)
# get the line height
LineHeight = 0
with open('SharedData/LineHeight.txt', 'r') as content_file:
LineHeight = int(float(content_file.read()))
# use the line height, and make the smoothing size 1/2 of that
SmoothSize = Statistics.RoundToEven(LineHeight / 2.0)
print "smooth size", SmoothSize
# use the recomended standard deviation
blur = cv2.GaussianBlur(img, (XFilterSize, XFilterSize), 0)
BlurredImageArray = Array.HorizontalArrayFromImage(Image.fromarray(blur))
RegularImageArray = Array.HorizontalArrayFromImage(Image.fromarray(img))
MeanArray = Array.MeanArray(RegularImageArray, SmoothSize)
MeanArrayBlurred = Array.MeanArray(BlurredImageArray, SmoothSize)
fig, ((ax1, ax2)) = plt.subplots(2, 1, sharex=False, sharey=False)
# LocalMaxes = sorted(Array.FindAllLocalMaxes(BlurredImageArray) + Array.FindAllLocalMins(BlurredImageArray))
LocalMaxes = Array.FindAllLocalMaxes(BlurredImageArray)
# sets how different maximums from the 'same' point can be
# this may be incorrect and will F**K us
NewMinimumThreshold = 5
# make sure that clusters of data are treated as one individual point
