def preprocess(image):
image = cv2.GaussianBlur(image, (5, 5), 0) # Blurring the image
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Grey scaling the image
image = cv2.adaptiveThreshold(
image,
255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,
11, 10)
''' Thresholding the image (Converts greyscale to binary),
using adaptive threshold for best results '''
im2, contours, hierarchy = cv2.findContours(
image,
cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
''' Finding the contours in the image'''
image.fill(255)
cv2.drawContours(image, contours, -1, (0, 0, 255))
''' Drawing the contours on the image before displaying'''
contours.reverse()
segments = segments_to_numpy([cv2.boundingRect(c) for c in contours])
segments = numpy.delete(segments, 0, 0)
euler_list, inner_segments = find_euler_and_inner_segments(segments, 1)
segments, euler_list, central_x, central_y = segment_blocks(
segments, inner_segments, euler_list)
return image, segments, euler_list, central_x, central_y
def _segment( self, image ):
self.image= image
image= cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image = cv2.adaptiveThreshold(image, maxValue=255, adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C, thresholdType=cv2.THRESH_BINARY, blockSize=self.block_size, C=self.c)
contours,hierarchy = cv2.findContours(image,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
segments= segments_to_numpy( [cv2.boundingRect(c) for c in contours] )
self.contours, self.hierarchy= contours, hierarchy #store, may be needed for debugging
return segments
def read_boxfile(path):
classes = []
segments = []
with open(path) as f:
for line in f:
s = line.split(" ")
assert len(s) == 6
assert s[5] == '0\n'
classes.append(s[0].decode('utf-8'))
segments.append(map(int, s[1:5]))
return classes_to_numpy(classes), segments_to_numpy(segments)
def read_boxfile( path ):
classes= []
segments= []
with open(path) as f:
for line in f:
s= line.split(" ")
assert len(s)==6
assert s[5]=='0\n'
classes.append( s[0].decode('utf-8') )
segments.append( map(int, s[1:5]))
return classes_to_numpy(classes), segments_to_numpy(segments)
def read_boxfile(path):
classes = []
segments = []
with io.open(path, encoding="utf-8") as f:
for line in f:
s = line.split(" ")
assert len(s) == 6
assert s[5] == '0\n'
classes.append(s[0])
segments.append(list(map(int, s[1:5])))
return classes_to_numpy(classes), segments_to_numpy(segments)
def _segment(self, image):
self.image = image
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.adaptiveThreshold(
image,
maxValue=255,
adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
thresholdType=cv2.THRESH_BINARY,
blockSize=self.block_size,
C=self.c)
contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
segments = segments_to_numpy([cv2.boundingRect(c) for c in contours])
self.contours, self.hierarchy = contours, hierarchy #store, may be needed for debugging
return segments
def preprocess_with_display(image):
copy = image.copy()
cv2.imshow('Display', image)
cv2.waitKey(0)
image = cv2.GaussianBlur(image, (5, 5), 0) # Blurring the image
cv2.imshow('Display', image)
print "After Guassian blurring"
cv2.waitKey(0)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Grey scaling the image
cv2.imshow('Display', image) # Displaying the image
print "After Greyscaling"
cv2.waitKey(0)
image = cv2.adaptiveThreshold(
image,
255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,
11, 10)
''' Thresholding the image (Converts greyscale to binary),
using adaptive threshold for best results '''
cv2.imshow('Display', image)
im2, contours, hierarchy = cv2.findContours(
image,
cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
''' Finding the contours in the image'''
image.fill(255)
cv2.drawContours(image, contours, -1, (0, 0, 255))
''' Drawing the contours on the image before displaying'''
cv2.imshow('Display', image)
print "After detecting Contours"
cv2.waitKey(0)
contours.reverse()
segments = segments_to_numpy([cv2.boundingRect(c) for c in contours])
segments = numpy.delete(segments, 0, 0)
euler_list, inner_segments = find_euler_and_inner_segments(segments, 1)
segments, euler_list, central_x, central_y = segment_blocks(
segments, inner_segments, euler_list)
draw_segments(copy, segments)
'''Draw the segments on the copy image (cant add color to greyscaled image)'''
cv2.imshow('Display', copy)
print "After Segmentation"
cv2.waitKey(0)
return image, segments, euler_list, central_x, central_y
