def process(self, frame, name="TrainingSamples/Image_"):
# preprocessing for contour detection
preprocessed = PreProcessing().background_contour_removal(frame)
# find contours using algorithm by Suzuki et al. (1985)
contours, hierarchy = cv.findContours(preprocessed, cv.RETR_TREE,
cv.CHAIN_APPROX_NONE)
# limit observed contours
if len(contours) > 500:
contours = contours[:500]
# ignore first contour, as it is outer border of the frame
contours = contours[1:]
hierarchy = hierarchy[0][1:] - 1
hierarchy = np.where(hierarchy < 0, -1, hierarchy)
if len(contours) == 0:
return preprocessed
# initialize contour object from each contour in contour list
binarized = PreProcessing().custom_binarize(frame)
contourList = [
Contour(contour=cnt, imgShape=frame.shape, frameBinary=binarized)
for cnt in contours
]
# filter, classify and group segmented contours
sg = Segmentation(contourList, hierarchy, frame.shape)
sg.group_and_classify()
filtered = sg.get_contours()
if len(filtered) == 0:
return preprocessed
# colouring preprocessing for ease in debugging
preprocessed = cv.cvtColor(preprocessed, cv.COLOR_GRAY2BGR)
lines = LineOrdering(filtered).get_lines(frame)
# label contours with additional positional information
lines = sg.label_contours(lines)
for l in range(len(lines)):
line = lines[l]
for i in range(len(line)):
cnt = line[i]
cv.putText(frame,
str(l) + str(i), (cnt.center[0], cnt.center[1]),
cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 1)
solutions = [
self.solver.solve([cnt.unwrap() for cnt in line], frame)
for line in lines if len(line) > 2
]
return preprocessed # orderedImage
