def plate_detection(self):
label_image = measure.label(self.binary_car_image)
image_height, image_width = label_image.shape
plate_dim = (0.05 * image_height, 0.2 * image_height,
0.15 * image_width, 0.6 * image_width)
plotting.plot_car_image(self.car_image, self.fig, self.axis)
self.lp_cands = []
self.lp_cand_dimension = []
for region in measure.regionprops(label_image):
minRow, minCol, maxRow, maxCol = region.bbox
(region_height, region_width) = (maxRow - minRow, maxCol - minCol)
if (region.area < 50 or region_height < 0.2 * region_width):
continue
candidate = np.invert(self.binary_car_image[minRow:maxRow,
minCol:maxCol])
if (region_height >= plate_dim[0] and region_height <= plate_dim[1]
and region_width >= plate_dim[2]
and region_width <= plate_dim[3]):
if self.elimininate_candidate(candidate):
continue
rectBorder = patches.Rectangle((minCol, minRow),
maxCol - minCol,
maxRow - minRow,
edgecolor="red",
linewidth=2,
fill=False)
self.lp_cands.append(candidate)
self.lp_cand_dimension.append(
((minRow, minCol), (maxRow - minRow, maxCol - minCol)))
plotting.add_borders(rectBorder, self.fig, self.axis)
def character_segmentation(self):
segmented_characters = []
idx = 0
for idx in range(len(self.lp_cands)):
cand = self.lp_cands[idx]
plotting.plot_car_image(cand, self.fig, self.axis1)
char_dim = (0.30 * cand.shape[0], 0.90 * cand.shape[0],
0.02 * cand.shape[1], 0.1 * cand.shape[1])
labelled_cand = measure.label(cand)
cnt = 0
border = []
temp_chars = []
for region in measure.regionprops(labelled_cand):
minRow, minCol, maxRow, maxCol = region.bbox
(region_height, region_width) = (maxRow - minRow,
maxCol - minCol)
if (maxRow == self.lp_cand_dimension[idx][1][0]):
continue
#print(region_height,region_width)
if (region_height >= char_dim[0]
and region_height <= char_dim[1]
and region_width >= char_dim[2]
and region_width <= char_dim[3]):
rectBorder = patches.Rectangle((minCol, minRow),
maxCol - minCol,
maxRow - minRow,
edgecolor="red",
linewidth=2,
fill=False)
border.append(rectBorder)
temp_chars.append((minRow, maxRow, minCol, maxCol))
plotting.add_borders(rectBorder, self.fig, self.axis1)
if (len(border) == 10):
for borders in border:
plotting.add_borders(borders, self.fig, self.axis1)
dim = self.lp_cand_dimension[idx]
for val in temp_chars:
r1 = dim[0][0] + val[0]
r2 = dim[0][0] + val[1]
c1 = dim[0][1] + val[2]
c2 = dim[0][1] + val[3]
segmented_characters.append(
(val[2],
resize(np.invert(self.binary_car_image[r1:r2, c1:c2]),
(20, 20))))
return segmented_characters