def extract_plate(img_original, list_of_matching_chars) -> PossiblePlate:
possible_plate = PossiblePlate.PossiblePlate()
# Ordenamos los chars de izquierda a derecha en base a la posicion X
list_of_matching_chars.sort(key=lambda matching_char: matching_char.intCenterX)
# Calculamos el punto central de la patente
flt_plate_center_x = (list_of_matching_chars[0].intCenterX + list_of_matching_chars[
len(list_of_matching_chars) - 1].intCenterX) / 2.0
flt_plate_center_y = (list_of_matching_chars[0].intCenterY + list_of_matching_chars[
len(list_of_matching_chars) - 1].intCenterY) / 2.0
pt_plate_center = flt_plate_center_x, flt_plate_center_y
# calculate plate width and height
int_plate_width = int(
(list_of_matching_chars[len(list_of_matching_chars) - 1].intBoundingRectX + list_of_matching_chars[
len(list_of_matching_chars) - 1].intBoundingRectWidth - list_of_matching_chars[
0].intBoundingRectX) * PLATE_WIDTH_PADDING_FACTOR)
int_total_of_char_heights = 0
for matching_char in list_of_matching_chars:
int_total_of_char_heights = int_total_of_char_heights + matching_char.intBoundingRectHeight
flt_average_char_height = int_total_of_char_heights / len(list_of_matching_chars)
int_plate_height = int(flt_average_char_height * PLATE_HEIGHT_PADDING_FACTOR)
# calculate correction angle of plate region
flt_opposite = list_of_matching_chars[len(list_of_matching_chars) - 1].intCenterY - list_of_matching_chars[
0].intCenterY
flt_hypotenuse = DetectChars.distance_between_chars(list_of_matching_chars[0],
list_of_matching_chars[len(list_of_matching_chars) - 1])
flt_correction_angle_in_rad = math.asin(flt_opposite / flt_hypotenuse)
flt_correction_angle_in_deg = flt_correction_angle_in_rad * (180.0 / math.pi)
# pack plate region center point, width and height, and correction angle into rotated rect member variable of plate
possible_plate.rrLocationOfPlateInScene = (
tuple(pt_plate_center), (int_plate_width, int_plate_height), flt_correction_angle_in_deg)
# final steps are to perform the actual rotation
# get the rotation matrix for our calculated correction angle
rotation_matrix = cv2.getRotationMatrix2D(tuple(pt_plate_center), flt_correction_angle_in_deg, 1.0)
height, width, num_channels = img_original.shape # unpack original image width and height
img_rotated = cv2.warpAffine(img_original, rotation_matrix, (width, height)) # rotate the entire image
img_cropped = cv2.getRectSubPix(img_rotated, (int_plate_width, int_plate_height), tuple(pt_plate_center))
possible_plate.imgPlate = img_cropped
return possible_plate
def extract_plate(img_original, chars):
possible_plate = PossiblePlate.PossiblePlate(
) # this will be the return value
chars.sort(key=lambda x: x.center_x
) # sort chars from left to right based on x position
# calculate the center point of the plate
plate_center_x = (chars[0].center_x + chars[-1].center_x) / 2.0
plate_center_y = (chars[0].center_y + chars[-1].center_y) / 2.0
# This is the probable centeral point of this plate.
plate_center = plate_center_x, plate_center_y
# calculate plate width and height
plate_width = int(
(chars[-1].pos_x + chars[-1].width - chars[0].pos_x) * 1.3)
# Here we calculate the probable width of this plate.
height_sum = 0
for matchingChar in chars:
height_sum += matchingChar.height
average_char_height = height_sum / len(chars)
plate_height = int(average_char_height *
1.5) # We include the padding factor.
# calculate correction angle of plate region
opposite = chars[-1].center_y - chars[0].center_y
hypotenuse = DetectChars.distance_between_chars(chars[0], chars[-1])
correction_angle_in_rad = math.asin(opposite / hypotenuse)
correction_angle_deg = correction_angle_in_rad * (180.0 / math.pi)
# pack plate region center point, width and height, and correction angle into rotated rect member variable of plate
possible_plate.location_plate_on_img = (tuple(plate_center),
(plate_width, plate_height),
correction_angle_deg)
# final steps are to perform the actual rotation
# get the rotation matrix for our calculated correction angle
rotation_matrix = cv2.getRotationMatrix2D(tuple(plate_center),
correction_angle_deg, 1.0)
height, width, _ = img_original.shape # unpack original image width and height
img_rotated = cv2.warpAffine(img_original, rotation_matrix,
(width, height)) # rotate the entire image
# imgTmp = cv2.getRectSubPix(img_rotated, (width, plate_height), tuple(plate_center))
img_cropped = cv2.getRectSubPix(img_rotated, (plate_width, plate_height),
tuple(plate_center))
possible_plate.img_plate = img_cropped
return possible_plate
def extract_plate(img_original, list_of_matching_chars):
possible_plate = PossiblePlate.PossiblePlate()
list_of_matching_chars.sort(
key=lambda matching_char: matching_char.intCenterX)
plate_center_x = (list_of_matching_chars[0].intCenterX +
list_of_matching_chars[len(list_of_matching_chars) -
1].intCenterX) / 2.0
plate_center_y = (list_of_matching_chars[0].intCenterY +
list_of_matching_chars[len(list_of_matching_chars) -
1].intCenterY) / 2.0
plate_center = plate_center_x, plate_center_y
plate_width = int((list_of_matching_chars[len(list_of_matching_chars) -
1].intBoundingRectX +
list_of_matching_chars[len(list_of_matching_chars) -
1].intBoundingRectWidth -
list_of_matching_chars[0].intBoundingRectX) *
PLATE_WIDTH_PADDING_FACTOR)
total_of_char_heights = 0
for matching_char in list_of_matching_chars:
total_of_char_heights = total_of_char_heights + matching_char.intBoundingRectHeight
average_char_height = total_of_char_heights / len(list_of_matching_chars)
plate_height = int(average_char_height * PLATE_HEIGHT_PADDING_FACTOR)
opposite = list_of_matching_chars[
len(list_of_matching_chars) -
1].intCenterY - list_of_matching_chars[0].intCenterY
hypotenuse = DetectChars.distance_between_chars(
list_of_matching_chars[0],
list_of_matching_chars[len(list_of_matching_chars) - 1])
correction_angle_in_rad = math.asin(opposite / hypotenuse)
correction_angle_in_deg = correction_angle_in_rad * (180.0 / math.pi)
possible_plate.rrLocationOfPlateInScene = (tuple(plate_center),
(plate_width, plate_height),
correction_angle_in_deg)
rotation_matrix = cv2.getRotationMatrix2D(tuple(plate_center),
correction_angle_in_deg, 1.0)
height, width, num_of_channels = img_original.shape
img_rotated = cv2.warpAffine(img_original, rotation_matrix,
(width, height))
img_cropped = cv2.getRectSubPix(img_rotated, (plate_width, plate_height),
tuple(plate_center))
possible_plate.imgPlate = img_cropped
return possible_plate
def rotate(img_orig, list_of_matching_chars):
"""
rotate image to recive straight line of chrs
"""
list_of_matching_chars.sort(key=lambda char: char.center_x)
# calculate the center point of the plate
plate_center_x = (
list_of_matching_chars[0].center_x +
list_of_matching_chars[len(list_of_matching_chars) - 1].center_x) / 2.0
plate_center_y = (
list_of_matching_chars[0].center_y +
list_of_matching_chars[len(list_of_matching_chars) - 1].center_y) / 2.0
# This is the probable centeral point of this plate.
pt_plate_center = plate_center_x, plate_center_y
# calculate correction angle of plate region
flt_opposite = list_of_matching_chars[
-1].center_y - list_of_matching_chars[0].center_y
flt_hypotenuse = DetectChars.distance_between_chars(
list_of_matching_chars[0], list_of_matching_chars[-1])
try:
flt_correction_angle_in_rad = math.asin(flt_opposite / flt_hypotenuse)
except ZeroDivisionError:
flt_correction_angle_in_rad = 0
flt_correction_angle_in_deg = flt_correction_angle_in_rad * (180.0 /
math.pi)
# final steps are to perform the actual rotation
# get the rotation matrix for our calculated correction angle
# The first poin tis the point of rotaion or center,theta and scaling factor
rotation_matrix = cv2.getRotationMatrix2D(tuple(pt_plate_center),
flt_correction_angle_in_deg, 1.0)
height, width = img_orig.shape # unpack original image width and height
img_rotated = cv2.warpAffine(img_orig, rotation_matrix,
(width, height)) # rotate the entire image
return img_rotated
