def preprocess_plate_without_croping(img_orig):
"""
some preparation to find False/True plate (it works)
"""
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(3, 3))
im = clahe.apply(cv2.cvtColor(img_orig, cv2.COLOR_BGR2GRAY))
img_thresh = half_thresh(im)
img_thresh = kray_fill(img_thresh, full_filling=True)
list_of_psb_chr, number_of_chars, _ = DetectChars.find_psbl_chr(
img_thresh, [70, 3, 15, 0.1, 1.5])
if number_of_chars > 7:
img_thresh = replace_last_first_symb(img_thresh, list_of_psb_chr,
number_of_chars)
img_thresh = del_blue(img_orig, list_of_psb_chr, img_thresh)
list_of_psb_chr, _, _ = DetectChars.find_psbl_chr(img_thresh,
[70, 3, 15, 0.1, 1.5])
img_thresh = draw_top_line(list_of_psb_chr, img_thresh)
img_thresh = draw_btm_line(list_of_psb_chr, img_thresh)
list_of_psb_chr, number_of_chars, none_char = DetectChars.find_psbl_chr(
img_thresh, [70, 3, 15, 0.1, 1.5])
if number_of_chars >= 2:
img_thresh = separate_sticked(img_thresh, list_of_psb_chr, none_char)
_, _, none_char = DetectChars.find_psbl_chr(img_thresh,
[100, 4, 15, 0.15, 1])
img_thresh = matching_broken_chars(img_thresh, none_char)
return img_thresh
def prepare_to_hist_clcl(im):
"""
preparations to left-right edge filling by histogram value
"""
img_thresh = half_thresh(im)
img_thresh = kray_fill(img_thresh, full_left_right=False)
list_of_psb_chr, nmbr_of_chrs, none_chars = DetectChars.find_psbl_chr(
img_thresh, [70, 3, 15, 0.1, 1.5])
if nmbr_of_chrs > 2:
img_thresh = separate_sticked(img_thresh, list_of_psb_chr, none_chars)
list_of_psb_chr, _, _ = DetectChars.find_psbl_chr(img_thresh,
[70, 3, 15, 0.1, 1.5])
return list_of_psb_chr
def find_most_likely_plts(list_of_psb_plates, number_chars_to_be_valid,
explore_btm, psbl_char_params, explr_area):
"""
the idea of function is do some tests to find the most truthful plate
in falsePlates, preprocess_for_scene delete more than 20% of plate's image on one of edges
"""
dif_list = []
for image in list_of_psb_plates:
image = image.img_plate
image = Preprocess.preprocess_plate_without_croping(image)
list_of_psb_chars, number_of_chars, _ = DetectChars.find_psbl_chr(
image, psbl_char_params, explr_area)
if number_of_chars < number_chars_to_be_valid: # plates with small nmbr of symbols is untruth
dif_list.append(1000) # add some value bigger than psbl differ
continue
if explore_btm:
dif_list = explore_btms(list_of_psb_chars, dif_list)
else:
dif_list.append(0)
return dif_list
def preprocess_for_plate(img_orig):
img_bl = cv2.cvtColor(img_orig, cv2.COLOR_BGR2GRAY)
img_grayscale = copy.deepcopy(img_bl)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(3, 3))
img_clashed = clahe.apply(img_bl)
# find chars on image
#######################################################################
list_of_psb_chr = prepare_to_hist_clcl(img_clashed)
img_thresh, img_grayscale = histogram_kray_fill(img_clashed, img_grayscale,
list_of_psb_chr)
img_thresh = half_thresh(img_thresh)
img_thresh = kray_fill(img_thresh)
list_of_psb_chr, _, _ = DetectChars.find_psbl_chr(img_thresh,
[70, 3, 15, 0.1, 1.5])
img_tmp = img_thresh.copy()
img_tmp = draw_top_line(list_of_psb_chr, img_tmp)
img_tmp = draw_btm_line(list_of_psb_chr, img_tmp)
list_of_psb_chr, number_of_chars, _ = DetectChars.find_psbl_chr(
img_tmp, [70, 3, 15, 0.1, 1.5])
if number_of_chars > 7:
img_thresh = replace_last_first_symb(img_thresh, list_of_psb_chr,
number_of_chars)
img_thresh = del_blue(img_orig, list_of_psb_chr, img_thresh)
list_of_psb_chr, _, _ = DetectChars.find_psbl_chr(img_thresh,
[70, 3, 15, 0.1, 1.5])
img_thresh = draw_top_line(list_of_psb_chr, img_thresh)
img_thresh = draw_btm_line(list_of_psb_chr, img_thresh)
list_of_psb_chr, nmbr_of_chrs, none_chars = DetectChars.find_psbl_chr(
img_thresh, [70, 3, 15, 0.1, 1.5])
if nmbr_of_chrs >= 2:
img_thresh = separate_sticked(img_thresh, list_of_psb_chr, none_chars)
_, _, none_chars = DetectChars.find_psbl_chr(img_thresh,
[100, 4, 15, 0.15, 1])
img_thresh = matching_broken_chars(img_thresh, none_chars)
##################################################################
# do two new image from grayscale, to further finding symbols and to recognize chars
#######################################################################
list_of_psb_chr, _, _ = DetectChars.find_psbl_chr(img_thresh,
[70, 3, 15, 0.1, 1.7])
height, width = img_thresh.shape
img_thresh = np.zeros((height, width), np.uint8)
img_for_rcgnz = np.zeros((height, width), np.uint8)
for i in list_of_psb_chr:
x1 = np.max([i.pos_x - 2, 0])
y2 = np.min([i.pos_y + i.height + 1, height - 1])
x2 = np.min([i.pos_x + i.width + 2, width - 1])
y1 = np.max([i.pos_y - 1, 0])
roi = img_grayscale[:, x1:x2]
_, img_for_rcgnz[y1:y2, x1:x2] = cv2.threshold(
roi[y1:y2, :], 2, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
_, img_thresh[y1:y2, x1:x2] = cv2.threshold(
roi[y1:y2, :], 2, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
list_of_psb_chr, number_of_chars, _ = DetectChars.find_psbl_chr(
img_tmp, [70, 3, 15, 0.1, 1.5])
if number_of_chars > 7:
img_thresh = replace_last_first_symb(img_thresh, list_of_psb_chr,
number_of_chars)
img_thresh = del_blue(img_orig, list_of_psb_chr, img_thresh)
list_of_psb_chr, nmbr_of_chrs, none_chars = DetectChars.find_psbl_chr(
img_thresh, [70, 3, 15, 0.1, 1.5])
if nmbr_of_chrs > 0:
img_thresh = rotate(img_thresh, list_of_psb_chr)
img_for_rcgnz = rotate(img_for_rcgnz, list_of_psb_chr)
_, img_thresh = cv2.threshold(img_thresh, 200, 255, cv2.THRESH_BINARY)
list_of_psb_chr, nmbr_of_chrs, none_chars = DetectChars.find_psbl_chr(
img_thresh, [70, 3, 15, 0.1, 1.5])
img_thresh = draw_top_line(list_of_psb_chr, img_thresh)
img_thresh = draw_btm_line(list_of_psb_chr, img_thresh)
list_of_psb_chr, nmbr_of_chrs, none_chars = DetectChars.find_psbl_chr(
img_thresh, [100, 4, 15, 0.15, 1])
if nmbr_of_chrs >= 2:
img_thresh = separate_sticked(img_thresh, list_of_psb_chr, none_chars)
list_of_psb_chr, nmbr_of_chrs, none_chars = DetectChars.find_psbl_chr(
img_thresh, [100, 4, 15, 0.15, 1])
img_thresh = matching_broken_chars(img_thresh, none_chars)
kernel = np.ones((3, 3), np.uint8)
img_for_rcgnz = cv2.erode(img_for_rcgnz, kernel)
return img_grayscale, img_thresh, img_for_rcgnz
