def segmentation(self, LpRegion):
"""
Hàm phân đoạn ảnh
:param LpRegion:
:return:
"""
# Áp dụng thresh để trích xuất vùng biển số
V = cv2.split(cv2.cvtColor(LpRegion, cv2.COLOR_BGR2HSV))[2]
# Phân ngưỡng bằng adaptive threshold
retval, threshold = cv2.threshold(V, 128, 255, cv2.THRESH_BINARY)
T = threshold_local(V, 15, offset=10, method="gaussian")
thresh = (V > T).astype("uint8") * 255
# Chuyển đổi pixel đen của chữ số thành pixel trắng
thresh = cv2.bitwise_not(thresh)
# Resize ảnh thresh với chiều rộng = 400px
thresh = imutils.resize(thresh, width=400)
# thresh = cv2.medianBlur(thresh, 5)
# Xóa nhiễu bằng thuật toán opening (erode => dilate)
kernel = np.ones((2, 2), np.uint8)
thresh = cv2.erode(thresh, kernel)
thresh = cv2.dilate(thresh, kernel)
# Thực hiện thuật toán connected components analysis
labels = measure.label(thresh, connectivity=2, background=0)
# Lặp qua các thành phần duy nhất
for label in np.unique(labels):
# if this is background label, ignore it
if label == 0:
continue
# Khởi tạo mặt nạ chứa vị trí của các ký tự ứng viên
mask = np.zeros(thresh.shape, dtype="uint8")
mask[labels == label] = 255
# Tìm contours từ mặt nạ
contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if len(contours) > 0:
contour = max(contours, key=cv2.contourArea)
(x, y, w, h) = cv2.boundingRect(contour)
# Xác định ký tự
aspectRatio = w / float(h)
solidity = cv2.contourArea(contour) / float(w * h)
heightRatio = h / float(LpRegion.shape[0])
if 0.1 < aspectRatio < 1.0 and solidity > 0.1 and 0.35 < heightRatio < 2.0:
# Trích xuất các ký tự
candidate = np.array(mask[y:y + h, x:x + w])
square_candidate = convert2Square(candidate)
square_candidate = cv2.resize(square_candidate, (28, 28),
cv2.INTER_AREA)
square_candidate = square_candidate.reshape((28, 28, 1))
self.candidates.append((square_candidate, (y, x)))
def segmentation(self, LpRegion, name):
# apply thresh to extracted licences plate
V = cv2.split(cv2.cvtColor(LpRegion, cv2.COLOR_BGR2HSV))[2]
# adaptive threshold
T = threshold_local(V, 15, offset=10, method="gaussian")
thresh = (V > T).astype("uint8") * 255
cv2.imwrite("output/lp/{}_step2_1.png".format(name), thresh)
# convert black pixel of digits to white pixel
thresh = cv2.bitwise_not(thresh)
cv2.imwrite("output/lp/{}_step2_2.png".format(name), thresh)
thresh = imutils.resize(thresh, width=400)
thresh = cv2.medianBlur(thresh, 5)
cv2.imwrite("output/lp/{}_step2_3.png".format(name), thresh)
# connected components analysis
labels = measure.label(thresh, connectivity=2, background=0)
# loop over the unique components
for label in np.unique(labels):
# if this is background label, ignore it
if label == 0:
continue
# init mask to store the location of the character candidates
mask = np.zeros(thresh.shape, dtype="uint8")
mask[labels == label] = 255
# find contours from mask
_, contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if len(contours) > 0:
contour = max(contours, key=cv2.contourArea)
(x, y, w, h) = cv2.boundingRect(contour)
# rule to determine characters
aspectRatio = w / float(h)
solidity = cv2.contourArea(contour) / float(w * h)
heightRatio = h / float(LpRegion.shape[0])
if 0.1 < aspectRatio < 1.0 and solidity > 0.1 and 0.35 < heightRatio < 2.0:
# extract characters
candidate = np.array(mask[y:y + h, x:x + w])
square_candidate = convert2Square(candidate)
square_candidate = cv2.resize(square_candidate, (28, 28),
cv2.INTER_AREA)
cv2.imwrite(
'./characters/' + str(x) + "_" + str(y) + ".png",
cv2.resize(square_candidate, (56, 56), cv2.INTER_AREA))
square_candidate = square_candidate.reshape((28, 28, 1))
self.candidates.append((square_candidate, (y, x)))
def segmentation(self, LpRegion):
LpRegion = self.clean_border(LpRegion)
# cv2.imshow("edge", edged)
V = cv2.split(cv2.cvtColor(LpRegion, cv2.COLOR_BGR2HSV))[2]
# adaptive threshold
T = threshold_local(V, 15, offset=10, method="gaussian")
thresh = (V > T).astype("uint8") * 255
# convert black pixel of digits to white pixel
thresh = cv2.bitwise_not(thresh)
thresh = imutils.resize(thresh, width=400)
thresh = clear_border(thresh)
# cv2.imwrite("step2_2.png", thresh)
cv2.imshow("thresh", thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
# try:
# lines = cv2.HoughLinesP(image=thresh,rho=1,theta=np.pi/180, threshold=200,lines=np.array([]), minLineLength=200,maxLineGap=20)
# angle = 0
# num = 0
# thresh = cv2.cvtColor(thresh, cv2.COLOR_GRAY2BGR)
# for line in lines:
# my_degree = math.degrees(math.atan2(line[0][3]-line[0][1], line[0][2]-line[0][0]))
# if -45 < my_degree < 45:
# angle += my_degree
# num += 1
# cv2.line(thresh, (line[0][0], line[0][1]), (line[0][2], line[0][3]), (255, 0, 0))
# angle /= num
# cv2.imshow("draw", thresh)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# # cv2.imwrite("draw.png", thresh)
# # Rotate image to deskew
# (h, w) = thresh.shape[:2]
# center = (w // 2, h // 2)
# M = cv2.getRotationMatrix2D(center, angle, 1.0)
# thresh = cv2.warpAffine(thresh, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
# except:
# pass
# edges = cv2.Canny(thresh,100,200)
# thresh = cv2.medianBlur(thresh, 5)
# cv2.imshow("thresh", edges)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# cv2.imwrite("thresh.png", thresh)
# connected components analysis
labels = measure.label(thresh, connectivity=2, background=0)
# loop over the unique components
for label in np.unique(labels):
# if this is background label, ignore it
if label == 0:
continue
# init mask to store the location of the character candidates
mask = np.zeros(thresh.shape, dtype="uint8")
mask[labels == label] = 255
# find contours from mask
contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if len(contours) > 0:
contour = max(contours, key=cv2.contourArea)
(x, y, w, h) = cv2.boundingRect(contour)
# rule to determine characters
aspectRatio = w / float(h)
solidity = cv2.contourArea(contour) / float(w * h)
heightRatio = h / float(LpRegion.shape[0])
if h * w > MIN_PIXEL_AREA and 0.25 < aspectRatio < 1.0 and solidity > 0.2 and 0.35 < heightRatio < 2.0:
# extract characters
candidate = np.array(mask[y:y + h, x:x + w])
square_candidate = convert2Square(candidate)
square_candidate = cv2.resize(square_candidate, (28, 28),
cv2.INTER_AREA)
# cv2.imwrite('./characters/' + str(y) + "_" + str(x) + ".png", cv2.resize(square_candidate, (56, 56), cv2.INTER_AREA))
square_candidate = square_candidate.reshape((28, 28, 1))
# cv2.imshow("square_candidate", square_candidate)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
self.candidates.append((square_candidate, (y, x)))