def opevCV_matching():
# Step 1. Image Pre-processing
root = tk.Tk()
root.withdraw()
file_path = filedialog.askopenfilename()
original_image = cv.imread(file_path)
image = original_image.copy()
carLicense_image = license_image(image)
# Step 2. Split the License Numbers and Characters
image = carLicense_image.copy()
word_images = license_spilt(image)
# Step 3. Characters and Numbers Matching with Template
word_images_ = word_images.copy()
result = template_matching(word_images_)
print(result)
tk.messagebox.showinfo('Result', result)
# Step 4. Image Rendering
height, weight = original_image.shape[0:2]
image = original_image.copy()
cv.rectangle(image, (int(0.2 * weight), int(0.75 * height)),
(int(weight * 0.8), int(height * 0.95)), (0, 255, 0), 5)
cv.putText(image, "".join(result),
(int(0.2 * weight) + 30, int(0.75 * height) + 80),
cv.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 8)
plt_show_raw(image)
def calculate_slope(image, contour):
"""
Calculate slope
:param image: licence plate image
:param contour:
:return: slop - k
"""
cnt = contour[1] # Coordinate points on the contour
image3 = image.copy()
h, w = image3.shape[:2]
# Returns the direction vector and the coordinate points on the x,y axis
[vx, vy, x, y] = cv.fitLine(cnt, cv.DIST_L2, 0, 0.01, 0.01)
print([vx, vy, x, y])
k = vy / vx # Calculate Slope
b = y - k * x # Calculate Y-intercept
print(k, b)
left = b
right = k * w + b
img = cv.line(image3, (w, right), (0, left), (0, 255, 0), 2)
plt_show_raw(img)
print((w, right))
print((0, left))
return k
def image_rotation(k):
"""
License Image Skew Correction
:param k: slop
:return: vehicle after correction
"""
a = math.atan(k)
a = math.degrees(a)
image4 = original_image.copy()
# Image rotation
h, w = image4.shape[:2]
print(h, w)
# Centre of rotation, angle of rotation, scaling
M = cv.getRotationMatrix2D((w / 2, h / 2), a, 0.8)
correct_image = cv.warpAffine(image4, M, (int(w * 1.1), int(h * 1.1)))
plt_show_raw(correct_image)
# Display of corrected license plate images
adjusted_image = boundingRect_draw(correct_image)
return adjusted_image
def license_image(slant_image):
"""
Extracting the license plate image
:param slant_image: a slant vehicle image
:return: Vehicle contours
"""
image_gray = gray_gauss(
slant_image) # Gaussian & greyscale noise reduction
# Sobel operator edge detection (detection in the Y direction)
Sobel_x = cv.Sobel(image_gray, cv.CV_16S, 1, 0) # x, y
absX = cv.convertScaleAbs(Sobel_x) # to unit 8
image_edge = absX
# plt_show_grey(image_edge)
# Adaptive threshold processing
ret, image = cv.threshold(image_edge, 0, 255, cv.THRESH_OTSU)
# plt_show_grey(image)
kernelX = cv.getStructuringElement(cv.MORPH_OPEN, (16, 6))
print(kernelX)
image = cv.morphologyEx(
image, cv.MORPH_CLOSE, kernelX,
iterations=1) # Closed, white parts seen as a whole
# plt_show_grey(image)
# Removal of some white spots
kernelX = cv.getStructuringElement(cv.MORPH_RECT, (10, 1))
kernelY = cv.getStructuringElement(cv.MORPH_RECT, (1, 10))
# Expansion and corrosion
image = cv.dilate(image, kernelX)
image = cv.erode(image, kernelX)
# Corrosion and expansion
image = cv.erode(image, kernelY)
image = cv.dilate(image, kernelY)
# plt_show_grey(image)
image = cv.medianBlur(image, 19) # Median filtering to remove noise
# plt_show_grey(image)
contours, hierarchy = cv.findContours(
image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) # Contours detection
image_rawEdge = slant_image.copy()
cv.drawContours(image_rawEdge, contours, -1, (0, 255),
5) # Draw the contours
plt_show_raw(image_rawEdge)
return image_rawEdge, contours
def boundingRect_draw(slant_image):
"""
Filter the outline of the location of the license plate
:param slant_image:
:return:
"""
image_rawEdge, contour = license_image(slant_image)
# Aspect ratio of 3:1 or 4:1 to judgement
for index, item in enumerate(contour):
rect = cv.boundingRect(
item) # Using the smallest rectangle, wrap the found shape
x = rect[0]
y = rect[1]
weight = rect[2]
height = rect[3]
if (weight > (height * 2.5)) and (weight < (height * 5)):
print(index)
image2 = slant_image.copy()
cv.drawContours(image2, contour, 1, (0, 0, 255), 5)
plt_show_raw(image2)
return image, contour