Python for license plate location and segmentation

Specific steps

1, the acquisition of the color license plate image conversion into a grayscale image
2, the gray image uses the Gaussian smoothing processing, then carries on the direct filtering to it
3, using Sobel operator to detect the edge of the image
4, the two value of the image for corrosion, expansion, open operation, closed operation of the morphological combination transformation
5, the morphological transformation of the image after the contour search, according to the license plate length and width ratio to extract license plate
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Code implementation

Grayscale of images

Gray = Cv2.cvtcolor (img, Cv2. Color_bgr2gray)

Gaussian smoothing, median filter processing

Gaussian = Cv2. Gaussianblur (Gray, (3, 3), 0, 0, Cv2. Border_default) median = Cv2.medianblur (Gaussian, 5)

Sobel Edge Detection

Sobel = Cv2. Sobel (median, cv2. cv_8u, 1, 0,  ksize = 3)

Binary Value

RET, binary = cv2.threshold (Sobel, 255, Cv2. Thresh_binary)

The morphological combination transformation of two-valued image for corrosion, expansion, open operation and closed operation

# Expansion and corrosion operation of the kernel function element1 = cv2.getstructuringelement (cv2. Morph_rect, (9, 1)) Element2 = Cv2.getstructuringelement (cv2. Morph_rect, (8, 6)) # Swell once, let contour highlight dilation = cv2.dilate (binary, element2, iterations = 1) # corrode Once, remove detail erosion = Cv2.erode (Dilat Ion, element1, iterations = 1) # Swell again, let the contour clear some dilation2 = cv2.dilate (erosion, element2,iterations = 3)

The image of morphological transformation is searched, and the license plate is extracted according to the aspect ratio of the license plate.

1. Find the license plate area

def findplatenumberregion (IMG): Region    = []    # find contour    contours,hierarchy = Cv2.findcontours (img, Cv2. Retr_tree, Cv2. Chain_approx_simple)    # Filter Area small for    I in range (len (contours)):        cnt = contours[i]        # Calculates the area of the contour areas        = Cv2.contourarea (CNT)        # The small area is filtered out        if (areas <):            continue        # contour approximation, very small function        epsilon = 0.001 * Cv2.arclength (cnt,true)        approx = CV2.APPROXPOLYDP (cnt, epsilon, True)        # finds the smallest rectangle, which may have a direction of        rect = Cv2.minarearect (CNT)        print "Rect is:" The        print rect        # box is a four point coordinate        box = cv2.cv.BoxPoints (rect)        box = np.int0 (Box)        # Calculates high and wide        height = ABS (box[0][1]-box[2][1])        width = ABS (box[0][0]-box[2][0])        # license plate Under normal conditions the height ratio is between 2.7-5        ratio =float (width)/float (height)        if (ratio > 5 or ratio < 2):            continue        Region.append (Box)    return region

2. Draw the license plate area with the green Line and cut the license plate

    # Draw these found outlines with the Green Line for the box in the region    :        cv2.drawcontours (IMG, [box], 0, (0, 255, 0), 2)    ys = [Box[0, 1], box[1, 1], Box[2, 1], box[3, 1]    xs = [box[0, 0], box[1, 0], box[2, 0], box[3, 0]]    ys_sorted_index = Np.argsort (ys)    Xs_so Rted_index = Np.argsort (xs)    x1 = box[xs_sorted_index[0], 0]    x2 = box[xs_sorted_index[3], 0]    y1 = Box[ys_ Sorted_index[0], 1]    y2 = box[ys_sorted_index[3], 1]    img_org2 = img.copy ()    img_plate = img_org2[y1:y2, x1 : X2]