main.py 文件源码

def find_contours(img):
    '''
    :param img: (numpy array)
    :return: all possible rectangles (contours)
    '''
    img_blurred = cv2.GaussianBlur(img, (5, 5), 1)  # remove noise
    img_gray = cv2.cvtColor(img_blurred, cv2.COLOR_BGR2GRAY)  # greyscale image
    # cv2.imshow('', img_gray)
    # cv2.waitKey(0)

    # Apply Sobel filter to find the vertical edges
    # Find vertical lines. Car plates have high density of vertical lines
    img_sobel_x = cv2.Sobel(img_gray, cv2.CV_8UC1, dx=1, dy=0, ksize=3, scale=1, delta=0, borderType=cv2.BORDER_DEFAULT)
    # cv2.imshow('img_sobel', img_sobel_x)

    # Apply optimal threshold by using Oslu algorithm
    retval, img_threshold = cv2.threshold(img_sobel_x, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)
    # cv2.imshow('s', img_threshold)
    # cv2.waitKey(0)

    # TODO: Try to apply AdaptiveThresh
    # Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on.
    # gaus_threshold = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 115, 1)
    # cv2.imshow('or', img)
    # cv2.imshow('gaus', gaus_threshold)
    # cv2.waitKey(0)

    # Define a stuctural element as rectangular of size 17x3 (we'll use it during the morphological cleaning)
    element = cv2.getStructuringElement(shape=cv2.MORPH_RECT, ksize=(17, 3))

    # And use this structural element in a close morphological operation
    morph_img_threshold = deepcopy(img_threshold)
    cv2.morphologyEx(src=img_threshold, op=cv2.MORPH_CLOSE, kernel=element, dst=morph_img_threshold)
    # cv2.dilate(img_threshold, kernel=np.ones((1,1), np.uint8), dst=img_threshold, iterations=1)
    # cv2.imshow('Normal Threshold', img_threshold)
    # cv2.imshow('Morphological Threshold based on rect. mask', morph_img_threshold)
    # cv2.waitKey(0)

    # Find contours that contain possible plates (in hierarchical relationship)
    contours, hierarchy = cv2.findContours(morph_img_threshold,
                                           mode=cv2.RETR_EXTERNAL,  # retrieve the external contours
                                           method=cv2.CHAIN_APPROX_NONE)  # all pixels of each contour

    plot_intermediate_steps = False
    if plot_intermediate_steps:
        plot(plt, 321, img, "Original image")
        plot(plt, 322, img_blurred, "Blurred image")
        plot(plt, 323, img_gray, "Grayscale image", cmap='gray')
        plot(plt, 324, img_sobel_x, "Sobel")
        plot(plt, 325, img_threshold, "Threshold image")
        # plot(plt, 326, morph_img_threshold, "After Morphological filter")
        plt.tight_layout()
        plt.show()

    return contours