示例#1
0
def detect_contours(input_file, thresh_val=255, k_size=5, iterations=30):
    """
    >>> contours = detect_contours(test_image)
    >>> isinstance(detect_contours(test_image), list)
    True

    >>> detect_contours(None)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: The input file can't be a None object

    >>> detect_contours("")
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: The input file can't be ''.

    >>> detect_contours("fakeRoute")
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: Input file not found.

    >>> detect_contours(test_image, thresh_val=270)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    ValueError: All threshold values must be between 0 and 255.

    >>> detect_contours(test_image, k_size=-10)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    ValueError: Kernel size value must be greater than 0.

    >>> detect_contours(test_image, iterations=-10)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    ValueError: Iterations value must be greater than 0.
    """

    gray = iu.get_image(input_file, 0)

    # Checking arguments
    check_threshold(thresh_val)
    check_kernel(k_size)
    check_iterations(iterations)



    gray = iu.pyramid_clean(gray)

    th2 = th.adaptive_threshold(gray, max_val=thresh_val,
                                mode=cv.ADAPTIVE_THRESH_MEAN_C)

    th2 = cv.erode(th2, kernel=(k_size, k_size), iterations=iterations)

    th2 = cv.bitwise_not(th2)

    contours, h = cv.findContours(th2, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

    return contours
示例#2
0
def detect_contours(input_file, thresh_val=255, k_size=5, iterations=30):
    """
    >>> contours = detect_contours(test_image)
    >>> isinstance(detect_contours(test_image), list)
    True

    >>> detect_contours(None)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: The input file can't be a None object

    >>> detect_contours("")
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: The input file can't be ''.

    >>> detect_contours("fakeRoute")
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: Input file not found.

    >>> detect_contours(test_image, thresh_val=270)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    ValueError: All threshold values must be between 0 and 255.

    >>> detect_contours(test_image, k_size=-10)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    ValueError: Kernel size value must be greater than 0.

    >>> detect_contours(test_image, iterations=-10)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    ValueError: Iterations value must be greater than 0.
    """

    gray = iu.get_image(input_file, 0)

    # Checking arguments
    check_threshold(thresh_val)
    check_kernel(k_size)
    check_iterations(iterations)



    gray = iu.pyramid_clean(gray)

    th2 = th.adaptive_threshold(gray, max_val=thresh_val,
                                mode=cv.ADAPTIVE_THRESH_MEAN_C)

    th2 = cv.erode(th2, kernel=(k_size, k_size), iterations=iterations)

    th2 = cv.bitwise_not(th2)

    contours, h = cv.findContours(th2, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

    return contours
示例#3
0
def get_analysis(input_file, min_dim=1000):

    original = iu.get_image(input_file)

    original = iu.pyramid_clean(original)

    cleaned = cc.delete_border_noise(original)

    contours = cc.detect_contours(cleaned)
    contours = cc.delete_small_contours(contours, min_dim)
    contours = cc.get_squares(contours)
    contours = cc.join_contours(contours, min_dist=10)

    blank = np.zeros(original.shape, np.uint8)
    blank[:] = 255
    blank2 = np.zeros(original.shape, np.uint8)
    blank2[:] = 255

    contours_lines = cc.draw_contours(blank, contours, color=(255, 0, 0),
                                      thickness=10)
    filled = cc.draw_contours(blank2, contours, color=(0, 0, 0), thickness=-1)

    if len(contours) > 0:
        dimension = 0
        area = 0
        squares = len(contours)
        for c in contours:
            dimension = dimension + cc.get_contour_dimension(c)
            area = area + cc.get_contour_area(c)

    else:
        dimension = -1
        area = -1
        squares = 0

    model = aa.get_model(filled)

    corners = cc.detect_corners(contours_lines)

    num_corners = len(corners)

    results = [input_file,
               dimension,
               area,
               squares,
               model,
               num_corners
               ]

    return results
示例#4
0
def get_analysis(input_file, min_dim=1000):

    original = iu.get_image(input_file)

    original = iu.pyramid_clean(original)

    cleaned = cc.delete_border_noise(original)

    contours = cc.detect_contours(cleaned)
    contours = cc.delete_small_contours(contours, min_dim)
    contours = cc.get_squares(contours)
    contours = cc.join_contours(contours, min_dist=10)

    blank = np.zeros(original.shape, np.uint8)
    blank[:] = 255
    blank2 = np.zeros(original.shape, np.uint8)
    blank2[:] = 255

    contours_lines = cc.draw_contours(blank,
                                      contours,
                                      color=(255, 0, 0),
                                      thickness=10)
    filled = cc.draw_contours(blank2, contours, color=(0, 0, 0), thickness=-1)

    if len(contours) > 0:
        dimension = 0
        area = 0
        squares = len(contours)
        for c in contours:
            dimension = dimension + cc.get_contour_dimension(c)
            area = area + cc.get_contour_area(c)

    else:
        dimension = -1
        area = -1
        squares = 0

    model = aa.get_model(filled)

    corners = cc.detect_corners(contours_lines)

    num_corners = len(corners)

    results = [input_file, dimension, area, squares, model, num_corners]

    return results
示例#5
0
def delete_border_noise(input_file, width=20, color=(255, 255, 255)):
    """
    >>> isinstance(delete_border_noise(test_image), np.ndarray)
    True

    >>> delete_border_noise(None)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: The input file can't be a None object

    >>> delete_border_noise("")
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: The input file can't be ''.

    >>> delete_border_noise("fakeRoute")
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: Input file not found.

    >>> delete_border_noise(test_image, width=-10)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    ValueError: Width value must be greater than 0.

    >>> delete_border_noise(test_image, color=(-10, 0, 0))
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    ValueError: Color value must be: (0-255, 0-255, 0-255).
    """

    # Checking arguments
    iu.check_color(color)
    check_width(width)

    image = iu.get_image(input_file)

    image = iu.pyramid_clean(image)

    image[0:width, :] = color
    image[:, 0:width] = color
    image[:, -width:] = color
    image[-width:, :] = color

    return image
示例#6
0
def delete_border_noise(input_file, width=20, color=(255, 255, 255)):
    """
    >>> isinstance(delete_border_noise(test_image), np.ndarray)
    True

    >>> delete_border_noise(None)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: The input file can't be a None object

    >>> delete_border_noise("")
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: The input file can't be ''.

    >>> delete_border_noise("fakeRoute")
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    IOError: Input file not found.

    >>> delete_border_noise(test_image, width=-10)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    ValueError: Width value must be greater than 0.

    >>> delete_border_noise(test_image, color=(-10, 0, 0))
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    ValueError: Color value must be: (0-255, 0-255, 0-255).
    """

    # Checking arguments
    iu.check_color(color)
    check_width(width)

    image = iu.get_image(input_file)

    image = iu.pyramid_clean(image)

    image[0:width, :] = color
    image[:, 0:width] = color
    image[:, -width:] = color
    image[-width:, :] = color

    return image