Python cv2.COLOR_BGR2YCrCb方法代碼示例

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_BGR2YCrCb [as 別名]
def describe(self, image, mask):
        image = cv.cvtColor(image, cv.COLOR_BGR2YCrCb)
        hist_y = cv.calcHist([image], [0], mask, self.bins,
                             [0, 256])
        hist_cr = cv.calcHist([image], [1], mask, self.bins,
                              [0, 256])
        hist_cb = cv.calcHist([image], [2], mask, self.bins,
                              [0, 256])
        hist_y = hist_y / np.sum(hist_y)
        hist_cr = hist_cr / np.sum(hist_cr)
        hist_cb = hist_cb / np.sum(hist_cb)

        # 24 dimensions
        return np.concatenate([hist_y, hist_cr, hist_cb], axis=0).reshape(-1) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_BGR2YCrCb [as 別名]
def make_dataset(dir):
    images = []
    assert os.path.isdir(dir), '%s is not a valid directory' % dir
    for root, _, fnames in sorted(os.walk(dir)):
        for fname in fnames:
            if is_image_file(fname):
                path = os.path.join(root, fname)
                images.append(path)
    return images

# Python opencv library (cv2) cv2.COLOR_BGR2YCrCb has different parameters with MATLAB color convertion.
# In order to have a fair comparison with the benchmark, we wrote these functions by ourselves. 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_BGR2YCrCb [as 別名]
def constrastLimit(image):
    img_hist_equalized = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb)
    channels = cv2.split(img_hist_equalized)
    channels[0] = cv2.equalizeHist(channels[0])
    img_hist_equalized = cv2.merge(channels)
    img_hist_equalized = cv2.cvtColor(img_hist_equalized, cv2.COLOR_YCrCb2BGR)
    return img_hist_equalized 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_BGR2YCrCb [as 別名]
def color_moments(image, mask, color_space):
    """
    function: Color Moment Features
    image: raw image
    mask: image mask
    color_space: 'rgb' or 'lab' or 'ycrcb' or 'hsv'
    """
    assert image.shape[:2] == mask.shape
    assert color_space.lower() in ['lab', 'rgb', 'ycrcb', 'hsv']

    if color_space.lower() == 'rgb':
        image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
    elif color_space.lower() == 'hsv':
        image = cv.cvtColor(image, cv.COLOR_BGR2HSV)
    elif color_space.lower() == 'lab':
        image = cv.cvtColor(image, cv.COLOR_BGR2LAB)
    elif color_space.lower() == 'ycrcb':
        image = cv.cvtColor(image, cv.COLOR_BGR2YCrCb)
    else:
        raise ValueError("Color space error...")

    # Split image channels info
    c1, c2, c3 = cv.split(image)
    color_feature = []

    # Only process mask != 0 channel region
    c1 = c1[np.where(mask != 0)]
    c2 = c2[np.where(mask != 0)]
    c3 = c3[np.where(mask != 0)]

    # Extract mean
    mean_1 = np.mean(c1)
    mean_2 = np.mean(c2)
    mean_3 = np.mean(c3)

    # Extract variance
    variance_1 = np.std(c1)
    variance_2 = np.std(c2)
    variance_3 = np.std(c3)

    # Extract skewness
    skewness_1 = np.mean(np.abs(c1 - mean_1) ** 3) ** (1. / 3)
    skewness_2 = np.mean(np.abs(c1 - mean_2) ** 3) ** (1. / 3)
    skewness_3 = np.mean(np.abs(c1 - mean_3) ** 3) ** (1. / 3)

    color_feature.extend(
        [mean_1, mean_2, mean_3, variance_1, variance_2,
            variance_3, skewness_1, skewness_2, skewness_3])

    return color_feature