Python cv2.COLOR_BGR2YCrCb() Examples

The following are 5 code examples of cv2.COLOR_BGR2YCrCb(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module cv2 , or try the search function .
Example #1
Source File: cal_histogram.py    From Face-skin-hair-segmentaiton-and-skin-color-evaluation with Apache License 2.0 5 votes vote down vote up 
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)
Example #2
Source File: Functions.py    From Jalali-Lab-Implementation-of-RAISR with GNU General Public License v3.0 5 votes vote down vote up 
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.
Example #3
Source File: main.py    From Traffic-Sign-Detection with MIT License 5 votes vote down vote up 
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
Example #4
Source File: cal_moments.py    From Face-skin-hair-segmentaiton-and-skin-color-evaluation with Apache License 2.0 4 votes vote down vote up 
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
Example #5
Source File: pipline_test.py    From Face-skin-hair-segmentaiton-and-skin-color-evaluation with Apache License 2.0 4 votes vote down vote up 
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