Python cv2.getGaussianKernel方法代碼示例

本文整理匯總了Python中cv2.getGaussianKernel方法的典型用法代碼示例。如果您正苦於以下問題：Python cv2.getGaussianKernel方法的具體用法？Python cv2.getGaussianKernel怎麽用？Python cv2.getGaussianKernel使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類cv2的用法示例。

在下文中一共展示了cv2.getGaussianKernel方法的8個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: make_edge_smooth

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import getGaussianKernel [as 別名]
def make_edge_smooth(dataset_name, img_size) :
    check_folder('./dataset/{}/{}'.format(dataset_name, 'trainB_smooth'))

    file_list = glob('./dataset/{}/{}/*.*'.format(dataset_name, 'trainB'))
    save_dir = './dataset/{}/trainB_smooth'.format(dataset_name)

    kernel_size = 5
    kernel = np.ones((kernel_size, kernel_size), np.uint8)
    gauss = cv2.getGaussianKernel(kernel_size, 0)
    gauss = gauss * gauss.transpose(1, 0)

    for f in tqdm(file_list) :
        file_name = os.path.basename(f)

        bgr_img = cv2.imread(f)
        gray_img = cv2.imread(f, 0)

        bgr_img = cv2.resize(bgr_img, (img_size, img_size))
        pad_img = np.pad(bgr_img, ((2, 2), (2, 2), (0, 0)), mode='reflect')
        gray_img = cv2.resize(gray_img, (img_size, img_size))

        edges = cv2.Canny(gray_img, 100, 200)
        dilation = cv2.dilate(edges, kernel)

        gauss_img = np.copy(bgr_img)
        idx = np.where(dilation != 0)
        for i in range(np.sum(dilation != 0)):
            gauss_img[idx[0][i], idx[1][i], 0] = np.sum(
                np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 0], gauss))
            gauss_img[idx[0][i], idx[1][i], 1] = np.sum(
                np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 1], gauss))
            gauss_img[idx[0][i], idx[1][i], 2] = np.sum(
                np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 2], gauss))

        cv2.imwrite(os.path.join(save_dir, file_name), gauss_img)

開發者ID:taki0112，項目名稱:CartoonGAN-Tensorflow，代碼行數:37，代碼來源:edge_smooth.py

示例2: ssim

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import getGaussianKernel [as 別名]
def ssim(img1, img2):
    C1 = (0.01 * 255)**2
    C2 = (0.03 * 255)**2

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())

    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
    mu1_sq = mu1**2
    mu2_sq = mu2**2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
                                                            (sigma1_sq + sigma2_sq + C2))
    return ssim_map.mean()

開發者ID:cszn，項目名稱:KAIR，代碼行數:23，代碼來源:utils_image.py

示例3: ssim

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import getGaussianKernel [as 別名]
def ssim(img1, img2):

    C1 = (0.01 * 255)**2
    C2 = (0.03 * 255)**2

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())

    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
    mu1_sq = mu1**2
    mu2_sq = mu2**2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
                                                            (sigma1_sq + sigma2_sq + C2))
    return ssim_map.mean()

開發者ID:Paper99，項目名稱:SRFBN_CVPR19，代碼行數:24，代碼來源:util.py

示例4: _ssim

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import getGaussianKernel [as 別名]
def _ssim(img1, img2):
        C1 = (0.01 * 255) ** 2
        C2 = (0.03 * 255) ** 2

        img1 = img1.astype(np.float64)
        img2 = img2.astype(np.float64)
        kernel = cv2.getGaussianKernel(11, 1.5)
        window = np.outer(kernel, kernel.transpose())

        mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
        mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
        mu1_sq = mu1 ** 2
        mu2_sq = mu2 ** 2
        mu1_mu2 = mu1 * mu2
        sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq
        sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq
        sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2

        ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
            (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
        )
        return ssim_map.mean()

開發者ID:bonlime，項目名稱:pytorch-tools，代碼行數:24，代碼來源:psnr.py

示例5: downsample

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import getGaussianKernel [as 別名]
def downsample(image, coordinates, **config):
    with tf.name_scope('gaussian_blur'):
        k_size = config['blur_size']
        kernel = cv.getGaussianKernel(k_size, 0)[:, 0]
        kernel = np.outer(kernel, kernel).astype(np.float32)
        kernel = tf.reshape(tf.convert_to_tensor(kernel), [k_size]*2+[1, 1])
        pad_size = int(k_size/2)
        image = tf.pad(image, [[pad_size]*2, [pad_size]*2, [0, 0]], 'REFLECT')
        image = tf.expand_dims(image, axis=0)  # add batch dim
        image = tf.nn.depthwise_conv2d(image, kernel, [1, 1, 1, 1], 'VALID')[0]

    with tf.name_scope('downsample'):
        ratio = tf.divide(tf.convert_to_tensor(config['resize']), tf.shape(image)[0:2])
        coordinates = coordinates * tf.cast(ratio, tf.float32)
        image = tf.image.resize_images(image, config['resize'],
                                       method=tf.image.ResizeMethod.BILINEAR)

    return image, coordinates

開發者ID:ethz-asl，項目名稱:hfnet，代碼行數:20，代碼來源:pipeline.py

示例6: make_edge_smooth

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import getGaussianKernel [as 別名]
def make_edge_smooth(path):
    file_list = glob(os.path.expanduser(os.path.join(path, 'trainB', '*')))
    save_dir = os.path.expanduser(os.path.join(path, 'trainB_smooth'))
    if not os.path.isdir(save_dir):
        os.makedirs(save_dir)

    kernel_size = 5
    kernel = np.ones((kernel_size, kernel_size), np.uint8)
    gauss = cv2.getGaussianKernel(kernel_size, 0)
    gauss = gauss * gauss.transpose(1, 0)

    for f in tqdm(file_list):
        file_name = os.path.basename(f)

        bgr_img = cv2.imread(f)
        gray_img = cv2.imread(f, 0)
        pad_img = np.pad(bgr_img, ((2, 2), (2, 2), (0, 0)), mode='reflect')
        edges = cv2.Canny(gray_img, 100, 200)
        dilation = cv2.dilate(edges, kernel)

        gauss_img = np.copy(bgr_img)
        idx = np.where(dilation != 0)
        for i in range(np.sum(dilation != 0)):
            gauss_img[idx[0][i], idx[1][i], 0] = np.sum(np.multiply(
                pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 0],
                gauss))
            gauss_img[idx[0][i], idx[1][i], 1] = np.sum(np.multiply(
                pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 1],
                gauss))
            gauss_img[idx[0][i], idx[1][i], 2] = np.sum(np.multiply(
                pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 2],
                gauss))

        cv2.imwrite(os.path.join(save_dir, file_name), gauss_img)

開發者ID:mnicnc404，項目名稱:CartoonGan-tensorflow，代碼行數:36，代碼來源:smooth.py

示例7: edge_promoting

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import getGaussianKernel [as 別名]
def edge_promoting(root, save):
    file_list = os.listdir(root)
    if not os.path.isdir(save):
        os.makedirs(save)
    kernel_size = 5
    kernel = np.ones((kernel_size, kernel_size), np.uint8)
    gauss = cv2.getGaussianKernel(kernel_size, 0)
    gauss = gauss * gauss.transpose(1, 0)
    n = 1
    for f in tqdm(file_list):
        rgb_img = cv2.imread(os.path.join(root, f))
        gray_img = cv2.imread(os.path.join(root, f), 0)
        rgb_img = cv2.resize(rgb_img, (256, 256))
        pad_img = np.pad(rgb_img, ((2,2), (2,2), (0,0)), mode='reflect')
        gray_img = cv2.resize(gray_img, (256, 256))
        edges = cv2.Canny(gray_img, 100, 200)
        dilation = cv2.dilate(edges, kernel)

        gauss_img = np.copy(rgb_img)
        idx = np.where(dilation != 0)
        for i in range(np.sum(dilation != 0)):
            gauss_img[idx[0][i], idx[1][i], 0] = np.sum(np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 0], gauss))
            gauss_img[idx[0][i], idx[1][i], 1] = np.sum(np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 1], gauss))
            gauss_img[idx[0][i], idx[1][i], 2] = np.sum(np.multiply(pad_img[idx[0][i]:idx[0][i] + kernel_size, idx[1][i]:idx[1][i] + kernel_size, 2], gauss))

        result = np.concatenate((rgb_img, gauss_img), 1)

        cv2.imwrite(os.path.join(save, str(n) + '.png'), result)
        n += 1

開發者ID:znxlwm，項目名稱:pytorch-CartoonGAN，代碼行數:31，代碼來源:edge_promoting.py

示例8: init

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import getGaussianKernel [as 別名]
def __init__(self, kernel_size=5):
    super(GaussianSmoother, self).__init__()
    self.sigma = 0.3*((kernel_size-1)*0.5-1)+0.8
    kernel = cv2.getGaussianKernel(kernel_size, -1)
    kernel2d = np.dot(kernel.reshape(kernel_size,1),kernel.reshape(1,kernel_size))
    data = torch.Tensor(1, 1, kernel_size, kernel_size)
    self.pad = (kernel_size-1)/2
    for i in range(0,1):
      data[i,0,:,:] = torch.from_numpy(kernel2d)
    self.blur_kernel = Variable(data, requires_grad=False)

開發者ID:masabdi，項目名稱:LSPS，代碼行數:12，代碼來源:common_net.py

注：本文中的cv2.getGaussianKernel方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。