python类COLOR_LAB2BGR的实例源码

def enhance(image_path, clip_limit=3):
    image = cv2.imread(image_path)
    # convert image to LAB color model
    image_lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)

    # split the image into L, A, and B channels
    l_channel, a_channel, b_channel = cv2.split(image_lab)

    # apply CLAHE to lightness channel
    clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=(8, 8))
    cl = clahe.apply(l_channel)

    # merge the CLAHE enhanced L channel with the original A and B channel
    merged_channels = cv2.merge((cl, a_channel, b_channel))

    # convert iamge from LAB color model back to RGB color model
    final_image = cv2.cvtColor(merged_channels, cv2.COLOR_LAB2BGR)
    return cv2_to_pil(final_image)

def __tiledoutput__(self, net_op, batch_size, num_cols=8, net_recon_const=None):

    num_rows = np.int_(np.ceil((batch_size*1.)/num_cols))
    out_img = np.zeros((num_rows*self.outshape[0], num_cols*self.outshape[1], 3), dtype='uint8')
    img_lab = np.zeros((self.outshape[0], self.outshape[1], 3), dtype='uint8')
    c = 0
    r = 0

    for i in range(batch_size):
      if(i % num_cols == 0 and i > 0):
        r = r + 1
        c = 0
      img_lab[..., 0] = self.__decodeimg__(net_recon_const[i, 0, :, :].reshape(\
        self.outshape[0], self.outshape[1]))
      img_lab[..., 1] = self.__decodeimg__(net_op[i, 0, :, :].reshape(\
        self.shape[0], self.shape[1]))
      img_lab[..., 2] = self.__decodeimg__(net_op[i, 1, :, :].reshape(\
        self.shape[0], self.shape[1]))
      img_rgb = cv2.cvtColor(img_lab, cv2.COLOR_LAB2BGR)
      out_img[r*self.outshape[0]:(r+1)*self.outshape[0], \
        c*self.outshape[1]:(c+1)*self.outshape[1], ...] = img_rgb
      c = c+1

    return out_img

def save_output(self, net_op, batch_size, num_cols=8, net_recon_const=None):
    num_rows = np.int_(np.ceil((batch_size*1.)/num_cols))
    out_img = np.zeros((num_rows*self.outshape[0], num_cols*self.outshape[1], 3), dtype='uint8')
    img_lab = np.zeros((self.outshape[0], self.outshape[1], 3), dtype='uint8')
    c = 0
    r = 0
    for i in range(batch_size):
      if(i % num_cols == 0 and i > 0):
        r = r + 1
        c = 0
      img_lab[..., 0] = self.__get_decoded_img(net_recon_const[i, ...].reshape(self.outshape[0], self.outshape[1]))
      img_lab[..., 1] = self.__get_decoded_img(net_op[i, :np.prod(self.shape)].reshape(self.shape[0], self.shape[1]))
      img_lab[..., 2] = self.__get_decoded_img(net_op[i, np.prod(self.shape):].reshape(self.shape[0], self.shape[1]))
      img_rgb = cv2.cvtColor(img_lab, cv2.COLOR_LAB2BGR)
      out_img[r*self.outshape[0]:(r+1)*self.outshape[0], c*self.outshape[1]:(c+1)*self.outshape[1], ...] = img_rgb
      c = c+1
    return out_img

def color_reduction(image, n_colors, method='kmeans', palette=None):
    """Reduce the number of colors in image to n_colors using method"""
    method = method.lower()
    if method not in ('kmeans', 'linear', 'max', 'median', 'octree'):
        method = 'kmeans'
    if n_colors < 2:
        n_colors = 2
    elif n_colors > 128:
        n_colors = 128
    if method == 'kmeans':
        n_clusters = n_colors
        h, w = image.shape[:2]
        img = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
        img = img.reshape((-1, 3))  # -1 -> img.shape[0] * img.shape[1]
        centers, labels = kmeans(img, n_clusters)
        if palette is not None:
            # palette comes in RGB
            centers = cv2.cvtColor(np.array([palette]), cv2.COLOR_RGB2LAB)[0]
        quant = centers[labels].reshape((h, w, 3))
        output = cv2.cvtColor(quant, cv2.COLOR_LAB2BGR)
    else:
        img = PIL.Image.fromarray(image[:, :, ::-1], mode='RGB')
        quant = img.quantize(colors=n_colors,
                             method=get_quantize_method(method))
        if palette is not None:
            palette = np.array(palette, dtype=np.uint8)
            quant.putpalette(palette.flatten())
        output = np.array(quant.convert('RGB'), dtype=np.uint8)[:, :, ::-1]
    return output

def visualisePCA(dstFolder):
    meanIsomap = pickle.load(open(os.path.join(dstFolder, "meanIsomap.p"), "rb"))
    pca = pickle.load(open(os.path.join(dstFolder, "pca.p"), "rb"))

    for i in range(10):
        for j in np.linspace(-1,1,3):
            X = [0,]*10
            X[i]=j*np.sqrt(pca.explained_variance_[i])
            displayIm = getIsoMapFromPCA(pca, meanIsomap, X)
            displayIm = cv2.cvtColor(displayIm[:, :, :3].astype(np.uint8), cv2.COLOR_LAB2BGR)
            cv2.imshow("pca_{}".format(j), displayIm.astype(np.uint8))
        # cv2.waitKey(-1)

    isomaps = pickle.load(open(os.path.join(dstFolder, "0_isomaps_centered.p"), "rb"))
    #play with variance
    for i in range(isomaps.shape[0]):
        isomap_just_color = isomaps[i, :, :, 1:3]

        # flatten to data
        X = isomap_just_color.reshape(1, -1)
        isomap_pca_color = pca.transform(X)[0]

        scale = 1
        # for j in range(5):
        new_isomap_pca_color = isomap_pca_color.copy()
        # new_isomap_pca_color[j] += np.sqrt(pca.explained_variance_[j]) * scale

        isomap_new_color = pca.inverse_transform(new_isomap_pca_color)
        isomap_new_color = isomap_new_color.reshape(isomap_just_color.shape[0],isomap_just_color.shape[1],isomap_just_color.shape[2])

        isomap_full = isomaps[i,:,:,:3].copy()
        isomap_full[:,:,1:] = isomap_new_color

        displayIm = isomaps[i, :, :, :3].copy() + meanIsomap[:, :, :3]
        displayIm = cv2.cvtColor(displayIm[:, :, :3].astype(np.uint8), cv2.COLOR_LAB2BGR)
        cv2.imshow("isomap_orig", displayIm.astype(np.uint8))

        displayIm = isomap_full.copy() + meanIsomap[:, :, :3]
        displayIm = cv2.cvtColor(displayIm[:, :, :3].astype(np.uint8), cv2.COLOR_LAB2BGR)
        cv2.imshow("isomap_new", displayIm.astype(np.uint8))
        cv2.waitKey(-1)

def save_divcolor(self, net_op, gt, epoch, itr_id, prefix, batch_size, imgname, num_cols=8, net_recon_const=None):
    img_lab = np.zeros((self.outshape[0], self.outshape[1], 3), dtype='uint8')
    img_lab_mat = np.zeros((self.shape[0], self.shape[1], 2), dtype='uint8')
    if not os.path.exists('%s/%s' % (self.out_directory, imgname)):
      os.makedirs('%s/%s' % (self.out_directory, imgname))
    for i in range(batch_size):
      img_lab[..., 0] = self.__get_decoded_img(net_recon_const[i, ...].reshape(self.outshape[0], self.outshape[1]))
      img_lab[..., 1] = self.__get_decoded_img(net_op[i, :np.prod(self.shape)].reshape(self.shape[0], self.shape[1]))
      img_lab[..., 2] = self.__get_decoded_img(net_op[i, np.prod(self.shape):].reshape(self.shape[0], self.shape[1]))
      img_lab_mat[..., 0] = 128.*net_op[i, :np.prod(self.shape)].reshape(self.shape[0], self.shape[1])+128.
      img_lab_mat[..., 1] = 128.*net_op[i, np.prod(self.shape):].reshape(self.shape[0], self.shape[1])+128.
      img_rgb = cv2.cvtColor(img_lab, cv2.COLOR_LAB2BGR)
      out_fn_pred = '%s/%s/%s_%03d.png' % (self.out_directory, imgname, prefix, i)
      cv2.imwrite(out_fn_pred, img_rgb)
#      out_fn_mat = '%s/%s/%s_%03d.mat' % (self.out_directory, imgname, prefix, i)
#      np.save(out_fn_mat, img_lab_mat)
    img_lab[..., 0] = self.__get_decoded_img(net_recon_const[i, ...].reshape(self.outshape[0], self.outshape[1]))
    img_lab[..., 1] = self.__get_decoded_img(gt[0, :np.prod(self.shape)].reshape(self.shape[0], self.shape[1]))
    img_lab[..., 2] = self.__get_decoded_img(gt[0, np.prod(self.shape):].reshape(self.shape[0], self.shape[1]))
    img_lab_mat[..., 0] = 128.*gt[0, :np.prod(self.shape)].reshape(self.shape[0], self.shape[1])+128.
    img_lab_mat[..., 1] = 128.*gt[0, np.prod(self.shape):].reshape(self.shape[0], self.shape[1])+128.
    out_fn_pred = '%s/%s/gt.png' % (self.out_directory, imgname)
    img_rgb = cv2.cvtColor(img_lab, cv2.COLOR_LAB2BGR)
    cv2.imwrite(out_fn_pred, img_rgb)
#    out_fn_mat = '%s/%s/gt.mat' % (self.out_directory, imgname)
#    np.save(out_fn_mat, img_lab_mat)