PaddleX/paddlex/modules/semantic_segmentation/dataset_checker/dataset_src/utils/visualizer.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.



import cv2
import numpy as np


def get_color_map_list(length):
    """Returns the color map for visualizing the segmentation mask"""
    length += 1
    color_map = length * [0, 0, 0]
    for i in range(0, length):
        j = 0
        lab = i
        while lab:
            color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
            color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
            color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
            j += 1
            lab >>= 3
    color_map = color_map[3:]
    return color_map


def visualize(image, result, weight=0.6, use_multilabel=False):
    """ Convert predict result to color image, and save added image. """
    color_map = get_color_map_list(256)
    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
    color_map = np.array(color_map).astype("uint8")

    if not use_multilabel:
        # Use OpenCV LUT for color mapping
        c1 = cv2.LUT(result, color_map[:, 0])
        c2 = cv2.LUT(result, color_map[:, 1])
        c3 = cv2.LUT(result, color_map[:, 2])
        pseudo_img = np.dstack((c3, c2, c1))

        vis_result = cv2.addWeighted(image, weight, pseudo_img, 1 - weight, 0)
    else:
        vis_result = image.copy()
        for i in range(result.shape[0]):
            mask = result[i]
            c1 = np.where(mask, color_map[i, 0], vis_result[..., 0])
            c2 = np.where(mask, color_map[i, 1], vis_result[..., 1])
            c3 = np.where(mask, color_map[i, 2], vis_result[..., 2])
            pseudo_img = np.dstack((c3, c2, c1)).astype('uint8')

            contour, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL,
                                          cv2.CHAIN_APPROX_SIMPLE)
            vis_result = cv2.addWeighted(vis_result, weight, pseudo_img,
                                         1 - weight, 0)
            contour_color = (int(color_map[i, 0]), int(color_map[i, 1]),
                             int(color_map[i, 2]))
            vis_result = cv2.drawContours(vis_result, contour, -1,
                                          contour_color, 1)

    return vis_result