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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 os
import cv2
import numpy as np
from PIL import Image as PILImage
def visualize(image, result, color_map, save_dir=None, weight=0.6):
"""
Convert predict result to color image, and save added image.
Args:
image (str): The path of origin image.
result (np.ndarray): The predict result of image.
color_map (list): The color used to save the prediction results.
save_dir (str): The directory for saving visual image. Default: None.
weight (float): The image weight of visual image, and the result weight is (1 - weight). Default: 0.6
Returns:
vis_result (np.ndarray): If `save_dir` is None, return the visualized result.
"""
color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
color_map = np.array(color_map).astype("uint8")
# 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))
im = cv2.imread(image)
vis_result = cv2.addWeighted(im, weight, pseudo_img, 1 - weight, 0)
if save_dir is not None:
if not os.path.exists(save_dir):
os.makedirs(save_dir)
image_name = os.path.split(image)[-1]
out_path = os.path.join(save_dir, image_name)
cv2.imwrite(out_path, vis_result)
else:
return vis_result
def get_pseudo_color_map(pred, color_map=None):
"""
Get the pseudo color image.
Args:
pred (numpy.ndarray): the origin predicted image.
color_map (list, optional): the palette color map. Default: None,
use paddleseg's default color map.
Returns:
(numpy.ndarray): the pseduo image.
"""
pred_mask = PILImage.fromarray(pred.astype(np.uint8), mode='P')
if color_map is None:
color_map = get_color_map_list(256)
pred_mask.putpalette(color_map)
return pred_mask
def get_color_map_list(num_classes, custom_color=None):
"""
Returns the color map for visualizing the segmentation mask,
which can support arbitrary number of classes.
Args:
num_classes (int): Number of classes.
custom_color (list, optional): Save images with a custom color map. Default: None, use paddleseg's default color map.
Returns:
(list). The color map.
"""
num_classes += 1
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
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:]
if custom_color:
color_map[:len(custom_color)] = custom_color
return color_map
def paste_images(image_list):
"""
Paste all image to a image.
Args:
image_list (List or Tuple): The images to be pasted and their size are the same.
Returns:
result_img (PIL.Image): The pasted image.
"""
assert isinstance(image_list,
(list, tuple)), "image_list should be a list or tuple"
assert len(
image_list) > 1, "The length of image_list should be greater than 1"
pil_img_list = []
for img in image_list:
if isinstance(img, str):
assert os.path.exists(img), "The image is not existed: {}".format(
img)
img = PILImage.open(img)
img = np.array(img)
elif isinstance(img, np.ndarray):
img = PILImage.fromarray(img)
pil_img_list.append(img)
sample_img = pil_img_list[0]
size = sample_img.size
for img in pil_img_list:
assert size == img.size, "The image size in image_list should be the same"
width, height = sample_img.size
result_img = PILImage.new(sample_img.mode,
(width * len(pil_img_list), height))
for i, img in enumerate(pil_img_list):
result_img.paste(img, box=(width * i, 0))
return result_img
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