[jacinto-ai/pytorch-jacinto-ai-devkit.git] / modules / pytorch_jacinto_ai / xnn / utils / image_utils.py
diff --git a/modules/pytorch_jacinto_ai/xnn/utils/image_utils.py b/modules/pytorch_jacinto_ai/xnn/utils/image_utils.py
index 2e37f478fda7b5555045ea232310628bb3838989..d36fa0d214b592cfe9b45f1b5396254ae160b343 100644 (file)
+#################################################################################
+# Copyright (c) 2018-2021, Texas Instruments Incorporated - http://www.ti.com
+# All Rights Reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+#################################################################################
+
+import copy
import cv2
import numpy as np
def chroma_blend_alpha(image, color):
alpha = 0.1
image = alpha*image + (1-alpha)*color
- return image
\ No newline at end of file
+ return image
+
+
+
+# Author: Manu Mathew
+# Date: 2021 March
+def get_color_palette(num_classes):
+ num_classes_3 = np.power(num_classes, 1.0/3)
+ delta_color = int(256/num_classes_3)
+ colors = [(r, g, b) for r in range(0,256,delta_color)
+ for g in range(0,256,delta_color)
+ for b in range(0,256,delta_color)]
+ # spread the colors list to num_classes
+ color_step = len(colors) / num_classes
+ colors_list = []
+ to_idx = 0
+ while len(colors_list) < num_classes:
+ from_idx = round(color_step * to_idx)
+ if from_idx < len(colors):
+ colors_list.append(colors[from_idx])
+ else:
+ break
+ #
+ to_idx = to_idx + 1
+ #
+ shortage = num_classes-len(colors_list)
+ if shortage > 0:
+ colors_list += colors[-shortage:]
+ #
+ if len(colors_list) < 256:
+ colors_list += [(255,255,255)] * (256-len(colors_list))
+ #
+ assert len(colors_list) == 256, f'incorrect length for color palette {len(colors_list)}'
+ return colors_list
+
+
+def segmap_to_color(seg_img, num_classes):
+ colors = get_color_palette(num_classes)
+ r = copy.deepcopy(seg_img)
+ g = copy.deepcopy(seg_img)
+ b = copy.deepcopy(seg_img)
+ for l in range(0, num_classes):
+ r[seg_img == l] = colors[l][0]
+ g[seg_img == l] = colors[l][1]
+ b[seg_img == l] = colors[l][2]
+ #
+ rgb = np.zeros((seg_img.shape[0], seg_img.shape[1], 3))
+ rgb[:, :, 0] = r / 255.0
+ rgb[:, :, 1] = g / 255.0
+ rgb[:, :, 2] = b / 255.0
+ return rgb