import cv2 import numpy as np def generate_colormap(num_classes): 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[i:i + 3] for i in range(0, len(color_map), 3)] return color_map class SegPostprocess(object): def __init__(self, class_num): self.class_num = class_num def __call__(self, image_with_result): if "filename" not in image_with_result: raise ("filename should be specified in postprocess") img_name = image_with_result["filename"] ori_img = cv2.imread(img_name, -1) ori_shape = ori_img.shape mask = None for key in image_with_result: if ".lod" in key or "filename" in key: continue mask = image_with_result[key][0] if mask is None: raise ("segment mask should be specified in postprocess") mask = np.argmax(mask, axis=0) mask = mask.astype("uint8") mask_png = mask score_png = mask_png[:, :, np.newaxis] score_png = np.concatenate([score_png] * 3, axis=2) color_map = generate_colormap(self.class_num) for i in range(score_png.shape[0]): for j in range(score_png.shape[1]): score_png[i, j] = color_map[score_png[i, j, 0]] ext_pos = img_name.rfind(".") img_name_fix = img_name[:ext_pos] + "_" + img_name[ext_pos + 1:] mask_save_name = img_name_fix + "_mask.png" cv2.imwrite(mask_save_name, mask_png, [cv2.CV_8UC1]) vis_result_name = img_name_fix + "_result.png" result_png = score_png result_png = cv2.resize(result_png, (ori_shape[1], ori_shape[0]), fx=0, fy=0, interpolation=cv2.INTER_CUBIC) cv2.imwrite(vis_result_name, result_png, [cv2.CV_8UC1])