#!/usr/bin/env python #-*- coding:utf-8 -*- # Author: Donny You(youansheng@gmail.com) # Visualizer for segmentation. import os import cv2 import numpy as np from lib.datasets.tools.transforms import DeNormalize from lib.utils.tools.logger import Logger as Log SEG_DIR = 'vis/results/seg' class SegVisualizer(object): def __init__(self, configer=None): self.configer = configer def vis_fn(self, preds, targets, ori_img_in=None, name='default', sub_dir='fn'): base_dir = os.path.join(self.configer.get('project_dir'), SEG_DIR, sub_dir) if not os.path.exists(base_dir): Log.error('Dir:{} not exists!'.format(base_dir)) os.makedirs(base_dir) if not isinstance(preds, np.ndarray): if len(preds.size()) > 3: Log.error('Preds size is not valid.') exit(1) if len(preds.size()) == 3: preds = preds.clone().data.cpu().numpy() if len(preds.size()) == 2: preds = preds.unsqueeze(0).data.cpu().numpy() else: if len(preds.shape) > 3: Log.error('Preds size is not valid.') exit(1) if len(preds.shape) == 2: preds = preds.unsqueeze(0) if not isinstance(targets, np.ndarray): if len(targets.size()) == 3: targets = targets.clone().data.cpu().numpy() if len(targets.size()) == 2: targets = targets.unsqueeze(0).data.cpu().numpy() else: if len(targets.shape) == 2: targets = targets.unsqueeze(0) if ori_img_in is not None: if not isinstance(ori_img_in, np.ndarray): if len(ori_img_in.size()) < 3: Log.error('Image size is not valid.') exit(1) if len(ori_img_in.size()) == 4: ori_img_in = ori_img_in.data.cpu() if len(ori_img_in.size()) == 3: ori_img_in = ori_img_in.unsqueeze(0).data.cpu() ori_img = ori_img_in.clone() for i in range(ori_img_in.size(0)): ori_img[i] = DeNormalize(div_value=self.configer.get('normalize', 'div_value'), mean=self.configer.get('normalize', 'mean'), std=self.configer.get('normalize', 'std'))(ori_img_in.clone()) ori_img = ori_img.numpy().transpose(2, 3, 1).astype(np.uint8) else: if len(ori_img_in.shape) == 3: ori_img_in = ori_img_in.unsqueeze(0) ori_img = ori_img_in.copy() for img_id in range(preds.shape[0]): label = targets[img_id] pred = preds[img_id] result = np.zeros(shape=(pred.shape[0], pred.shape[1], 3), dtype=np.uint8) for i in range(self.configer.get('data', 'num_classes')): mask0 = np.zeros_like(label, dtype=np.uint8) mask1 = np.zeros_like(label, dtype=np.uint8) mask0[label[:] == i] += 1 mask0[pred[:] == i] += 1 mask1[pred[:] == i] += 1 result[mask0[:] == 1] = self.configer.get('details', 'color_list')[i] result[mask1[:] == 1] = (0, 0, 0) image_result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB) if ori_img_in is not None: image_result = cv2.addWeighted(ori_img[i], 0.6, image_result, 0.4, 0) cv2.imwrite(os.path.join(base_dir, '{}_{}.jpg'.format(name, img_id)), image_result) def vis_fp(self, preds, targets, ori_img_in=None, name='default', sub_dir='fp'): base_dir = os.path.join(self.configer.get('project_dir'), SEG_DIR, sub_dir) if not os.path.exists(base_dir): Log.error('Dir:{} not exists!'.format(base_dir)) os.makedirs(base_dir) if not isinstance(preds, np.ndarray): if len(preds.size()) > 3: Log.error('Preds size is not valid.') exit(1) if len(preds.size()) == 3: preds = preds.clone().data.cpu().numpy() if len(preds.size()) == 2: preds = preds.unsqueeze(0).data.cpu().numpy() else: if len(preds.shape) > 3: Log.error('Preds size is not valid.') exit(1) if len(preds.shape) == 2: preds = preds.unsqueeze(0) if not isinstance(targets, np.ndarray): if len(targets.size()) == 3: targets = targets.clone().data.cpu().numpy() if len(targets.size()) == 2: targets = targets.unsqueeze(0).data.cpu().numpy() else: if len(targets.shape) == 2: targets = targets.unsqueeze(0) if ori_img_in is not None: if not isinstance(ori_img_in, np.ndarray): if len(ori_img_in.size()) < 3: Log.error('Image size is not valid.') exit(1) if len(ori_img_in.size()) == 4: ori_img_in = ori_img_in.data.cpu() if len(ori_img_in.size()) == 3: ori_img_in = ori_img_in.unsqueeze(0).data.cpu() ori_img = ori_img_in.clone() for i in range(ori_img_in.size(0)): ori_img[i] = DeNormalize(div_value=self.configer.get('normalize', 'div_value'), mean=self.configer.get('normalize', 'mean'), std=self.configer.get('normalize', 'std'))(ori_img_in.clone()) ori_img = ori_img.numpy().transpose(2, 3, 1).astype(np.uint8) else: if len(ori_img_in.shape) == 3: ori_img_in = ori_img_in.unsqueeze(0) ori_img = ori_img_in.copy() for img_id in range(preds.shape[0]): label = targets[img_id] pred = preds[img_id] result = np.zeros(shape=(pred.shape[0], pred.shape[1], 3), dtype=np.uint8) for i in range(self.configer.get('data', 'num_classes')): mask0 = np.zeros_like(label, dtype=np.uint8) mask1 = np.zeros_like(label, dtype=np.uint8) mask0[label[:] == i] += 1 mask0[pred[:] == i] += 1 mask1[label[:] == i] += 1 result[mask0[:] == 1] = self.configer.get('details', 'color_list')[i] result[mask1[:] == 1] = (0, 0, 0) image_result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB) if ori_img_in is not None: image_result = cv2.addWeighted(ori_img[i], 0.6, image_result, 0.4, 0) cv2.imwrite(os.path.join(base_dir, '{}_{}.jpg'.format(name, img_id)), image_result) def error_map(self, im, pred, gt): canvas = im.copy() canvas[np.where((gt - pred != [0, 0, 0]).all(axis=2))] = [0, 0, 0] pred[np.where((gt - pred == [0, 0, 0]).all(axis=2))] = [0, 0, 0] canvas = cv2.addWeighted(canvas, 1.0, pred, 1.0, 0) # canvas = cv2.addWeighted(im, 0.3, canvas, 0.7, 0) canvas[np.where((gt == [0, 0, 0]).all(axis=2))] = [0, 0, 0] return canvas