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| import os |
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| import cv2 |
| import numpy as np |
|
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| from lib.datasets.tools.transforms import DeNormalize |
| from lib.utils.tools.logger import Logger as Log |
|
|
| SEG_DIR = 'vis/results/seg' |
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|
| class SegVisualizer(object): |
|
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| 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[np.where((gt == [0, 0, 0]).all(axis=2))] = [0, 0, 0] |
| return canvas |
|
|
