def run(args): dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set, data_dir=args.voc12_root) labels = [ dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset)) ] preds = [] for id in dataset.ids: ###################修改代码######################### cam_dict = np.load(os.path.join(args.cam_out_aug_dir, id + '.npy'), allow_pickle=True).item() ###################修改代码######################### cams = cam_dict['high_res'] cams = np.pad(cams, ((1, 0), (0, 0), (0, 0)), mode='constant', constant_values=args.cam_eval_thres) keys = np.pad(cam_dict['keys'] + 1, (1, 0), mode='constant') cls_labels = np.argmax(cams, axis=0) cls_labels = keys[cls_labels] preds.append(cls_labels.copy()) confusion = calc_semantic_segmentation_confusion(preds, labels) gtj = confusion.sum(axis=1) resj = confusion.sum(axis=0) gtjresj = np.diag(confusion) denominator = gtj + resj - gtjresj iou = gtjresj / denominator print({'iou': iou, 'miou': np.nanmean(iou)})
def run(args): assert args.voc12_root is not None assert args.chainer_eval_set is not None assert args.sem_seg_out_dir is not None dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set, data_dir=args.voc12_root) labels = [ dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset)) ] preds = [] for id in tqdm(dataset.ids): cls_labels = imageio.imread( os.path.join(args.sem_seg_out_dir, id + '.png')).astype(np.uint8) cls_labels[cls_labels == 255] = 0 preds.append(cls_labels.copy()) confusion = calc_semantic_segmentation_confusion(preds, labels)[:21, :21] gtj = confusion.sum(axis=1) resj = confusion.sum(axis=0) gtjresj = np.diag(confusion) denominator = gtj + resj - gtjresj fp = 1. - gtj / denominator fn = 1. - resj / denominator iou = gtjresj / denominator print(fp[0], fn[0]) print(np.mean(fp[1:]), np.mean(fn[1:])) print({'iou': iou, 'miou': np.nanmean(iou)})
def run_app(cfg: DictConfig) -> None: dataset = VOCSemanticSegmentationDataset(split=cfg.chainer_eval_set, data_dir=cfg.voc12_root) labels = [ dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset)) ] debug = True if debug: preds = [] for idx in dataset.ids: pred = _work(cfg.cam_out_dir, cfg.cv_out_dir, cfg.cam_eval_thres, cfg.area_threshold, idx) preds.append(pred) else: with mp.Pool(processes=mp.cpu_count() // 2) as pool: preds = pool.map( partial(_work, cfg.cam_out_dir, cfg.cv_out_dir, cfg.cam_eval_thres, cfg.area_threshold), list(dataset.ids)) print(len(preds)) confusion = calc_semantic_segmentation_confusion(preds, labels) gtj = confusion.sum(axis=1) resj = confusion.sum(axis=0) gtjresj = np.diag(confusion) denominator = gtj + resj - gtjresj iou = gtjresj / denominator print({'iou': iou, 'miou': np.nanmean(iou)}) logging.info({'iou': iou, 'miou': np.nanmean(iou)})
def run_app(cfg: DictConfig) -> None: dataset = VOCSemanticSegmentationDataset(split=cfg.chainer_eval_set, data_dir=cfg.voc12_root) labels = [ dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset)) ] preds = [] for id in dataset.ids: cls_labels = imageio.imread( os.path.join(cfg.sem_seg_out_dir, id + '.png')).astype(np.uint8) cls_labels[cls_labels == 255] = 0 if cfg.cv_out_dir: cls_labels = add_cv_results(cls_labels.copy(), id, cfg.cv_out_dir, cfg.area_threshold) preds.append(cls_labels.copy()) confusion = calc_semantic_segmentation_confusion(preds, labels)[:21, :21] gtj = confusion.sum(axis=1) resj = confusion.sum(axis=0) gtjresj = np.diag(confusion) denominator = gtj + resj - gtjresj fp = 1. - gtj / denominator fn = 1. - resj / denominator iou = gtjresj / denominator print(fp[0], fn[0]) print(np.mean(fp[1:]), np.mean(fn[1:])) print({'iou': iou, 'miou': np.nanmean(iou)})
def run(args): if args.dataset == 'l8biome': dataset = l8biome.dataloader.L8BiomeDataset(args.data_root, 'train', mask_file='mask.tif') # Only compute CAM for cloudy images - we know the segmentation label for clear already. dataset.images = [img for img in dataset.images if 'cloudy' in img[2]] labels = [dataset.load_mask(x[0]) for x in dataset.images] ids = [x[2] for x in dataset.images] else: dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set, data_dir=args.data_root) labels = [ dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset)) ] ids = dataset.ids preds = [] for id in tqdm(ids): cam_dict = np.load(os.path.join(args.cam_out_dir, id + '.npy'), allow_pickle=True).item() cams = cam_dict['high_res'] cams = np.pad(cams, ((1, 0), (0, 0), (0, 0)), mode='constant', constant_values=args.cam_eval_thres) if args.dataset == 'l8biome': # background class (in our case 'clear') corresponds to class 0 already keys = np.pad(cam_dict['keys'], (1, 0), mode='constant') else: keys = np.pad(cam_dict['keys'] + 1, (1, 0), mode='constant') cls_labels = np.argmax(cams, axis=0) cls_labels = keys[cls_labels] preds.append(cls_labels.copy()) if args.dataset == 'l8biome': # Compute metrics as FCD pass else: confusion = calc_semantic_segmentation_confusion(preds, labels) gtj = confusion.sum(axis=1) resj = confusion.sum(axis=0) gtjresj = np.diag(confusion) denominator = gtj + resj - gtjresj iou = gtjresj / denominator print({'iou': iou, 'miou': np.nanmean(iou)})
def run(args): dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set, data_dir=args.voc12_root) # labels = [dataset.get_example_by_keys(i, (1,))[0] for i in range(len(dataset))] preds = [] labels = [] n_images = 0 for i, id in enumerate(dataset.ids): n_images += 1 # print(os.path.join(args.cam_out_dir, id + '.npy')) cam_dict = np.load(os.path.join(args.cam_out_dir, id + '.npy'), allow_pickle=True).item() cams = cam_dict['high_res'] cams = np.pad(cams, ((1, 0), (0, 0), (0, 0)), mode='constant', constant_values=args.cam_eval_thres) keys = np.pad(cam_dict['keys'] + 1, (1, 0), mode='constant') cls_labels = np.argmax(cams, axis=0) cls_labels = keys[cls_labels] preds.append(cls_labels.copy()) labels.append(dataset.get_example_by_keys(i, (1, ))[0]) confusion = calc_semantic_segmentation_confusion(preds, labels) gtj = confusion.sum(axis=1) resj = confusion.sum(axis=0) gtjresj = np.diag(confusion) denominator = gtj + resj - gtjresj iou = gtjresj / denominator print("threshold:", args.cam_eval_thres, 'miou:', np.nanmean(iou), "i_imgs", n_images) print('among_predfg_bg', float((resj[1:].sum() - confusion[1:, 1:].sum()) / (resj[1:].sum()))) return np.nanmean(iou)
def run(args): if args.dataset == 'voc12': dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set, data_dir=args.dev_root) outsize = None elif args.dataset in ['adp_morph', 'adp_func']: dataset = ADPSemanticSegmentationDataset(split=args.chainer_eval_set, data_dir=args.dev_root, htt_type=args.dataset.split('_')[-1]) outsize = (1088, 1088) elif args.dataset in ['deepglobe', 'deepglobe_balanced']: dataset = DeepGlobeSemanticSegmentationDataset(split=args.chainer_eval_set, data_dir=args.dev_root, is_balanced=args.dataset == 'deepglobe_balanced') outsize = (2448, 2448) else: raise KeyError('Dataset %s not yet implemented' % args.dataset) labels = [dataset.get_example_by_keys(i, (1,))[0] for i in range(len(dataset))] preds = [] with tqdm(total=len(dataset)) as pbar: for id in dataset.ids: if args.dataset == 'voc12': img_path = voc12.dataloader.get_img_path(id, args.dev_root) elif args.dataset in ['adp_morph', 'adp_func']: img_path = adp.dataloader.get_img_path(id, args.dev_root, args.split == 'evaluation') elif args.dataset in ['deepglobe', 'deepglobe_balanced']: img_path = deepglobe.dataloader.get_img_path(id, args.dev_root) else: raise KeyError('Dataset %s not yet implemented' % args.dataset) cam_dict = np.load(os.path.join(args.cam_out_dir, id + '.npy'), allow_pickle=True).item() if args.dataset == 'voc12': cams = cam_dict['high_res'] cams = np.pad(cams, ((1, 0), (0, 0), (0, 0)), mode='constant', constant_values=args.cam_eval_thres) keys = np.pad(cam_dict['keys'] + 1, (1, 0), mode='constant') elif args.dataset in ['adp_morph', 'adp_func']: keys = cam_dict['keys'] cams = cam_dict['high_res'] elif args.dataset in ['deepglobe', 'deepglobe_balanced']: keys = cam_dict['keys'] cams = cam_dict['cam'] else: raise KeyError('Dataset %s not yet implemented' % args.dataset) cls_labels = np.argmax(cams, axis=0) cls_labels = keys[cls_labels] if outsize is not None: cls_labels = cv2.resize(cls_labels, outsize, interpolation=cv2.INTER_NEAREST) imageio.imsave(os.path.join(args.cam_clr_out_dir, id + '.png'), cls_labels.astype(np.uint8)) # Save with colour rw_pred_clr = np.zeros(list(cls_labels.shape) + [3], dtype=np.uint8) off = 0 for t in ['bg', 'fg']: for i, c in enumerate(args.class_colours[t]): for ch in range(3): rw_pred_clr[:, :, ch] += c[ch] * np.uint8(cls_labels == (i + off)) off += len(args.class_colours[t]) imageio.imsave(os.path.join(args.cam_clr_out_dir, id + '.png'), rw_pred_clr) # Save with colour, overlaid on original image if args.dataset not in ['deepglobe', 'deepglobe_balanced']: orig_img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB) else: orig_img = cv2.resize(cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB), rw_pred_clr.shape[:2]) if args.dataset in ['adp_morph', 'adp_func']: rw_pred_clr = cv2.resize(rw_pred_clr, orig_img.shape[:2]) rw_pred_clr_over = np.uint8((1 - args.overlay_r) * np.float32(orig_img) + args.overlay_r * np.float32(rw_pred_clr)) imageio.imsave(os.path.join(args.cam_clr_out_dir, id + '_overlay.png'), rw_pred_clr_over) preds.append(cls_labels.copy()) pbar.update(1) confusion = calc_semantic_segmentation_confusion(preds, labels) gtj = confusion.sum(axis=1) resj = confusion.sum(axis=0) gtjresj = np.diag(confusion) denominator = gtj + resj - gtjresj iou = gtjresj / denominator precision = gtjresj / resj recall = gtjresj / gtj miou = np.array([np.nanmean(iou)]) mprecision = np.array([np.nanmean(precision)]) mrecall = np.array([np.nanmean(recall)]) iou_data = np.concatenate((iou, miou), axis=0) pr_data = np.concatenate((precision, mprecision), axis=0) re_data = np.concatenate((recall, mrecall), axis=0) data = np.column_stack((iou_data, pr_data, re_data)) if args.dataset in ['deepglobe', 'deepglobe_balanced']: row_names = args.class_names['bg'] + args.class_names['fg'][:-1] + ['mean'] else: row_names = args.class_names['bg'] + args.class_names['fg'] + ['mean'] df = pd.DataFrame(data, index=row_names, columns=['iou', 'precision', 'recall']) df.to_csv(os.path.join(args.eval_dir, args.run_name + '_' + args.split + '_cam_iou.csv'), index=True) with open(args.logfile, 'a') as f: f.write('[eval_cam, ' + args.split + '] iou: ' + str(list(iou)) + '\n') f.write('[eval_cam, ' + args.split + '] miou: ' + str(miou[0]) + '\n') # args.logger.write('[eval_cam] iou: ' + iou + '\n') # args.logger.write('[eval_cam] miou: ' + miou+ '\n')
def run(args): if args.dataset == 'voc12': dataset = VOCSemanticSegmentationDataset(split=args.chainer_eval_set, data_dir=args.dev_root) outsize = None elif args.dataset in ['adp_morph', 'adp_func']: dataset = ADPSemanticSegmentationDataset( split=args.chainer_eval_set, data_dir=args.dev_root, htt_type=args.dataset.split('_')[-1]) outsize = (1088, 1088) elif args.dataset in ['deepglobe', 'deepglobe_balanced']: dataset = DeepGlobeSemanticSegmentationDataset( split=args.chainer_eval_set, data_dir=args.dev_root, is_balanced=args.dataset == 'deepglobe_balanced') outsize = (2448, 2448) else: raise KeyError('Dataset %s not yet implemented' % args.dataset) labels = [ dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset)) ] preds = [] with tqdm(total=len(dataset)) as pbar: for id in dataset.ids: cls_labels = imageio.imread( os.path.join(args.sem_seg_out_dir, id + '.png')).astype(np.uint8) cls_labels[cls_labels == 255] = 0 if outsize is not None: cls_labels = cv2.resize(cls_labels, outsize, interpolation=cv2.INTER_NEAREST) preds.append(cls_labels.copy()) pbar.update(1) confusion = calc_semantic_segmentation_confusion(preds, labels) #[:21, :21] gtj = confusion.sum(axis=1) resj = confusion.sum(axis=0) gtjresj = np.diag(confusion) denominator = gtj + resj - gtjresj fp = 1. - gtj / denominator fn = 1. - resj / denominator iou = gtjresj / denominator miou = np.array([np.nanmean(iou)]) print(fp[0], fn[0]) print(np.mean(fp[1:]), np.mean(fn[1:])) data = np.concatenate((iou, miou), axis=0) if args.dataset in ['deepglobe', 'deepglobe_balanced']: row_names = args.class_names['bg'] + args.class_names['fg'][:-1] + [ 'miou' ] else: row_names = args.class_names['bg'] + args.class_names['fg'] + ['miou'] df = pd.DataFrame(data, index=row_names, columns=['iou']) df.to_csv(os.path.join(args.eval_dir, args.run_name + '_' + args.split + '_iou.csv'), index=True) with open(args.logfile, 'a') as f: f.write('[eval_sem_seg, ' + args.split + '] iou: ' + str(list(iou)) + '\n') f.write('[eval_sem_seg, ' + args.split + '] miou: ' + str(miou[0]) + '\n')
from config import * import numpy as np import os from chainercv.datasets import VOCSemanticSegmentationDataset from chainercv.evaluations import calc_semantic_segmentation_confusion from PIL import Image import cv2 rgb_dict = [[0, 0, 0]] + [[255, 0, 0], [0, 255, 0], [0, 0, 255], [255, 255, 0], [255, 0, 255], [0, 255, 255]] * 20 # rgb_dict = [[0, 0, 0]] + [[0, 0, 0], [0, 255, 0], [0, 0, 0], [255, 255, 0], [ 255, 0, 255], [0, 255,255]]*20 if __name__ == "__main__": dataset = VOCSemanticSegmentationDataset(split=chainer_eval_set, data_dir=voc12_root) labels = [ dataset.get_example_by_keys(i, (1, ))[0] for i in range(len(dataset)) ] preds = [] for idx, id in enumerate(dataset.ids): cam_dict = np.load(os.path.join(cam_out_dir, id + '.npy'), allow_pickle=True).item() cams = cam_dict['high_res'] cams[:2, :] = 0 cams[3:, :] = 0 cams = np.pad(cams, ((1, 0), (0, 0), (0, 0)), mode='constant', constant_values=cam_eval_thres) # 添加背景的阈值 keys = np.pad(cam_dict['keys'] + 1, (1, 0), mode='constant') # 添加背景 cls_labels = np.argmax(cams, axis=0) cls_labels = keys[cls_labels]