File size: 2,637 Bytes
26aa799 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
import os
import numpy as np
from mmseg.apis import init_model, inference_model, show_result_pyplot#, inference_segmentor
import torch
from PIL import Image
from sklearn.metrics import confusion_matrix
from torchmetrics import JaccardIndex
def main():
config_file = 'mmsegmentation/configs/mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py'
checkpoint_file = 'evaluations/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640_20221203_235933-7120c214.pth'
# build the model from a config file and a checkpoint file
model = init_model(config_file, checkpoint_file, device='cuda:0')
# Image and segmentation labels directories
img_dir = 'sample/ade20k/visualization'
ann_dir = 'sample/ade20k/annotations'
# List all image files
img_fns = [f for f in sorted(os.listdir(img_dir)) if f.endswith(".png")]
# ann_fns = [f for f in sorted(os.listdir(ann_dir)) if f.endswith(".png")]
total_mIoU = 0
from tqdm import tqdm
i = 0
jaccard_index = JaccardIndex(task="multiclass", num_classes=150)
num_classes = 150
conf_matrix = np.zeros((num_classes + 1, num_classes + 1), dtype=np.int64)
for img_fn in tqdm(img_fns):
i += 1
# if i >= 100:
# break
# try:
img_path = os.path.join(img_dir, img_fn)
ann_path = os.path.join(ann_dir, img_fn)
result = inference_model(model, img_path)
# except Exception as e:
# continue
# Read ground truth segmentation map
gt_semantic_seg = np.array(Image.open(ann_path))
ignore_label = 0
gt = gt_semantic_seg.copy()
pred = result.pred_sem_seg.data[0].cpu().numpy().copy()+1
gt[gt == ignore_label] = num_classes
conf_matrix += np.bincount(
(num_classes + 1) * pred.reshape(-1) + gt.reshape(-1),
minlength=conf_matrix.size,
).reshape(conf_matrix.shape)
# calculate miou
acc = np.full(num_classes, np.nan, dtype=np.float64)
iou = np.full(num_classes, np.nan, dtype=np.float64)
tp = conf_matrix.diagonal()[:-1].astype(np.float64)
pos_gt = np.sum(conf_matrix[:-1, :-1], axis=0).astype(np.float64)
pos_pred = np.sum(conf_matrix[:-1, :-1], axis=1).astype(np.float64)
acc_valid = pos_gt > 0
acc[acc_valid] = tp[acc_valid] / pos_gt[acc_valid]
iou_valid = (pos_gt + pos_pred) > 0
union = pos_gt + pos_pred - tp
iou[acc_valid] = tp[acc_valid] / union[acc_valid]
miou = np.sum(iou[acc_valid]) / np.sum(iou_valid)
print(f"mIoU: {miou}")
if __name__ == '__main__':
main() |