atom-predict/msunet/metric_only_one.py

53 lines
866 B
Python
Executable File

import json
import glob
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from multiprocessing import Pool
from utils.labelme import get_mask_v2
from utils.e2e_metrics import get_metrics
def get_score():
res = get_metrics(
label,
get_mask_v2(pred[0]))
return res
def get_score_v():
res = get_metrics(
np.array(label > 1),
get_mask_v2(pred[0])
)
return res
with open('/home/gao/mouclear/cc/data/new_v3/patch_unet/test_10s/version_4/test_10s.json') as f:
data = json.load(f)
img_path = np.array(data['img_path'])
label = np.array(data['label']) # [metric_idx]
pred = np.array(data['pred']) # [metric_idx]
# label = np.array([label])
nums = label.shape[0]; nums
a = get_score()
b = get_score_v()
print(a)
print(b)
# print(np.mean(a, axis=0))
# print(np.mean(b, axis=0))