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import json
import argparse
from pycocotools import mask as mask_utils
import numpy as np
import tqdm
from sklearn.metrics import balanced_accuracy_score
import utils
import cv2
import os
from PIL import Image
def evaluate_take():
#实验需改动
pred_path = "/data/work2-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap/predictions/ego_query5/92b2221b-ae92-44f0-bb31-e2d27cb736d6/gp02"
root_path = "/data/work2-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap"
tmp = pred_path.split("/")
take_id = tmp[-2]
exo = tmp[-1]
gt_path = f"{root_path}/{take_id}/annotation.json"
with open(gt_path, 'r') as fp:
gt = json.load(fp)
#实验需改动
ego_cams = [x for x in gt['masks']["piano"].keys() if 'aria' in x]
ego = ego_cams[0]
IoUs = []
ShapeAcc = []
ExistenceAcc = []
LocationScores = []
frames = os.listdir(pred_path)
idx = [f.split(".")[0] for f in frames]
for id in idx:
#实验需改动
gt_mask = gt["masks"]["piano"][exo][id]
gt_mask = mask_utils.decode(gt_mask)
#修改，将解码后gt_mask调整大小为pred_mask的大小
gt_mask = cv2.resize(gt_mask, (960, 540), interpolation=cv2.INTER_NEAREST)
pred_mask = Image.open(f"{pred_path}/{id}.png")
pred_mask = np.array(pred_mask)
iou, shape_acc = utils.eval_mask(gt_mask, pred_mask)
ex_acc = utils.existence_accuracy(gt_mask, pred_mask)
location_score = utils.location_score(gt_mask, pred_mask, size=(540, 960))
IoUs.append(iou)
ShapeAcc.append(shape_acc)
ExistenceAcc.append(ex_acc)
LocationScores.append(location_score)
IoUs = np.array(IoUs)
ShapeAcc = np.array(ShapeAcc)
ExistenceAcc = np.array(ExistenceAcc)
LocationScores = np.array(LocationScores)
print("iou:", np.mean(IoUs))
print("LocationScores:", np.mean(LocationScores))
print("ShapeAcc:", np.mean(ShapeAcc))
if __name__ == '__main__':
evaluate_take()
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