#!/usr/bin/env python
# coding: utf-8

# In[1]:


import json
import logging
import faster_coco_eval
from faster_coco_eval import COCO, COCOeval_faster
from faster_coco_eval.extra import Curves

print(f"{faster_coco_eval.__version__=}")

logging.root.setLevel("INFO")
logging.debug("Запись.")


# In[2]:


prepared_coco_in_dict = COCO.load_json("../tests/synth_test_dataset/test.json")
prepared_anns = COCO.load_json("../tests/synth_test_dataset/result_data.json")


# In[3]:


iouType = "keypoints"


# In[4]:


cocoGt = COCO(prepared_coco_in_dict)
cocoDt = cocoGt.loadRes(prepared_anns)

cocoEval = COCOeval_faster(
    cocoGt, cocoDt, iouType, extra_calc=True, kpt_oks_sigmas=[0.01] * 4
)

cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()


# In[5]:


cocoEval.stats_as_dict


# In[6]:


cur = Curves(cocoGt, cocoDt, iouType=iouType, kpt_oks_sigmas=[0.025] * 4)

cur.plot_ced_metric()
cur.plot_f1_confidence()


# In[7]:


from faster_coco_eval.extra import PreviewResults

preview = PreviewResults(cur.cocoGt, cur.cocoDt, iouType=iouType, kpt_oks_sigmas=[0.01] * 4)


# In[8]:


imgs = []

for img_id, anns in cur.cocoDt.img_ann_map.items():
    for ann in anns:
        if ann.get("mae", 0) > 3.5:
            imgs.append([img_id, ann.get("mae", 0)])

imgs.sort(key=lambda x: x[1], reverse=True)
print(imgs)

imgs = [i[0] for i in imgs]
imgs


# In[9]:


preview.display_tp_fp_fn(
    data_folder="../tests/synth_test_dataset/",
    image_ids=imgs,
    display_gt=True,
)


# In[ ]: