coco MAPテスト
1.TXT結果に基づいて、jsonファイルが生成される.
2.テスト
#!/usr/bin/env python
# coding=utf-8
# Copyright 2017 challenger.ai
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Evaluation utility for human skeleton system keypoint task.
This python script is used for calculating the final score (mAP) of the test result,
based on your submited file and the reference file containing ground truth.
usage
python keypoint_eval.py --submit SUBMIT_FILEPATH --ref REF_FILEPATH
A test case is provided, submited file is submit.json, reference file is ref.json, test it by:
python keypoint_eval.py --submit ./keypoint_sample_predictions.json \
--ref ./keypoint_sample_annotations.json
The final score of the submited result, error message and warning message will be printed.
"""
import json
import time
import argparse
import pprint, sys
from glob2 import glob
import numpy as np
from pycocotools.coco import COCO
import pylab
import os
import csv
save_json = r'F:\f\f\prediction_4.json'
file_name = r'F:\f\f\pre_coco_test\*.txt'
if __name__ == "__main__":
predictions = []
for i in glob(file_name):
file_object = open(i, 'rU')
human_dict = {}
temp = 1
keypoint_ls = []
img_id = 0
#get img_id
for line in file_object:
person = 0
#TODO
if len(line) == 1:
continue
line = line.replace('
','')
line = line.replace(' ','')
human_ls = line.split(',')
keypoint_ls.append(float(human_ls[1]))
keypoint_ls.append(float(human_ls[2]))
if human_ls[1] == '0' and human_ls[2] == '0':
keypoint_ls.append(2)
else:
keypoint_ls.append(2)
img_id = int(i[-10: -4])
temp +=1
temp1 = 1
point_ls = []
# get keypoint_ls
for kp in keypoint_ls:
if temp1 == 54*(person+1)+1:
point_ls = []
person += 1
point_ls.append(kp)
temp1 += 1
human_dict['human'+str(person)] = point_ls
for per in human_dict:
prediction = {}
anno_keypoint = np.reshape(human_dict[per], (18, 3))
anno_keypoint = np.delete(anno_keypoint, 1, 0) # A ， 17 （）
anno_keypoint_copy = anno_keypoint.copy()
# 15-2
anno_keypoint[1] = anno_keypoint_copy[14]
# 14-3
anno_keypoint[2] = anno_keypoint_copy[13]
# 17-4
anno_keypoint[3] = anno_keypoint_copy[16]
# 16-5
anno_keypoint[4] = anno_keypoint_copy[15]
# 5-6
anno_keypoint[5] = anno_keypoint_copy[4]
# 2-7
anno_keypoint[6] = anno_keypoint_copy[1]
# 6-8
anno_keypoint[7] = anno_keypoint_copy[5]
# 3-9
anno_keypoint[8] = anno_keypoint_copy[2]
# 7-10
anno_keypoint[9] = anno_keypoint_copy[6]
# 4-11
anno_keypoint[10] = anno_keypoint_copy[3]
# 11-12
anno_keypoint[11] = anno_keypoint_copy[10]
# 8-13
anno_keypoint[12] = anno_keypoint_copy[7]
# 12-14
anno_keypoint[13] = anno_keypoint_copy[11]
# 9-15
anno_keypoint[14] = anno_keypoint_copy[8]
# 13-16
anno_keypoint[15] = anno_keypoint_copy[12]
# 10-17
anno_keypoint[16] = anno_keypoint_copy[9]
temp_ls = anno_keypoint.tolist()
# temp_str = "".join(anno_keypoint.tolist())
temp_str = str(temp_ls)
temp_str = temp_str.replace("[",',')
temp_str = temp_str.replace(']',',')
temp_str = temp_str.replace(',, ,',',')
temp_str = temp_str.replace(',,','')
temp_ls = temp_str.split(',')
temp_ls = map(float, temp_ls)
prediction['keypoints'] = temp_ls
prediction['image_id'] = img_id
prediction['score'] = 1
prediction['category_id'] = 1
predictions.append(prediction)
# print ls_human
jsobj = json.dumps(predictions)
fileObject = open(save_json, 'w')
fileObject.write(jsobj)
fileObject.close()
# ========
# # 15-2
# anno_keypoint[1] = anno_keypoint_copy[14]
# # 14-3
# anno_keypoint[2] = anno_keypoint_copy[13]
# # 17-4
# anno_keypoint[3] = anno_keypoint_copy[16]
# # 16-5
# anno_keypoint[4] = anno_keypoint_copy[15]
# # 5-6
# anno_keypoint[5] = anno_keypoint_copy[4]
# # 2-7
# anno_keypoint[6] = anno_keypoint_copy[1]
# # 6-8
# anno_keypoint[7] = anno_keypoint_copy[5]
# # 3-9
# anno_keypoint[8] = anno_keypoint_copy[2]
# # 7-10
# anno_keypoint[9] = anno_keypoint_copy[6]
# # 4-11
# anno_keypoint[10] = anno_keypoint_copy[3]
# # 11-12
# anno_keypoint[11] = anno_keypoint_copy[10]
# # 8-13
# anno_keypoint[12] = anno_keypoint_copy[7]
# # 12-14
# anno_keypoint[13] = anno_keypoint_copy[11]
# # 9-15
# anno_keypoint[14] = anno_keypoint_copy[8]
# # 13-16
# anno_keypoint[15] = anno_keypoint_copy[12]
# # 10-17
# anno_keypoint[16] = anno_keypoint_copy[9]2.テスト
#-*- coding:utf-8 -*-
import matplotlib.pyplot as plt
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
import numpy as np
import skimage.io as io
import pylab,json
pylab.rcParams['figure.figsize'] = (10.0, 8.0)
def get_img_id(file_name):
ls = []
myset = []
annos = json.load(open(file_name, 'r'))
for anno in annos:
ls.append(anno['image_id'])
myset = {}.fromkeys(ls).keys()
return myset
if __name__ == '__main__':
annType = ['segm', 'bbox', 'keypoints']#set iouType to 'segm', 'bbox' or 'keypoints'
annType = annType[2] # specify type here
cocoGt_file = r"F:\coco_data\person_keypoints_val2014.json"# json
cocoGt = COCO(cocoGt_file)# coco json
cocoDt_file = r"F:\f\f\prediction_4.json"# json
imgIds = get_img_id(cocoDt_file)
print len(imgIds)
cocoDt = cocoGt.loadRes(cocoDt_file)# image json
imgIds = sorted(imgIds)# coco image_id
imgIds = imgIds[0:100]# image
# imgId = imgIds[np.random.randint(100)]#numpy.random.randint(low,high=None,size=None,dtype) [low,high) ; high=None， [0,low)
cocoEval = COCOeval(cocoGt, cocoDt, annType)
cocoEval.params.imgIds = imgIds#
cocoEval.evaluate()#
cocoEval.accumulate()#
cocoEval.summarize()#
# =====================