def validate(annFile, dt_path):
mean_MR = []
for id_setup in range(0, 4):
cocoGt = COCO(annFile)
cocoDt = cocoGt.loadRes(dt_path)
imgIds = sorted(cocoGt.getImgIds())
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
cocoEval.params.imgIds = imgIds
cocoEval.evaluate(id_setup)
cocoEval.accumulate()
mean_MR.append(cocoEval.summarize_nofile(id_setup))
return mean_MR
def eval_json_reasonable(annFile, resFile, annType='bbox'):
dt_path = os.path.split(resFile)[0]
respath = os.path.join(dt_path, 'results.txt')
res_file = open(respath, "w")
mr_reasonable = None
for id_setup in range(6):
cocoGt = COCO(annFile)
cocoDt = cocoGt.loadRes(resFile)
imgIds = sorted(cocoGt.getImgIds())
cocoEval = COCOeval(cocoGt, cocoDt, annType)
cocoEval.params.imgIds = imgIds
cocoEval.evaluate(id_setup)
cocoEval.accumulate()
mean_mr = cocoEval.summarize(id_setup, res_file)
if id_setup == 0:
mr_reasonable = mean_mr
print('')
res_file.close()
return mr_reasonable
if C.offset:
boxes = bbox_process.parse_det_offset(Y, C, score=0.1, down=4)
else:
boxes = bbox_process.parse_det(Y, C, score=0.1, down=4, scale=C.scale)
if len(boxes) > 0:
f_res = np.repeat(f + 1, len(boxes), axis=0).reshape((-1, 1))
boxes[:, [2, 3]] -= boxes[:, [0, 1]]
res_all += np.concatenate((f_res, boxes), axis=-1).tolist()
np.savetxt(res_file_txt, np.array(res_all), fmt='%6f')
print(time.time() - start_time)
dt_path = res_path
json_path = convert_file(res_file_txt)
annFile_test = '/home/vobecant/PhD/CSP/eval_city/val_gt.json'
resFile = os.path.join(dt_path, 'val_dt.json')
respath = os.path.join(dt_path, 'results_testSet.txt')
res_file = open(respath, "w")
print('\nResults on the TEST set:')
for id_setup in range(6):
print('setup {}'.format(id_setup))
cocoGt = COCO(annFile_test)
cocoDt = cocoGt.loadRes(resFile)
imgIds = sorted(cocoGt.getImgIds())
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
cocoEval.params.imgIds = imgIds
cocoEval.evaluate(id_setup)
cocoEval.accumulate()
cocoEval.summarize(id_setup, res_file)