tensorflow2.0 - cocoEval summarize() return all mAP -1

Question

I create a json with annotations and execute the following code:

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
import json 

pylab.rcParams['figure.figsize'] = (10.0, 8.0)


annType = ['segm','bbox','keypoints']
annType = annType[1]      #specify type here
prefix = 'person_keypoints' if annType=='keypoints' else 'instances'
print ('Running demo for *%s* results.'%(annType))

# use the valadation labelme file
annFile = '/content/json_moded.json'
cocoGt=COCO(annFile)
#initialize COCO detections api
# use the generated results
resFile = '/content/test_data_normal.json'
cocoDt=cocoGt.loadRes(resFile)

'''
dts = json.load(open(resFile,'r'))
imgIds = [imid['image_id'] for imid in dts]
imgIds = sorted(list(set(imgIds)))
'''
imgIds=sorted(cocoGt.getImgIds())

'''
imgIds=sorted(cocoGt.getImgIds())
imgIds=imgIds[0:24]
imgId = imgIds[np.random.randint(24)]
'''

# running box evaluation
cocoEval = COCOeval(cocoGt,cocoDt,annType)


cocoEval.params.imgIds  = imgIds
'''
cocoEval.params.catIds = [3] # 1 stands for the 'person' class, you can increase or decrease the category as needed
'''
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()

but the results are the following:

Running demo for *bbox* results.
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.11s).
Accumulating evaluation results...
DONE (t=0.04s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000

why every mAP return -1?

I dont know why return these result, i think that both json are corrects.

I dont know why return these result, i think that both json are corrects.

I dont know why return these result, i think that both json are corrects.

question from:https://stackoverflow.com/questions/66051351/cocoeval-summarize-return-all-map-1

Answer 1

Waitting for answers