def _do_segmentation_eval(json_dataset, res_file, output_dir):
coco_dt = json_dataset.COCO.loadRes(str(res_file))
coco_eval = denseposeCOCOeval(json_dataset.COCO, coco_dt, 'segm')
coco_eval.evaluate()
coco_eval.accumulate()
_log_detection_eval_metrics(json_dataset, coco_eval)
eval_file = os.path.join(output_dir, 'segmentation_results.pkl')
save_object(coco_eval, eval_file)
logger.info('Wrote json eval results to: {}'.format(eval_file))
return coco_eval
def _do_detection_eval(json_dataset, res_file, output_dir):
coco_dt = json_dataset.COCO.loadRes(str(res_file))
coco_eval = denseposeCOCOeval(json_dataset.COCO, coco_dt, 'bbox')
coco_eval.evaluate()
coco_eval.accumulate()
_log_detection_eval_metrics(json_dataset, coco_eval)
eval_file = os.path.join(output_dir, 'detection_results.pkl')
save_object(coco_eval, eval_file)
logger.info('Wrote json eval results to: {}'.format(eval_file))
return coco_eval
def _do_keypoint_eval(json_dataset, res_file, output_dir):
ann_type = 'keypoints'
imgIds = json_dataset.COCO.getImgIds()
imgIds.sort()
coco_dt = json_dataset.COCO.loadRes(res_file)
coco_eval = denseposeCOCOeval(json_dataset.COCO, coco_dt, ann_type)
coco_eval.params.imgIds = imgIds
coco_eval.evaluate()
coco_eval.accumulate()
eval_file = os.path.join(output_dir, 'keypoint_results.pkl')
save_object(coco_eval, eval_file)
logger.info('Wrote json eval results to: {}'.format(eval_file))
coco_eval.summarize()
return coco_eval
def _do_keypoint_eval(json_dataset, res_file, output_dir):
ann_type = 'keypoints'
imgIds = json_dataset.COCO.getImgIds()
imgIds.sort()
coco_dt = json_dataset.COCO.loadRes(res_file)
coco_eval = denseposeCOCOeval(json_dataset.COCO, coco_dt, ann_type)
coco_eval.params.imgIds = imgIds
coco_eval.evaluate()
coco_eval.accumulate()
eval_file = os.path.join(output_dir, 'keypoint_results.pkl')
save_object(coco_eval, eval_file)
logger.info('Wrote json eval results to: {}'.format(eval_file))
coco_eval.summarize()
return coco_eval
def _do_body_uv_eval(json_dataset, res_file, output_dir):
ann_type = 'uv'
imgIds = json_dataset.COCO.getImgIds()
imgIds.sort()
with open(res_file, 'rb') as f:
res=pickle.load(f)
coco_dt = json_dataset.COCO.loadRes(res)
# Non-standard params used by the modified COCO API version
# from the DensePose fork
test_sigma = 0.255
coco_eval = denseposeCOCOeval(json_dataset.COCO, coco_dt, ann_type, test_sigma)
coco_eval.params.imgIds = imgIds
coco_eval.evaluate()
coco_eval.accumulate()
#eval_file = os.path.join(output_dir, 'body_uv_results.pkl')
#save_object(coco_eval, eval_file)
#logger.info('Wrote json eval results to: {}'.format(eval_file))
coco_eval.summarize()
return coco_eval
def _do_body_uv_eval(json_dataset, res_file, output_dir):
ann_type = 'uv'
imgIds = json_dataset.COCO.getImgIds()
imgIds.sort()
with open(res_file, 'rb') as f:
res=pickle.load(f)
coco_dt = json_dataset.COCO.loadRes(res)
# Non-standard params used by the modified COCO API version
# from the DensePose fork
test_sigma = 0.255
coco_eval = denseposeCOCOeval(json_dataset.COCO, coco_dt, ann_type, test_sigma)
coco_eval.params.imgIds = imgIds
coco_eval.evaluate()
coco_eval.accumulate()
#eval_file = os.path.join(output_dir, 'body_uv_results.pkl')
#save_object(coco_eval, eval_file)
#logger.info('Wrote json eval results to: {}'.format(eval_file))
coco_eval.summarize()
return coco_eval
def _do_body_uv_eval(json_dataset, res_file, output_dir):
ann_type = 'uv'
imgIds = json_dataset.COCO.getImgIds()
imgIds.sort()
res = load_object(res_file)
coco_dt = json_dataset.COCO.loadRes(res)
# Non-standard params used by the modified COCO API version
# from the DensePose fork
# global normalization factor used in per-instance evaluation
test_sigma = 0.255
coco_eval = denseposeCOCOeval(json_dataset.COCO, coco_dt, ann_type,
test_sigma)
coco_eval.params.imgIds = imgIds
coco_eval.evaluate()
coco_eval.accumulate()
eval_file = os.path.join(output_dir, 'body_uv_results.pkl')
save_object(coco_eval, eval_file)
logger.info('Wrote pickle eval results to: {}'.format(eval_file))
coco_eval.summarize()
return coco_eval