def inference(base_model_name, path_to_npz, data_format, input_files, plot):
model_func = get_model(base_model_name)
height, width = (368, 432)
e = measure(
lambda: TfPoseEstimator(path_to_npz,
model_func,
target_size=(width, height),
data_format=data_format),
'create TfPoseEstimator')
t0 = time.time()
for idx, img_name in enumerate(input_files):
image = measure(
lambda: read_imgfile(
img_name, width, height, data_format=data_format),
'read_imgfile')
humans, heatMap, pafMap = measure(lambda: e.inference(image),
'e.inference')
tl.logging.info('got %d humans from %s' % (len(humans), img_name))
if humans:
for h in humans:
tl.logging.debug(h)
if plot:
if data_format == 'channels_first':
image = image.transpose([1, 2, 0])
plot_humans(image, heatMap, pafMap, humans, '%02d' % (idx + 1),
'inferece')
tot = time.time() - t0
mean = tot / len(input_files)
tl.logging.info('inference all took: %f, mean: %f, FPS: %f' %
(tot, mean, 1.0 / mean))
def model_func():
target_size = (args.width, args.height)
return get_model(args.base_model)(target_size, args.data_format)
def model_func():
target_size = (config.MODEL.win, config.MODEL.hin)
return get_model(config.MODEL.name)(target_size,
config.MODEL.data_format)
config.DATA.data_path, 'mscoco%s' % config.DATA.coco_version,
'annotations/person_keypoints_val%s.json' % config.DATA.coco_version)
cocoGt = COCO(coco_json_file)
catIds = cocoGt.getCatIds(catNms=['person'])
keys = cocoGt.getImgIds(catIds=catIds)
if config.EVAL.data_idx < 0:
if config.EVAL.eval_size > 0:
keys = keys[:config.EVAL.
eval_size] # only use the first #eval_size elements.
pass
else:
keys = [keys[config.EVAL.data_idx]]
logger.info('validation %s set size=%d' % (coco_json_file, len(keys)))
height, width = (config.MODEL.hin, config.MODEL.win)
model_func = get_model(config.MODEL.name)
estimator = TfPoseEstimator(os.path.join(config.MODEL.model_path,
config.EVAL.model),
model_func,
target_size=(width, height))
result = []
for i, k in enumerate(tqdm(keys)):
img_meta = cocoGt.loadImgs(k)[0]
img_idx = img_meta['id']
img_name = os.path.join(image_dir, img_meta['file_name'])
image = read_imgfile(img_name, width, height)
if image is None:
logger.error('image not found, path=%s' % img_name)
sys.exit(-1)