def test():
opt = TrainingOpt()
config = GetConfig(opt.config_name)
hm = Heatmapper(config)
heatmap_test = np.load("heatmap_test.npz")
img = heatmap_test['img']
joints = heatmap_test['joints']
mask_all = heatmap_test['mask_all']
mask_miss = heatmap_test['mask_miss']
plt.imshow(img[:, :, [2, 0, 1]])
plt.show()
plt.imshow(mask_all)
plt.show()
plt.imshow(mask_miss)
plt.show()
labels = hm.create_heatmaps(joints, mask_all)
print()
"If the norm of the gradient vector exceeds this, re-normalize it to have the norm equal to max_grad_norm"
)
parser.add_argument('--output',
type=str,
default='result.jpg',
help='output image')
parser.add_argument('--opt-level', type=str, default='O1')
parser.add_argument('--keep-batchnorm-fp32', type=str, default=None)
parser.add_argument('--loss-scale', type=str, default=None)
parser.add_argument('--run_refactor', action='store_true')
parser.add_argument('--run_cpp', action='store_true')
args = parser.parse_args()
# ################################### Setup for some configurations ###########################################
opt = TrainingOpt()
config = GetConfig(opt.config_name)
joint2limb_pairs = config.limbs_conn # > 30
dt_gt_mapping = config.dt_gt_mapping
NUM_KEYPOINTS = 18
NUM_COCO_KEYPOINTS = 17
NUM_TEST_IMG = -1
TEST_SET = 'val2017'
# ###################################### For evaluating time ######################################
def process(input_image_path, model, test_cfg, model_cfg, heat_layers,
paf_layers):
ori_img = cv2.imread(input_image_path)