def __init__(self, model_path):
self.model = bodypose_model()
if torch.cuda.is_available():
self.model = self.model.cuda()
model_dict = util.transfer(self.model, torch.load(model_path))
self.model.load_state_dict(model_dict)
self.model.eval()
def __init__(self, model_path, add_model_path, add=True):
self.model = bodypose_model()
if torch.cuda.is_available():
self.model = self.model.cuda()
model_dict = util.transfer(self.model, torch.load(model_path))
self.model.load_state_dict(model_dict)
self.model.eval()
if add:
self.add_model = add_model()
if torch.cuda.is_available():
self.add_model = self.add_model.cuda()
add_model_dict = util.add_transfer(
self.add_model, torch.load(
add_model_path)) #, map_location=torch.device('cpu')))
self.add_model.load_state_dict(add_model_dict)
# self.add_model.load_state_dict(torch.load(add_model_path))
self.add_model.eval()
inWidth = 368
inHeight = 368
thr = 0.1
import torch
from src import util
torchmodelfile = 'model/body_pose_model.pth'
from src.model import bodypose_model
torchnet = bodypose_model()
if torch.cuda.is_available():
torchmodel = torchnet.cuda()
else:
torchmodel = torchnet.cpu()
model_dict = util.transfer(torchmodel, torch.load(torchmodelfile))
torchmodel.load_state_dict(model_dict)
torchmodel.eval()
frame = cv.imread("images/demo.jpg")
frameWidth = frame.shape[1]
frameHeight = frame.shape[0]
stride = 8
padValue = 128
#imageToTest = cv.resize(frame, (0, 0), fx=368, fy=368, interpolation=cv.INTER_CUBIC)
imageToTest = cv.resize(frame, (368, 368))
imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride,
padValue)