def main(_):
#----edited by parker call to graph
# graphviz = GraphvizOutput()
# graphviz.output_file = 'call_to_graph.png'
img = preprocess_image(opts.img_path, img_size=opts.img_size)
print("opts:", opts.gpu_id)
# with PyCallGraph(output=graphviz):
#img的維度是3x257x257
# 創建一個pytorch tensor 的batch 維度是["img"][1][3][257][257]而且值為 1.0
batch = {'img': torch.Tensor(np.expand_dims(img, 0))}
predictor = pred_util.MeshPredictor(opts)
#-----------------得到預測好的vertice
outputs = predictor.predict(batch)
#-----------------------------draw predited mesh
#----------------------------------
# This is resolution
renderer = predictor.vis_rend
renderer.set_light_dir([0, 1, -1], 0.4)
# output["verts"]是已經預測好的vertce
visualize(img, outputs, predictor.vis_rend)
def main(_):
if opts.dataset == 'cub':
self.data_module = cub_data
else:
raise NotImplementedError
print('opts.split', opts.split)
self.dataloader = self.data_module.data_loader(opts)
import ipdb
pdb.set_trace
# import pdb; pdb.set_trace()
# return img array (3, 257, 257)
img = preprocess_image(opts.img_path, img_size=opts.img_size)
batch = {'img': torch.Tensor(np.expand_dims(img, 0))}
# init predictor, opts.texture = True, opts.use_sfm_ms = False
predictor = pred_util.MeshPredictor(opts)
# outputs keys: ['kp_pred', 'verts', 'kp_verts', 'cam_pred', 'mask_pred', 'texture', 'texture_pred', 'uv_image', 'uv_flow']
# [(k,v.shape) for k, v in outputs.items()]
# ('texture', torch.Size([1, 1280, 6, 6, 6, 3])), ('texture_pred', torch.Size([1, 3, 256, 256])), ('uv_image', torch.Size([1, 3, 128, 256])), ('uv_flow', torch.Size([1, 128, 256, 2]))]
outputs = predictor.predict(batch)
# This is resolution
renderer = predictor.vis_rend
renderer.set_light_dir([0, 1, -1], 0.4)
visualize(img, outputs, predictor.vis_rend)
def define_model(self):
opts = self.opts
self.predictor = pred_utils.MeshPredictor(opts)
# for visualization
self.renderer = self.predictor.vis_rend
self.renderer.set_bgcolor([1., 1., 1.])
self.renderer.renderer.renderer.image_size = 512
self.renderer.set_light_dir([0, 1, -1], 0.38)
def main(_):
for i, img_path in enumerate(opts.img_paths):
img = preprocess_image(img_path, img_size=opts.img_size)
batch = {'img': torch.Tensor(np.expand_dims(img, 0))}
predictor = pred_util.MeshPredictor(opts)
outputs = predictor.predict(batch)
# This is resolution
renderer = predictor.vis_rend
renderer.set_light_dir([0, 1, -1], 0.4)
visualize(img, outputs, predictor.vis_rend, f"demo_{i}.png")
def main(_):
# import pdb; pdb.set_trace()
# return img array (3, 257, 257)
img = preprocess_image(opts.img_path, img_size=opts.img_size)
batch = {'img': torch.Tensor(np.expand_dims(img, 0))}
# init predictor, opts.texture = True, opts.use_sfm_ms = False
predictor = pred_util.MeshPredictor(opts)
# outputs keys: ['kp_pred', 'verts', 'kp_verts', 'cam_pred', 'mask_pred', 'texture', 'texture_pred', 'uv_image', 'uv_flow']
# [(k,v.shape) for k, v in outputs.items()]
# ('texture', torch.Size([1, 1280, 6, 6, 6, 3])), ('texture_pred', torch.Size([1, 3, 256, 256])), ('uv_image', torch.Size([1, 3, 128, 256])), ('uv_flow', torch.Size([1, 128, 256, 2]))]
outputs = predictor.predict(batch)
# This is resolution
renderer = predictor.vis_rend
renderer.set_light_dir([0, 1, -1], 0.4)
visualize(img, outputs, predictor.vis_rend)
def main(_):
img = preprocess_image(opts.img_path, img_size=opts.img_size)
batch = {'img': torch.Tensor(np.expand_dims(img, 0))}
predictor = pred_util.MeshPredictor(opts)
outputs = predictor.predict(batch)
print(predictor.faces.shape)
save_obj('/content/test.obj',
outputs['verts'][0],
predictor.faces[0],
textures=outputs['texture'][0])
# This is resolution
renderer = predictor.vis_rend
renderer.set_light_dir([0, 1, -1], 0.4)
visualize(img, outputs, predictor.vis_rend)
