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inference.py
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import torch
import argparse
import numpy as np
import pdb
import os
from PIL import Image
from model.IAGNet import get_IAGNet
from utils.visualization_point import get_affordance_label
from torchvision import transforms
import open3d as o3d
from utils.get_box import get_crop, get_resize_box
def pc_normalize(pc):
centroid = np.mean(pc, axis=0)
pc = pc - centroid
m = np.max(np.sqrt(np.sum(pc**2, axis=1)))
pc = pc / m
return pc
def img_normalize(img):
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
img = transform(img)
return img
def extract_point_file(point_path):
with open(point_path,'r') as f:
coordinates = []
lines = f.readlines()
for line in lines:
line = line.strip('\n')
line = line.strip(' ')
data = line.split(' ')
coordinate = [float(x) for x in data[2:]]
coordinates.append(coordinate)
data_array = np.array(coordinates)
points_coordinates = data_array[:, 0:3]
return points_coordinates
def inference_single(img_path, box_path, GT_path, model_path, results_folder):
model = get_IAGNet(pre_train=False)
checkpoint = torch.load(model_path, map_location='cpu')
model.load_state_dict(checkpoint['model'])
model = model.cuda()
model.eval()
Img = Image.open(img_path).convert('RGB')
subject, object = get_crop(box_path, Img, 'inference')
sub_box, obj_box = get_resize_box(Img, (224, 224), subject, object)
sub_box, obj_box = torch.tensor(sub_box).float(), torch.tensor(obj_box).float()
Img = Img.resize((224, 224))
Img = img_normalize(Img)
Img = Img.unsqueeze(0).cuda()
sub_box = sub_box.unsqueeze(0).cuda()
obj_box = obj_box.unsqueeze(0).cuda()
with open(GT_path,'r') as f:
coordinates = []
lines = f.readlines()
for line in lines:
line = line.strip('\n')
line = line.strip(' ')
data = line.split(' ')
coordinate = [float(x) for x in data[2:]]
coordinates.append(coordinate)
data_array = np.array(coordinates)
points_coordinates = data_array[:, 0:3]
affordance_label = data_array[: , 3:]
affordance_label = get_affordance_label(img_path, affordance_label)
Point = pc_normalize(points_coordinates)
Point = Point.transpose()
Points = torch.from_numpy(Point)
Points = torch.unsqueeze(Points, 0)
Points = Points.float().cuda()
pred,_,_ = model(Img, Points, sub_box, obj_box)
pred = torch.squeeze(pred)
affordance_pred = pred.cpu().detach().numpy()
gt_point = o3d.geometry.PointCloud()
gt_point.points = o3d.utility.Vector3dVector(points_coordinates)
pred_point = o3d.geometry.PointCloud()
pred_point.points = o3d.utility.Vector3dVector(points_coordinates)
color = np.zeros((2048,3))
reference_color = np.array([255, 0, 0])
back_color = np.array([190, 190, 190])
for i, point_affordacne in enumerate(affordance_label):
scale_i = point_affordacne
color[i] = (reference_color-back_color) * scale_i + back_color
gt_point.colors = o3d.utility.Vector3dVector(color.astype(np.float64) / 255.0)
pred_color = np.zeros((2048,3))
for i, aff_pred in enumerate(affordance_pred):
scale_i = aff_pred
pred_color[i] = (reference_color-back_color) * scale_i + back_color
pred_point.colors = o3d.utility.Vector3dVector(pred_color.astype(np.float64) / 255.0)
pred_point.translate((2, 0, 0), relative=True)
object = GT_path.split('_')[-2]
affordance_type = img_path.split('_')[-2]
num = (GT_path.split('_')[-1]).split('.')[0]
GT_file = results_folder + object + '_' + affordance_type + '_' + num + '_GT' + '.ply'
pred_file = results_folder + object + '_' + affordance_type + '_' + num + '_Pred' + '.ply'
o3d.visualization.draw_geometries([gt_point, pred_point], window_name='GT point', width=600, height=600)
o3d.io.write_point_cloud(pred_file, pred_point)
o3d.io.write_point_cloud(GT_file, gt_point)
f.close()
if __name__=='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', type=str, default='ckpts/IAG_Seen.pt', help='model path')
parser.add_argument('--img_path', type=str, default='Demo/Img_Test_Bag_lift_1.jpg', help='test img path')
parser.add_argument('--point_path', type=str, default='Demo/Point_Test_Bag_1.txt', help='test point path')
parser.add_argument('--box_path', type=str, default='Demo/Img_Test_Bag_lift_1.json', help='test point path')
parser.add_argument('--results_path', type=str, default='Demo/', help='save Demo path')
opt = parser.parse_args()
inference_single(opt.img_path, opt.box_path, opt.point_path, opt.model_path, opt.results_path)