FGT_codes/RAFT/demo.py

import sys
import argparse
import os
import cv2
import glob
import numpy as np
import torch
from PIL import Image

from .raft import RAFT
from .utils import flow_viz
from .utils.utils import InputPadder



DEVICE = 'cuda'

def load_image(imfile):
    img = np.array(Image.open(imfile)).astype(np.uint8)
    img = torch.from_numpy(img).permute(2, 0, 1).float()
    return img


def load_image_list(image_files):
    images = []
    for imfile in sorted(image_files):
        images.append(load_image(imfile))

    images = torch.stack(images, dim=0)
    images = images.to(DEVICE)

    padder = InputPadder(images.shape)
    return padder.pad(images)[0]


def viz(img, flo):
    img = img[0].permute(1,2,0).cpu().numpy()
    flo = flo[0].permute(1,2,0).cpu().numpy()

    # map flow to rgb image
    flo = flow_viz.flow_to_image(flo)
    # img_flo = np.concatenate([img, flo], axis=0)
    img_flo = flo

    cv2.imwrite('/home/chengao/test/flow.png', img_flo[:, :, [2,1,0]])
    # cv2.imshow('image', img_flo[:, :, [2,1,0]]/255.0)
    # cv2.waitKey()


def demo(args):
    model = torch.nn.DataParallel(RAFT(args))
    model.load_state_dict(torch.load(args.model))

    model = model.module
    model.to(DEVICE)
    model.eval()

    with torch.no_grad():
        images = glob.glob(os.path.join(args.path, '*.png')) + \
                 glob.glob(os.path.join(args.path, '*.jpg'))

        images = load_image_list(images)
        for i in range(images.shape[0]-1):
            image1 = images[i,None]
            image2 = images[i+1,None]

            flow_low, flow_up = model(image1, image2, iters=20, test_mode=True)
            viz(image1, flow_up)


def RAFT_infer(args):
    model = torch.nn.DataParallel(RAFT(args))
    model.load_state_dict(torch.load(args.model))

    model = model.module
    model.to(DEVICE)
    model.eval()

    return model