infer.py

#test end to end benchmark data test
import sys, os
import torch
import argparse
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import cv2
#import scipy.misc as m ## USE scipy (< v1.2.0) TO READ THE IMAGE TO PRODUCE THE RESULTS REPORTED IN THE PAPER
from torch.autograd import Variable
from torch.utils import data
from tqdm import tqdm
import matplotlib.pyplot as plt


from models import get_model
from loaders import get_loader
from utils import convert_state_dict

DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')


def unwarp(img, bm):
    w,h=img.shape[0],img.shape[1]
    bm = bm.transpose(1, 2).transpose(2, 3).detach().cpu().numpy()[0,:,:,:]
    bm0=cv2.blur(bm[:,:,0],(3,3))
    bm1=cv2.blur(bm[:,:,1],(3,3))
    bm0=cv2.resize(bm0,(h,w))
    bm1=cv2.resize(bm1,(h,w))
    bm=np.stack([bm0,bm1],axis=-1)
    bm=np.expand_dims(bm,0)
    bm=torch.from_numpy(bm).double()

    img = img.astype(float) / 255.0
    img = img.transpose((2, 0, 1))
    img = np.expand_dims(img, 0)
    img = torch.from_numpy(img).double()

    res = F.grid_sample(input=img, grid=bm)
    res = res[0].numpy().transpose((1, 2, 0))

    return res


def test(args,img_path,fname):
    wc_model_file_name = os.path.split(args.wc_model_path)[1]
    wc_model_name = wc_model_file_name[:wc_model_file_name.find('_')]

    bm_model_file_name = os.path.split(args.bm_model_path)[1]
    bm_model_name = bm_model_file_name[:bm_model_file_name.find('_')]

    wc_n_classes = 3
    bm_n_classes = 2

    wc_img_size=(256,256)
    bm_img_size=(128,128)

    # Setup image
    print("Read Input Image from : {}".format(img_path))
    imgorg = cv2.imread(img_path)
    imgorg = cv2.cvtColor(imgorg, cv2.COLOR_BGR2RGB)
    img = cv2.resize(imgorg, wc_img_size)
    
    '''
    # Alternatively use scipy (< v1.2.0) 
    # TO PRODUCE THE RESULTS REPORTED IN THE PAPER
    # Comment line 61-63 and uncomment 68-69
    # For details refer to https://github.com/cvlab-stonybrook/DewarpNet/issues/38 
    imgorg = m.imread(img_path,mode='RGB')
    img = m.imresize(imgorg, wc_loaderimg_size)
    '''
    
    img = img[:, :, ::-1]
    img = img.astype(float) / 255.0
    img = img.transpose(2, 0, 1) # NHWC -> NCHW
    img = np.expand_dims(img, 0)
    img = torch.from_numpy(img).float()

    # Predict
    htan = nn.Hardtanh(0,1.0)
    wc_model = get_model(wc_model_name, wc_n_classes, in_channels=3)
    if DEVICE.type == 'cpu':
        wc_state = convert_state_dict(torch.load(args.wc_model_path, map_location='cpu')['model_state'])
    else:
        wc_state = convert_state_dict(torch.load(args.wc_model_path)['model_state'])
    wc_model.load_state_dict(wc_state)
    wc_model.eval()
    bm_model = get_model(bm_model_name, bm_n_classes, in_channels=3)
    if DEVICE.type == 'cpu':
        bm_state = convert_state_dict(torch.load(args.bm_model_path, map_location='cpu')['model_state'])
    else:
        bm_state = convert_state_dict(torch.load(args.bm_model_path)['model_state'])
    bm_model.load_state_dict(bm_state)
    bm_model.eval()

    if torch.cuda.is_available():
        wc_model.cuda()
        bm_model.cuda()
        images = Variable(img.cuda())
    else:
        images = Variable(img)

    with torch.no_grad():
        wc_outputs = wc_model(images)
        pred_wc = htan(wc_outputs)
        bm_input=F.interpolate(pred_wc, bm_img_size)
        outputs_bm = bm_model(bm_input)

    # call unwarp
    uwpred=unwarp(imgorg, outputs_bm)

    if args.show:
        f1, axarr1 = plt.subplots(1, 2)
        axarr1[0].imshow(imgorg)
        axarr1[1].imshow(uwpred)
        plt.show()

    # Save the output
    outp=os.path.join(args.out_path,fname)
    cv2.imwrite(outp,uwpred[:,:,::-1]*255)

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Params')
    parser.add_argument('--wc_model_path', nargs='?', type=str, default='',
                        help='Path to the saved wc model')
    parser.add_argument('--bm_model_path', nargs='?', type=str, default='',
                        help='Path to the saved bm model')
    parser.add_argument('--img_path', nargs='?', type=str, default='./eval/inp/',
                        help='Path of the input image')
    parser.add_argument('--out_path', nargs='?', type=str, default='./eval/uw/',
                        help='Path of the output unwarped image')
    parser.add_argument('--show', dest='show', action='store_true',
                        help='Show the input image and output unwarped')
    parser.set_defaults(show=False)
    args = parser.parse_args()
    for fname in os.listdir(args.img_path):
        if '.jpg' in fname or '.JPG' in fname or '.png' in fname:
            img_path=os.path.join( args.img_path,fname)
            test(args,img_path,fname)


# python infer.py --wc_model_path ./eval/models/unetnc_doc3d.pkl --bm_model_path ./eval/models/dnetccnl_doc3d.pkl --show