import os import numpy as np import os.path as osp import cv2 import argparse import torch #from torch.utils.data import DataLoader import torchvision from RCFPyTorch0.dataset import BSDS_Dataset from RCFPyTorch0.models import RCF import gradio as gr from PIL import Image import sys import torch.nn as nn import torch.nn.functional as F import torchvision.transforms as transforms from MODNet.src.models.modnet import MODNet # 网页制作 import cv2 def single_scale_test(image): ref_size = 512 # define image to tensor transform im_transform = transforms.Compose( [ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ] ) # create MODNet and load the pre-trained ckpt modnet = MODNet(backbone_pretrained=False) modnet = nn.DataParallel(modnet).cuda() modnet.load_state_dict(torch.load('MODNet/pretrained/modnet_photographic.ckpt')) modnet.eval() # 注:程序中的数字仅表示某张输入图片尺寸,如1080x1440,此处只为记住其转换过程。 # inference images # im_names = os.listdir(args.input_path) # for im_name in im_names: # print('Process image: {0}'.format(im_name)) # read image # unify image channels to 3 image = np.asarray(image) if len(image.shape) == 2: image = image[:, :, None] if image.shape[2] == 1: image = np.repeat(image, 3, axis=2) elif image.shape[2] == 4: image = image[:, :, 0:3] im_org = image # 保存numpy原始数组 (1080,1440,3) # convert image to PyTorch tensor image = Image.fromarray(image) image = im_transform(image) # add mini-batch dim image = image[None, :, :, :] # resize image for input im_b, im_c, im_h, im_w = image.shape if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size: if im_w >= im_h: im_rh = ref_size im_rw = int(im_w / im_h * ref_size) elif im_w < im_h: im_rw = ref_size im_rh = int(im_h / im_w * ref_size) else: im_rh = im_h im_rw = im_w im_rw = im_rw - im_rw % 32 im_rh = im_rh - im_rh % 32 image = F.interpolate(image, size=(im_rh, im_rw), mode='area') # inference _, _, matte = modnet(image.cuda(), True) # 从模型获得的 matte ([1,1,512, 672]) # resize and save matte,foreground picture matte = F.interpolate(matte, size=(im_h, im_w), mode='area') #内插,扩展到([1,1,1080,1440]) 范围[0,1] matte = matte[0][0].data.cpu().numpy() # torch 张量转换成numpy (1080, 1440) # matte_name = im_name.split('.')[0] + '_matte.png' # Image.fromarray(((matte * 255).astype('uint8')), mode='L').save(os.path.join(args.output_path, matte_name)) matte_org = np.repeat(np.asarray(matte)[:, :, None], 3, axis=2) # 扩展到 (1080, 1440, 3) 以便和im_org计算 foreground = im_org * matte_org + np.full(im_org.shape, 255) * (1 - matte_org) # 计算前景,获得抠像 # fg_name = im_name.split('.')[0] + '_fg.png' Image.fromarray(((foreground).astype('uint8')), mode='RGB').save(os.path.join('MODNet/output-img', 'fg_name.png')) output = Image.open(os.path.join('MODNet/output-img', 'fg_name.png')) image = np.array(output) model = RCF().cuda() checkpoint = torch.load("RCFPyTorch0/bsds500_pascal_model.pth") model.load_state_dict(checkpoint) model.eval() # if not osp.isdir(save_dir): # os.makedirs(save_dir) # for idx, image in enumerate(test_loader): image = torch.from_numpy(image).float().permute(2,0,1).unsqueeze(0) image = image.cuda() _, _, H, W = image.shape results = model(image) all_res = torch.zeros((len(results), 1, H, W)) for i in range(len(results)): all_res[i, 0, :, :] = results[i] #filename = osp.splitext(test_list[idx])[0] torchvision.utils.save_image(1 - all_res, osp.join('RCFPyTorch0/results/RCF', 'result.jpg')) fuse_res = torch.squeeze(results[1].detach()).cpu().numpy() fuse_res = ((1 - fuse_res) * 255).astype(np.uint8) cv2.imwrite(osp.join("RCFPyTorch0/results/RCF", 'result_ss.png'), fuse_res) #print('\rRunning single-scale test [%d/%d]' % (idx + 1, len(test_loader)), end='') #print('Running single-scale test done') output = Image.open(os.path.join('RCFPyTorch0/results/RCF', 'result_ss.png')) return output parser = argparse.ArgumentParser(description='PyTorch Testing') parser.add_argument('--gpu', default='0', type=str, help='GPU ID') #parser.add_argument('--checkpoint', default=None, type=str, help='path to latest checkpoint') #parser.add_argument('--save-dir', help='output folder', default='results/RCF') #parser.add_argument('--dataset', help='root folder of dataset', default='data/HED-BSDS') args = parser.parse_args() os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu #if not osp.isdir(args.save_dir): # os.makedirs(args.save_dir) #test_dataset = BSDS_Dataset(root=args.dataset, split='test') #test_loader = DataLoader(test_dataset, batch_size=1, num_workers=1, drop_last=False, shuffle=False) #test_list = [osp.split(i.rstrip())[1] for i in test_dataset.file_list] #assert len(test_list) == len(test_loader) #if osp.isfile(args.checkpoint): # print("=> loading checkpoint from '{}'".format(args.checkpoint)) # checkpoint = torch.load(args.checkpoint) # model.load_state_dict(checkpoint) # print("=> checkpoint loaded") #else: # print("=> no checkpoint found at '{}'".format(args.checkpoint)) #print('Performing the testing...') interface = gr.Interface(fn=single_scale_test, inputs="image", outputs="image") interface.launch(share=True)