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129 | # Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE file in the project root for full license information.
import torch.utils.data as data
from PIL import Image
import os
import struct
import os.path as osp
import numpy as np
import cv2
def parse_images(dir):
dir = osp.expanduser(dir)
image_pairs = []
pair_file = osp.join(dir, 'pairs.txt')
if osp.exists(pair_file):
with open(pair_file, "r") as f:
for line in f:
pair = line.strip().split(" ")
if len(pair) >=2 :
item0 = (pair[0], pair[1])
image_pairs.append(item0)
else:
raise (RuntimeError("Found no pair.txt in folder of: " + dir+ "\n"))
return image_pairs
def pil_loader(path):
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
def combo5_loader(path, real_w, real_h):
f = open(path, 'rb')
# width, height
d = f.read(4)
im_sz = struct.unpack("i", d)
h = im_sz[0]
d = f.read(4)
im_sz = struct.unpack("i", d)
w = im_sz[0]
# warp_ba_layer 4
d = f.read(4)
im_sz = struct.unpack("i", d)
d = f.read(im_sz[0])
file_bytes = np.asarray(bytearray(d), dtype=np.uint8)
img_data_ndarray = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
img_data_ndarray = cv2.cvtColor(img_data_ndarray, cv2.COLOR_BGR2RGB)
warp_ba = Image.fromarray(img_data_ndarray)
# warp_aba_layer 4
d = f.read(4)
im_sz = struct.unpack("i", d)
d = f.read(im_sz[0])
file_bytes = np.asarray(bytearray(d), dtype=np.uint8)
img_data_ndarray = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
img_data_ndarray = cv2.cvtColor(img_data_ndarray, cv2.COLOR_BGR2RGB)
warp_aba = Image.fromarray(img_data_ndarray)
# 5 layers: err_aba, err_ba, err_ab
errs = []
for l in range(5):
d = f.read(4)
im_sz = struct.unpack("i", d)
d = f.read(im_sz[0])
file_bytes = np.asarray(bytearray(d), dtype=np.uint8)
img_data_ndarray = cv2.imdecode(file_bytes, cv2.IMREAD_GRAYSCALE)
err_ba = Image.fromarray(img_data_ndarray)
d = f.read(4)
im_sz = struct.unpack("i", d)
d = f.read(im_sz[0])
file_bytes = np.asarray(bytearray(d), dtype=np.uint8)
img_data_ndarray = cv2.imdecode(file_bytes, cv2.IMREAD_GRAYSCALE)
err_ab = Image.fromarray(img_data_ndarray)
errs.append([err_ba, err_ab])
f.close()
return errs, warp_ba, warp_aba
class TestDataset(data.Dataset):
def __init__(self, data_root, transform=None):
image_pairs = parse_images(data_root)
if len(image_pairs) == 0:
raise (RuntimeError("Found 0 image pairs in dataroot"))
self.data_root = data_root
self.image_pairs = image_pairs
self.transform = transform
def get_out_name(self, index):
img_name0, img_name1 = self.image_pairs[index]
out_name = '%s_%s.png' % (os.path.splitext(img_name0)[0], os.path.splitext(img_name1)[0])
return out_name
def __getitem__(self, index):
image_id = 0
pair_id = index
image_names = ["", ""]
image_names[0], image_names[1] = self.image_pairs[pair_id]
image_path = osp.join(self.data_root, "input", image_names[image_id])
image = pil_loader(image_path)
inputs = [image, image]
w, h = image.size
image_comb_name = "%s_%s" % (os.path.splitext(image_names[image_id])[0], os.path.splitext(image_names[1 - image_id])[0])
combo_path = osp.join(self.data_root, "combo_new", "%s.combo" % image_comb_name)
errs, warp_ba, warp_aba = combo5_loader(combo_path, w, h)
inputs.append(warp_ba)
inputs.append(warp_aba)
inputs = inputs + errs
if self.transform is not None:
inputs = self.transform(inputs)
return inputs
def __len__(self):
return len(self.image_pairs)
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