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import os
import cv2
import torch
import numpy as np
from scipy import misc
def load_test_data(image_path, size=256):
img = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
if img is None:
return None
h, w, c = img.shape
if img.shape[2] == 4:
white = np.ones((h, w, 3), np.uint8) * 255
img_rgb = img[:, :, :3].copy()
mask = img[:, :, 3].copy()
mask = (mask / 255).astype(np.uint8)
img = (img_rgb * mask[:, :, np.newaxis]).astype(np.uint8) + white * (1 - mask[:, :, np.newaxis])
img = cv2.resize(img, (size, size), cv2.INTER_AREA)
img = RGB2BGR(img)
img = np.expand_dims(img, axis=0)
img = preprocessing(img)
return img
def preprocessing(x):
x = x/127.5 - 1
# -1 ~ 1
return x
def save_images(images, size, image_path):
return imsave(inverse_transform(images), size, image_path)
def inverse_transform(images):
return (images+1.) / 2
def imsave(images, size, path):
return misc.imsave(path, merge(images, size))
def merge(images, size):
h, w = images.shape[1], images.shape[2]
img = np.zeros((h * size[0], w * size[1], 3))
for idx, image in enumerate(images):
i = idx % size[1]
j = idx // size[1]
img[h*j:h*(j+1), w*i:w*(i+1), :] = image
return img
def check_folder(log_dir):
if not os.path.exists(log_dir):
os.makedirs(log_dir)
return log_dir
def str2bool(x):
return x.lower() in ('true')
def cam(x, size=256):
x = x - np.min(x)
cam_img = x / np.max(x)
cam_img = np.uint8(255 * cam_img)
cam_img = cv2.resize(cam_img, (size, size))
cam_img = cv2.applyColorMap(cam_img, cv2.COLORMAP_JET)
return cam_img / 255.0
def imagenet_norm(x):
mean = [0.485, 0.456, 0.406]
std = [0.299, 0.224, 0.225]
mean = torch.FloatTensor(mean).unsqueeze(0).unsqueeze(2).unsqueeze(3).to(x.device)
std = torch.FloatTensor(std).unsqueeze(0).unsqueeze(2).unsqueeze(3).to(x.device)
return (x - mean) / std
def denorm(x):
return x * 0.5 + 0.5
def tensor2numpy(x):
return x.detach().cpu().numpy().transpose(1, 2, 0)
def RGB2BGR(x):
return cv2.cvtColor(x, cv2.COLOR_RGB2BGR)
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