90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
import os
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torchvision.transforms as transforms
|
|
from PIL import Image
|
|
|
|
from .models import create_model
|
|
|
|
|
|
def tensor2im(input_image, imtype=np.uint8):
|
|
if not isinstance(input_image, np.ndarray):
|
|
if isinstance(input_image, torch.Tensor): # get the data from a variable
|
|
image_tensor = input_image.data
|
|
else:
|
|
return input_image
|
|
image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array
|
|
if image_numpy.shape[0] == 1: # grayscale to RGB
|
|
image_numpy = np.tile(image_numpy, (3, 1, 1))
|
|
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling
|
|
else: # if it is a numpy array, do nothing
|
|
image_numpy = input_image
|
|
return image_numpy.astype(imtype)
|
|
|
|
|
|
def save_image(image_numpy, image_path, w, h, aspect_ratio=1.0):
|
|
"""Save a numpy image to the disk
|
|
|
|
Parameters:
|
|
image_numpy (numpy array) -- input numpy array
|
|
image_path (str) -- the path of the image
|
|
"""
|
|
|
|
image_pil = Image.fromarray(image_numpy)
|
|
image_pil = image_pil.resize((w, h))
|
|
image_pil.save(image_path)
|
|
|
|
|
|
def save_img(image_tensor, w, h, filename):
|
|
image_pil = tensor2im(image_tensor)
|
|
|
|
save_image(image_pil, filename, w, h, aspect_ratio=1.0)
|
|
print("Image saved as {}".format(filename))
|
|
|
|
|
|
def load_img(filepath):
|
|
img = Image.open(filepath).convert('L')
|
|
# print(img.size)
|
|
width = img.size[0]
|
|
height = img.size[1]
|
|
# img = img.resize((512, 512), Image.BICUBIC)
|
|
return img, width, height
|
|
|
|
|
|
if __name__ == '__main__':
|
|
img_A = "/workspace/Semi_ref2sketch_code/datasets/ref_unpair/testA/real_Dress_732caedc416a0cbfedd0e6528040eac7.jpg_Img.jpg"
|
|
img_B = "/workspace/Semi_ref2sketch_code/datasets/ref_unpair/testC/style_3.png"
|
|
from opt import Config
|
|
|
|
opt = Config() # get test options
|
|
# hard-code some parameters for test
|
|
opt.num_threads = 0 # test code only supports num_threads = 0
|
|
opt.batch_size = 1 # test code only supports batch_size = 1
|
|
opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.
|
|
opt.no_flip = True # no flip; comment this line if results on flipped images are needed.
|
|
opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file.
|
|
device = torch.device("cuda:0")
|
|
model = create_model(opt) # create a model given opt.model and other options
|
|
model.setup(opt)
|
|
transform_list = [transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]
|
|
transform = transforms.Compose(transform_list)
|
|
if opt.eval:
|
|
model.eval()
|
|
data = {}
|
|
print(os.getcwd())
|
|
B = reference, _, _ = load_img(r"/app/service/image2sketch/datasets/ref_unpair/testC/style_3.png")
|
|
style_img = transform(reference)
|
|
data['B'] = style_img
|
|
data['B'] = data['B'].unsqueeze(0).to(device)
|
|
A = Image.open(r"E:\workspace\trinity_client_aida\app\service\image2sketch\datasets\ref_unpair\testA\real_Dress_3200fecdc83d0c556c2bd96aedbd7fbf.jpg_Img.jpg")
|
|
width = A.size[0]
|
|
height = A.size[1]
|
|
# data['A'] = A.resize((512, 512))
|
|
data['A'] = transform(A)
|
|
data['A'] = data['A'].unsqueeze(0).to(device)
|
|
model.set_input(data)
|
|
model.test() # run inference
|
|
visuals = model.get_current_visuals() # get image results
|
|
save_img(visuals['content_output'].cpu(), width, height, "result/result.jpg")
|