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