Delete

test.py

@@ -1,63 +0,0 @@
-import numpy as np
-from label_colors import colorMap
-from PIL import Image
-from spade.model import Pix2PixModel
-from spade.dataset import get_transform
-from torchvision.transforms import ToPILImage
-
-'''colors = np.array([[56, 79, 131], [239, 239, 239],
-                   [93, 110, 50], [183, 210, 78],
-                   [60, 59, 75], [250, 250, 250]])'''
-colors = [key['color'] for key in colorMap]
-id_list = [key['id'] for key in colorMap]
-
-
-def semantic(img):
-    print("semantic", type(img))
-    h, w = img.size
-    imrgb = img.convert("RGB")
-    pix = list(imrgb.getdata())
-    mask = [id_list[colors.index(i)] if i in colors else 156 for i in pix]
-    return np.array(mask).reshape(h, w)
-
-
-def evaluate(labelmap):
-    opt = {
-        'label_nc': 182,  # num classes in coco model
-        'crop_size': 512,
-        'load_size': 512,
-        'aspect_ratio': 1.0,
-        'isTrain': False,
-        'checkpoints_dir': 'app',
-        'which_epoch': 'latest',
-        'use_gpu': False
-    }
-    model = Pix2PixModel(opt)
-    model.eval()
-    image = Image.fromarray(np.array(labelmap).astype(np.uint8))
-    transform_label = get_transform(opt, method=Image.NEAREST, normalize=False)
-    # transforms.ToTensor in transform_label rescales image from [0,255] to [0.0,1.0]
-    # lets rescale it back to [0,255] to match our label ids
-    label_tensor = transform_label(image) * 255.0
-    label_tensor[label_tensor == 255] = opt['label_nc']  # 'unknown' is opt.label_nc
-    print("label_tensor:", label_tensor.shape)
-
-    # not using encoder, so creating a blank image...
-    transform_image = get_transform(opt)
-    image_tensor = transform_image(Image.new('RGB', (500, 500)))
-
-    data = {
-        'label': label_tensor.unsqueeze(0),
-        'instance': label_tensor.unsqueeze(0),
-        'image': image_tensor.unsqueeze(0)
-    }
-    generated = model(data, mode='inference')
-    print("generated_image:", generated.shape)
-
-    return generated
-
-
-def to_image(generated):
-    to_img = ToPILImage()
-    normalized_img = ((generated.reshape([3, 512, 512]) + 1) / 2.0) * 255.0
-    return to_img(normalized_img.byte().cpu())