no dependencies

app.py

@@ -1,8 +1,8 @@
 from argparse import Namespace
 
-import cv2
+#import cv2
 import gradio as gr
-import numpy as np
+#import numpy as np
 import torch
 import torchvision.transforms as transforms
 from huggingface_hub import hf_hub_download
@@ -17,15 +17,6 @@ transfroms = transforms.Compose([
     transforms.ToTensor()]
 )
 
-def log_input_image(x, opts):
-	if opts.label_nc == 0:
-		return tensor2im(x)
-	elif opts.label_nc == 1:
-		return tensor2sketch(x)
-	else:
-		return tensor2map(x)
-
-
 def tensor2im(var):
 	var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy()
 	var = ((var + 1) / 2)
@@ -34,28 +25,6 @@ def tensor2im(var):
 	var = var * 255
 	return Image.fromarray(var.astype('uint8'))
 
-def tensor2map(var):
-	mask = np.argmax(var.data.cpu().numpy(), axis=0)
-	print(np.unique(mask))
-	colors = get_colors()
-	mask_image = np.zeros(shape=(mask.shape[0], mask.shape[1], 3))
-	for class_idx in np.unique(mask):
-		mask_image[mask == class_idx] = colors[class_idx]
-	mask_image = mask_image.astype('uint8')
-	return Image.fromarray(mask_image)
-
-def tensor2sketch(var):
-	im = var[0].cpu().detach().numpy()
-	im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)
-	im = (im * 255).astype(np.uint8)
-	return Image.fromarray(im)
-def get_colors():
-	# currently support up to 19 classes (for the celebs-hq-mask dataset)
-	colors = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255],
-			  [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204],
-			  [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]]
-	return colors
-
 def sketch_recognition(img):
     from_im = transfroms(Image.fromarray(img))
     with torch.no_grad():