app.py

import os

os.system("wget https://github.com/Sxela/ArcaneGAN/releases/download/v0.2/ArcaneGANv0.2.jit")
os.system("pip -qq install facenet_pytorch")


from facenet_pytorch import MTCNN
from torchvision import transforms
import torch, PIL

from tqdm.notebook import tqdm
import gradio as gr

mtcnn = MTCNN(image_size=256, margin=80)

# simplest ye olde trustworthy MTCNN for face detection with landmarks
def detect(img):
 
        # Detect faces
        batch_boxes, batch_probs, batch_points = mtcnn.detect(img, landmarks=True)
        # Select faces
        if not mtcnn.keep_all:
            batch_boxes, batch_probs, batch_points = mtcnn.select_boxes(
                batch_boxes, batch_probs, batch_points, img, method=mtcnn.selection_method
            )
 
        return batch_boxes, batch_points

# my version of isOdd, should make a separate repo for it :D
def makeEven(_x):
  return _x if (_x % 2 == 0) else _x+1

# the actual scaler function
def scale(boxes, _img, max_res=1_500_000, target_face=256, fixed_ratio=0, max_upscale=2, VERBOSE=False):
 
    x, y = _img.size
 
    ratio = 2 #initial ratio
 
    #scale to desired face size
    if (boxes is not None):
      if len(boxes)>0:
        ratio = target_face/max(boxes[0][2:]-boxes[0][:2]); 
        ratio = min(ratio, max_upscale)
        if VERBOSE: print('up by', ratio)

    if fixed_ratio>0:
      if VERBOSE: print('fixed ratio')
      ratio = fixed_ratio
 
    x*=ratio
    y*=ratio
 
    #downscale to fit into max res 
    res = x*y
    if res > max_res:
      ratio = pow(res/max_res,1/2); 
      if VERBOSE: print(ratio)
      x=int(x/ratio)
      y=int(y/ratio)
 
    #make dimensions even, because usually NNs fail on uneven dimensions due skip connection size mismatch
    x = makeEven(int(x))
    y = makeEven(int(y))
    
    size = (x, y)

    return _img.resize(size)

""" 
    A useful scaler algorithm, based on face detection.
    Takes PIL.Image, returns a uniformly scaled PIL.Image
    boxes: a list of detected bboxes
    _img: PIL.Image
    max_res: maximum pixel area to fit into. Use to stay below the VRAM limits of your GPU.
    target_face: desired face size. Upscale or downscale the whole image to fit the detected face into that dimension.
    fixed_ratio: fixed scale. Ignores the face size, but doesn't ignore the max_res limit.
    max_upscale: maximum upscale ratio. Prevents from scaling images with tiny faces to a blurry mess.
"""

def scale_by_face_size(_img, max_res=1_500_000, target_face=256, fix_ratio=0, max_upscale=2, VERBOSE=False):
    boxes = None
    boxes, _ = detect(_img)
    if VERBOSE: print('boxes',boxes)
    img_resized = scale(boxes, _img, max_res, target_face, fix_ratio, max_upscale, VERBOSE)
    return img_resized


size = 256

means = [0.485, 0.456, 0.406]
stds = [0.229, 0.224, 0.225]

t_stds = torch.tensor(stds).cpu()[:,None,None]
t_means = torch.tensor(means).cpu()[:,None,None]

def makeEven(_x):
  return int(_x) if (_x % 2 == 0) else int(_x+1)

img_transforms = transforms.Compose([                        
            transforms.ToTensor(),
            transforms.Normalize(means,stds)])
 
def tensor2im(var):
     return var.mul(t_stds).add(t_means).mul(255.).clamp(0,255).permute(1,2,0)

def proc_pil_img(input_image, model):
    transformed_image = img_transforms(input_image)[None,...].cpu()
            
    with torch.no_grad():
        result_image = model(transformed_image)[0]; print(result_image.shape)
        output_image = tensor2im(result_image)
        output_image = output_image.detach().cpu().numpy().astype('uint8')
        output_image = PIL.Image.fromarray(output_image)
    return output_image



model_path = './ArcaneGANv0.2.jit' 

model = torch.jit.load(model_path,map_location='cpu').to('cpu').float().eval().cpu()

def fit(img,maxsize=512):
  maxdim = max(*img.size)
  if maxdim>maxsize:
    ratio = maxsize/maxdim
    x,y = img.size
    size = (int(x*ratio),int(y*ratio)) 
    img = img.resize(size)
  return img
 


def process(img):
    im = scale_by_face_size(im, target_face=300, max_res=1_500_000, max_upscale=2)
    res = proc_pil_img(im, model)
    return res
        
title = "ArcaneGAN"
description = "Gradio demo for ArcaneGan. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.05703'>Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis</a> | <a href='https://github.com/Mukosame/Anime2Sketch'>Github Repo</a></p>"

gr.Interface(
    process, 
    gr.inputs.Image(type="pil", label="Input"), 
    gr.outputs.Image(type="pil", label="Output"),
    title=title,
    description=description,
    article=article,
   ).launch(debug=True)