Update app.py

app.py

@@ -1,117 +1,8 @@
-import os
-os.system("pip install dlib")
-import sys
-import face_detection
-from PIL import Image, ImageOps, ImageFile
-import numpy as np
-import cv2 as cv
-import torch
 import gradio as gr
+from paintingface import generate
 
-torch.set_grad_enabled(False)
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", device=device).eval()
-model2 = torch.hub.load("AK391/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1",  device=device).eval()
-face2paint = torch.hub.load("bryandlee/animegan2-pytorch:main", "face2paint", device=device)
-image_format = "png" #@param ["jpeg", "png"]
-
-def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=2.0, threshold=0):
-    """Return a sharpened version of the image, using an unsharp mask."""
-    blurred = cv.GaussianBlur(image, kernel_size, sigma)
-    sharpened = float(amount + 1) * image - float(amount) * blurred
-    sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
-    sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
-    sharpened = sharpened.round().astype(np.uint8)
-    if threshold > 0:
-        low_contrast_mask = np.absolute(image - blurred) < threshold
-        np.copyto(sharpened, image, where=low_contrast_mask)
-    return sharpened
-
-def normPRED(d):
-    ma = np.max(d)
-    mi = np.min(d)
-
-    dn = (d-mi)/(ma-mi)
-
-    return dn
-
-def array_to_np(array_in):
-    array_in = normPRED(array_in)
-    array_in = np.squeeze(255.0*(array_in)) 
-    array_in = np.transpose(array_in, (1, 2, 0))
-    return array_in
-
-def array_to_image(array_in):    
-    array_in = normPRED(array_in)
-    array_in = np.squeeze(255.0*(array_in)) 
-    array_in = np.transpose(array_in, (1, 2, 0))
-    im = Image.fromarray(array_in.astype(np.uint8))
-    return im
-
-
-def image_as_array(image_in):
-    image_in = np.array(image_in, np.float32)
-    tmpImg = np.zeros((image_in.shape[0],image_in.shape[1],3))
-    image_in = image_in/np.max(image_in)
-    if image_in.shape[2]==1:
-        tmpImg[:,:,0] = (image_in[:,:,0]-0.485)/0.229
-        tmpImg[:,:,1] = (image_in[:,:,0]-0.485)/0.229
-        tmpImg[:,:,2] = (image_in[:,:,0]-0.485)/0.229
-    else:
-        tmpImg[:,:,0] = (image_in[:,:,0]-0.485)/0.229
-        tmpImg[:,:,1] = (image_in[:,:,1]-0.456)/0.224
-        tmpImg[:,:,2] = (image_in[:,:,2]-0.406)/0.225
-
-    tmpImg = tmpImg.transpose((2, 0, 1))
-    image_out = np.expand_dims(tmpImg, 0)
-    return image_out
-
-# detect a face
-def find_aligned_face(image_in, size=400):   
-    aligned_image, n_faces, quad = face_detection.align(image_in, face_index=0, output_size=size)
-    return aligned_image, n_faces, quad
-
-# clip the face, return array
-def align_first_face(image_in, size=400):  
-    aligned_image, n_faces, quad = find_aligned_face(image_in,size=size)
-    if n_faces == 0:
-        try:
-            image_in = ImageOps.exif_transpose(image_in)
-        except:
-            print("exif problem, not rotating")
-        image_in = image_in.resize((size, size))
-        im_array = image_as_array(image_in)
-    else:
-        im_array = image_as_array(aligned_image)
-
-    return im_array
-
-def img_concat_h(im1, im2):
-    dst = Image.new('RGB', (im1.width + im2.width, im1.height))
-    dst.paste(im1, (0, 0))
-    dst.paste(im2, (im1.width, 0))
-    return dst
-
-def paintface(img: Image.Image,size: int) -> Image.Image:
-    aligned_img = align_first_face(img,size)
-    if aligned_img is None:
-        output=None
-    else:
-        im_in = array_to_image(aligned_img).convert("RGB")
-        im_out1 = face2paint(model, im_in, side_by_side=False)
-        im_out2 = face2paint(model2, im_in, side_by_side=False)
-
-        output = img_concat_h(im_out1, im_out2)         
-    return output
-
-def generate(img):
-    out = paintface(img, 400)
-    return out
-      
-  
 title = "Face from Photo into Handed-paint"
-description = "Upload a photo, this Ai will detect and transfer only the main face into cartoon/anime handed painting style. (If cannot detect face, try the edit button to crop the photo manually on the right corner of the picture.)"
+description = "Upload a photo, this Ai will detect and transfer only the main face into cartoon/anime handed-painting style. (If cannot detect a face, try the edit button on the right corner of the picture to crop the photo manually.)"
 article = "Examples are from Internet"
 
 Example=[['Example01.jpg'],['Example02.jpg'],['Example03.jpg']]