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import os |
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os.system("pip install dlib") |
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import sys |
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import face_detection |
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from PIL import Image, ImageOps, ImageFile |
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import numpy as np |
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import cv2 as cv |
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import torch |
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import gradio as gr |
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torch.set_grad_enabled(False) |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", device=device).eval() |
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model2 = torch.hub.load("AK391/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1", device=device).eval() |
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face2paint = torch.hub.load("bryandlee/animegan2-pytorch:main", "face2paint", device=device) |
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image_format = "png" |
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def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=2.0, threshold=0): |
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"""Return a sharpened version of the image, using an unsharp mask.""" |
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blurred = cv.GaussianBlur(image, kernel_size, sigma) |
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sharpened = float(amount + 1) * image - float(amount) * blurred |
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sharpened = np.maximum(sharpened, np.zeros(sharpened.shape)) |
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sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape)) |
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sharpened = sharpened.round().astype(np.uint8) |
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if threshold > 0: |
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low_contrast_mask = np.absolute(image - blurred) < threshold |
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np.copyto(sharpened, image, where=low_contrast_mask) |
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return sharpened |
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def normPRED(d): |
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ma = np.max(d) |
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mi = np.min(d) |
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dn = (d-mi)/(ma-mi) |
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return dn |
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def array_to_np(array_in): |
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array_in = normPRED(array_in) |
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array_in = np.squeeze(255.0*(array_in)) |
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array_in = np.transpose(array_in, (1, 2, 0)) |
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return array_in |
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def array_to_image(array_in): |
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array_in = normPRED(array_in) |
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array_in = np.squeeze(255.0*(array_in)) |
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array_in = np.transpose(array_in, (1, 2, 0)) |
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im = Image.fromarray(array_in.astype(np.uint8)) |
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return im |
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def image_as_array(image_in): |
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image_in = np.array(image_in, np.float32) |
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tmpImg = np.zeros((image_in.shape[0],image_in.shape[1],3)) |
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image_in = image_in/np.max(image_in) |
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if image_in.shape[2]==1: |
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tmpImg[:,:,0] = (image_in[:,:,0]-0.485)/0.229 |
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tmpImg[:,:,1] = (image_in[:,:,0]-0.485)/0.229 |
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tmpImg[:,:,2] = (image_in[:,:,0]-0.485)/0.229 |
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else: |
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tmpImg[:,:,0] = (image_in[:,:,0]-0.485)/0.229 |
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tmpImg[:,:,1] = (image_in[:,:,1]-0.456)/0.224 |
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tmpImg[:,:,2] = (image_in[:,:,2]-0.406)/0.225 |
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tmpImg = tmpImg.transpose((2, 0, 1)) |
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image_out = np.expand_dims(tmpImg, 0) |
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return image_out |
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def find_aligned_face(image_in, size=400): |
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aligned_image, n_faces, quad = face_detection.align(image_in, face_index=0, output_size=size) |
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return aligned_image, n_faces, quad |
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def align_first_face(image_in, size=400): |
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aligned_image, n_faces, quad = find_aligned_face(image_in,size=size) |
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if n_faces == 0: |
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try: |
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image_in = ImageOps.exif_transpose(image_in) |
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except: |
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print("exif problem, not rotating") |
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image_in = image_in.resize((size, size)) |
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im_array = image_as_array(image_in) |
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else: |
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im_array = image_as_array(aligned_image) |
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return im_array |
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def img_concat_h(im1, im2): |
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dst = Image.new('RGB', (im1.width + im2.width, im1.height)) |
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dst.paste(im1, (0, 0)) |
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dst.paste(im2, (im1.width, 0)) |
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return dst |
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def paintface(img: Image.Image,size: int) -> Image.Image: |
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aligned_img = align_first_face(img,size) |
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if aligned_img is None: |
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output=None |
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else: |
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im_in = array_to_image(aligned_img).convert("RGB") |
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im_out1 = face2paint(model, im_in, side_by_side=False) |
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im_out2 = face2paint(model2, im_in, side_by_side=False) |
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output = img_concat_h(im_out1, im_out2) |
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return output |
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def generate(img): |
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out = paintface(img, 400) |
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return out |
