161 lines
5.6 KiB
Python
161 lines
5.6 KiB
Python
from facenet_pytorch import MTCNN
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from torchvision import transforms
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from huggingface_hub import hf_hub_download
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import torch, PIL
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import gradio as gr
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modelarcanev4 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.4", filename="ArcaneGANv0.4.jit")
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modelarcanev3 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.3", filename="ArcaneGANv0.3.jit")
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modelarcanev2 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.2", filename="ArcaneGANv0.2.jit")
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# modelarcanev4 = torch.load("ArcaneGANv0.4/ArcaneGANv0.4.jit")
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# modelarcanev3 = torch.load("ArcaneGANv0.3/ArcaneGANv0.3.jit")
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# modelarcanev2 = torch.load("ArcaneGANv0.2/ArcaneGANv0.2.jit")
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mtcnn = MTCNN(image_size=256, margin=80)
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# simplest ye olde trustworthy MTCNN for face detection with landmarks
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def detect(img):
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# Detect faces
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batch_boxes, batch_probs, batch_points = mtcnn.detect(img, landmarks=True)
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# Select faces
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if not mtcnn.keep_all:
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batch_boxes, batch_probs, batch_points = mtcnn.select_boxes(
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batch_boxes, batch_probs, batch_points, img, method=mtcnn.selection_method
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)
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return batch_boxes, batch_points
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# my version of isOdd, should make a separate repo for it :D
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def makeEven(_x):
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return _x if (_x % 2 == 0) else _x + 1
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# the actual scaler function
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def scale(boxes, _img, max_res=1_500_000, target_face=256, fixed_ratio=0, max_upscale=2, VERBOSE=False):
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x, y = _img.size
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ratio = 2 # initial ratio
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# scale to desired face size
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if (boxes is not None):
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if len(boxes) > 0:
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ratio = target_face / max(boxes[0][2:] - boxes[0][:2]);
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ratio = min(ratio, max_upscale)
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if VERBOSE: print('up by', ratio)
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if fixed_ratio > 0:
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if VERBOSE: print('fixed ratio')
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ratio = fixed_ratio
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x *= ratio
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y *= ratio
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# downscale to fit into max res
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res = x * y
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if res > max_res:
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ratio = pow(res / max_res, 1 / 2);
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if VERBOSE: print(ratio)
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x = int(x / ratio)
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y = int(y / ratio)
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# make dimensions even, because usually NNs fail on uneven dimensions due skip connection size mismatch
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x = makeEven(int(x))
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y = makeEven(int(y))
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size = (x, y)
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return _img.resize(size)
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"""
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A useful scaler algorithm, based on face detection.
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Takes PIL.Image, returns a uniformly scaled PIL.Image
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boxes: a list of detected bboxes
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_img: PIL.Image
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max_res: maximum pixel area to fit into. Use to stay below the VRAM limits of your GPU.
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target_face: desired face size. Upscale or downscale the whole image to fit the detected face into that dimension.
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fixed_ratio: fixed scale. Ignores the face size, but doesn't ignore the max_res limit.
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max_upscale: maximum upscale ratio. Prevents from scaling images with tiny faces to a blurry mess.
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"""
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def scale_by_face_size(_img, max_res=1_500_000, target_face=256, fix_ratio=0, max_upscale=2, VERBOSE=False):
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boxes = None
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boxes, _ = detect(_img)
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if VERBOSE: print('boxes', boxes)
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img_resized = scale(boxes, _img, max_res, target_face, fix_ratio, max_upscale, VERBOSE)
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return img_resized
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size = 256
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means = [0.485, 0.456, 0.406]
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stds = [0.229, 0.224, 0.225]
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t_stds = torch.tensor(stds).cuda().half()[:, None, None]
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t_means = torch.tensor(means).cuda().half()[:, None, None]
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def makeEven(_x):
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return int(_x) if (_x % 2 == 0) else int(_x + 1)
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img_transforms = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize(means, stds)])
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def tensor2im(var):
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return var.mul(t_stds).add(t_means).mul(255.).clamp(0, 255).permute(1, 2, 0)
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def proc_pil_img(input_image, model):
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transformed_image = img_transforms(input_image)[None, ...].cuda().half()
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with torch.no_grad():
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result_image = model(transformed_image)[0]
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output_image = tensor2im(result_image)
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output_image = output_image.detach().cpu().numpy().astype('uint8')
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output_image = PIL.Image.fromarray(output_image)
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return output_image
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modelv4 = torch.jit.load(modelarcanev4).eval().cuda().half()
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modelv3 = torch.jit.load(modelarcanev3).eval().cuda().half()
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modelv2 = torch.jit.load(modelarcanev2).eval().cuda().half()
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def process(im, version):
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if version == 'version 0.4':
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im = scale_by_face_size(im, target_face=256, max_res=1_500_000, max_upscale=1)
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res = proc_pil_img(im, modelv4)
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elif version == 'version 0.3':
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im = scale_by_face_size(im, target_face=256, max_res=1_500_000, max_upscale=1)
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res = proc_pil_img(im, modelv3)
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else:
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im = scale_by_face_size(im, target_face=256, max_res=1_500_000, max_upscale=1)
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res = proc_pil_img(im, modelv2)
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return res
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title = "ArcaneGAN"
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description = "ArcaneGAN的Gradio demo, 把图片转换成Arcane style。上传你想要的图像或者点击下面的示例来加载它。"
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article = "<div style='text-align: center;'>ArcaneGan by <a href='https://twitter.com/devdef' target='_blank'>Alexander S</a> | <a href='https://github.com/Sxela/ArcaneGAN' target='_blank'>Github Repo</a> | <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_arcanegan' alt='visitor badge'></center></div>"
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gr.Interface(
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process,
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[gr.inputs.Image(type="pil", label="Input"),
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gr.inputs.Radio(choices=['version 0.2', 'version 0.3', 'version 0.4'], type="value", default='version 0.4',
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label='version')
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],
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gr.outputs.Image(type="pil", label="Output"),
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title=title,
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description=description,
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article=article,
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examples=[['images/bill.png', 'version 0.3'], ['images/keanu.png', 'version 0.4'], ['images/will.jpeg', 'version 0.4']],
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allow_flagging=False,
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allow_screenshot=False
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).launch(server_name="0.0.0.0") |