Update app.py

app.py

@@ -1,151 +1,3 @@
-import os
-from huggingface_hub import hf_hub_download
-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
-import torch
-
-modelarcanev4 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.4", filename="ArcaneGANv0.4.jit")
-modelarcanev3 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.3", filename="ArcaneGANv0.3.jit")
-modelarcanev2 = hf_hub_download(repo_id="akhaliq/ArcaneGANv0.2", filename="ArcaneGANv0.2.jit")
-
-
-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).cuda().half()[:,None,None]
-t_means = torch.tensor(means).cuda().half()[:,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,...].cuda().half()
-            
-    with torch.no_grad():
-        result_image = model(transformed_image)[0]
-        output_image = tensor2im(result_image)
-        output_image = output_image.detach().cpu().numpy().astype('uint8')
-        output_image = PIL.Image.fromarray(output_image)
-    return output_image
-    
-    
- 
-modelv4 = torch.jit.load(modelarcanev4).eval().cuda().half()
-modelv3 = torch.jit.load(modelarcanev3).eval().cuda().half()
-modelv2 = torch.jit.load(modelarcanev2).eval().cuda().half()
-
-def process(im, version):
-    if version == 'version 0.4':
-        im = scale_by_face_size(im, target_face=256, max_res=1_500_000, max_upscale=1)
-        res = proc_pil_img(im, modelv4)
-    elif version == 'version 0.3':
-        im = scale_by_face_size(im, target_face=256, max_res=1_500_000, max_upscale=1)
-        res = proc_pil_img(im, modelv3)
-    else:
-        im = scale_by_face_size(im, target_face=256, max_res=1_500_000, max_upscale=1)
-        res = proc_pil_img(im, modelv2)
-    return res
-        
-title = "ArcaneGAN"
-description = "Gradio demo for ArcaneGAN, portrait to Arcane style. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
-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>"
-
-gr.Interface(
-    process, 
-    [gr.inputs.Image(source="webcam", type="pil", label="Input"),gr.inputs.Radio(choices=['version 0.2','version 0.3','version 0.4'], type="value", default='version 0.4', label='version')
-], 
-    gr.outputs.Image(type="pil", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    allow_flagging=False,
-    allow_screenshot=False
-    ).launch()
+gr.Interface.load(
+    "spaces/abidlabs/remove-bg", inputs="webcam", title="Remove your webcam background!"
+).launch()