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| import fastai | |
| from fastai.vision import * | |
| from fastai.utils.mem import * | |
| from fastai.vision import open_image, load_learner, image, torch | |
| import numpy as np | |
| import urllib.request | |
| import PIL.Image | |
| from io import BytesIO | |
| import torchvision.transforms as T | |
| from PIL import Image | |
| import requests | |
| from io import BytesIO | |
| import fastai | |
| from fastai.vision import * | |
| from fastai.utils.mem import * | |
| from fastai.vision import open_image, load_learner, image, torch | |
| import numpy as np | |
| import urllib.request | |
| import PIL.Image | |
| from PIL import Image | |
| from io import BytesIO | |
| import torchvision.transforms as T | |
| class FeatureLoss(nn.Module): | |
| def __init__(self, m_feat, layer_ids, layer_wgts): | |
| super().__init__() | |
| self.m_feat = m_feat | |
| self.loss_features = [self.m_feat[i] for i in layer_ids] | |
| self.hooks = hook_outputs(self.loss_features, detach=False) | |
| self.wgts = layer_wgts | |
| self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids)) | |
| ] + [f'gram_{i}' for i in range(len(layer_ids))] | |
| def make_features(self, x, clone=False): | |
| self.m_feat(x) | |
| return [(o.clone() if clone else o) for o in self.hooks.stored] | |
| def forward(self, input, target): | |
| out_feat = self.make_features(target, clone=True) | |
| in_feat = self.make_features(input) | |
| self.feat_losses = [base_loss(input,target)] | |
| self.feat_losses += [base_loss(f_in, f_out)*w | |
| for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)] | |
| self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out))*w**2 * 5e3 | |
| for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)] | |
| self.metrics = dict(zip(self.metric_names, self.feat_losses)) | |
| return sum(self.feat_losses) | |
| def __del__(self): self.hooks.remove() | |
| def add_margin(pil_img, top, right, bottom, left, color): | |
| width, height = pil_img.size | |
| new_width = width + right + left | |
| new_height = height + top + bottom | |
| result = Image.new(pil_img.mode, (new_width, new_height), color) | |
| result.paste(pil_img, (left, top)) | |
| return result | |
| MODEL_URL = "https://www.dropbox.com/s/04suaimdpru76h3/ArtLine_920.pkl?dl=1 " | |
| urllib.request.urlretrieve(MODEL_URL, "ArtLine_920.pkl") | |
| path = Path(".") | |
| print(os.listdir('.')) | |
| learn=load_learner(path, 'ArtLine_920.pkl') | |
| import gradio as gr | |
| import cv2 | |
| def get_filename(prefix="sketch"): | |
| from datetime import datetime | |
| from pytz import timezone | |
| return datetime.now(timezone('Asia/Seoul')).strftime('sketch__%Y-%m-%d %H:%M:%S.jpg') | |
| def predict(img): | |
| img = PIL.Image.fromarray(img) | |
| img = add_margin(img, 250, 250, 250, 250, (255, 255, 255)) | |
| img = np.array(img) | |
| h, w = img.shape[:-1] | |
| cv2.imwrite("test.jpg", img) | |
| img_test = open_image("test.jpg") | |
| p,img_hr,b = learn.predict(img_test) | |
| res = (img_hr / img_hr.max()).numpy() | |
| res = res[0] # take only first channel as result | |
| res = cv2.resize(res, (w,h)) | |
| output_file = get_filename() | |
| cv2.imwrite(output_file, (res * 255).astype(np.uint8), [cv2.IMWRITE_JPEG_QUALITY, 50]) | |
| return res, output_file | |
| gr.Interface(predict, | |
| inputs="image", | |
| outputs=[gr.Image(label="Sketch Image"), gr.File(label="Result File")], | |
| title="Image-to-sketch", | |
| description="Transfer any image into BW cartoon-style sketch!").launch() | |