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| import cv2 as cv | |
| import numpy as np | |
| import torch | |
| from PIL import Image, ImageOps | |
| from comic_style.face_detection import align | |
| torch.set_grad_enabled(False) | |
| model = torch.jit.load('comic_style/u2net_bce_itr_16000_train_3.835149_tar_0.542587-400x_360x.jit.pt') | |
| model.eval() | |
| # https://en.wikipedia.org/wiki/Unsharp_masking | |
| # https://stackoverflow.com/a/55590133/1495606 | |
| 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 | |
| def find_aligned_face(image_in, size=400): | |
| aligned_image, n_faces, quad = align(image_in, face_index=0, output_size=size) | |
| return aligned_image, n_faces, quad | |
| 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 face2hero( | |
| img: Image.Image, | |
| size: int | |
| ) -> Image.Image: | |
| aligned_img = align_first_face(img) | |
| if aligned_img is None: | |
| output = None | |
| else: | |
| input = torch.Tensor(aligned_img) | |
| results = model(input) | |
| hero_np_image = array_to_np(results[1].detach().numpy()) | |
| hero_image = unsharp_mask(hero_np_image) | |
| hero_image = Image.fromarray(hero_image) | |
| # hero_image = hero_image.resize((int(hero_image.width * 0.3), int(hero_image.height * 0.3)), Image.ANTIALIAS) | |
| # output = img_concat_h(array_to_image(aligned_img), hero_image) | |
| del results | |
| return hero_image | |
| def inference(img): | |
| out = face2hero(img, 400) | |
| return out | |