import torch import torch.nn.functional as F import numpy as np from PIL import Image import os current = os.getcwd() print(current) full = current + "/inference" print(full) os.chdir(full) print(os.getcwd()) import network import morphology import math import gradio as gr from torchvision import transforms import torchtext idx = 0 torchtext.utils.download_from_url("https://drive.google.com/uc?id=1NDD54BLligyr8tzo8QGI5eihZisXK1nq", root=".") def save_img(img, output_path): result = Image.fromarray((img.data.cpu().numpy().transpose((1, 2, 0)) * 255).astype(np.uint8)) result.save(output_path) def param2stroke(param, H, W, meta_brushes): """ Input a set of stroke parameters and output its corresponding foregrounds and alpha maps. Args: param: a tensor with shape n_strokes x n_param_per_stroke. Here, param_per_stroke is 8: x_center, y_center, width, height, theta, R, G, and B. H: output height. W: output width. meta_brushes: a tensor with shape 2 x 3 x meta_brush_height x meta_brush_width. The first slice on the batch dimension denotes vertical brush and the second one denotes horizontal brush. Returns: foregrounds: a tensor with shape n_strokes x 3 x H x W, containing color information. alphas: a tensor with shape n_strokes x 3 x H x W, containing binary information of whether a pixel is belonging to the stroke (alpha mat), for painting process. """ # Firstly, resize the meta brushes to the required shape, # in order to decrease GPU memory especially when the required shape is small. meta_brushes_resize = F.interpolate(meta_brushes, (H, W)) b = param.shape[0] # Extract shape parameters and color parameters. param_list = torch.split(param, 1, dim=1) x0, y0, w, h, theta = [item.squeeze(-1) for item in param_list[:5]] R, G, B = param_list[5:] # Pre-compute sin theta and cos theta sin_theta = torch.sin(torch.acos(torch.tensor(-1., device=param.device)) * theta) cos_theta = torch.cos(torch.acos(torch.tensor(-1., device=param.device)) * theta) # index means each stroke should use which meta stroke? Vertical meta stroke or horizontal meta stroke. # When h > w, vertical stroke should be used. When h <= w, horizontal stroke should be used. index = torch.full((b,), -1, device=param.device, dtype=torch.long) index[h > w] = 0 index[h <= w] = 1 brush = meta_brushes_resize[index.long()] # Calculate warp matrix according to the rules defined by pytorch, in order for warping. warp_00 = cos_theta / w warp_01 = sin_theta * H / (W * w) warp_02 = (1 - 2 * x0) * cos_theta / w + (1 - 2 * y0) * sin_theta * H / (W * w) warp_10 = -sin_theta * W / (H * h) warp_11 = cos_theta / h warp_12 = (1 - 2 * y0) * cos_theta / h - (1 - 2 * x0) * sin_theta * W / (H * h) warp_0 = torch.stack([warp_00, warp_01, warp_02], dim=1) warp_1 = torch.stack([warp_10, warp_11, warp_12], dim=1) warp = torch.stack([warp_0, warp_1], dim=1) # Conduct warping. grid = F.affine_grid(warp, [b, 3, H, W], align_corners=False) brush = F.grid_sample(brush, grid, align_corners=False) # alphas is the binary information suggesting whether a pixel is belonging to the stroke. alphas = (brush > 0).float() brush = brush.repeat(1, 3, 1, 1) alphas = alphas.repeat(1, 3, 1, 1) # Give color to foreground strokes. color_map = torch.cat([R, G, B], dim=1) color_map = color_map.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, H, W) foreground = brush * color_map # Dilation and erosion are used for foregrounds and alphas respectively to prevent artifacts on stroke borders. foreground = morphology.dilation(foreground) alphas = morphology.erosion(alphas) return foreground, alphas def param2img_serial( param, decision, meta_brushes, cur_canvas, frame_dir, has_border=False, original_h=None, original_w=None): """ Input stroke parameters and decisions for each patch, meta brushes, current canvas, frame directory, and whether there is a border (if intermediate painting results are required). Output the painting results of adding the corresponding strokes on the current canvas. Args: param: a tensor with shape batch size x patch along height dimension x patch along width dimension x n_stroke_per_patch x n_param_per_stroke decision: a 01 tensor with shape batch size x patch along height dimension x patch along width dimension x n_stroke_per_patch meta_brushes: a tensor with shape 2 x 3 x meta_brush_height x meta_brush_width. The first slice on the batch dimension denotes vertical brush and the second one denotes horizontal brush. cur_canvas: a tensor with shape batch size x 3 x H x W, where H and W denote height and width of padded results of original images. frame_dir: directory to save intermediate painting results. None means intermediate results are not required. has_border: on the last painting layer, in order to make sure that the painting results do not miss any important detail, we choose to paint again on this layer but shift patch_size // 2 pixels when cutting patches. In this case, if intermediate results are required, we need to cut the shifted length on the border before saving, or there would be a black border. original_h: to indicate the original height for cropping when saving intermediate results. original_w: to indicate the original width for cropping when saving intermediate results. Returns: cur_canvas: a tensor with shape batch size x 3 x H x W, denoting painting results. """ # param: b, h, w, stroke_per_patch, param_per_stroke # decision: b, h, w, stroke_per_patch b, h, w, s, p = param.shape H, W = cur_canvas.shape[-2:] is_odd_y = h % 2 == 1 is_odd_x = w % 2 == 1 patch_size_y = 2 * H // h patch_size_x = 2 * W // w even_idx_y = torch.arange(0, h, 2, device=cur_canvas.device) even_idx_x = torch.arange(0, w, 2, device=cur_canvas.device) odd_idx_y = torch.arange(1, h, 2, device=cur_canvas.device) odd_idx_x = torch.arange(1, w, 2, device=cur_canvas.device) even_y_even_x_coord_y, even_y_even_x_coord_x = torch.meshgrid([even_idx_y, even_idx_x]) odd_y_odd_x_coord_y, odd_y_odd_x_coord_x = torch.meshgrid([odd_idx_y, odd_idx_x]) even_y_odd_x_coord_y, even_y_odd_x_coord_x = torch.meshgrid([even_idx_y, odd_idx_x]) odd_y_even_x_coord_y, odd_y_even_x_coord_x = torch.meshgrid([odd_idx_y, even_idx_x]) cur_canvas = F.pad(cur_canvas, [patch_size_x // 4, patch_size_x // 4, patch_size_y // 4, patch_size_y // 4, 0, 0, 0, 0]) def partial_render(this_canvas, patch_coord_y, patch_coord_x, stroke_id): canvas_patch = F.unfold(this_canvas, (patch_size_y, patch_size_x), stride=(patch_size_y // 2, patch_size_x // 2)) # canvas_patch: b, 3 * py * px, h * w canvas_patch = canvas_patch.view(b, 3, patch_size_y, patch_size_x, h, w).contiguous() canvas_patch = canvas_patch.permute(0, 4, 5, 1, 2, 3).contiguous() # canvas_patch: b, h, w, 3, py, px selected_canvas_patch = canvas_patch[:, patch_coord_y, patch_coord_x, :, :, :] selected_h, selected_w = selected_canvas_patch.shape[1:3] selected_param = param[:, patch_coord_y, patch_coord_x, stroke_id, :].view(-1, p).contiguous() selected_decision = decision[:, patch_coord_y, patch_coord_x, stroke_id].view(-1).contiguous() selected_foregrounds = torch.zeros(selected_param.shape[0], 3, patch_size_y, patch_size_x, device=this_canvas.device) selected_alphas = torch.zeros(selected_param.shape[0], 3, patch_size_y, patch_size_x, device=this_canvas.device) if selected_param[selected_decision, :].shape[0] > 0: selected_foregrounds[selected_decision, :, :, :], selected_alphas[selected_decision, :, :, :] = \ param2stroke(selected_param[selected_decision, :], patch_size_y, patch_size_x, meta_brushes) selected_foregrounds = selected_foregrounds.view( b, selected_h, selected_w, 3, patch_size_y, patch_size_x).contiguous() selected_alphas = selected_alphas.view(b, selected_h, selected_w, 3, patch_size_y, patch_size_x).contiguous() selected_decision = selected_decision.view(b, selected_h, selected_w, 1, 1, 1).contiguous() selected_canvas_patch = selected_foregrounds * selected_alphas * selected_decision + selected_canvas_patch * ( 1 - selected_alphas * selected_decision) this_canvas = selected_canvas_patch.permute(0, 3, 1, 4, 2, 5).contiguous() # this_canvas: b, 3, selected_h, py, selected_w, px this_canvas = this_canvas.view(b, 3, selected_h * patch_size_y, selected_w * patch_size_x).contiguous() # this_canvas: b, 3, selected_h * py, selected_w * px return this_canvas global idx if has_border: factor = 2 else: factor = 4 if even_idx_y.shape[0] > 0 and even_idx_x.shape[0] > 0: for i in range(s): canvas = partial_render(cur_canvas, even_y_even_x_coord_y, even_y_even_x_coord_x, i) if not is_odd_y: canvas = torch.cat([canvas, cur_canvas[:, :, -patch_size_y // 2:, :canvas.shape[3]]], dim=2) if not is_odd_x: canvas = torch.cat([canvas, cur_canvas[:, :, :canvas.shape[2], -patch_size_x // 2:]], dim=3) cur_canvas = canvas idx += 1 if frame_dir is not None: frame = crop(cur_canvas[:, :, patch_size_y // factor:-patch_size_y // factor, patch_size_x // factor:-patch_size_x // factor], original_h, original_w) save_img(frame[0], os.path.join(frame_dir, '%03d.jpg' % idx)) if odd_idx_y.shape[0] > 0 and odd_idx_x.shape[0] > 0: for i in range(s): canvas = partial_render(cur_canvas, odd_y_odd_x_coord_y, odd_y_odd_x_coord_x, i) canvas = torch.cat([cur_canvas[:, :, :patch_size_y // 2, -canvas.shape[3]:], canvas], dim=2) canvas = torch.cat([cur_canvas[:, :, -canvas.shape[2]:, :patch_size_x // 2], canvas], dim=3) if is_odd_y: canvas = torch.cat([canvas, cur_canvas[:, :, -patch_size_y // 2:, :canvas.shape[3]]], dim=2) if is_odd_x: canvas = torch.cat([canvas, cur_canvas[:, :, :canvas.shape[2], -patch_size_x // 2:]], dim=3) cur_canvas = canvas idx += 1 if frame_dir is not None: frame = crop(cur_canvas[:, :, patch_size_y // factor:-patch_size_y // factor, patch_size_x // factor:-patch_size_x // factor], original_h, original_w) save_img(frame[0], os.path.join(frame_dir, '%03d.jpg' % idx)) if odd_idx_y.shape[0] > 0 and even_idx_x.shape[0] > 0: for i in range(s): canvas = partial_render(cur_canvas, odd_y_even_x_coord_y, odd_y_even_x_coord_x, i) canvas = torch.cat([cur_canvas[:, :, :patch_size_y // 2, :canvas.shape[3]], canvas], dim=2) if is_odd_y: canvas = torch.cat([canvas, cur_canvas[:, :, -patch_size_y // 2:, :canvas.shape[3]]], dim=2) if not is_odd_x: canvas = torch.cat([canvas, cur_canvas[:, :, :canvas.shape[2], -patch_size_x // 2:]], dim=3) cur_canvas = canvas idx += 1 if frame_dir is not None: frame = crop(cur_canvas[:, :, patch_size_y // factor:-patch_size_y // factor, patch_size_x // factor:-patch_size_x // factor], original_h, original_w) save_img(frame[0], os.path.join(frame_dir, '%03d.jpg' % idx)) if even_idx_y.shape[0] > 0 and odd_idx_x.shape[0] > 0: for i in range(s): canvas = partial_render(cur_canvas, even_y_odd_x_coord_y, even_y_odd_x_coord_x, i) canvas = torch.cat([cur_canvas[:, :, :canvas.shape[2], :patch_size_x // 2], canvas], dim=3) if not is_odd_y: canvas = torch.cat([canvas, cur_canvas[:, :, -patch_size_y // 2:, -canvas.shape[3]:]], dim=2) if is_odd_x: canvas = torch.cat([canvas, cur_canvas[:, :, :canvas.shape[2], -patch_size_x // 2:]], dim=3) cur_canvas = canvas idx += 1 if frame_dir is not None: frame = crop(cur_canvas[:, :, patch_size_y // factor:-patch_size_y // factor, patch_size_x // factor:-patch_size_x // factor], original_h, original_w) save_img(frame[0], os.path.join(frame_dir, '%03d.jpg' % idx)) cur_canvas = cur_canvas[:, :, patch_size_y // 4:-patch_size_y // 4, patch_size_x // 4:-patch_size_x // 4] return cur_canvas def param2img_parallel(param, decision, meta_brushes, cur_canvas): """ Input stroke parameters and decisions for each patch, meta brushes, current canvas, frame directory, and whether there is a border (if intermediate painting results are required). Output the painting results of adding the corresponding strokes on the current canvas. Args: param: a tensor with shape batch size x patch along height dimension x patch along width dimension x n_stroke_per_patch x n_param_per_stroke decision: a 01 tensor with shape batch size x patch along height dimension x patch along width dimension x n_stroke_per_patch meta_brushes: a tensor with shape 2 x 3 x meta_brush_height x meta_brush_width. The first slice on the batch dimension denotes vertical brush and the second one denotes horizontal brush. cur_canvas: a tensor with shape batch size x 3 x H x W, where H and W denote height and width of padded results of original images. Returns: cur_canvas: a tensor with shape batch size x 3 x H x W, denoting painting results. """ # param: b, h, w, stroke_per_patch, param_per_stroke # decision: b, h, w, stroke_per_patch b, h, w, s, p = param.shape param = param.view(-1, 8).contiguous() decision = decision.view(-1).contiguous().bool() H, W = cur_canvas.shape[-2:] is_odd_y = h % 2 == 1 is_odd_x = w % 2 == 1 patch_size_y = 2 * H // h patch_size_x = 2 * W // w even_idx_y = torch.arange(0, h, 2, device=cur_canvas.device) even_idx_x = torch.arange(0, w, 2, device=cur_canvas.device) odd_idx_y = torch.arange(1, h, 2, device=cur_canvas.device) odd_idx_x = torch.arange(1, w, 2, device=cur_canvas.device) even_y_even_x_coord_y, even_y_even_x_coord_x = torch.meshgrid([even_idx_y, even_idx_x]) odd_y_odd_x_coord_y, odd_y_odd_x_coord_x = torch.meshgrid([odd_idx_y, odd_idx_x]) even_y_odd_x_coord_y, even_y_odd_x_coord_x = torch.meshgrid([even_idx_y, odd_idx_x]) odd_y_even_x_coord_y, odd_y_even_x_coord_x = torch.meshgrid([odd_idx_y, even_idx_x]) cur_canvas = F.pad(cur_canvas, [patch_size_x // 4, patch_size_x // 4, patch_size_y // 4, patch_size_y // 4, 0, 0, 0, 0]) foregrounds = torch.zeros(param.shape[0], 3, patch_size_y, patch_size_x, device=cur_canvas.device) alphas = torch.zeros(param.shape[0], 3, patch_size_y, patch_size_x, device=cur_canvas.device) valid_foregrounds, valid_alphas = param2stroke(param[decision, :], patch_size_y, patch_size_x, meta_brushes) foregrounds[decision, :, :, :] = valid_foregrounds alphas[decision, :, :, :] = valid_alphas # foreground, alpha: b * h * w * stroke_per_patch, 3, patch_size_y, patch_size_x foregrounds = foregrounds.view(-1, h, w, s, 3, patch_size_y, patch_size_x).contiguous() alphas = alphas.view(-1, h, w, s, 3, patch_size_y, patch_size_x).contiguous() # foreground, alpha: b, h, w, stroke_per_patch, 3, render_size_y, render_size_x decision = decision.view(-1, h, w, s, 1, 1, 1).contiguous() # decision: b, h, w, stroke_per_patch, 1, 1, 1 def partial_render(this_canvas, patch_coord_y, patch_coord_x): canvas_patch = F.unfold(this_canvas, (patch_size_y, patch_size_x), stride=(patch_size_y // 2, patch_size_x // 2)) # canvas_patch: b, 3 * py * px, h * w canvas_patch = canvas_patch.view(b, 3, patch_size_y, patch_size_x, h, w).contiguous() canvas_patch = canvas_patch.permute(0, 4, 5, 1, 2, 3).contiguous() # canvas_patch: b, h, w, 3, py, px selected_canvas_patch = canvas_patch[:, patch_coord_y, patch_coord_x, :, :, :] selected_foregrounds = foregrounds[:, patch_coord_y, patch_coord_x, :, :, :, :] selected_alphas = alphas[:, patch_coord_y, patch_coord_x, :, :, :, :] selected_decisions = decision[:, patch_coord_y, patch_coord_x, :, :, :, :] for i in range(s): cur_foreground = selected_foregrounds[:, :, :, i, :, :, :] cur_alpha = selected_alphas[:, :, :, i, :, :, :] cur_decision = selected_decisions[:, :, :, i, :, :, :] selected_canvas_patch = cur_foreground * cur_alpha * cur_decision + selected_canvas_patch * ( 1 - cur_alpha * cur_decision) this_canvas = selected_canvas_patch.permute(0, 3, 1, 4, 2, 5).contiguous() # this_canvas: b, 3, h_half, py, w_half, px h_half = this_canvas.shape[2] w_half = this_canvas.shape[4] this_canvas = this_canvas.view(b, 3, h_half * patch_size_y, w_half * patch_size_x).contiguous() # this_canvas: b, 3, h_half * py, w_half * px return this_canvas if even_idx_y.shape[0] > 0 and even_idx_x.shape[0] > 0: canvas = partial_render(cur_canvas, even_y_even_x_coord_y, even_y_even_x_coord_x) if not is_odd_y: canvas = torch.cat([canvas, cur_canvas[:, :, -patch_size_y // 2:, :canvas.shape[3]]], dim=2) if not is_odd_x: canvas = torch.cat([canvas, cur_canvas[:, :, :canvas.shape[2], -patch_size_x // 2:]], dim=3) cur_canvas = canvas if odd_idx_y.shape[0] > 0 and odd_idx_x.shape[0] > 0: canvas = partial_render(cur_canvas, odd_y_odd_x_coord_y, odd_y_odd_x_coord_x) canvas = torch.cat([cur_canvas[:, :, :patch_size_y // 2, -canvas.shape[3]:], canvas], dim=2) canvas = torch.cat([cur_canvas[:, :, -canvas.shape[2]:, :patch_size_x // 2], canvas], dim=3) if is_odd_y: canvas = torch.cat([canvas, cur_canvas[:, :, -patch_size_y // 2:, :canvas.shape[3]]], dim=2) if is_odd_x: canvas = torch.cat([canvas, cur_canvas[:, :, :canvas.shape[2], -patch_size_x // 2:]], dim=3) cur_canvas = canvas if odd_idx_y.shape[0] > 0 and even_idx_x.shape[0] > 0: canvas = partial_render(cur_canvas, odd_y_even_x_coord_y, odd_y_even_x_coord_x) canvas = torch.cat([cur_canvas[:, :, :patch_size_y // 2, :canvas.shape[3]], canvas], dim=2) if is_odd_y: canvas = torch.cat([canvas, cur_canvas[:, :, -patch_size_y // 2:, :canvas.shape[3]]], dim=2) if not is_odd_x: canvas = torch.cat([canvas, cur_canvas[:, :, :canvas.shape[2], -patch_size_x // 2:]], dim=3) cur_canvas = canvas if even_idx_y.shape[0] > 0 and odd_idx_x.shape[0] > 0: canvas = partial_render(cur_canvas, even_y_odd_x_coord_y, even_y_odd_x_coord_x) canvas = torch.cat([cur_canvas[:, :, :canvas.shape[2], :patch_size_x // 2], canvas], dim=3) if not is_odd_y: canvas = torch.cat([canvas, cur_canvas[:, :, -patch_size_y // 2:, -canvas.shape[3]:]], dim=2) if is_odd_x: canvas = torch.cat([canvas, cur_canvas[:, :, :canvas.shape[2], -patch_size_x // 2:]], dim=3) cur_canvas = canvas cur_canvas = cur_canvas[:, :, patch_size_y // 4:-patch_size_y // 4, patch_size_x // 4:-patch_size_x // 4] return cur_canvas def read_img(img_path, img_type='RGB', h=None, w=None): img = Image.open(img_path).convert(img_type) if h is not None and w is not None: img = img.resize((w, h), resample=Image.NEAREST) img = np.array(img) if img.ndim == 2: img = np.expand_dims(img, axis=-1) img = img.transpose((2, 0, 1)) img = torch.from_numpy(img).unsqueeze(0).float() / 255. return img def pad(img, H, W): b, c, h, w = img.shape pad_h = (H - h) // 2 pad_w = (W - w) // 2 remainder_h = (H - h) % 2 remainder_w = (W - w) % 2 img = torch.cat([torch.zeros((b, c, pad_h, w), device=img.device), img, torch.zeros((b, c, pad_h + remainder_h, w), device=img.device)], dim=-2) img = torch.cat([torch.zeros((b, c, H, pad_w), device=img.device), img, torch.zeros((b, c, H, pad_w + remainder_w), device=img.device)], dim=-1) return img def crop(img, h, w): H, W = img.shape[-2:] pad_h = (H - h) // 2 pad_w = (W - w) // 2 remainder_h = (H - h) % 2 remainder_w = (W - w) % 2 img = img[:, :, pad_h:H - pad_h - remainder_h, pad_w:W - pad_w - remainder_w] return img def main(input_path, model_path, output_dir, need_animation=False, resize_h=None, resize_w=None, serial=False): # if not os.path.exists(output_dir): # os.mkdir(output_dir) input_name = os.path.basename(input_path) # output_path = os.path.join(output_dir, input_name) frame_dir = None if need_animation: if not serial: print('It must be under serial mode if animation results are required, so serial flag is set to True!') serial = True frame_dir = os.path.join(output_dir, input_name[:input_name.find('.')]) if not os.path.exists(frame_dir): os.mkdir(frame_dir) patch_size = 32 stroke_num = 8 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") net_g = network.Painter(5, stroke_num, 256, 8, 3, 3).to(device) net_g.load_state_dict(torch.load(model_path)) net_g.eval() for param in net_g.parameters(): param.requires_grad = False brush_large_vertical = read_img('brush/brush_large_vertical.png', 'L').to(device) brush_large_horizontal = read_img('brush/brush_large_horizontal.png', 'L').to(device) meta_brushes = torch.cat( [brush_large_vertical, brush_large_horizontal], dim=0) with torch.no_grad(): original_img = read_img(input_path, 'RGB', resize_h, resize_w).to(device) original_h, original_w = original_img.shape[-2:] K = max(math.ceil(math.log2(max(original_h, original_w) / patch_size)), 0) original_img_pad_size = patch_size * (2 ** K) original_img_pad = pad(original_img, original_img_pad_size, original_img_pad_size) final_result = torch.zeros_like(original_img_pad).to(device) for layer in range(0, K + 1): layer_size = patch_size * (2 ** layer) img = F.interpolate(original_img_pad, (layer_size, layer_size)) result = F.interpolate(final_result, (patch_size * (2 ** layer), patch_size * (2 ** layer))) img_patch = F.unfold(img, (patch_size, patch_size), stride=(patch_size, patch_size)) result_patch = F.unfold(result, (patch_size, patch_size), stride=(patch_size, patch_size)) # There are patch_num * patch_num patches in total patch_num = (layer_size - patch_size) // patch_size + 1 # img_patch, result_patch: b, 3 * output_size * output_size, h * w img_patch = img_patch.permute(0, 2, 1).contiguous().view(-1, 3, patch_size, patch_size).contiguous() result_patch = result_patch.permute(0, 2, 1).contiguous().view( -1, 3, patch_size, patch_size).contiguous() shape_param, stroke_decision = net_g(img_patch, result_patch) stroke_decision = network.SignWithSigmoidGrad.apply(stroke_decision) grid = shape_param[:, :, :2].view(img_patch.shape[0] * stroke_num, 1, 1, 2).contiguous() img_temp = img_patch.unsqueeze(1).contiguous().repeat(1, stroke_num, 1, 1, 1).view( img_patch.shape[0] * stroke_num, 3, patch_size, patch_size).contiguous() color = F.grid_sample(img_temp, 2 * grid - 1, align_corners=False).view( img_patch.shape[0], stroke_num, 3).contiguous() stroke_param = torch.cat([shape_param, color], dim=-1) # stroke_param: b * h * w, stroke_per_patch, param_per_stroke # stroke_decision: b * h * w, stroke_per_patch, 1 param = stroke_param.view(1, patch_num, patch_num, stroke_num, 8).contiguous() decision = stroke_decision.view(1, patch_num, patch_num, stroke_num).contiguous().bool() # param: b, h, w, stroke_per_patch, 8 # decision: b, h, w, stroke_per_patch param[..., :2] = param[..., :2] / 2 + 0.25 param[..., 2:4] = param[..., 2:4] / 2 if serial: final_result = param2img_serial(param, decision, meta_brushes, final_result, frame_dir, False, original_h, original_w) else: final_result = param2img_parallel(param, decision, meta_brushes, final_result) border_size = original_img_pad_size // (2 * patch_num) img = F.interpolate(original_img_pad, (patch_size * (2 ** layer), patch_size * (2 ** layer))) result = F.interpolate(final_result, (patch_size * (2 ** layer), patch_size * (2 ** layer))) img = F.pad(img, [patch_size // 2, patch_size // 2, patch_size // 2, patch_size // 2, 0, 0, 0, 0]) result = F.pad(result, [patch_size // 2, patch_size // 2, patch_size // 2, patch_size // 2, 0, 0, 0, 0]) img_patch = F.unfold(img, (patch_size, patch_size), stride=(patch_size, patch_size)) result_patch = F.unfold(result, (patch_size, patch_size), stride=(patch_size, patch_size)) final_result = F.pad(final_result, [border_size, border_size, border_size, border_size, 0, 0, 0, 0]) h = (img.shape[2] - patch_size) // patch_size + 1 w = (img.shape[3] - patch_size) // patch_size + 1 # img_patch, result_patch: b, 3 * output_size * output_size, h * w img_patch = img_patch.permute(0, 2, 1).contiguous().view(-1, 3, patch_size, patch_size).contiguous() result_patch = result_patch.permute(0, 2, 1).contiguous().view(-1, 3, patch_size, patch_size).contiguous() shape_param, stroke_decision = net_g(img_patch, result_patch) grid = shape_param[:, :, :2].view(img_patch.shape[0] * stroke_num, 1, 1, 2).contiguous() img_temp = img_patch.unsqueeze(1).contiguous().repeat(1, stroke_num, 1, 1, 1).view( img_patch.shape[0] * stroke_num, 3, patch_size, patch_size).contiguous() color = F.grid_sample(img_temp, 2 * grid - 1, align_corners=False).view( img_patch.shape[0], stroke_num, 3).contiguous() stroke_param = torch.cat([shape_param, color], dim=-1) # stroke_param: b * h * w, stroke_per_patch, param_per_stroke # stroke_decision: b * h * w, stroke_per_patch, 1 param = stroke_param.view(1, h, w, stroke_num, 8).contiguous() decision = stroke_decision.view(1, h, w, stroke_num).contiguous().bool() # param: b, h, w, stroke_per_patch, 8 # decision: b, h, w, stroke_per_patch param[..., :2] = param[..., :2] / 2 + 0.25 param[..., 2:4] = param[..., 2:4] / 2 if serial: final_result = param2img_serial(param, decision, meta_brushes, final_result, frame_dir, True, original_h, original_w) else: final_result = param2img_parallel(param, decision, meta_brushes, final_result) final_result = final_result[:, :, border_size:-border_size, border_size:-border_size] final_result = crop(final_result, original_h, original_w) # save_img(final_result[0], output_path) tensor_to_pil = transforms.ToPILImage()(final_result[0].squeeze_(0)) return tensor_to_pil def gradio_inference(image): return main(input_path=image.name, model_path='model.pth', output_dir='output/', need_animation=False, # whether need intermediate results for animation. resize_h=512, # resize original input to this size. None means do not resize. resize_w=512, # resize original input to this size. None means do not resize. serial=False) # if need animation, serial must be True. title = "Anime2Sketch" description = "demo for Anime2Sketch. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." article = "

Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis | Github Repo

" gr.Interface( gradio_inference, [gr.inputs.Image(type="file", label="Input")], gr.outputs.Image(type="pil", label="Output"), title=title, description=description, article=article ).launch(debug=True)