import os import time import cv2 import torch import torch.nn.functional as F from lama_cleaner.helper import get_cache_path_by_url, load_jit_model from lama_cleaner.schema import Config import numpy as np from lama_cleaner.model.base import InpaintModel ZITS_INPAINT_MODEL_URL = os.environ.get( "ZITS_INPAINT_MODEL_URL", "https://github.com/Sanster/models/releases/download/add_zits/zits-inpaint-0717.pt", ) ZITS_INPAINT_MODEL_MD5 = os.environ.get( "ZITS_INPAINT_MODEL_MD5", "9978cc7157dc29699e42308d675b2154" ) ZITS_EDGE_LINE_MODEL_URL = os.environ.get( "ZITS_EDGE_LINE_MODEL_URL", "https://github.com/Sanster/models/releases/download/add_zits/zits-edge-line-0717.pt", ) ZITS_EDGE_LINE_MODEL_MD5 = os.environ.get( "ZITS_EDGE_LINE_MODEL_MD5", "55e31af21ba96bbf0c80603c76ea8c5f" ) ZITS_STRUCTURE_UPSAMPLE_MODEL_URL = os.environ.get( "ZITS_STRUCTURE_UPSAMPLE_MODEL_URL", "https://github.com/Sanster/models/releases/download/add_zits/zits-structure-upsample-0717.pt", ) ZITS_STRUCTURE_UPSAMPLE_MODEL_MD5 = os.environ.get( "ZITS_STRUCTURE_UPSAMPLE_MODEL_MD5", "3d88a07211bd41b2ec8cc0d999f29927" ) ZITS_WIRE_FRAME_MODEL_URL = os.environ.get( "ZITS_WIRE_FRAME_MODEL_URL", "https://github.com/Sanster/models/releases/download/add_zits/zits-wireframe-0717.pt", ) ZITS_WIRE_FRAME_MODEL_MD5 = os.environ.get( "ZITS_WIRE_FRAME_MODEL_MD5", "a9727c63a8b48b65c905d351b21ce46b" ) def resize(img, height, width, center_crop=False): imgh, imgw = img.shape[0:2] if center_crop and imgh != imgw: # center crop side = np.minimum(imgh, imgw) j = (imgh - side) // 2 i = (imgw - side) // 2 img = img[j : j + side, i : i + side, ...] if imgh > height and imgw > width: inter = cv2.INTER_AREA else: inter = cv2.INTER_LINEAR img = cv2.resize(img, (height, width), interpolation=inter) return img def to_tensor(img, scale=True, norm=False): if img.ndim == 2: img = img[:, :, np.newaxis] c = img.shape[-1] if scale: img_t = torch.from_numpy(img).permute(2, 0, 1).float().div(255) else: img_t = torch.from_numpy(img).permute(2, 0, 1).float() if norm: mean = torch.tensor([0.5, 0.5, 0.5]).reshape(c, 1, 1) std = torch.tensor([0.5, 0.5, 0.5]).reshape(c, 1, 1) img_t = (img_t - mean) / std return img_t def load_masked_position_encoding(mask): ones_filter = np.ones((3, 3), dtype=np.float32) d_filter1 = np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=np.float32) d_filter2 = np.array([[0, 0, 0], [1, 1, 0], [1, 1, 0]], dtype=np.float32) d_filter3 = np.array([[0, 1, 1], [0, 1, 1], [0, 0, 0]], dtype=np.float32) d_filter4 = np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], dtype=np.float32) str_size = 256 pos_num = 128 ori_mask = mask.copy() ori_h, ori_w = ori_mask.shape[0:2] ori_mask = ori_mask / 255 mask = cv2.resize(mask, (str_size, str_size), interpolation=cv2.INTER_AREA) mask[mask > 0] = 255 h, w = mask.shape[0:2] mask3 = mask.copy() mask3 = 1.0 - (mask3 / 255.0) pos = np.zeros((h, w), dtype=np.int32) direct = np.zeros((h, w, 4), dtype=np.int32) i = 0 while np.sum(1 - mask3) > 0: i += 1 mask3_ = cv2.filter2D(mask3, -1, ones_filter) mask3_[mask3_ > 0] = 1 sub_mask = mask3_ - mask3 pos[sub_mask == 1] = i m = cv2.filter2D(mask3, -1, d_filter1) m[m > 0] = 1 m = m - mask3 direct[m == 1, 0] = 1 m = cv2.filter2D(mask3, -1, d_filter2) m[m > 0] = 1 m = m - mask3 direct[m == 1, 1] = 1 m = cv2.filter2D(mask3, -1, d_filter3) m[m > 0] = 1 m = m - mask3 direct[m == 1, 2] = 1 m = cv2.filter2D(mask3, -1, d_filter4) m[m > 0] = 1 m = m - mask3 direct[m == 1, 3] = 1 mask3 = mask3_ abs_pos = pos.copy() rel_pos = pos / (str_size / 2) # to 0~1 maybe larger than 1 rel_pos = (rel_pos * pos_num).astype(np.int32) rel_pos = np.clip(rel_pos, 0, pos_num - 1) if ori_w != w or ori_h != h: rel_pos = cv2.resize(rel_pos, (ori_w, ori_h), interpolation=cv2.INTER_NEAREST) rel_pos[ori_mask == 0] = 0 direct = cv2.resize(direct, (ori_w, ori_h), interpolation=cv2.INTER_NEAREST) direct[ori_mask == 0, :] = 0 return rel_pos, abs_pos, direct def load_image(img, mask, device, sigma256=3.0): """ Args: img: [H, W, C] RGB mask: [H, W] 255 为 masks 区域 sigma256: Returns: """ h, w, _ = img.shape imgh, imgw = img.shape[0:2] img_256 = resize(img, 256, 256) mask = (mask > 127).astype(np.uint8) * 255 mask_256 = cv2.resize(mask, (256, 256), interpolation=cv2.INTER_AREA) mask_256[mask_256 > 0] = 255 mask_512 = cv2.resize(mask, (512, 512), interpolation=cv2.INTER_AREA) mask_512[mask_512 > 0] = 255 # original skimage implemention # https://scikit-image.org/docs/stable/api/skimage.feature.html#skimage.feature.canny # low_threshold: Lower bound for hysteresis thresholding (linking edges). If None, low_threshold is set to 10% of dtype’s max. # high_threshold: Upper bound for hysteresis thresholding (linking edges). If None, high_threshold is set to 20% of dtype’s max. try: import skimage gray_256 = skimage.color.rgb2gray(img_256) edge_256 = skimage.feature.canny(gray_256, sigma=3.0, mask=None).astype(float) # cv2.imwrite("skimage_gray.jpg", (gray_256*255).astype(np.uint8)) # cv2.imwrite("skimage_edge.jpg", (edge_256*255).astype(np.uint8)) except: gray_256 = cv2.cvtColor(img_256, cv2.COLOR_RGB2GRAY) gray_256_blured = cv2.GaussianBlur(gray_256, ksize=(7, 7), sigmaX=sigma256, sigmaY=sigma256) edge_256 = cv2.Canny(gray_256_blured, threshold1=int(255*0.1), threshold2=int(255*0.2)) # cv2.imwrite("opencv_edge.jpg", edge_256) # line img_512 = resize(img, 512, 512) rel_pos, abs_pos, direct = load_masked_position_encoding(mask) batch = dict() batch["images"] = to_tensor(img.copy()).unsqueeze(0).to(device) batch["img_256"] = to_tensor(img_256, norm=True).unsqueeze(0).to(device) batch["masks"] = to_tensor(mask).unsqueeze(0).to(device) batch["mask_256"] = to_tensor(mask_256).unsqueeze(0).to(device) batch["mask_512"] = to_tensor(mask_512).unsqueeze(0).to(device) batch["edge_256"] = to_tensor(edge_256, scale=False).unsqueeze(0).to(device) batch["img_512"] = to_tensor(img_512).unsqueeze(0).to(device) batch["rel_pos"] = torch.LongTensor(rel_pos).unsqueeze(0).to(device) batch["abs_pos"] = torch.LongTensor(abs_pos).unsqueeze(0).to(device) batch["direct"] = torch.LongTensor(direct).unsqueeze(0).to(device) batch["h"] = imgh batch["w"] = imgw return batch def to_device(data, device): if isinstance(data, torch.Tensor): return data.to(device) if isinstance(data, dict): for key in data: if isinstance(data[key], torch.Tensor): data[key] = data[key].to(device) return data if isinstance(data, list): return [to_device(d, device) for d in data] class ZITS(InpaintModel): name = "zits" min_size = 256 pad_mod = 32 pad_to_square = True def __init__(self, device, **kwargs): """ Args: device: """ super().__init__(device) self.device = device self.sample_edge_line_iterations = 1 def init_model(self, device, **kwargs): self.wireframe = load_jit_model(ZITS_WIRE_FRAME_MODEL_URL, device, ZITS_WIRE_FRAME_MODEL_MD5) self.edge_line = load_jit_model(ZITS_EDGE_LINE_MODEL_URL, device, ZITS_EDGE_LINE_MODEL_MD5) self.structure_upsample = load_jit_model( ZITS_STRUCTURE_UPSAMPLE_MODEL_URL, device, ZITS_STRUCTURE_UPSAMPLE_MODEL_MD5 ) self.inpaint = load_jit_model(ZITS_INPAINT_MODEL_URL, device, ZITS_INPAINT_MODEL_MD5) @staticmethod def is_downloaded() -> bool: model_paths = [ get_cache_path_by_url(ZITS_WIRE_FRAME_MODEL_URL), get_cache_path_by_url(ZITS_EDGE_LINE_MODEL_URL), get_cache_path_by_url(ZITS_STRUCTURE_UPSAMPLE_MODEL_URL), get_cache_path_by_url(ZITS_INPAINT_MODEL_URL), ] return all([os.path.exists(it) for it in model_paths]) def wireframe_edge_and_line(self, items, enable: bool): # 最终向 items 中添加 edge 和 line key if not enable: items["edge"] = torch.zeros_like(items["masks"]) items["line"] = torch.zeros_like(items["masks"]) return start = time.time() try: line_256 = self.wireframe_forward( items["img_512"], h=256, w=256, masks=items["mask_512"], mask_th=0.85, ) except: line_256 = torch.zeros_like(items["mask_256"]) print(f"wireframe_forward time: {(time.time() - start) * 1000:.2f}ms") # np_line = (line[0][0].numpy() * 255).astype(np.uint8) # cv2.imwrite("line.jpg", np_line) start = time.time() edge_pred, line_pred = self.sample_edge_line_logits( context=[items["img_256"], items["edge_256"], line_256], mask=items["mask_256"].clone(), iterations=self.sample_edge_line_iterations, add_v=0.05, mul_v=4, ) print(f"sample_edge_line_logits time: {(time.time() - start) * 1000:.2f}ms") # np_edge_pred = (edge_pred[0][0].numpy() * 255).astype(np.uint8) # cv2.imwrite("edge_pred.jpg", np_edge_pred) # np_line_pred = (line_pred[0][0].numpy() * 255).astype(np.uint8) # cv2.imwrite("line_pred.jpg", np_line_pred) # exit() input_size = min(items["h"], items["w"]) if input_size != 256 and input_size > 256: while edge_pred.shape[2] < input_size: edge_pred = self.structure_upsample(edge_pred) edge_pred = torch.sigmoid((edge_pred + 2) * 2) line_pred = self.structure_upsample(line_pred) line_pred = torch.sigmoid((line_pred + 2) * 2) edge_pred = F.interpolate( edge_pred, size=(input_size, input_size), mode="bilinear", align_corners=False, ) line_pred = F.interpolate( line_pred, size=(input_size, input_size), mode="bilinear", align_corners=False, ) # np_edge_pred = (edge_pred[0][0].numpy() * 255).astype(np.uint8) # cv2.imwrite("edge_pred_upsample.jpg", np_edge_pred) # np_line_pred = (line_pred[0][0].numpy() * 255).astype(np.uint8) # cv2.imwrite("line_pred_upsample.jpg", np_line_pred) # exit() items["edge"] = edge_pred.detach() items["line"] = line_pred.detach() @torch.no_grad() def forward(self, image, mask, config: Config): """Input images and output images have same size images: [H, W, C] RGB masks: [H, W] return: BGR IMAGE """ mask = mask[:, :, 0] items = load_image(image, mask, device=self.device) self.wireframe_edge_and_line(items, config.zits_wireframe) inpainted_image = self.inpaint( items["images"], items["masks"], items["edge"], items["line"], items["rel_pos"], items["direct"], ) inpainted_image = inpainted_image * 255.0 inpainted_image = ( inpainted_image.cpu().permute(0, 2, 3, 1)[0].numpy().astype(np.uint8) ) inpainted_image = inpainted_image[:, :, ::-1] # cv2.imwrite("inpainted.jpg", inpainted_image) # exit() return inpainted_image def wireframe_forward(self, images, h, w, masks, mask_th=0.925): lcnn_mean = torch.tensor([109.730, 103.832, 98.681]).reshape(1, 3, 1, 1) lcnn_std = torch.tensor([22.275, 22.124, 23.229]).reshape(1, 3, 1, 1) images = images * 255.0 # the masks value of lcnn is 127.5 masked_images = images * (1 - masks) + torch.ones_like(images) * masks * 127.5 masked_images = (masked_images - lcnn_mean) / lcnn_std def to_int(x): return tuple(map(int, x)) lines_tensor = [] lmap = np.zeros((h, w)) output_masked = self.wireframe(masked_images) output_masked = to_device(output_masked, "cpu") if output_masked["num_proposals"] == 0: lines_masked = [] scores_masked = [] else: lines_masked = output_masked["lines_pred"].numpy() lines_masked = [ [line[1] * h, line[0] * w, line[3] * h, line[2] * w] for line in lines_masked ] scores_masked = output_masked["lines_score"].numpy() for line, score in zip(lines_masked, scores_masked): if score > mask_th: try: import skimage rr, cc, value = skimage.draw.line_aa( *to_int(line[0:2]), *to_int(line[2:4]) ) lmap[rr, cc] = np.maximum(lmap[rr, cc], value) except: cv2.line(lmap, to_int(line[0:2][::-1]), to_int(line[2:4][::-1]), (1, 1, 1), 1, cv2.LINE_AA) lmap = np.clip(lmap * 255, 0, 255).astype(np.uint8) lines_tensor.append(to_tensor(lmap).unsqueeze(0)) lines_tensor = torch.cat(lines_tensor, dim=0) return lines_tensor.detach().to(self.device) def sample_edge_line_logits( self, context, mask=None, iterations=1, add_v=0, mul_v=4 ): [img, edge, line] = context img = img * (1 - mask) edge = edge * (1 - mask) line = line * (1 - mask) for i in range(iterations): edge_logits, line_logits = self.edge_line(img, edge, line, masks=mask) edge_pred = torch.sigmoid(edge_logits) line_pred = torch.sigmoid((line_logits + add_v) * mul_v) edge = edge + edge_pred * mask edge[edge >= 0.25] = 1 edge[edge < 0.25] = 0 line = line + line_pred * mask b, _, h, w = edge_pred.shape edge_pred = edge_pred.reshape(b, -1, 1) line_pred = line_pred.reshape(b, -1, 1) mask = mask.reshape(b, -1) edge_probs = torch.cat([1 - edge_pred, edge_pred], dim=-1) line_probs = torch.cat([1 - line_pred, line_pred], dim=-1) edge_probs[:, :, 1] += 0.5 line_probs[:, :, 1] += 0.5 edge_max_probs = edge_probs.max(dim=-1)[0] + (1 - mask) * (-100) line_max_probs = line_probs.max(dim=-1)[0] + (1 - mask) * (-100) indices = torch.sort( edge_max_probs + line_max_probs, dim=-1, descending=True )[1] for ii in range(b): keep = int((i + 1) / iterations * torch.sum(mask[ii, ...])) assert torch.sum(mask[ii][indices[ii, :keep]]) == keep, "Error!!!" mask[ii][indices[ii, :keep]] = 0 mask = mask.reshape(b, 1, h, w) edge = edge * (1 - mask) line = line * (1 - mask) edge, line = edge.to(torch.float32), line.to(torch.float32) return edge, line