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