423 lines
14 KiB
Python
423 lines
14 KiB
Python
import abc
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from typing import Optional
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import cv2
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import torch
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import numpy as np
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from loguru import logger
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from iopaint.helper import (
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boxes_from_mask,
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resize_max_size,
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pad_img_to_modulo,
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switch_mps_device,
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)
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from iopaint.schema import InpaintRequest, HDStrategy, SDSampler
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from .helper.g_diffuser_bot import expand_image
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from .utils import get_scheduler
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class InpaintModel:
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name = "base"
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min_size: Optional[int] = None
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pad_mod = 8
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pad_to_square = False
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is_erase_model = False
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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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device = switch_mps_device(self.name, device)
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self.device = device
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self.init_model(device, **kwargs)
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@abc.abstractmethod
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def init_model(self, device, **kwargs):
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...
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@staticmethod
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@abc.abstractmethod
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def is_downloaded() -> bool:
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return False
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@abc.abstractmethod
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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, 1] 255 为 masks 区域
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return: BGR IMAGE
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"""
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...
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@staticmethod
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def download():
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...
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def _pad_forward(self, image, mask, config: InpaintRequest):
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origin_height, origin_width = image.shape[:2]
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pad_image = pad_img_to_modulo(
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image, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size
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)
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pad_mask = pad_img_to_modulo(
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mask, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size
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)
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# logger.info(f"final forward pad size: {pad_image.shape}")
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image, mask = self.forward_pre_process(image, mask, config)
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result = self.forward(pad_image, pad_mask, config)
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result = result[0:origin_height, 0:origin_width, :]
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result, image, mask = self.forward_post_process(result, image, mask, config)
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if config.sd_keep_unmasked_area:
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mask = mask[:, :, np.newaxis]
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result = result * (mask / 255) + image[:, :, ::-1] * (1 - (mask / 255))
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return result
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def forward_pre_process(self, image, mask, config):
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return image, mask
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def forward_post_process(self, result, image, mask, config):
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return result, image, mask
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@torch.no_grad()
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def __call__(self, image, mask, config: InpaintRequest):
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"""
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images: [H, W, C] RGB, not normalized
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masks: [H, W]
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return: BGR IMAGE
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"""
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inpaint_result = None
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# logger.info(f"hd_strategy: {config.hd_strategy}")
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if config.hd_strategy == HDStrategy.CROP:
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if max(image.shape) > config.hd_strategy_crop_trigger_size:
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logger.info(f"Run crop strategy")
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boxes = boxes_from_mask(mask)
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crop_result = []
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for box in boxes:
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crop_image, crop_box = self._run_box(image, mask, box, config)
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crop_result.append((crop_image, crop_box))
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inpaint_result = image[:, :, ::-1]
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for crop_image, crop_box in crop_result:
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x1, y1, x2, y2 = crop_box
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inpaint_result[y1:y2, x1:x2, :] = crop_image
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elif config.hd_strategy == HDStrategy.RESIZE:
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if max(image.shape) > config.hd_strategy_resize_limit:
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origin_size = image.shape[:2]
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downsize_image = resize_max_size(
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image, size_limit=config.hd_strategy_resize_limit
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)
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downsize_mask = resize_max_size(
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mask, size_limit=config.hd_strategy_resize_limit
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)
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logger.info(
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f"Run resize strategy, origin size: {image.shape} forward size: {downsize_image.shape}"
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)
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inpaint_result = self._pad_forward(
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downsize_image, downsize_mask, config
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)
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# only paste masked area result
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inpaint_result = cv2.resize(
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inpaint_result,
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(origin_size[1], origin_size[0]),
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interpolation=cv2.INTER_CUBIC,
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)
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original_pixel_indices = mask < 127
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inpaint_result[original_pixel_indices] = image[:, :, ::-1][
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original_pixel_indices
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]
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if inpaint_result is None:
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inpaint_result = self._pad_forward(image, mask, config)
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return inpaint_result
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def _crop_box(self, image, mask, box, config: InpaintRequest):
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"""
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Args:
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image: [H, W, C] RGB
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mask: [H, W, 1]
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box: [left,top,right,bottom]
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Returns:
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BGR IMAGE, (l, r, r, b)
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"""
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box_h = box[3] - box[1]
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box_w = box[2] - box[0]
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cx = (box[0] + box[2]) // 2
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cy = (box[1] + box[3]) // 2
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img_h, img_w = image.shape[:2]
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w = box_w + config.hd_strategy_crop_margin * 2
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h = box_h + config.hd_strategy_crop_margin * 2
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_l = cx - w // 2
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_r = cx + w // 2
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_t = cy - h // 2
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_b = cy + h // 2
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l = max(_l, 0)
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r = min(_r, img_w)
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t = max(_t, 0)
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b = min(_b, img_h)
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# try to get more context when crop around image edge
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if _l < 0:
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r += abs(_l)
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if _r > img_w:
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l -= _r - img_w
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if _t < 0:
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b += abs(_t)
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if _b > img_h:
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t -= _b - img_h
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l = max(l, 0)
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r = min(r, img_w)
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t = max(t, 0)
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b = min(b, img_h)
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crop_img = image[t:b, l:r, :]
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crop_mask = mask[t:b, l:r]
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# logger.info(f"box size: ({box_h},{box_w}) crop size: {crop_img.shape}")
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return crop_img, crop_mask, [l, t, r, b]
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def _calculate_cdf(self, histogram):
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cdf = histogram.cumsum()
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normalized_cdf = cdf / float(cdf.max())
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return normalized_cdf
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def _calculate_lookup(self, source_cdf, reference_cdf):
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lookup_table = np.zeros(256)
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lookup_val = 0
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for source_index, source_val in enumerate(source_cdf):
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for reference_index, reference_val in enumerate(reference_cdf):
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if reference_val >= source_val:
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lookup_val = reference_index
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break
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lookup_table[source_index] = lookup_val
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return lookup_table
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def _match_histograms(self, source, reference, mask):
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transformed_channels = []
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for channel in range(source.shape[-1]):
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source_channel = source[:, :, channel]
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reference_channel = reference[:, :, channel]
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# only calculate histograms for non-masked parts
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source_histogram, _ = np.histogram(source_channel[mask == 0], 256, [0, 256])
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reference_histogram, _ = np.histogram(
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reference_channel[mask == 0], 256, [0, 256]
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)
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source_cdf = self._calculate_cdf(source_histogram)
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reference_cdf = self._calculate_cdf(reference_histogram)
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lookup = self._calculate_lookup(source_cdf, reference_cdf)
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transformed_channels.append(cv2.LUT(source_channel, lookup))
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result = cv2.merge(transformed_channels)
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result = cv2.convertScaleAbs(result)
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return result
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def _apply_cropper(self, image, mask, config: InpaintRequest):
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img_h, img_w = image.shape[:2]
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l, t, w, h = (
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config.croper_x,
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config.croper_y,
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config.croper_width,
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config.croper_height,
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)
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r = l + w
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b = t + h
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l = max(l, 0)
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r = min(r, img_w)
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t = max(t, 0)
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b = min(b, img_h)
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crop_img = image[t:b, l:r, :]
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crop_mask = mask[t:b, l:r]
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return crop_img, crop_mask, (l, t, r, b)
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def _run_box(self, image, mask, box, config: InpaintRequest):
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"""
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Args:
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image: [H, W, C] RGB
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mask: [H, W, 1]
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box: [left,top,right,bottom]
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Returns:
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BGR IMAGE
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"""
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crop_img, crop_mask, [l, t, r, b] = self._crop_box(image, mask, box, config)
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return self._pad_forward(crop_img, crop_mask, config), [l, t, r, b]
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class DiffusionInpaintModel(InpaintModel):
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def __init__(self, device, **kwargs):
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self.model_info = kwargs["model_info"]
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self.model_id_or_path = self.model_info.path
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super().__init__(device, **kwargs)
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@torch.no_grad()
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def __call__(self, image, mask, config: InpaintRequest):
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"""
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images: [H, W, C] RGB, not normalized
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masks: [H, W]
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return: BGR IMAGE
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"""
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# boxes = boxes_from_mask(mask)
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if config.use_croper:
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crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
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crop_image = self._scaled_pad_forward(crop_img, crop_mask, config)
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inpaint_result = image[:, :, ::-1]
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inpaint_result[t:b, l:r, :] = crop_image
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elif config.use_extender:
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inpaint_result = self._do_outpainting(image, config)
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else:
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inpaint_result = self._scaled_pad_forward(image, mask, config)
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return inpaint_result
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def _do_outpainting(self, image, config: InpaintRequest):
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# cropper 和 image 在同一个坐标系下，croper_x/y 可能为负数
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# 从 image 中 crop 出 outpainting 区域
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image_h, image_w = image.shape[:2]
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cropper_l = config.extender_x
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cropper_t = config.extender_y
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cropper_r = config.extender_x + config.extender_width
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cropper_b = config.extender_y + config.extender_height
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image_l = 0
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image_t = 0
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image_r = image_w
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image_b = image_h
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# 类似求 IOU
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l = max(cropper_l, image_l)
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t = max(cropper_t, image_t)
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r = min(cropper_r, image_r)
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b = min(cropper_b, image_b)
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assert (
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0 <= l < r and 0 <= t < b
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), f"cropper and image not overlap, {l},{t},{r},{b}"
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cropped_image = image[t:b, l:r, :]
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padding_l = max(0, image_l - cropper_l)
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padding_t = max(0, image_t - cropper_t)
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padding_r = max(0, cropper_r - image_r)
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padding_b = max(0, cropper_b - image_b)
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zero_padding_count = [padding_l, padding_t, padding_r, padding_b].count(0)
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if zero_padding_count not in [0, 3]:
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logger.warning(
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f"padding count({zero_padding_count}) not 0 or 3, may result in bad edge outpainting"
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)
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expanded_image, mask_image = expand_image(
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cropped_image,
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left=padding_l,
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top=padding_t,
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right=padding_r,
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bottom=padding_b,
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softness=config.sd_outpainting_softness,
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space=config.sd_outpainting_space,
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)
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# 最终扩大了的 image, BGR
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expanded_cropped_result_image = self._scaled_pad_forward(
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expanded_image, mask_image, config
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)
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# RGB -> BGR
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outpainting_image = cv2.copyMakeBorder(
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image,
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left=padding_l,
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top=padding_t,
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right=padding_r,
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bottom=padding_b,
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borderType=cv2.BORDER_CONSTANT,
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value=0,
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)[:, :, ::-1]
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# 把 cropped_result_image 贴到 outpainting_image 上，这一步不需要 blend
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paste_t = 0 if config.extender_y < 0 else config.extender_y
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paste_l = 0 if config.extender_x < 0 else config.extender_x
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outpainting_image[
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paste_t : paste_t + expanded_cropped_result_image.shape[0],
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paste_l : paste_l + expanded_cropped_result_image.shape[1],
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:,
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] = expanded_cropped_result_image
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return outpainting_image
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def _scaled_pad_forward(self, image, mask, config: InpaintRequest):
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longer_side_length = int(config.sd_scale * max(image.shape[:2]))
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origin_size = image.shape[:2]
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downsize_image = resize_max_size(image, size_limit=longer_side_length)
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downsize_mask = resize_max_size(mask, size_limit=longer_side_length)
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if config.sd_scale != 1:
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logger.info(
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f"Resize image to do sd inpainting: {image.shape} -> {downsize_image.shape}"
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)
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inpaint_result = self._pad_forward(downsize_image, downsize_mask, config)
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# only paste masked area result
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inpaint_result = cv2.resize(
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inpaint_result,
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(origin_size[1], origin_size[0]),
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interpolation=cv2.INTER_CUBIC,
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)
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# blend result, copy from g_diffuser_bot
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# mask_rgb = 1.0 - np_img_grey_to_rgb(mask / 255.0)
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# inpaint_result = np.clip(
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# inpaint_result * (1.0 - mask_rgb) + image * mask_rgb, 0.0, 255.0
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# )
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# original_pixel_indices = mask < 127
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# inpaint_result[original_pixel_indices] = image[:, :, ::-1][
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# original_pixel_indices
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# ]
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return inpaint_result
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def set_scheduler(self, config: InpaintRequest):
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scheduler_config = self.model.scheduler.config
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sd_sampler = config.sd_sampler
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if config.sd_lcm_lora:
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sd_sampler = SDSampler.lcm
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logger.info(f"LCM Lora enabled, use {sd_sampler} sampler")
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scheduler = get_scheduler(sd_sampler, scheduler_config)
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self.model.scheduler = scheduler
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def forward_pre_process(self, image, mask, config):
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if config.sd_mask_blur != 0:
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k = 2 * config.sd_mask_blur + 1
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mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
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return image, mask
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def forward_post_process(self, result, image, mask, config):
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if config.sd_match_histograms:
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result = self._match_histograms(result, image[:, :, ::-1], mask)
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if config.sd_mask_blur != 0:
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k = 2 * config.sd_mask_blur + 1
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mask = cv2.GaussianBlur(mask, (k, k), 0)
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return result, image, mask
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