265 lines
8.3 KiB
Python
265 lines
8.3 KiB
Python
import json
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import os
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from typing import Tuple, List
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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from loguru import logger
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from pydantic import BaseModel
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from lama_cleaner.helper import (
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load_jit_model,
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load_img,
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)
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from lama_cleaner.plugins.base_plugin import BasePlugin
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class Click(BaseModel):
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# [y, x]
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coords: Tuple[float, float]
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is_positive: bool
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indx: int
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@property
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def coords_and_indx(self):
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return (*self.coords, self.indx)
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def scale(self, x_ratio: float, y_ratio: float) -> "Click":
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return Click(
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coords=(self.coords[0] * x_ratio, self.coords[1] * y_ratio),
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is_positive=self.is_positive,
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indx=self.indx,
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)
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class ResizeTrans:
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def __init__(self, size=480):
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super().__init__()
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self.crop_height = size
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self.crop_width = size
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def transform(self, image_nd, clicks_lists):
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assert image_nd.shape[0] == 1 and len(clicks_lists) == 1
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image_height, image_width = image_nd.shape[2:4]
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self.image_height = image_height
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self.image_width = image_width
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image_nd_r = F.interpolate(
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image_nd,
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(self.crop_height, self.crop_width),
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mode="bilinear",
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align_corners=True,
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)
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y_ratio = self.crop_height / image_height
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x_ratio = self.crop_width / image_width
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clicks_lists_resized = []
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for clicks_list in clicks_lists:
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clicks_list_resized = [
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click.scale(y_ratio, x_ratio) for click in clicks_list
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]
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clicks_lists_resized.append(clicks_list_resized)
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return image_nd_r, clicks_lists_resized
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def inv_transform(self, prob_map):
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new_prob_map = F.interpolate(
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prob_map,
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(self.image_height, self.image_width),
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mode="bilinear",
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align_corners=True,
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)
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return new_prob_map
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class ISPredictor(object):
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def __init__(
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self,
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model,
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device,
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open_kernel_size: int,
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dilate_kernel_size: int,
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net_clicks_limit=None,
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zoom_in=None,
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infer_size=384,
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):
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self.model = model
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self.open_kernel_size = open_kernel_size
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self.dilate_kernel_size = dilate_kernel_size
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self.net_clicks_limit = net_clicks_limit
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self.device = device
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self.zoom_in = zoom_in
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self.infer_size = infer_size
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# self.transforms = [zoom_in] if zoom_in is not None else []
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def __call__(self, input_image: torch.Tensor, clicks: List[Click], prev_mask):
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"""
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Args:
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input_image: [1, 3, H, W] [0~1]
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clicks: List[Click]
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prev_mask: [1, 1, H, W]
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Returns:
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"""
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transforms = [ResizeTrans(self.infer_size)]
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input_image = torch.cat((input_image, prev_mask), dim=1)
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# image_nd resized to infer_size
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for t in transforms:
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image_nd, clicks_lists = t.transform(input_image, [clicks])
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# image_nd.shape = [1, 4, 256, 256]
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# points_nd.sha[e = [1, 2, 3]
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# clicks_lists[0][0] Click 类
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points_nd = self.get_points_nd(clicks_lists)
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pred_logits = self.model(image_nd, points_nd)
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pred = torch.sigmoid(pred_logits)
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pred = self.post_process(pred)
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prediction = F.interpolate(
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pred, mode="bilinear", align_corners=True, size=image_nd.size()[2:]
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)
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for t in reversed(transforms):
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prediction = t.inv_transform(prediction)
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# if self.zoom_in is not None and self.zoom_in.check_possible_recalculation():
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# return self.get_prediction(clicker)
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return prediction.cpu().numpy()[0, 0]
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def post_process(self, pred: torch.Tensor) -> torch.Tensor:
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pred_mask = pred.cpu().numpy()[0][0]
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# morph_open to remove small noise
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kernel_size = self.open_kernel_size
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kernel = cv2.getStructuringElement(
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cv2.MORPH_ELLIPSE, (kernel_size, kernel_size)
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)
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pred_mask = cv2.morphologyEx(pred_mask, cv2.MORPH_OPEN, kernel, iterations=1)
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# Why dilate: make region slightly larger to avoid missing some pixels, this generally works better
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dilate_kernel_size = self.dilate_kernel_size
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if dilate_kernel_size > 1:
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kernel = cv2.getStructuringElement(
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cv2.MORPH_DILATE, (dilate_kernel_size, dilate_kernel_size)
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)
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pred_mask = cv2.dilate(pred_mask, kernel, 1)
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return torch.from_numpy(pred_mask).unsqueeze(0).unsqueeze(0)
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def get_points_nd(self, clicks_lists):
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total_clicks = []
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num_pos_clicks = [
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sum(x.is_positive for x in clicks_list) for clicks_list in clicks_lists
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]
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num_neg_clicks = [
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len(clicks_list) - num_pos
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for clicks_list, num_pos in zip(clicks_lists, num_pos_clicks)
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]
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num_max_points = max(num_pos_clicks + num_neg_clicks)
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if self.net_clicks_limit is not None:
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num_max_points = min(self.net_clicks_limit, num_max_points)
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num_max_points = max(1, num_max_points)
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for clicks_list in clicks_lists:
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clicks_list = clicks_list[: self.net_clicks_limit]
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pos_clicks = [
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click.coords_and_indx for click in clicks_list if click.is_positive
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]
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pos_clicks = pos_clicks + (num_max_points - len(pos_clicks)) * [
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(-1, -1, -1)
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]
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neg_clicks = [
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click.coords_and_indx for click in clicks_list if not click.is_positive
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]
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neg_clicks = neg_clicks + (num_max_points - len(neg_clicks)) * [
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(-1, -1, -1)
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]
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total_clicks.append(pos_clicks + neg_clicks)
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return torch.tensor(total_clicks, device=self.device)
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INTERACTIVE_SEG_MODEL_URL = os.environ.get(
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"INTERACTIVE_SEG_MODEL_URL",
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"https://github.com/Sanster/models/releases/download/clickseg_pplnet/clickseg_pplnet.pt",
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)
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INTERACTIVE_SEG_MODEL_MD5 = os.environ.get(
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"INTERACTIVE_SEG_MODEL_MD5", "8ca44b6e02bca78f62ec26a3c32376cf"
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)
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class InteractiveSeg(BasePlugin):
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name = "InteractiveSeg"
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def __init__(self, infer_size=384, open_kernel_size=3, dilate_kernel_size=3):
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super().__init__()
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device = torch.device("cpu")
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model = load_jit_model(
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INTERACTIVE_SEG_MODEL_URL, device, INTERACTIVE_SEG_MODEL_MD5
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).eval()
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self.predictor = ISPredictor(
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model,
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device,
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infer_size=infer_size,
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open_kernel_size=open_kernel_size,
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dilate_kernel_size=dilate_kernel_size,
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)
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def __call__(self, rgb_np_img, files, form):
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image = rgb_np_img
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if "mask" in files:
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mask, _ = load_img(files["mask"].read(), gray=True)
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else:
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mask = None
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_clicks = json.loads(form["clicks"])
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clicks = []
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for i, click in enumerate(_clicks):
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clicks.append(
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Click(coords=(click[1], click[0]), indx=i, is_positive=click[2] == 1)
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)
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new_mask = self.forward(image, clicks=clicks, prev_mask=mask)
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return new_mask
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def forward(self, image, clicks, prev_mask=None):
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"""
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Args:
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image: [H,W,C] RGB
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clicks:
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Returns:
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"""
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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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image = torch.from_numpy((image / 255).transpose(2, 0, 1)).unsqueeze(0).float()
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if prev_mask is None:
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mask = torch.zeros_like(image[:, :1, :, :])
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else:
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logger.info("InteractiveSeg run with prev_mask")
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mask = torch.from_numpy(prev_mask / 255).unsqueeze(0).unsqueeze(0).float()
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pred_probs = self.predictor(image, clicks, mask)
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pred_mask = pred_probs > 0.5
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pred_mask = (pred_mask * 255).astype(np.uint8)
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# Find largest contour
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# pred_mask = only_keep_largest_contour(pred_mask)
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# To simplify frontend process, add mask brush color here
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fg = pred_mask == 255
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bg = pred_mask != 255
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pred_mask = cv2.cvtColor(pred_mask, cv2.COLOR_GRAY2BGRA)
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# frontend brush color "ffcc00bb"
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pred_mask[bg] = 0
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pred_mask[fg] = [255, 203, 0, int(255 * 0.73)]
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pred_mask = cv2.cvtColor(pred_mask, cv2.COLOR_BGRA2RGBA)
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return pred_mask
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