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