import time import torch import cv2 from PIL import Image, ImageDraw, ImageOps import numpy as np from typing import Union from segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator import matplotlib.pyplot as plt import PIL from .mask_painter import mask_painter as mask_painter2 from .base_segmenter import BaseSegmenter from .painter import mask_painter, point_painter import os import requests import sys mask_color = 3 mask_alpha = 0.7 contour_color = 1 contour_width = 5 point_color_ne = 8 point_color_ps = 50 point_alpha = 0.9 point_radius = 15 contour_color = 2 contour_width = 5 class SamControler(): def __init__(self, SAM_checkpoint, model_type, device): ''' initialize sam controler ''' self.sam_controler = BaseSegmenter(SAM_checkpoint, model_type, device) # def seg_again(self, image: np.ndarray): # ''' # it is used when interact in video # ''' # self.sam_controler.reset_image() # self.sam_controler.set_image(image) # return def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True,mask_color=3): ''' it is used in first frame in video return: mask, logit, painted image(mask+point) ''' # self.sam_controler.set_image(image) origal_image = self.sam_controler.orignal_image neg_flag = labels[-1] if neg_flag==1: #find neg prompts = { 'point_coords': points, 'point_labels': labels, } masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask) mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :] prompts = { 'point_coords': points, 'point_labels': labels, 'mask_input': logit[None, :, :] } masks, scores, logits = self.sam_controler.predict(prompts, 'both', multimask) mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :] else: #find positive prompts = { 'point_coords': points, 'point_labels': labels, } masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask) mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :] assert len(points)==len(labels) painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width) painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width) painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width) painted_image = Image.fromarray(painted_image) return mask, logit, painted_image # def interact_loop(self, image:np.ndarray, same: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True): # origal_image = self.sam_controler.orignal_image # if same: # ''' # true; loop in the same image # ''' # prompts = { # 'point_coords': points, # 'point_labels': labels, # 'mask_input': logits[None, :, :] # } # masks, scores, logits = self.sam_controler.predict(prompts, 'both', multimask) # mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :] # painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width) # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width) # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width) # painted_image = Image.fromarray(painted_image) # return mask, logit, painted_image # else: # ''' # loop in the different image, interact in the video # ''' # if image is None: # raise('Image error') # else: # self.seg_again(image) # prompts = { # 'point_coords': points, # 'point_labels': labels, # } # masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask) # mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :] # painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width) # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width) # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width) # painted_image = Image.fromarray(painted_image) # return mask, logit, painted_image # def initialize(): # ''' # initialize sam controler # ''' # checkpoint_url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth" # folder = "segmenter" # SAM_checkpoint= './checkpoints/sam_vit_h_4b8939.pth' # download_checkpoint(checkpoint_url, folder, SAM_checkpoint) # model_type = 'vit_h' # device = "cuda:0" # sam_controler = BaseSegmenter(SAM_checkpoint, model_type, device) # return sam_controler # def seg_again(sam_controler, image: np.ndarray): # ''' # it is used when interact in video # ''' # sam_controler.reset_image() # sam_controler.set_image(image) # return # def first_frame_click(sam_controler, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True): # ''' # it is used in first frame in video # return: mask, logit, painted image(mask+point) # ''' # sam_controler.set_image(image) # prompts = { # 'point_coords': points, # 'point_labels': labels, # } # masks, scores, logits = sam_controler.predict(prompts, 'point', multimask) # mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :] # assert len(points)==len(labels) # painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width) # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width) # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width) # painted_image = Image.fromarray(painted_image) # return mask, logit, painted_image # def interact_loop(sam_controler, image:np.ndarray, same: bool, points:np.ndarray, labels: np.ndarray, logits: np.ndarray=None, multimask=True): # if same: # ''' # true; loop in the same image # ''' # prompts = { # 'point_coords': points, # 'point_labels': labels, # 'mask_input': logits[None, :, :] # } # masks, scores, logits = sam_controler.predict(prompts, 'both', multimask) # mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :] # painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width) # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width) # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width) # painted_image = Image.fromarray(painted_image) # return mask, logit, painted_image # else: # ''' # loop in the different image, interact in the video # ''' # if image is None: # raise('Image error') # else: # seg_again(sam_controler, image) # prompts = { # 'point_coords': points, # 'point_labels': labels, # } # masks, scores, logits = sam_controler.predict(prompts, 'point', multimask) # mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :] # painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width) # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width) # painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width) # painted_image = Image.fromarray(painted_image) # return mask, logit, painted_image # if __name__ == "__main__": # points = np.array([[500, 375], [1125, 625]]) # labels = np.array([1, 1]) # image = cv2.imread('/hhd3/gaoshang/truck.jpg') # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # sam_controler = initialize() # mask, logit, painted_image_full = first_frame_click(sam_controler,image, points, labels, multimask=True) # painted_image = mask_painter2(image, mask.astype('uint8'), background_alpha=0.8) # painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR) # numpy array (h, w, 3) # cv2.imwrite('/hhd3/gaoshang/truck_point.jpg', painted_image) # cv2.imwrite('/hhd3/gaoshang/truck_change.jpg', image) # painted_image_full.save('/hhd3/gaoshang/truck_point_full.jpg') # mask, logit, painted_image_full = interact_loop(sam_controler,image,True, points, np.array([1, 0]), logit, multimask=True) # painted_image = mask_painter2(image, mask.astype('uint8'), background_alpha=0.8) # painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR) # numpy array (h, w, 3) # cv2.imwrite('/hhd3/gaoshang/truck_same.jpg', painted_image) # painted_image_full.save('/hhd3/gaoshang/truck_same_full.jpg') # mask, logit, painted_image_full = interact_loop(sam_controler,image, False, points, labels, multimask=True) # painted_image = mask_painter2(image, mask.astype('uint8'), background_alpha=0.8) # painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR) # numpy array (h, w, 3) # cv2.imwrite('/hhd3/gaoshang/truck_diff.jpg', painted_image) # painted_image_full.save('/hhd3/gaoshang/truck_diff_full.jpg')