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')