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"""
Contains all the types of interaction related to the GUI
Not related to automatic evaluation in the DAVIS dataset
You can inherit the Interaction class to create new interaction types
undo is (sometimes partially) supported
"""
import torch
import torch.nn.functional as F
import numpy as np
import cv2
import time
from .interactive_utils import color_map, index_numpy_to_one_hot_torch
def aggregate_sbg(prob, keep_bg=False, hard=False):
device = prob.device
k, h, w = prob.shape
ex_prob = torch.zeros((k+1, h, w), device=device)
ex_prob[0] = 0.5
ex_prob[1:] = prob
ex_prob = torch.clamp(ex_prob, 1e-7, 1-1e-7)
logits = torch.log((ex_prob /(1-ex_prob)))
if hard:
# Very low temperature o((⊙﹏⊙))o 🥶
logits *= 1000
if keep_bg:
return F.softmax(logits, dim=0)
else:
return F.softmax(logits, dim=0)[1:]
def aggregate_wbg(prob, keep_bg=False, hard=False):
k, h, w = prob.shape
new_prob = torch.cat([
torch.prod(1-prob, dim=0, keepdim=True),
prob
], 0).clamp(1e-7, 1-1e-7)
logits = torch.log((new_prob /(1-new_prob)))
if hard:
# Very low temperature o((⊙﹏⊙))o 🥶
logits *= 1000
if keep_bg:
return F.softmax(logits, dim=0)
else:
return F.softmax(logits, dim=0)[1:]
class Interaction:
def __init__(self, image, prev_mask, true_size, controller):
self.image = image
self.prev_mask = prev_mask
self.controller = controller
self.start_time = time.time()
self.h, self.w = true_size
self.out_prob = None
self.out_mask = None
def predict(self):
pass
class FreeInteraction(Interaction):
def __init__(self, image, prev_mask, true_size, num_objects):
"""
prev_mask should be index format numpy array
"""
super().__init__(image, prev_mask, true_size, None)
self.K = num_objects
self.drawn_map = self.prev_mask.copy()
self.curr_path = [[] for _ in range(self.K + 1)]
self.size = None
def set_size(self, size):
self.size = size
"""
k - object id
vis - a tuple (visualization map, pass through alpha). None if not needed.
"""
def push_point(self, x, y, k, vis=None):
if vis is not None:
vis_map, vis_alpha = vis
selected = self.curr_path[k]
selected.append((x, y))
if len(selected) >= 2:
cv2.line(self.drawn_map,
(int(round(selected[-2][0])), int(round(selected[-2][1]))),
(int(round(selected[-1][0])), int(round(selected[-1][1]))),
k, thickness=self.size)
# Plot visualization
if vis is not None:
# Visualization for drawing
if k == 0:
vis_map = cv2.line(vis_map,
(int(round(selected[-2][0])), int(round(selected[-2][1]))),
(int(round(selected[-1][0])), int(round(selected[-1][1]))),
color_map[k], thickness=self.size)
else:
vis_map = cv2.line(vis_map,
(int(round(selected[-2][0])), int(round(selected[-2][1]))),
(int(round(selected[-1][0])), int(round(selected[-1][1]))),
color_map[k], thickness=self.size)
# Visualization on/off boolean filter
vis_alpha = cv2.line(vis_alpha,
(int(round(selected[-2][0])), int(round(selected[-2][1]))),
(int(round(selected[-1][0])), int(round(selected[-1][1]))),
0.75, thickness=self.size)
if vis is not None:
return vis_map, vis_alpha
def end_path(self):
# Complete the drawing
self.curr_path = [[] for _ in range(self.K + 1)]
def predict(self):
self.out_prob = index_numpy_to_one_hot_torch(self.drawn_map, self.K+1).cuda()
# self.out_prob = torch.from_numpy(self.drawn_map).float().cuda()
# self.out_prob, _ = pad_divide_by(self.out_prob, 16, self.out_prob.shape[-2:])
# self.out_prob = aggregate_sbg(self.out_prob, keep_bg=True)
return self.out_prob
class ScribbleInteraction(Interaction):
def __init__(self, image, prev_mask, true_size, controller, num_objects):
"""
prev_mask should be in an indexed form
"""
super().__init__(image, prev_mask, true_size, controller)
self.K = num_objects
self.drawn_map = np.empty((self.h, self.w), dtype=np.uint8)
self.drawn_map.fill(255)
# background + k
self.curr_path = [[] for _ in range(self.K + 1)]
self.size = 3
"""
k - object id
vis - a tuple (visualization map, pass through alpha). None if not needed.
"""
def push_point(self, x, y, k, vis=None):
if vis is not None:
vis_map, vis_alpha = vis
selected = self.curr_path[k]
selected.append((x, y))
if len(selected) >= 2:
self.drawn_map = cv2.line(self.drawn_map,
(int(round(selected[-2][0])), int(round(selected[-2][1]))),
(int(round(selected[-1][0])), int(round(selected[-1][1]))),
k, thickness=self.size)
# Plot visualization
if vis is not None:
# Visualization for drawing
if k == 0:
vis_map = cv2.line(vis_map,
(int(round(selected[-2][0])), int(round(selected[-2][1]))),
(int(round(selected[-1][0])), int(round(selected[-1][1]))),
color_map[k], thickness=self.size)
else:
vis_map = cv2.line(vis_map,
(int(round(selected[-2][0])), int(round(selected[-2][1]))),
(int(round(selected[-1][0])), int(round(selected[-1][1]))),
color_map[k], thickness=self.size)
# Visualization on/off boolean filter
vis_alpha = cv2.line(vis_alpha,
(int(round(selected[-2][0])), int(round(selected[-2][1]))),
(int(round(selected[-1][0])), int(round(selected[-1][1]))),
0.75, thickness=self.size)
# Optional vis return
if vis is not None:
return vis_map, vis_alpha
def end_path(self):
# Complete the drawing
self.curr_path = [[] for _ in range(self.K + 1)]
def predict(self):
self.out_prob = self.controller.interact(self.image.unsqueeze(0), self.prev_mask, self.drawn_map)
self.out_prob = aggregate_wbg(self.out_prob, keep_bg=True, hard=True)
return self.out_prob
class ClickInteraction(Interaction):
def __init__(self, image, prev_mask, true_size, controller, tar_obj):
"""
prev_mask in a prob. form
"""
super().__init__(image, prev_mask, true_size, controller)
self.tar_obj = tar_obj
# negative/positive for each object
self.pos_clicks = []
self.neg_clicks = []
self.out_prob = self.prev_mask.clone()
"""
neg - Negative interaction or not
vis - a tuple (visualization map, pass through alpha). None if not needed.
"""
def push_point(self, x, y, neg, vis=None):
# Clicks
if neg:
self.neg_clicks.append((x, y))
else:
self.pos_clicks.append((x, y))
# Do the prediction
self.obj_mask = self.controller.interact(self.image.unsqueeze(0), x, y, not neg)
# Plot visualization
if vis is not None:
vis_map, vis_alpha = vis
# Visualization for clicks
if neg:
vis_map = cv2.circle(vis_map,
(int(round(x)), int(round(y))),
2, color_map[0], thickness=-1)
else:
vis_map = cv2.circle(vis_map,
(int(round(x)), int(round(y))),
2, color_map[self.tar_obj], thickness=-1)
vis_alpha = cv2.circle(vis_alpha,
(int(round(x)), int(round(y))),
2, 1, thickness=-1)
# Optional vis return
return vis_map, vis_alpha
def predict(self):
self.out_prob = self.prev_mask.clone()
# a small hack to allow the interacting object to overwrite existing masks
# without remembering all the object probabilities
self.out_prob = torch.clamp(self.out_prob, max=0.9)
self.out_prob[self.tar_obj] = self.obj_mask
self.out_prob = aggregate_wbg(self.out_prob[1:], keep_bg=True, hard=True)
return self.out_prob
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