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from time import time
import numpy as np
import torch
import cv2
from isegm.inference import utils
from isegm.inference.clicker import Click, Clicker
try:
get_ipython()
from tqdm import tqdm_notebook as tqdm
except NameError:
from tqdm import tqdm
def evaluate_dataset(dataset, predictor, sam_type=None, oracle=False, gra_oracle=False, **kwargs):
all_ious = []
start_time = time()
all_gras = {}
for index in tqdm(range(len(dataset)), leave=False):
sample = dataset.get_sample(index)
for object_id in sample.objects_ids:
if gra_oracle:
sample_ious, gra_idx = evaluate_sample_oracle(sample.image, sample.gt_mask(object_id), predictor,
sample_id=index, sam_type=sam_type, oracle=oracle, **kwargs)
all_gras[gra_idx] = all_gras.get(gra_idx, 0) + 1
else:
_, sample_ious, _ = evaluate_sample(sample.image, sample.gt_mask(object_id), predictor,
sample_id=index, sam_type=sam_type, oracle=oracle, **kwargs)
all_ious.append(sample_ious)
end_time = time()
elapsed_time = end_time - start_time
if len(all_gras) > 0:
print(all_gras)
return all_ious, elapsed_time
def evaluate_sample(image, gt_mask, predictor, max_iou_thr,
pred_thr=0.49, min_clicks=1, max_clicks=20,
sample_id=None, sam_type=False, oracle=False, callback=None):
clicker = Clicker(gt_mask=gt_mask)
pred_mask = np.zeros_like(gt_mask)
ious_list = []
with torch.no_grad():
predictor.set_input_image(image)
if sam_type == 'SAM':
for click_indx in range(max_clicks):
clicker.make_next_click(pred_mask)
point_coords, point_labels = get_sam_input(clicker)
if oracle:
ious = []
pred_masks = []
pred_probs, _, _ = predictor.predict(point_coords, point_labels, multimask_output=True, return_logits=True)
for idx in range(pred_probs.shape[0]):
pred_masks.append(pred_probs[idx] > predictor.model.mask_threshold)
ious.append(utils.get_iou(gt_mask, pred_masks[-1]))
tgt_idx = np.argmax(np.array(ious))
iou = ious[tgt_idx]
pred_mask = pred_masks[tgt_idx]
else:
pred_probs, _, _ = predictor.predict(point_coords, point_labels, multimask_output=False, return_logits=True)
pred_probs = pred_probs[0]
pred_mask = pred_probs > predictor.model.mask_threshold
iou = utils.get_iou(gt_mask, pred_mask)
if callback is not None:
callback(image, gt_mask, pred_probs, sample_id, click_indx, clicker.clicks_list)
ious_list.append(iou)
if iou >= max_iou_thr and click_indx + 1 >= min_clicks:
break
return clicker.clicks_list, np.array(ious_list, dtype=np.float32), pred_probs
else:
for click_indx in range(max_clicks):
clicker.make_next_click(pred_mask)
pred_probs = predictor.get_prediction(clicker)
pred_mask = pred_probs > pred_thr
iou = utils.get_iou(gt_mask, pred_mask)
if callback is not None:
callback(image, gt_mask, pred_probs, sample_id, click_indx, clicker.clicks_list)
ious_list.append(iou)
if iou >= max_iou_thr and click_indx + 1 >= min_clicks:
break
return clicker.clicks_list, np.array(ious_list, dtype=np.float32), pred_probs
def evaluate_sample_oracle(image, gt_mask, predictor, max_iou_thr,
pred_thr=0.49, min_clicks=1, max_clicks=20,
sample_id=None, sam_type=False, oracle=False, callback=None):
clicker = Clicker(gt_mask=gt_mask)
ious_lists = []
click_indxs = []
with torch.no_grad():
predictor.set_input_image(image)
min_num = 100
for gra in range(1, 11):
cur_gra = round(gra * 0.1, 1)
ious_list = []
clicker.reset_clicks()
pred_mask = np.zeros_like(gt_mask)
if sam_type == 'SAM_GraCo':
for click_indx in range(max_clicks):
clicker.make_next_click(pred_mask)
point_coords, point_labels = get_sam_input(clicker)
if oracle:
ious = []
pred_masks = []
pred_probs, _, _ = predictor.predict(point_coords, point_labels, gra=cur_gra, multimask_output=True, return_logits=True)
for idx in range(pred_probs.shape[0]):
pred_masks.append(pred_probs[idx] > predictor.model.mask_threshold)
ious.append(utils.get_iou(gt_mask, pred_masks[-1]))
tgt_idx = np.argmax(np.array(ious))
iou = ious[tgt_idx]
pred_mask = pred_masks[tgt_idx]
else:
pred_probs, _, _ = predictor.predict(point_coords, point_labels, gra=cur_gra, multimask_output=False, return_logits=True)
pred_probs = pred_probs[0]
pred_mask = pred_probs > predictor.model.mask_threshold
iou = utils.get_iou(gt_mask, pred_mask)
if callback is not None:
callback(image, gt_mask, pred_probs, sample_id, click_indx, clicker.clicks_list)
ious_list.append(iou)
if iou >= max_iou_thr and click_indx + 1 >= min_clicks:
min_num = min(min_num, click_indx + 1)
break
if min_num <= max_clicks and click_indx + 1 > min_num:
break
else:
predictor.prev_prediction = torch.zeros_like(predictor.original_image[:, :1, :, :])
for click_indx in range(max_clicks):
clicker.make_next_click(pred_mask)
pred_probs = predictor.get_prediction(clicker, gra=cur_gra)
pred_mask = pred_probs > pred_thr
iou = utils.get_iou(gt_mask, pred_mask)
if callback is not None:
callback(image, gt_mask, pred_probs, sample_id, click_indx, clicker.clicks_list)
ious_list.append(iou)
if iou >= max_iou_thr and click_indx + 1 >= min_clicks:
min_num = min(min_num, click_indx + 1)
break
if min_num <= max_clicks and click_indx + 1 > min_num:
break
ious_lists.append(np.array(ious_list, dtype=np.float32))
click_indxs.append(click_indx)
click_indxs = np.array(click_indxs)
tgt_idxs = np.squeeze(np.argwhere(click_indxs == np.min(click_indxs)), axis=1)
selected_ious = [ious_lists[i] for i in tgt_idxs]
max_index = np.argmax([ious[0] for ious in selected_ious])
ious = selected_ious[max_index]
tgt_idx = tgt_idxs[max_index]
return ious, tgt_idx
def get_sam_input(clicker, reverse=True):
clicks_list = clicker.get_clicks()
points_nd = get_points_nd([clicks_list])
point_length = len(points_nd[0]) // 2
point_coords = []
point_labels = []
for i, point in enumerate(points_nd[0]):
if point[0] == -1:
continue
if i < point_length:
point_labels.append(1)
else:
point_labels.append(0)
if reverse:
point_coords.append([point[1], point[0]]) # for SAM
return np.array(point_coords), np.array(point_labels)
def get_points_nd(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)
num_max_points = max(1, num_max_points)
for clicks_list in clicks_lists:
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 total_clicks
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