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import sys
sys.path.append("..")
sys.path.append("./sam")
from sam.segment_anything import sam_model_registry, SamAutomaticMaskGenerator
from aot_tracker import get_aot
import numpy as np
from tool.segmentor import Segmentor
from tool.detector import Detector
from tool.transfer_tools import draw_outline, draw_points
import cv2
from seg_track_anything import draw_mask
class SegTracker():
def __init__(self,segtracker_args, sam_args, aot_args) -> None:
"""
Initialize SAM and AOT.
"""
self.sam = Segmentor(sam_args)
self.tracker = get_aot(aot_args)
self.detector = Detector(self.sam.device)
self.sam_gap = segtracker_args['sam_gap']
self.min_area = segtracker_args['min_area']
self.max_obj_num = segtracker_args['max_obj_num']
self.min_new_obj_iou = segtracker_args['min_new_obj_iou']
self.reference_objs_list = []
self.object_idx = 1
self.curr_idx = 1
self.origin_merged_mask = None # init by segment-everything or update
self.first_frame_mask = None
# debug
self.everything_points = []
self.everything_labels = []
print("SegTracker has been initialized")
def seg(self,frame):
'''
Arguments:
frame: numpy array (h,w,3)
Return:
origin_merged_mask: numpy array (h,w)
'''
frame = frame[:, :, ::-1]
anns = self.sam.everything_generator.generate(frame)
# anns is a list recording all predictions in an image
if len(anns) == 0:
return
# merge all predictions into one mask (h,w)
# note that the merged mask may lost some objects due to the overlapping
self.origin_merged_mask = np.zeros(anns[0]['segmentation'].shape,dtype=np.uint8)
idx = 1
for ann in anns:
if ann['area'] > self.min_area:
m = ann['segmentation']
self.origin_merged_mask[m==1] = idx
idx += 1
self.everything_points.append(ann["point_coords"][0])
self.everything_labels.append(1)
obj_ids = np.unique(self.origin_merged_mask)
obj_ids = obj_ids[obj_ids!=0]
self.object_idx = 1
for id in obj_ids:
if np.sum(self.origin_merged_mask==id) < self.min_area or self.object_idx > self.max_obj_num:
self.origin_merged_mask[self.origin_merged_mask==id] = 0
else:
self.origin_merged_mask[self.origin_merged_mask==id] = self.object_idx
self.object_idx += 1
self.first_frame_mask = self.origin_merged_mask
return self.origin_merged_mask
def update_origin_merged_mask(self, updated_merged_mask):
self.origin_merged_mask = updated_merged_mask
# obj_ids = np.unique(updated_merged_mask)
# obj_ids = obj_ids[obj_ids!=0]
# self.object_idx = int(max(obj_ids)) + 1
def reset_origin_merged_mask(self, mask, id):
self.origin_merged_mask = mask
self.curr_idx = id
def add_reference(self,frame,mask,frame_step=0):
'''
Add objects in a mask for tracking.
Arguments:
frame: numpy array (h,w,3)
mask: numpy array (h,w)
'''
self.reference_objs_list.append(np.unique(mask))
self.curr_idx = self.get_obj_num() + 1
self.tracker.add_reference_frame(frame,mask, self.curr_idx - 1, frame_step)
def track(self,frame,update_memory=False):
'''
Track all known objects.
Arguments:
frame: numpy array (h,w,3)
Return:
origin_merged_mask: numpy array (h,w)
'''
pred_mask = self.tracker.track(frame)
if update_memory:
self.tracker.update_memory(pred_mask)
return pred_mask.squeeze(0).squeeze(0).detach().cpu().numpy().astype(np.uint8)
def get_tracking_objs(self):
objs = set()
for ref in self.reference_objs_list:
objs.update(set(ref))
objs = list(sorted(list(objs)))
objs = [i for i in objs if i!=0]
return objs
def get_obj_num(self):
objs = self.get_tracking_objs()
if len(objs) == 0: return 0
return int(max(objs))
def find_new_objs(self, track_mask, seg_mask):
'''
Compare tracked results from AOT with segmented results from SAM. Select objects from background if they are not tracked.
Arguments:
track_mask: numpy array (h,w)
seg_mask: numpy array (h,w)
Return:
new_obj_mask: numpy array (h,w)
'''
new_obj_mask = (track_mask==0) * seg_mask
new_obj_ids = np.unique(new_obj_mask)
new_obj_ids = new_obj_ids[new_obj_ids!=0]
# obj_num = self.get_obj_num() + 1
obj_num = self.curr_idx
for idx in new_obj_ids:
new_obj_area = np.sum(new_obj_mask==idx)
obj_area = np.sum(seg_mask==idx)
if new_obj_area/obj_area < self.min_new_obj_iou or new_obj_area < self.min_area\
or obj_num > self.max_obj_num:
new_obj_mask[new_obj_mask==idx] = 0
else:
new_obj_mask[new_obj_mask==idx] = obj_num
obj_num += 1
return new_obj_mask
def restart_tracker(self):
self.tracker.restart()
def seg_acc_bbox(self, origin_frame: np.ndarray, bbox: np.ndarray,):
''''
Use bbox-prompt to get mask
Parameters:
origin_frame: H, W, C
bbox: [[x0, y0], [x1, y1]]
Return:
refined_merged_mask: numpy array (h, w)
masked_frame: numpy array (h, w, c)
'''
# get interactive_mask
interactive_mask = self.sam.segment_with_box(origin_frame, bbox)[0]
refined_merged_mask = self.add_mask(interactive_mask)
# draw mask
masked_frame = draw_mask(origin_frame.copy(), refined_merged_mask)
# draw bbox
masked_frame = cv2.rectangle(masked_frame, bbox[0], bbox[1], (0, 0, 255))
return refined_merged_mask, masked_frame
def seg_acc_click(self, origin_frame: np.ndarray, coords: np.ndarray, modes: np.ndarray, multimask=True):
'''
Use point-prompt to get mask
Parameters:
origin_frame: H, W, C
coords: nd.array [[x, y]]
modes: nd.array [[1]]
Return:
refined_merged_mask: numpy array (h, w)
masked_frame: numpy array (h, w, c)
'''
# get interactive_mask
interactive_mask = self.sam.segment_with_click(origin_frame, coords, modes, multimask)
refined_merged_mask = self.add_mask(interactive_mask)
# draw mask
masked_frame = draw_mask(origin_frame.copy(), refined_merged_mask)
# draw points
# self.everything_labels = np.array(self.everything_labels).astype(np.int64)
# self.everything_points = np.array(self.everything_points).astype(np.int64)
masked_frame = draw_points(coords, modes, masked_frame)
# draw outline
masked_frame = draw_outline(interactive_mask, masked_frame)
return refined_merged_mask, masked_frame
def add_mask(self, interactive_mask: np.ndarray):
'''
Merge interactive mask with self.origin_merged_mask
Parameters:
interactive_mask: numpy array (h, w)
Return:
refined_merged_mask: numpy array (h, w)
'''
if self.origin_merged_mask is None:
self.origin_merged_mask = np.zeros(interactive_mask.shape,dtype=np.uint8)
refined_merged_mask = self.origin_merged_mask.copy()
refined_merged_mask[interactive_mask > 0] = self.curr_idx
return refined_merged_mask
def detect_and_seg(self, origin_frame: np.ndarray, grounding_caption, box_threshold, text_threshold, box_size_threshold=1, reset_image=False):
'''
Using Grounding-DINO to detect object acc Text-prompts
Retrun:
refined_merged_mask: numpy array (h, w)
annotated_frame: numpy array (h, w, 3)
'''
# backup id and origin-merged-mask
bc_id = self.curr_idx
bc_mask = self.origin_merged_mask
# get annotated_frame and boxes
annotated_frame, boxes = self.detector.run_grounding(origin_frame, grounding_caption, box_threshold, text_threshold)
for i in range(len(boxes)):
bbox = boxes[i]
if (bbox[1][0] - bbox[0][0]) * (bbox[1][1] - bbox[0][1]) > annotated_frame.shape[0] * annotated_frame.shape[1] * box_size_threshold:
continue
interactive_mask = self.sam.segment_with_box(origin_frame, bbox, reset_image)[0]
refined_merged_mask = self.add_mask(interactive_mask)
self.update_origin_merged_mask(refined_merged_mask)
self.curr_idx += 1
# reset origin_mask
self.reset_origin_merged_mask(bc_mask, bc_id)
return refined_merged_mask, annotated_frame
if __name__ == '__main__':
from model_args import segtracker_args,sam_args,aot_args
Seg_Tracker = SegTracker(segtracker_args, sam_args, aot_args)
# ------------------ detect test ----------------------
origin_frame = cv2.imread('/data2/cym/Seg_Tra_any/Segment-and-Track-Anything/debug/point.png')
origin_frame = cv2.cvtColor(origin_frame, cv2.COLOR_BGR2RGB)
grounding_caption = "swan.water"
box_threshold = 0.25
text_threshold = 0.25
predicted_mask, annotated_frame = Seg_Tracker.detect_and_seg(origin_frame, grounding_caption, box_threshold, text_threshold)
masked_frame = draw_mask(annotated_frame, predicted_mask)
origin_frame = cv2.cvtColor(origin_frame, cv2.COLOR_RGB2BGR)
cv2.imwrite('./debug/masked_frame.png', masked_frame)
cv2.imwrite('./debug/x.png', annotated_frame) |