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import cv2
import numpy as np
import psutil
from tqdm import tqdm
from tools.mask_merge import merge_masks
from tracker_core_xmem2 import TrackerCore
from tools.overlay_image import painter_borders
from XMem2.inference.interact.interactive_utils import overlay_davis
from sam_controller import SegmenterController
from interactive_video import InteractVideo
class Tracker:
def __init__(
self, segmenter_controller: SegmenterController, tracker_core: TrackerCore
):
self.sam_controller = segmenter_controller
self.tracker = tracker_core
print(f'used {TrackerCore.name_version}')
def select_object(self, prompts: dict) -> np.ndarray:
# maskss = []
# for point in points:
# prompts = {
# 'point_coords': np.array([point]),
# 'point_labels': np.array([1]),
# }
# masks, scores, logits = self.segmenter.predict(prompts, 'point')
# maskss.append(masks[np.argmax(scores)])
results = self.sam_controller.predict_from_prompts(prompts)
results_masks = [
result[np.argmax(scores)] for result, scores, logits in results
]
mask, unique_mask = merge_masks(results_masks)
return unique_mask
def tracking(
self,
frames: list[np.ndarray],
template_mask: np.ndarray,
exhaustive: bool = False,
) -> list:
masks = []
for i in tqdm(range(len(frames)), desc='Tracking'):
current_memory_usage = psutil.virtual_memory().percent
if current_memory_usage > 90:
break
"""
TODO: улучшение точности
- надо проверять сколько масок в трекере
- смотреть сколько объектов обнаруживается
- если они не совпадают добавлять к новым маскам маску из трекера
"""
if i == 0:
mask = self.tracker.track(frames[i], template_mask, exhaustive)
masks.append(mask)
else:
mask = self.tracker.track(frames[i])
masks.append(mask)
return masks
def tracking_cut(
self,
frames: list[np.ndarray],
templates_masks: dict[str, np.ndarray],
exhaustive: bool = False,
):
masks = []
for i in tqdm(range(len(frames)), desc='Tracking_cut'):
current_memory_usage = psutil.virtual_memory().percent
if current_memory_usage > 90:
break
if str(i) in templates_masks:
template_mask = templates_masks[str(i)]
if i == 0 and str(i) in templates_masks:
mask = self.tracker.track(frames[i], template_mask, exhaustive)
masks.append(mask)
else:
mask = self.tracker.track(frames[i])
masks.append(mask)
if len(templates_masks) > 1:
exhaustive = True
return masks
if __name__ == '__main__':
path = 'video-test/VID_20241218_134328.mp4'
key_interval = 3
controller = InteractVideo(path, key_interval)
controller.extract_frames()
controller.collect_keypoints()
results = controller.get_results()
segmenter_controller = SegmenterController()
tracker_core = TrackerCore()
tracker = Tracker(segmenter_controller, tracker_core)
frames = results['frames']
# prompts = {
# 'mode': 'point',
# 'point_coords': [[531, 230], [45, 321], [226, 360], [194, 313]],
# 'point_labels': [1, 1, 1, 1],
# }
frames_idx = list(map(int, results['keypoints'].keys()))
result = []
for i in range(len(frames_idx) - 1):
current_frame = frames_idx[i]
current_coords = results['keypoints'][str(current_frame)]
next_frame = frames_idx[i + 1]
print(current_frame, next_frame)
if current_coords:
tracker.sam_controller.load_image(frames[current_frame])
prompts = {
'mode': 'point',
'point_coords': current_coords,
'point_labels': [1] * len(current_coords),
}
mask = tracker.select_object(prompts)
tracker.sam_controller.reset_image()
result.append(
{
"gap": [current_frame, next_frame],
"frame": current_frame,
"mask": mask,
}
)
else:
result.append(
{
"gap": [current_frame, next_frame],
"frame": current_frame,
"mask": None,
}
)
# masks = tracking.tracking(frames, mask)
masks = []
for res in result:
current_frame, next_frame = res['gap']
if res['mask'] is not None:
print(current_frame, next_frame)
mask = tracker.tracking(frames[current_frame:next_frame], res['mask'])
tracker.tracker.clear_memory()
masks += mask
else:
print(current_frame, next_frame)
m = []
for _ in range(current_frame, next_frame):
height, width, _ = frames[current_frame].shape
binary_mask = np.zeros((height, width), dtype=np.uint8)
binary_mask[:, :] = 1
m.append(binary_mask)
masks += m
filename = 'output_video_from_file_mem2_ved_pot.mp4'
output = cv2.VideoWriter(
filename, cv2.VideoWriter_fourcc(*'XVID'), controller.fps, controller.frame_size
)
for frame, mask in zip(frames, masks):
# f = painter_borders(frame, mask)
f = overlay_davis(frame, mask)
output.write(f)
# Освобождаем ресурсы
output.release()
cv2.destroyAllWindows()
print(f'Видео записано в файл: {filename}')
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