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import cv2
import numpy as np
import psutil
import torch
from XMem2.inference.inference_core import InferenceCore
from XMem2.model.network import XMem
from XMem2.inference.data.mask_mapper import MaskMapper
from config import XMEM_CONFIG, DEVICE
from torchvision import transforms
from XMem2.util.range_transform import im_normalization
from XMem2.inference.interact.interactive_utils import overlay_davis
from segmenter import Segmenter
from tools.mask_display import visualize_wb_mask, mask_map
from tools.contour_detector import getting_coordinates
from tools.mask_merge import merge_masks
class TrackerCore:
name_version = 'XMem2'
def __init__(self, device: str = DEVICE):
self.device = device
if self.device.lower() != 'cpu':
self.network = XMem(XMEM_CONFIG, 'checkpoints/XMem.pth').eval().to('cuda')
else:
self.network = XMem(
XMEM_CONFIG, 'checkpoints/XMem.pth', map_location='cpu'
).eval()
self.processor = InferenceCore(self.network, XMEM_CONFIG)
self.im_transform = transforms.Compose(
[transforms.ToTensor(), im_normalization]
)
self.mapper = MaskMapper()
@torch.no_grad()
def track(
self, frame: np.ndarray, mask_segmet: np.ndarray = None, exhaustive=False
):
if mask_segmet is not None:
mask, labels = self.mapper.convert_mask(mask_segmet, exhaustive)
mask = torch.Tensor(mask).to(self.device)
self.processor.set_all_labels(list(self.mapper.remappings.values()))
else:
mask = None
labels = None
frame_tensor = self.im_transform(frame).to(self.device)
probs = self.processor.step(frame_tensor, mask, labels)
out_mask = torch.argmax(probs, dim=0)
out_mask = (out_mask.detach().cpu().numpy()).astype(np.uint8)
final_mask = np.zeros_like(out_mask)
# map back
for k, v in self.mapper.remappings.items():
final_mask[out_mask == v] = k
return final_mask
@torch.no_grad()
def clear_memory(self):
self.processor.clear_memory()
self.mapper.clear_lables()
torch.cuda.empty_cache()
if __name__ == '__main__':
path = 'video-test/video.mp4'
video = cv2.VideoCapture(path)
ret, frame = video.read()
frame_cop = frame.copy()
video.release()
bboxes = [(476, 166, 102, 154), (8, 252, 91, 149), (106, 335, 211, 90)]
points = [[531, 230], [45, 321], [226, 360], [194, 313]]
mode = 'point'
prompts = {
'point_coords': np.array([[531, 230], [45, 321], [226, 360], [194, 313]]),
'point_labels': np.array([1] * len(points)),
}
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
seg = Segmenter()
seg.set_image(frame)
maskss = []
for point in points:
prompts = {
'point_coords': np.array([point]),
'point_labels': np.array([1]),
}
masks, scores, logits = seg.predict(prompts, mode)
maskss.append(masks[np.argmax(scores)])
mask, unique_mask = merge_masks(maskss)
masks = []
images = []
traker = TrackerCore()
frames_to_propagate = 200
current_frame_index = 0
cap = cv2.VideoCapture(path)
while cap.isOpened():
current_memory_usage = psutil.virtual_memory().percent
# print(current_memory_usage)
if current_memory_usage > 90:
break
ret, frame_v = cap.read()
if not ret:
break
if current_frame_index > frames_to_propagate:
break
if current_frame_index == 0:
mask = traker.track(frame_v, unique_mask)
masks.append(mask)
images.append(frame_v)
else:
mask = traker.track(frame_v)
masks.append(mask)
images.append(frame_v)
current_frame_index += 1
video.release()
im3 = visualize_wb_mask(masks[200])
ima = images[200].copy()
for m in mask_map(masks[200]):
for box in getting_coordinates(m):
(x, y, w, h) = [v for v in box]
cv2.rectangle(ima, (x, y), (x + w, y + h), (0, 255, 0), 2)
image_m = overlay_davis(images[200], masks[200])
cv2.imshow('image_m200', image_m)
cv2.imshow('ima_rect', ima)
cv2.imshow('im2', im3)
cv2.waitKey(0)
cv2.destroyAllWindows()
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