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import os
import cv2
import torch
import numpy as np
from tqdm import tqdm
from torchvision import transforms
import imageio
import argparse
import sys
sys.path.append("RAFT/core")
from raft import RAFT
from utils.utils import InputPadder
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
def load_raft_model(ckpt_path):
args = argparse.Namespace(
small=False,
mixed_precision=False,
alternate_corr=False,
dropout=0.0,
max_depth=8,
depth_network=False,
depth_residual=False,
depth_scale=1.0
)
model = torch.nn.DataParallel(RAFT(args))
model.load_state_dict(torch.load(ckpt_path, map_location=DEVICE))
return model.module.to(DEVICE).eval()
def run_masking(video_path, output_path, mask_path, raft):
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
print(f"Failed to open video: {video_path}")
return
fps = cap.get(cv2.CAP_PROP_FPS)
n_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
ok, first = cap.read()
if not ok:
print(f"Failed to read first frame in {video_path}")
return
resize_to = (720, 480)
first = cv2.resize(first, resize_to)
H, W, _ = first.shape
area_thresh = (H * W) // 6
grid = np.stack(np.meshgrid(np.arange(W), np.arange(H)), -1).astype(np.float32)
pos = grid.copy()
vis = np.ones((H, W), dtype=bool)
writer = imageio.get_writer(output_path, fps=int(fps))
prev = first.copy()
frames_since_corr = 0
freeze_mask = False
frozen_mask = None
all_masks = []
writer.append_data(first[:, :, ::-1])
all_masks.append(np.ones((H, W), dtype=bool))
def to_tensor(bgr):
return transforms.ToTensor()(bgr).unsqueeze(0).to(DEVICE)
def raft_flow(img1_bgr, img2_bgr):
t1, t2 = to_tensor(img1_bgr), to_tensor(img2_bgr)
padder = InputPadder(t1.shape)
i1, i2 = padder.pad(t1, t2)
with torch.no_grad():
_, flow = raft(i1, i2, iters=20, test_mode=True)
return padder.unpad(flow)[0].permute(1, 2, 0).cpu().numpy()
for _ in range(1, n_frames):
ok, cur = cap.read()
if not ok:
break
cur = cv2.resize(cur, resize_to)
if not freeze_mask:
flow_fw = raft_flow(prev, cur)
pos += flow_fw
frames_since_corr += 1
x_ok = (0 <= pos[..., 0]) & (pos[..., 0] < W)
y_ok = (0 <= pos[..., 1]) & (pos[..., 1] < H)
vis &= x_ok & y_ok
m = np.zeros((H, W), np.uint8)
ys, xs = np.where(vis)
px = np.round(pos[ys, xs, 0]).astype(int)
py = np.round(pos[ys, xs, 1]).astype(int)
inb = (0 <= px) & (px < W) & (0 <= py) & (py < H)
m[py[inb], px[inb]] = 1
m = cv2.dilate(m, np.ones((2, 2), np.uint8))
visible_ratio = m.sum() / (H * W)
if visible_ratio < 0.3:
flow_0t = raft_flow(first, cur)
pos = grid + flow_0t
vis = np.ones((H, W), dtype=bool)
x_ok = (0 <= pos[..., 0]) & (pos[..., 0] < W)
y_ok = (0 <= pos[..., 1]) & (pos[..., 1] < H)
vis &= x_ok & y_ok
m.fill(0)
ys, xs = np.where(vis)
px = np.round(pos[ys, xs, 0]).astype(int)
py = np.round(pos[ys, xs, 1]).astype(int)
inb = (0 <= px) & (px < W) & (0 <= py) & (py < H)
m[py[inb], px[inb]] = 1
m = cv2.dilate(m, np.ones((2, 2), np.uint8))
if m.sum() < area_thresh:
freeze_mask = True
frozen_mask = m.copy()
frames_since_corr = 0
else:
m = frozen_mask
effective_mask = m.astype(bool)
all_masks.append(effective_mask)
out = cur.copy()
out[~effective_mask] = 0
writer.append_data(out[:, :, ::-1])
prev = cur if not freeze_mask else prev
writer.close()
cap.release()
all_masks_array = np.stack(all_masks, axis=0)
np.savez_compressed(mask_path, mask=all_masks_array)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--video_path", type=str, required=True)
parser.add_argument("--output_path", type=str, required=True)
parser.add_argument("--mask_path", type=str, required=True)
parser.add_argument("--raft_ckpt", type=str, required=True)
parser.add_argument("--start_idx", type=int, required=True)
parser.add_argument("--end_idx", type=int, required=True)
parser.add_argument("--gpu_id", type=int, required=True)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_id)
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
os.makedirs(args.output_path, exist_ok=True)
os.makedirs(args.mask_path, exist_ok=True)
video_list = sorted([
f for f in os.listdir(args.video_path)
if f.endswith(".mp4")
])
selected_videos = video_list[args.start_idx : args.end_idx]
print(f"[GPU {args.gpu_id}] Processing {len(selected_videos)} videos: {args.start_idx} to {args.end_idx}")
model = load_raft_model(args.raft_ckpt)
for fname in tqdm(selected_videos, desc="Batch Processing"):
input_path = os.path.join(args.video_path, fname)
mask_path = os.path.join(args.mask_path, fname.replace(".mp4", ".npz"))
output_path = os.path.join(args.output_path, fname)
if os.path.exists(mask_path):
try:
np.load(mask_path)["mask"]
continue
except:
print(f"⚠️ Mask corrupt or unreadable: {mask_path} - Regenerating")
if os.path.exists(output_path):
continue
run_masking(input_path, output_path, mask_path, model) |