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static_filter.py
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static_filter.py
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import os
import cv2
import glob
import numpy as np
import torch
from tqdm import tqdm
import argparse
from pathlib import Path
import json
import shutil
import logging
logging.basicConfig(level = logging.INFO,format = '%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
from vbench.utils import CACHE_DIR, get_prompt_from_filename, load_json
from vbench.third_party.RAFT.core.raft import RAFT
from vbench.third_party.RAFT.core.utils_core.utils import InputPadder
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
DEVICE = 'cuda'
class StaticFilter:
def __init__(self, args, device):
self.args = args
self.device = device
self.load_model()
def load_model(self):
self.model = torch.nn.DataParallel(RAFT(self.args))
self.model.load_state_dict(torch.load(self.args.model))
self.model = self.model.module
self.model.to(self.device)
self.model.eval()
def get_score(self, img, flo):
img = img[0].permute(1,2,0).cpu().numpy()
flo = flo[0].permute(1,2,0).cpu().numpy()
u = flo[:,:,0]
v = flo[:,:,1]
rad = np.sqrt(np.square(u) + np.square(v))
h, w = rad.shape
rad_flat = rad.flatten()
cut_index = int(h*w*0.02)
max_rad = np.mean(abs(np.sort(-rad_flat))[:cut_index])
return max_rad
def check_static(self, score_list):
thres = self.params["thres"]
count_num = self.params["count_num"]
count = 0
for score in score_list[:-2]:
if score > thres:
count += 1
if count > count_num:
return False
for score in score_list[-2:]:
if score > thres*count_num*2:
return False
return True
def set_params(self, frame, count):
scale = min(list(frame.shape)[-2:])
self.params = {"thres":3.0*(scale/256.0), "count_num":round(2*(count/16.0))}
def infer(self, path):
with torch.no_grad():
frames = self.get_frames(path)
self.set_params(frame=frames[0], count=len(frames))
static_score = []
for image1, image2 in zip(frames[:-1]+[frames[0],frames[-1]], frames[1:]+[frames[-1],frames[0]]):
padder = InputPadder(image1.shape)
image1, image2 = padder.pad(image1, image2)
_, flow_up = self.model(image1, image2, iters=20, test_mode=True)
max_rad = self.get_score(image1, flow_up)
static_score.append(max_rad)
whether_static = self.check_static(static_score)
return whether_static
def get_frames(self, video_path):
frame_list = []
video = cv2.VideoCapture(video_path)
while video.isOpened():
success, frame = video.read()
if success:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # convert to rgb
frame = torch.from_numpy(frame.astype(np.uint8)).permute(2, 0, 1).float()
frame = frame[None].to(DEVICE)
frame_list.append(frame)
else:
break
video.release()
assert frame_list != []
return frame_list
def check_and_move(args, filter_results, target_path=None):
if target_path is None:
target_path = os.path.join(args.result_path, "filtered_videos")
os.makedirs(target_path, exist_ok=True)
for prompt, v in filter_results.items():
if v["static_count"] < 5 and args.filter_scope=='temporal_flickering':
logger.warning(f"Prompt: '{prompt}' has fewer than 5 filter results.")
for i, video_path in enumerate(v["static_path"]):
target_name = os.path.join(target_path, f"{prompt}-{i}.mp4")
shutil.copy(video_path, target_name)
logger.info(f"All filtered videos are saved in the '{target_path}' path")
def static_filter(args):
static_filter = StaticFilter(args, device=DEVICE)
prompt_dict = {}
prompt_list = []
paths = sorted(glob.glob(os.path.join(args.videos_path, "*.mp4")))
if args.filter_scope=='temporal_flickering':
full_prompt_list = load_json(f"{CUR_DIR}/vbench/VBench_full_info.json")
for prompt in full_prompt_list:
if 'temporal_flickering' in prompt['dimension']:
prompt_dict[prompt['prompt_en']] = {"static_count":0, "static_path":[]}
prompt_list.append(prompt['prompt_en'])
elif args.filter_scope=='all':
for prompt in paths:
prompt = get_prompt_from_filename(prompt)
prompt_dict[prompt] = {"static_count":0, "static_path":[]}
prompt_list.append(prompt)
else:
assert os.path.isfile(args.filter_scope) and Path(args.filter_scope).suffix.lower() == '.json', f"""
--filter_scope flag is not correctly set, set to 'all' to filter all videos in the --videos_path directory,
or provide the correct path to the JSON file
"""
full_prompt_list = load_json(args.filter_scope)
for prompt in full_prompt_list:
prompt = get_prompt_from_filename(prompt)
prompt_dict[prompt] = {"static_count":0, "static_path":[]}
prompt_list.append(prompt)
for path in tqdm(paths):
name = get_prompt_from_filename(path)
if name in prompt_list:
if prompt_dict[name]["static_count"] < 5 or args.filter_scope != 'temporal_flickering':
if static_filter.infer(path):
prompt_dict[name]["static_count"] += 1
prompt_dict[name]["static_path"].append(path)
os.makedirs(args.result_path, exist_ok=True)
info_file = os.path.join(args.result_path, args.store_name)
json.dump(prompt_dict, open(info_file, "w"))
logger.info(f"Filtered results info is saved in the '{info_file}' file")
check_and_move(args, prompt_dict)
def parse_args():
parser = argparse.ArgumentParser(description='static_filter', formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('--model', type=str, default=f"{CACHE_DIR}/raft_model/models/raft-things.pth", help="restore checkpoint")
parser.add_argument('--videos_path', default="", required=True, help="video path for filtering")
parser.add_argument('--result_path', type=str, default="./filter_results", help='result save path')
parser.add_argument('--store_name', type=str, default="filtered_static_video.json", help='result file name')
parser.add_argument('--small', action='store_true', help='use small model')
parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
parser.add_argument('--alternate_corr', action='store_true', help='use efficent correlation implementation')
parser.add_argument('--filter_scope', default='temporal_flickering', help=f'''For specifying the scope for filtering videos
1. 'temporal_flickering' (default): filter videos based on matches with temporal_flickering dimension of VBench.
2. 'all': filter all video in the current directory.
3. '$filename': if a filepath to a JSON file is provided, only the filename exists in JSON file will be filtered.
> usage: --filter_scope example.json
''')
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
static_filter(args)