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# -*- encoding: utf-8 -*-
# here put the import lib
import os
import sys
import argparse
import glob
import pickle
import json
import cv2
import parser
import random
import pdb
import traceback
import hashlib
from moviepy.editor import VideoFileClip
from tqdm import tqdm
import pandas as pd
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torchvision.ops import nms
try:
from ..wenlan.bbox_extractor.bbox_extractor import BboxExtractor
from ..wenlan.img_feat_extractor import generate_folder_csv
from ..wenlan.utils import getLanMask
from ..wenlan.utils.config import cfg_from_yaml_file, cfg
from ..wenlan.models.vl_model import *
except:
pass
class ImgModel(nn.Module):
def __init__(self, model_cfg):
super(ImgModel, self).__init__()
self.model_cfg = model_cfg
self.learnable = nn.ModuleDict()
self.learnable["imgencoder"] = ImgLearnableEncoder(model_cfg)
def forward(self, imgFea, maskImages, image_boxs):
imgFea = self.learnable["imgencoder"](
imgFea, maskImages, image_boxs
) # <bsz, img_dim>
imgFea = F.normalize(imgFea, p=2, dim=-1)
return imgFea
class ImgFeatureExtractor:
def __init__(self, cfg_file, model_weights, gpu_id=0):
self.gpu_id = gpu_id
self.cfg_file = cfg_file
self.cfg = cfg_from_yaml_file(self.cfg_file, cfg)
self.img_model = ImgModel(model_cfg=self.cfg.MODEL)
self.img_model = self.img_model.cuda(self.gpu_id)
model_component = torch.load(
model_weights, map_location=torch.device("cuda:{}".format(self.gpu_id))
)
img_model_component = {}
for key in model_component["learnable"].keys():
if "imgencoder." in key:
img_model_component[key] = model_component["learnable"][key]
self.img_model.learnable.load_state_dict(img_model_component)
self.img_model.eval()
self.visual_transform = self.visual_transforms_box(self.cfg.MODEL.IMG_SIZE)
def visual_transforms_box(self, new_size=456):
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
return transforms.Compose(
[transforms.ToTensor(), transforms.Resize((new_size, new_size)), normalize]
)
def extract(self, img_path, bboxes):
if type(img_path) == str:
image = Image.open(img_path).convert("RGB")
else:
image = Image.fromarray(img_path)
if image is None:
return None
else:
width, height = image.size
new_size = self.cfg.MODEL.IMG_SIZE
img_box_s = []
for box_i in bboxes[: self.cfg.MODEL.MAX_IMG_LEN - 1]: # [x1, y1, x2, y2]
x1, y1, x2, y2 = (
box_i[0] * (new_size / width),
box_i[1] * (new_size / height),
box_i[2] * (new_size / width),
box_i[3] * (new_size / height),
)
img_box_s.append(
torch.from_numpy(np.array([x1, y1, x2, y2]).astype(np.float32))
)
img_box_s.append(
torch.from_numpy(
np.array([0, 0, new_size, new_size]).astype(np.float32)
)
)
image_boxs = torch.stack(img_box_s, 0) # <36, 4>
image = self.visual_transform(image)
img_len = torch.full((1,), self.cfg.MODEL.MAX_IMG_LEN, dtype=torch.long)
with torch.no_grad():
imgs = image.unsqueeze(0) # <batchsize, 3, image_size, image_size>
img_lens = img_len.unsqueeze(0).view(-1)
image_boxs = image_boxs.unsqueeze(0) # <BSZ, 36, 4>
# get image mask
imgMask = getLanMask(img_lens, cfg.MODEL.MAX_IMG_LEN)
imgMask = imgMask.cuda(self.gpu_id)
imgs = imgs.cuda(self.gpu_id)
image_boxs = image_boxs.cuda(self.gpu_id) # <BSZ, 36, 4>
img_fea = self.img_model(imgs, imgMask, image_boxs)
img_fea = img_fea.cpu().numpy()
return img_fea
def main(video_path, video_map, vf_extractor, bbx_extr):
video_name = ".".join(video_path.split("/")[-1].split(".")[:-1])
if not os.path.exists(save_path):
os.makedirs(save_path)
# video_hash_code = (os.popen('md5sum {}'.format(video_path))).readlines()[0].split(' ')[0]
with open(video_path, "rb") as fd:
data = fd.read()
video_hash_code = hashlib.md5(data).hexdigest()
save_path = os.path.join(
save_path, "{}_{}.json".format(video_name, video_hash_code[:8])
)
assert video_hash_code == video_map["video_file_hash_code"]
fps = 1
max_frame_num = 5
select_frame_idx = []
select_frame_clip = []
for i in range(len(video_map["clips"])):
clip = video_map["clips"][i]
if clip["cliptype"] == "transition":
continue
select_frame_num = int(min(np.ceil(clip["duration"] * fps), max_frame_num))
clip_total_frame_num = clip["frame_end"] - clip["frame_start"]
frame_duration = clip_total_frame_num // (select_frame_num + 1)
for j in range(select_frame_num):
select_frame_idx.append(clip["frame_start"] + (j + 1) * frame_duration)
select_frame_clip.append(i)
print(len(select_frame_idx), len(set(select_frame_idx)))
# Capture video
video = VideoFileClip(video_path)
video = video.crop(*video_map["content_box"])
fps = video.fps
duration = video.duration
total_frames = int(duration * fps)
width, height = video.size
print("fps, frame_count, width, height:", fps, total_frames, width, height)
cnt_frame, step = 0, 0
for frame in video.iter_frames(fps=video_map["sample_fps"]):
if step == len(select_frame_idx):
break
if cnt_frame == select_frame_idx[step]:
bboxes = bbx_extr.extract_bboxes(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
bboxes = bboxes.tolist()
fea = vf_extractor.extract(frame, bboxes)
fea = fea.squeeze(axis=0).tolist()
if "feat" in video_map["clips"][select_frame_clip[step]]:
video_map["clips"][select_frame_clip[step]]["feat"].append(fea)
else:
video_map["clips"][select_frame_clip[step]]["feat"] = [fea]
step += 1
cnt_frame += 1
for clip in video_map["clips"]:
clip["multi_factor"] = {"semantics": None}
if "feat" in clip:
clip["multi_factor"]["semantics"] = np.mean(
np.array(clip["feat"]), axis=0
).tolist()
return video_map
class WenLanVisualPredictor(object):
def __init__(
self,
brivl_checkpoint,
cfg_file="cfg/test_xyb.yml",
bbox_extractor_cfg="bbox_extractor/configs/bua-caffe/extract-bua-caffe-r101.yaml",
) -> None:
# brivl_checkpoint = '/innovation_cfs/mmatch/infguo/weights/BriVL-1.0-5500w.pth',
abs_path = os.path.dirname(os.path.abspath(__file__))
cfg_file = os.path.join(abs_path, cfg_file)
bbox_extractor_cfg = os.path.join(abs_path, bbox_extractor_cfg)
self.vf_extractor = ImgFeatureExtractor(cfg_file, brivl_checkpoint)
self.bbx_extr = BboxExtractor(bbox_extractor_cfg)
def __call__(
self,
video_path,
video_map,
):
video_map = main(video_path, video_map, self.vf_extractor, self.bbx_extr)
return video_map
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