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test_video.py
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test_video.py
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"""
Example:
python test_video.py \
--video "PATH_TO_INPUT_VIDEO" \
--output-video "PATH_TO_OUTPUT_VIDEO" \
--pretrained-weight ./pretrained/SGHM-ResNet50.pth
"""
import argparse
import os
import glob
import cv2
import numpy as np
from PIL import Image
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.utils import save_image
import torchvision.transforms as transforms
from model.model import HumanSegment, HumanMatting
import utils
import segpeo.inference as inference
pil_to_tensor = transforms.Compose(
[
transforms.ToTensor()
]
)
# --------------- Arguments ---------------
parser = argparse.ArgumentParser(description='Test Video')
parser.add_argument('--video', type=str, required=True)
parser.add_argument('--output-video', type=str, required=True)
parser.add_argument('--pretrained-weight', type=str, required=True)
args = parser.parse_args()
if not os.path.exists(args.pretrained_weight):
print('Cannot find the pretrained model: {0}'.format(args.pretrained_weight))
exit()
# --------------- Main ---------------
# Load Model
model = HumanMatting(backbone='resnet50')
model = nn.DataParallel(model).cuda().eval()
model.load_state_dict(torch.load(args.pretrained_weight))
print("Load checkpoint successfully ...")
# Load Video
vc = cv2.VideoCapture(args.video)
if vc.isOpened():
ret, frame = vc.read()
else:
ret = False
if not ret:
print('Failed to read the input video: {0}'.format(args.video))
exit()
num_frame = vc.get(cv2.CAP_PROP_FRAME_COUNT)
fps = vc.get(cv2.CAP_PROP_FPS)
h, w = frame.shape[:2]
infer_size = 1280
if min(h, w) > infer_size:
if w >= h:
rh = infer_size
rw = int(w / h * infer_size)
else:
rw = infer_size
rh = int(h / w * infer_size)
else:
rh, rw = h, w
rh = rh - rh % 64
rw = rw - rw % 64
# Create output
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
video_writer = cv2.VideoWriter(args.output_video, fourcc, fps, (w, h))
# Background Color
back_np = np.full(frame.shape, 0)
back_np[:, :, 0] = 120
back_np[:, :, 1] = 255
back_np[:, :, 2] = 155
# Process Video
with tqdm(range(int(num_frame)))as t:
for c in t:
if frame is None:
print("Frame is empty, process finished ...")
break
frame_np = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_pil = Image.fromarray(frame_np)
frame_tensor = pil_to_tensor(frame_pil)
frame_tensor = frame_tensor[None, :, :, :].cuda()
input_tensor = F.interpolate(frame_tensor, size=(rh, rw), mode='bilinear')
with torch.no_grad():
pred = model(input_tensor)
alpha_pred_os1, alpha_pred_os4, alpha_pred_os8 = pred['alpha_os1'], pred['alpha_os4'], pred['alpha_os8']
pred_alpha = alpha_pred_os8.clone().detach()
weight_os4 = utils.get_unknown_tensor_from_pred(pred_alpha, rand_width=30, train_mode=False)
pred_alpha[weight_os4>0] = alpha_pred_os4[weight_os4>0]
weight_os1 = utils.get_unknown_tensor_from_pred(pred_alpha, rand_width=15, train_mode=False)
pred_alpha[weight_os1>0] = alpha_pred_os1[weight_os1>0]
pred_alpha = pred_alpha.repeat(1, 3, 1, 1)
pred_alpha = F.interpolate(pred_alpha, size=(h, w), mode='bilinear')
alpha_np = pred_alpha[0].data.cpu().numpy().transpose(1, 2, 0)
comp_np = alpha_np * frame_np + (1 - alpha_np) * back_np
comp_np = comp_np.astype(np.uint8)
video_writer.write(cv2.cvtColor(comp_np, cv2.COLOR_RGB2BGR))
ret, frame = vc.read()
c += 1