Spaces:
Sleeping
Sleeping
File size: 9,354 Bytes
a1d02b9 c0174af a1d02b9 f4c1e03 e3b2a81 a1d02b9 02f1a14 0836db1 eadcf1b a1d02b9 0836db1 c7aed3b a1d02b9 d0f72b7 a1d02b9 11be581 a1d02b9 4983ef5 a1d02b9 c0fcc98 a1d02b9 eb50f43 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 |
from PIL import Image
import gradio as gr
from FGT_codes.tool.video_inpainting import video_inpainting
from SiamMask.tools.test import *
from SiamMask.experiments.siammask_sharp.custom import Custom
from types import SimpleNamespace
import torch
import numpy as np
import torchvision
import cv2
import sys
from os.path import exists, join, basename, splitext
import os
import argparse
project_name = ''
sys.path.append(project_name)
sys.path.append(os.path.abspath(join(project_name, 'FGT_codes')))
sys.path.append(os.path.abspath(join(project_name, 'FGT_codes', 'tool')))
sys.path.append(os.path.abspath(join(project_name, 'FGT_codes', 'LAFC', 'flowCheckPoint')))
sys.path.append(os.path.abspath(join(project_name, 'FGT_codes', 'LAFC', 'checkpoint')))
sys.path.append(os.path.abspath(join(project_name, 'FGT_codes', 'FGT', 'checkpoint')))
sys.path.append(os.path.abspath(join(project_name, 'FGT_codes', 'LAFC',
'flowCheckPoint', 'raft-things.pth')))
# sys.path.append(join(project_name, 'SiamMask',
# 'experiments', 'siammask_sharp'))
# sys.path.append(join(project_name, 'SiamMask', 'models'))
# sys.path.append(join(project_name, 'SiamMask'))
exp_path = join(project_name, 'SiamMask/experiments/siammask_sharp')
pretrained_path1 = join(exp_path, 'SiamMask_DAVIS.pth')
print(sys.path)
torch.set_grad_enabled(False)
# init SiamMask
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
cfg = load_config(SimpleNamespace(config=join(exp_path, 'config_davis.json')))
siammask = Custom(anchors=cfg['anchors'])
siammask = load_pretrain(siammask, pretrained_path1)
siammask = siammask.eval().to(device)
# constants
object_x = 0
object_y = 0
object_width = 0
object_height = 0
original_frame_list = []
mask_list = []
parser = argparse.ArgumentParser()
parser.add_argument('--opt', default='configs/object_removal.yaml',
help='Please select your config file for inference')
# video completion
parser.add_argument('--mode', default='object_removal', choices=[
'object_removal', 'watermark_removal', 'video_extrapolation'], help="modes: object_removal / video_extrapolation")
parser.add_argument(
'--path', default='/myData/davis_resized/walking', help="dataset for evaluation")
parser.add_argument(
'--path_mask', default='/myData/dilateAnnotations_4/walking', help="mask for object removal")
parser.add_argument(
'--outroot', default='quick_start/walking3', help="output directory")
parser.add_argument('--consistencyThres', dest='consistencyThres', default=5, type=float,
help='flow consistency error threshold')
parser.add_argument('--alpha', dest='alpha', default=0.1, type=float)
parser.add_argument('--Nonlocal', dest='Nonlocal',
default=False, type=bool)
# RAFT
parser.add_argument(
'--raft_model', default='../LAFC/flowCheckPoint/raft-things.pth', help="restore checkpoint")
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')
# LAFC
parser.add_argument('--lafc_ckpts', type=str, default='../LAFC/checkpoint')
# FGT
parser.add_argument('--fgt_ckpts', type=str, default='../FGT/checkpoint')
# extrapolation
parser.add_argument('--H_scale', dest='H_scale', default=2,
type=float, help='H extrapolation scale')
parser.add_argument('--W_scale', dest='W_scale', default=2,
type=float, help='W extrapolation scale')
# Image basic information
parser.add_argument('--imgH', type=int, default=256)
parser.add_argument('--imgW', type=int, default=432)
parser.add_argument('--flow_mask_dilates', type=int, default=8)
parser.add_argument('--frame_dilates', type=int, default=0)
parser.add_argument('--gpu', type=int, default=0)
# FGT inference parameters
parser.add_argument('--step', type=int, default=10)
parser.add_argument('--num_ref', type=int, default=-1)
parser.add_argument('--neighbor_stride', type=int, default=5)
# visualization
parser.add_argument('--vis_flows', action='store_true',
help='Visualize the initialized flows')
parser.add_argument('--vis_completed_flows',
action='store_true', help='Visualize the completed flows')
parser.add_argument('--vis_prop', action='store_true',
help='Visualize the frames after stage-I filling (flow guided content propagation)')
parser.add_argument('--vis_frame', action='store_true',
help='Visualize frames')
args = parser.parse_args()
def getBoundaries(mask):
if mask is None:
return 0, 0, 0, 0
indexes = np.where((mask == [255, 255, 255]).all(axis=2))
print(indexes)
x1 = min(indexes[1])
y1 = min(indexes[0])
x2 = max(indexes[1])
y2 = max(indexes[0])
return x1, y1, (x2-x1), (y2-y1)
def track_and_mask(vid, original_frame, masked_frame):
x, y, w, h = getBoundaries(masked_frame)
f = 0
video_capture = cv2.VideoCapture()
if video_capture.open(vid):
width, height = int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(
video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = video_capture.get(cv2.CAP_PROP_FPS)
# can't write out mp4, so try to write into an AVI file
video_writer = cv2.VideoWriter(
"output.avi", cv2.VideoWriter_fourcc(*'MP42'), fps, (width, height))
video_writer2 = cv2.VideoWriter(
"output_mask.avi", cv2.VideoWriter_fourcc(*'MP42'), fps, (width, height))
while video_capture.isOpened():
ret, frame = video_capture.read()
if not ret:
break
# frame = cv2.resize(frame, (w - w % 8, h - h % 8))
if f == 0:
target_pos = np.array([x + w / 2, y + h / 2])
target_sz = np.array([w, h])
# init tracker
state = siamese_init(
frame, target_pos, target_sz, siammask, cfg['hp'], device=device)
else:
# track
state = siamese_track(
state, frame, mask_enable=True, refine_enable=True, device=device)
location = state['ploygon'].flatten()
mask = state['mask'] > state['p'].seg_thr
frame[:, :, 2] = (mask > 0) * 255 + \
(mask == 0) * frame[:, :, 2]
mask = mask.astype(np.uint8) # convert to an unsigned byte
mask = mask * 255
mask_list.append(mask)
cv2.polylines(frame, [np.int0(location).reshape(
(-1, 1, 2))], True, (0, 255, 0), 3)
original_frame_list.append(frame)
mask_list.append(mask)
video_writer.write(frame)
video_writer2.write(mask)
f = f + 1
video_capture.release()
video_writer.release()
video_writer2.release()
else:
print("can't open the given input video file!")
return "output.avi"
def inpaint_video():
video_inpainting(args, original_frame_list, mask_list)
return "result.mp4"
def get_first_frame(video):
video_capture = cv2.VideoCapture()
if video_capture.open(video):
width, height = int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(
video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
if video_capture.isOpened():
ret, frame = video_capture.read()
RGB_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
return RGB_frame
def drawRectangle(frame, mask):
x1, y1, x2, y2 = getBoundaries(mask)
return cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
def getStartEndPoints(mask):
if mask is None:
return 0, 0, 0, 0
indexes = np.where((mask == [255, 255, 255]).all(axis=2))
print(indexes)
x1 = min(indexes[1])
y1 = min(indexes[0])
x2 = max(indexes[1])
y2 = max(indexes[0])
return x1, y1, x2, y2
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=2):
with gr.Row():
in_video = gr.PlayableVideo()
with gr.Row():
first_frame = gr.ImageMask()
with gr.Row():
approve_mask = gr.Button(value="Approve Mask")
with gr.Column(scale=1):
with gr.Row():
original_image = gr.Image(interactive=False)
with gr.Row():
masked_image = gr.Image(interactive=False)
with gr.Column(scale=2):
out_video = gr.Video()
out_video_inpaint = gr.Video()
track_mask = gr.Button(value="Track and Mask")
inpaint = gr.Button(value="Inpaint")
in_video.change(fn=get_first_frame, inputs=[
in_video], outputs=[first_frame])
approve_mask.click(lambda x: [x['image'], x['mask']], first_frame, [
original_image, masked_image])
track_mask.click(fn=track_and_mask, inputs=[
in_video, original_image, masked_image], outputs=[out_video])
inpaint.click(fn=inpaint_video, outputs=[out_video_inpaint])
demo.launch(debug=True)
|