File size: 11,883 Bytes
6b59f61 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 |
# --------------------------------------------------------
# SEEM -- Segment Everything Everywhere All At Once
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Xueyan Zou (xueyan@cs.wisc.edu)
# --------------------------------------------------------
import torch
import numpy as np
import torch.nn.functional as F
from PIL import Image
from torchvision import transforms
from utils.visualizer import Visualizer
from detectron2.utils.colormap import random_color
from detectron2.data import MetadataCatalog
from detectron2.structures import BitMasks
from modeling.language.loss import vl_similarity
from utils.constants import COCO_PANOPTIC_CLASSES
from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
import cv2
import os
import glob
import subprocess
from PIL import Image
import random
t = []
t.append(transforms.Resize(512, interpolation=Image.BICUBIC))
transform = transforms.Compose(t)
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
all_classes = [name.replace('-other','').replace('-merged','') for name in COCO_PANOPTIC_CLASSES] + ["others"]
colors_list = [(np.array(color['color'])/255).tolist() for color in COCO_CATEGORIES] + [[1, 1, 1]]
def interactive_infer_image(model, audio_model, image, tasks, refimg=None, reftxt=None, audio_pth=None, video_pth=None):
image_ori = transform(image['image'])
mask_ori = image['mask']
width = image_ori.size[0]
height = image_ori.size[1]
image_ori = np.asarray(image_ori)
visual = Visualizer(image_ori, metadata=metadata)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
# stroke_inimg = None
# stroke_refimg = None
data = {"image": images, "height": height, "width": width}
if len(tasks) == 0:
tasks = ["Panoptic"]
# inistalize task
model.model.task_switch['spatial'] = False
model.model.task_switch['visual'] = False
model.model.task_switch['grounding'] = False
model.model.task_switch['audio'] = False
example = None
if 'Example' in tasks:
model.model.task_switch['visual'] = True
model.model.task_switch['spatial'] = True
refimg_ori, refimg_mask = refimg['image'], refimg['mask']
refimg_ori = transform(refimg_ori)
_width = refimg_ori.size[0]
_height = refimg_ori.size[1]
refimg_ori = np.asarray(refimg_ori)
refimg_ori_np = refimg_ori.copy()
images = torch.from_numpy(refimg_ori.copy()).permute(2,0,1).cuda()
batched_inputs = [{'image': images, 'height': _height, 'width': _width, 'spatial_query':{}}]
refimg_mask = np.asarray(refimg_mask)[:,:,0:1].copy()
refimg_mask = torch.from_numpy(refimg_mask).permute(2,0,1)[None,]
refimg_mask = (F.interpolate(refimg_mask, (_height, _width), mode='bilinear') > 0)
batched_inputs[0]['spatial_query']['rand_shape'] = refimg_mask
outputs_refimg, img_shape = model.model.evaluate_referring_image(batched_inputs)
model.model.task_switch['spatial'] = False
data['visual'] = outputs_refimg
# overlay = refimg_mask[0,0].float().numpy()[:,:,None] * np.array([0,0,255])
# x = refimg_ori_np
# stroke_refimg = x * (1 - refimg_mask[0,0].float().numpy()[:,:,None]) + (x * refimg_mask[0,0].numpy()[:,:,None] * 0.2 + overlay * 0.8)
# stroke_refimg = Image.fromarray(stroke_refimg.astype(np.uint8))
stroke = None
if 'Stroke' in tasks:
model.model.task_switch['spatial'] = True
mask_ori = np.asarray(mask_ori)[:,:,0:1].copy()
mask_ori = torch.from_numpy(mask_ori).permute(2,0,1)[None,]
mask_ori = (F.interpolate(mask_ori, (height, width), mode='bilinear') > 0)
data['stroke'] = mask_ori
# overlay = mask_ori[0,0].float().numpy()[:,:,None] * np.array([0,255,0])
# x = image_ori
# stroke_inimg = x * (1 - mask_ori[0,0].float().numpy()[:,:,None]) + (x * mask_ori[0,0].numpy()[:,:,None] * 0.2 + overlay * 0.8)
# stroke_inimg = Image.fromarray(stroke_inimg.astype(np.uint8))
text = None
if 'Text' in tasks:
model.model.task_switch['grounding'] = True
data['text'] = [reftxt]
audio = None
if 'Audio' in tasks:
model.model.task_switch['audio'] = True
audio_result = audio_model.transcribe(audio_pth)
data['audio'] = [audio_result['text']]
batch_inputs = [data]
if 'Panoptic' in tasks:
model.model.metadata = metadata
results = model.model.evaluate(batch_inputs)
pano_seg = results[-1]['panoptic_seg'][0]
pano_seg_info = results[-1]['panoptic_seg'][1]
demo = visual.draw_panoptic_seg(pano_seg.cpu(), pano_seg_info) # rgb Image
res = demo.get_image()
return Image.fromarray(res), None
else:
results,image_size,extra = model.model.evaluate_demo(batch_inputs)
# If contians spatial use spatial:
if 'Stroke' in tasks:
v_emb = results['pred_maskembs']
s_emb = results['pred_pspatials']
pred_masks = results['pred_masks']
pred_logits = v_emb @ s_emb.transpose(1,2)
logits_idx_y = pred_logits[:,:,0].max(dim=1)[1]
logits_idx_x = torch.arange(len(logits_idx_y), device=logits_idx_y.device)
logits_idx = torch.stack([logits_idx_x, logits_idx_y]).tolist()
pred_masks_pos = pred_masks[logits_idx]
pred_class = results['pred_logits'][logits_idx].max(dim=-1)[1]
elif 'Example' in tasks:
v_emb = results['pred_maskembs']
s_emb = results['pred_pvisuals']
pred_masks = results['pred_masks']
pred_logits = v_emb @ s_emb.transpose(1,2)
logits_idx_y = pred_logits[:,:,0].max(dim=1)[1]
logits_idx_x = torch.arange(len(logits_idx_y), device=logits_idx_y.device)
logits_idx = torch.stack([logits_idx_x, logits_idx_y]).tolist()
pred_masks_pos = pred_masks[logits_idx]
pred_class = results['pred_logits'][logits_idx].max(dim=-1)[1]
elif 'Text' in tasks:
pred_masks = results['pred_masks'][0]
v_emb = results['pred_captions'][0]
t_emb = extra['grounding_class']
t_emb = t_emb / (t_emb.norm(dim=-1, keepdim=True) + 1e-7)
v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7)
temperature = model.model.sem_seg_head.predictor.lang_encoder.logit_scale
out_prob = vl_similarity(v_emb, t_emb, temperature=temperature)
matched_id = out_prob.max(0)[1]
pred_masks_pos = pred_masks[matched_id,:,:]
pred_class = results['pred_logits'][0][matched_id].max(dim=-1)[1]
elif 'Audio' in tasks:
pred_masks = results['pred_masks'][0]
v_emb = results['pred_captions'][0]
t_emb = extra['audio_class']
t_emb = t_emb / (t_emb.norm(dim=-1, keepdim=True) + 1e-7)
v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7)
temperature = model.model.sem_seg_head.predictor.lang_encoder.logit_scale
out_prob = vl_similarity(v_emb, t_emb, temperature=temperature)
matched_id = out_prob.max(0)[1]
pred_masks_pos = pred_masks[matched_id,:,:]
pred_class = results['pred_logits'][0][matched_id].max(dim=-1)[1]
# interpolate mask to ori size
pred_masks_pos = (F.interpolate(pred_masks_pos[None,], image_size[-2:], mode='bilinear')[0,:,:data['height'],:data['width']] > 0.0).float().cpu().numpy()
texts = [all_classes[pred_class[0]]]
for idx, mask in enumerate(pred_masks_pos):
# color = random_color(rgb=True, maximum=1).astype(np.int32).tolist()
out_txt = texts[idx] if 'Text' not in tasks else reftxt
demo = visual.draw_binary_mask(mask, color=colors_list[pred_class[0]%133], text=out_txt)
res = demo.get_image()
torch.cuda.empty_cache()
# return Image.fromarray(res), stroke_inimg, stroke_refimg
return Image.fromarray(res), None
def interactive_infer_video(model, audio_model, image, tasks, refimg=None, reftxt=None, audio_pth=None, video_pth=None):
if 'Video' in tasks:
input_dir = video_pth.replace('.mp4', '')
input_name = input_dir.split('/')[-1]
random_number = str(random.randint(10000, 99999))
output_dir = input_dir + '_output'
output_name = output_dir.split('/')[-1]
output_file = video_pth.replace('.mp4', '_{}_output.mp4'.format(random_number))
frame_interval = 10
# Ensure output directory exists
if not os.path.exists(input_dir):
os.makedirs(input_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Build the FFmpeg command
ffmpeg_cmd = "ffmpeg -i {} -vf \"fps=5\" {}/%04d.png".format(video_pth, input_dir)
os.system(ffmpeg_cmd)
data = {}
model.model.task_switch['visual'] = True
model.model.task_switch['spatial'] = True
refimg_ori, refimg_mask = refimg['image'], refimg['mask']
refimg_ori = transform(refimg_ori)
_width = refimg_ori.size[0]
_height = refimg_ori.size[1]
refimg_ori = np.asarray(refimg_ori)
refimg_ori_np = refimg_ori.copy()
images = torch.from_numpy(refimg_ori.copy()).permute(2,0,1).cuda()
batched_inputs = [{'image': images, 'height': _height, 'width': _width, 'spatial_query':{}}]
refimg_mask = np.asarray(refimg_mask)[:,:,0:1].copy()
refimg_mask = torch.from_numpy(refimg_mask).permute(2,0,1)[None,]
refimg_mask = (F.interpolate(refimg_mask, (_height, _width), mode='bilinear') > 0)
batched_inputs[0]['spatial_query']['rand_shape'] = refimg_mask
outputs_refimg, img_shape = model.model.evaluate_referring_image(batched_inputs)
model.model.task_switch['visual'] = False
model.model.task_switch['spatial'] = False
data['visual'] = outputs_refimg
model.model.task_switch['visual'] = True
frame_pths = sorted(glob.glob(os.path.join(input_dir, '*.png')))
for frame_pth in frame_pths:
image_ori = transform(Image.open(frame_pth))
width = image_ori.size[0]
height = image_ori.size[1]
image_ori = np.asarray(image_ori)
visual = Visualizer(image_ori[:,:,::-1], metadata=metadata)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
data.update({"image": images, "height": height, "width": width})
batch_inputs = [data]
results,image_size,extra = model.model.evaluate_demo(batch_inputs)
v_emb = results['pred_maskembs']
s_emb = results['pred_pvisuals']
pred_masks = results['pred_masks']
pred_logits = v_emb @ s_emb.transpose(1,2)
logits_idx_y = pred_logits[:,:,0].max(dim=1)[1]
logits_idx_x = torch.arange(len(logits_idx_y), device=logits_idx_y.device)
logits_idx = torch.stack([logits_idx_x, logits_idx_y]).tolist()
pred_masks_pos = pred_masks[logits_idx]
pred_class = results['pred_logits'][logits_idx].max(dim=-1)[1]
pred_masks_pos = (F.interpolate(pred_masks_pos[None,], image_size[-2:], mode='bilinear')[0,:,:data['height'],:data['width']] > 0.0).float().cpu().numpy()
texts = [all_classes[pred_class[0]]]
for idx, mask in enumerate(pred_masks_pos):
out_txt = texts[idx]
demo = visual.draw_binary_mask(mask, color=colors_list[pred_class[0]%133], text=out_txt)
res = demo.get_image()
output_pth = frame_pth.replace(input_name, output_name)
cv2.imwrite(output_pth, res)
ffmpeg_cmd = "ffmpeg -framerate 5 -pattern_type glob -i '{}/*.png' -c:v libx264 {}".format(output_dir, output_file)
os.system(ffmpeg_cmd)
return None, output_file
|