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Add functionality to save segmentation mask during inference for images #50

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Add functionality to save segmentation mask during inference for images #50

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69c894f
save mask separately
abdullahmeda Jul 14, 2023
8693568
Merge branch 'baaivision:main' into main
abdullahmeda Jul 14, 2023
e05f02f
minor changes
abdullahmeda Jul 14, 2023
499317e
Merge remote-tracking branch 'origin/main'
abdullahmeda Jul 14, 2023
0ef9c32
minor changes
abdullahmeda Jul 14, 2023
ae8fef4
minor changes
abdullahmeda Jul 14, 2023
f159097
minor changes
abdullahmeda Jul 16, 2023
bfb6a78
multi input image
abdullahmeda Aug 13, 2023
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22 changes: 17 additions & 5 deletions SegGPT/SegGPT_inference/seggpt_engine.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
import os

import torch
import torch.nn.functional as F
import numpy as np
Expand Down Expand Up @@ -53,7 +55,7 @@ def run_one_image(img, tgt, model, device):
return output


def inference_image(model, device, img_path, img2_paths, tgt2_paths, out_path):
def inference_image(model, device, img_path, img2_paths, tgt2_paths, output_dir):
res, hres = 448, 448

image = Image.open(img_path).convert("RGB")
Expand Down Expand Up @@ -93,15 +95,25 @@ def inference_image(model, device, img_path, img2_paths, tgt2_paths, out_path):
"""### Run SegGPT on the image"""
# make random mask reproducible (comment out to make it change)
torch.manual_seed(2)
output = run_one_image(img, tgt, model, device)
output = F.interpolate(
output[None, ...].permute(0, 3, 1, 2),
mask = run_one_image(img, tgt, model, device)
mask = F.interpolate(
mask[None, ...].permute(0, 3, 1, 2),
size=[size[1], size[0]],
mode='nearest',
).permute(0, 2, 3, 1)[0].numpy()
output = Image.fromarray((input_image * (0.6 * output / 255 + 0.4)).astype(np.uint8))
output = Image.fromarray((input_image * (0.6 * mask / 255 + 0.4)).astype(np.uint8))

img_name = os.path.basename(img_path)

# save segmented output
out_path = os.path.join(output_dir, "output_" + '.'.join(img_name.split('.')[:-1]) + '.png')
output.save(out_path)

# save binary mask
mask_path = os.path.join(output_dir, "mask_" + '.'.join(img_name.split('.')[:-1]) + '.png')
mask_image = Image.fromarray(mask.astype(np.uint8))
mask_image.save(mask_path)


def inference_video(model, device, vid_path, num_frames, img2_paths, tgt2_paths, out_path):
res, hres = 448, 448
Expand Down
13 changes: 7 additions & 6 deletions SegGPT/SegGPT_inference/seggpt_inference.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import os
import os, gc
import argparse
from tqdm import tqdm

import torch
import numpy as np
Expand All @@ -18,7 +19,7 @@ def get_args_parser():
default='seggpt_vit_large.pth')
parser.add_argument('--model', type=str, help='dir to ckpt',
default='seggpt_vit_large_patch16_input896x448')
parser.add_argument('--input_image', type=str, help='path to input image to be tested',
parser.add_argument('--input_image', type=str, nargs='+', help='path to input image to be tested',
default=None)
parser.add_argument('--input_video', type=str, help='path to input video to be tested',
default=None)
Expand Down Expand Up @@ -59,10 +60,10 @@ def prepare_model(chkpt_dir, arch='seggpt_vit_large_patch16_input896x448', seg_t
if args.input_image is not None:
assert args.prompt_image is not None and args.prompt_target is not None

img_name = os.path.basename(args.input_image)
out_path = os.path.join(args.output_dir, "output_" + '.'.join(img_name.split('.')[:-1]) + '.png')

inference_image(model, device, args.input_image, args.prompt_image, args.prompt_target, out_path)
for image in tqdm(args.input_image):
inference_image(model, device, image, args.prompt_image, args.prompt_target, args.output_dir)
torch.cuda.empty_cache()
gc.collect()

if args.input_video is not None:
assert args.prompt_target is not None and len(args.prompt_target) == 1
Expand Down