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import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
import gradio as gr
from unimatch.unimatch import UniMatch
from utils.flow_viz import flow_to_image
from dataloader.stereo import transforms
from utils.visualization import vis_disparity
IMAGENET_MEAN = [0.485, 0.456, 0.406]
IMAGENET_STD = [0.229, 0.224, 0.225]
@torch.no_grad()
def inference(image1, image2, task='flow'):
"""Inference on an image pair for optical flow or stereo disparity prediction"""
model = UniMatch(feature_channels=128,
num_scales=2,
upsample_factor=4,
ffn_dim_expansion=4,
num_transformer_layers=6,
reg_refine=True,
task=task)
model.eval()
if task == 'flow':
checkpoint_path = 'pretrained/gmflow-scale2-regrefine6-mixdata-train320x576-4e7b215d.pth'
else:
checkpoint_path = 'pretrained/gmstereo-scale2-regrefine3-resumeflowthings-mixdata-train320x640-ft640x960-e4e291fd.pth'
checkpoint_flow = torch.load(checkpoint_path)
model.load_state_dict(checkpoint_flow['model'], strict=True)
padding_factor = 32
attn_type = 'swin' if task == 'flow' else 'self_swin2d_cross_swin1d'
attn_splits_list = [2, 8]
corr_radius_list = [-1, 4]
prop_radius_list = [-1, 1]
num_reg_refine = 6 if task == 'flow' else 3
# smaller inference size for faster speed
max_inference_size = [384, 768] if task == 'flow' else [640, 960]
transpose_img = False
image1 = np.array(image1).astype(np.float32)
image2 = np.array(image2).astype(np.float32)
if len(image1.shape) == 2: # gray image
image1 = np.tile(image1[..., None], (1, 1, 3))
image2 = np.tile(image2[..., None], (1, 1, 3))
else:
image1 = image1[..., :3]
image2 = image2[..., :3]
if task == 'flow':
image1 = torch.from_numpy(image1).permute(2, 0, 1).float().unsqueeze(0)
image2 = torch.from_numpy(image2).permute(2, 0, 1).float().unsqueeze(0)
else:
val_transform_list = [transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
]
val_transform = transforms.Compose(val_transform_list)
sample = {'left': image1, 'right': image2}
sample = val_transform(sample)
image1 = sample['left'].unsqueeze(0) # [1, 3, H, W]
image2 = sample['right'].unsqueeze(0) # [1, 3, H, W]
# the model is trained with size: width > height
if task == 'flow' and image1.size(-2) > image1.size(-1):
image1 = torch.transpose(image1, -2, -1)
image2 = torch.transpose(image2, -2, -1)
transpose_img = True
nearest_size = [int(np.ceil(image1.size(-2) / padding_factor)) * padding_factor,
int(np.ceil(image1.size(-1) / padding_factor)) * padding_factor]
inference_size = [min(max_inference_size[0], nearest_size[0]), min(max_inference_size[1], nearest_size[1])]
assert isinstance(inference_size, list) or isinstance(inference_size, tuple)
ori_size = image1.shape[-2:]
# resize before inference
if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]:
image1 = F.interpolate(image1, size=inference_size, mode='bilinear',
align_corners=True)
image2 = F.interpolate(image2, size=inference_size, mode='bilinear',
align_corners=True)
results_dict = model(image1, image2,
attn_type=attn_type,
attn_splits_list=attn_splits_list,
corr_radius_list=corr_radius_list,
prop_radius_list=prop_radius_list,
num_reg_refine=num_reg_refine,
task=task,
)
flow_pr = results_dict['flow_preds'][-1] # [1, 2, H, W] or [1, H, W]
# resize back
if task == 'flow':
if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]:
flow_pr = F.interpolate(flow_pr, size=ori_size, mode='bilinear',
align_corners=True)
flow_pr[:, 0] = flow_pr[:, 0] * ori_size[-1] / inference_size[-1]
flow_pr[:, 1] = flow_pr[:, 1] * ori_size[-2] / inference_size[-2]
else:
if inference_size[0] != ori_size[0] or inference_size[1] != ori_size[1]:
pred_disp = F.interpolate(flow_pr.unsqueeze(1), size=ori_size,
mode='bilinear',
align_corners=True).squeeze(1) # [1, H, W]
pred_disp = pred_disp * ori_size[-1] / float(inference_size[-1])
if task == 'flow':
if transpose_img:
flow_pr = torch.transpose(flow_pr, -2, -1)
flow = flow_pr[0].permute(1, 2, 0).cpu().numpy() # [H, W, 2]
output = flow_to_image(flow) # [H, W, 3]
else:
disp = pred_disp[0].cpu().numpy()
output = vis_disparity(disp, return_rgb=True)
return Image.fromarray(output)
title = "UniMatch"
description = "<p style='text-align: center'>Optical flow and stereo matching demo for <a href='https://haofeixu.github.io/unimatch/' target='_blank'>Unifying Flow, Stereo and Depth Estimation</a> | <a href='https://arxiv.org/abs/2211.05783' target='_blank'>Paper</a> | <a href='https://github.com/autonomousvision/unimatch' target='_blank'>Code</a> | <a href='https://colab.research.google.com/drive/1r5m-xVy3Kw60U-m5VB-aQ98oqqg_6cab?usp=sharing' target='_blank'>Colab</a><br>Task <strong>flow</strong>: Image1: <strong>video frame t</strong>, Image2: <strong>video frame t+1</strong>; Task <strong>stereo</strong>: Image1: <strong>left</strong> image, Image2: <strong>right</strong> image<br>Simply upload your images or click one of the provided examples.<br><strong>Select the task type according to your input images</strong>.</p>"
examples = [
['demo/stereo_drivingstereo_test_2018-07-11-14-48-52_2018-07-11-14-58-34-673_left.jpg',
'demo/stereo_drivingstereo_test_2018-07-11-14-48-52_2018-07-11-14-58-34-673_right.jpg', 'stereo'],
['demo/stereo_middlebury_plants_im0.png', 'demo/stereo_middlebury_plants_im1.png', 'stereo'],
['demo/stereo_holopix_left.png', 'demo/stereo_holopix_right.png', 'stereo'],
['demo/flow_kitti_test_000197_10.png', 'demo/flow_kitti_test_000197_11.png', 'flow'],
['demo/flow_sintel_cave_3_frame_0049.png', 'demo/flow_sintel_cave_3_frame_0050.png', 'flow'],
['demo/flow_davis_skate-jump_00059.jpg', 'demo/flow_davis_skate-jump_00060.jpg', 'flow']
]
gr.Interface(
inference,
[gr.Image(type="pil", label="Image1"), gr.Image(type="pil", label="Image2"), gr.Radio(choices=['flow', 'stereo'], value='flow', label='Task')],
gr.Image(type="pil", label="flow/disparity"),
title=title,
description=description,
examples=examples,
).launch(debug=True, quiet=True)
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