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from pathlib import Path
from typing import Any, Dict, Optional, Tuple
import gradio as gr
import numpy as np
from easydict import EasyDict as edict
from omegaconf import OmegaConf
from common.sfm import SfmEngine
from common.utils import (
GRADIO_VERSION,
gen_examples,
generate_warp_images,
get_matcher_zoo,
load_config,
ransac_zoo,
run_matching,
run_ransac,
send_to_match,
)
DESCRIPTION = """
# Image Matching WebUI
This Space demonstrates [Image Matching WebUI](https://github.com/Vincentqyw/image-matching-webui) by vincent qin. Feel free to play with it, or duplicate to run image matching without a queue!
<br/>
🔎 For more details about supported local features and matchers, please refer to https://github.com/Vincentqyw/image-matching-webui
🚀 All algorithms run on CPU for inference, causing slow speeds and high latency. For faster inference, please download the [source code](https://github.com/Vincentqyw/image-matching-webui) for local deployment.
🐛 Your feedback is valuable to me. Please do not hesitate to report any bugs [here](https://github.com/Vincentqyw/image-matching-webui/issues).
"""
class ImageMatchingApp:
def __init__(self, server_name="0.0.0.0", server_port=7860, **kwargs):
self.server_name = server_name
self.server_port = server_port
self.config_path = kwargs.get(
"config", Path(__file__).parent / "config.yaml"
)
self.cfg = load_config(self.config_path)
self.matcher_zoo = get_matcher_zoo(self.cfg["matcher_zoo"])
self.app = None
self.init_interface()
# print all the keys
def init_matcher_dropdown(self):
algos = []
for k, v in self.cfg["matcher_zoo"].items():
if v.get("enable", True):
algos.append(k)
return algos
def init_interface(self):
with gr.Blocks() as self.app:
with gr.Tab("Image Matching"):
with gr.Row():
with gr.Column(scale=1):
gr.Image(
str(
Path(__file__).parent.parent
/ "assets/logo.webp"
),
elem_id="logo-img",
show_label=False,
show_share_button=False,
show_download_button=False,
)
with gr.Column(scale=3):
gr.Markdown(DESCRIPTION)
with gr.Row(equal_height=False):
with gr.Column():
with gr.Row():
matcher_list = gr.Dropdown(
choices=self.init_matcher_dropdown(),
value="disk+lightglue",
label="Matching Model",
interactive=True,
)
match_image_src = gr.Radio(
(
["upload", "webcam", "clipboard"]
if GRADIO_VERSION > "3"
else ["upload", "webcam", "canvas"]
),
label="Image Source",
value="upload",
)
with gr.Row():
input_image0 = gr.Image(
label="Image 0",
type="numpy",
image_mode="RGB",
height=300 if GRADIO_VERSION > "3" else None,
interactive=True,
)
input_image1 = gr.Image(
label="Image 1",
type="numpy",
image_mode="RGB",
height=300 if GRADIO_VERSION > "3" else None,
interactive=True,
)
with gr.Row():
button_reset = gr.Button(value="Reset")
button_run = gr.Button(
value="Run Match", variant="primary"
)
with gr.Accordion("Advanced Setting", open=False):
with gr.Accordion("Matching Setting", open=True):
with gr.Row():
match_setting_threshold = gr.Slider(
minimum=0.0,
maximum=1,
step=0.001,
label="Match thres.",
value=0.1,
)
match_setting_max_keypoints = gr.Slider(
minimum=10,
maximum=10000,
step=10,
label="Max features",
value=1000,
)
# TODO: add line settings
with gr.Row():
detect_keypoints_threshold = gr.Slider(
minimum=0,
maximum=1,
step=0.001,
label="Keypoint thres.",
value=0.015,
)
detect_line_threshold = ( # noqa: F841
gr.Slider(
minimum=0.1,
maximum=1,
step=0.01,
label="Line thres.",
value=0.2,
)
)
# matcher_lists = gr.Radio(
# ["NN-mutual", "Dual-Softmax"],
# label="Matcher mode",
# value="NN-mutual",
# )
with gr.Accordion("RANSAC Setting", open=True):
with gr.Row(equal_height=False):
ransac_method = gr.Dropdown(
choices=ransac_zoo.keys(),
value=self.cfg["defaults"][
"ransac_method"
],
label="RANSAC Method",
interactive=True,
)
ransac_reproj_threshold = gr.Slider(
minimum=0.0,
maximum=12,
step=0.01,
label="Ransac Reproj threshold",
value=8.0,
)
ransac_confidence = gr.Slider(
minimum=0.0,
maximum=1,
step=0.00001,
label="Ransac Confidence",
value=self.cfg["defaults"][
"ransac_confidence"
],
)
ransac_max_iter = gr.Slider(
minimum=0.0,
maximum=100000,
step=100,
label="Ransac Iterations",
value=self.cfg["defaults"][
"ransac_max_iter"
],
)
button_ransac = gr.Button(
value="Rerun RANSAC", variant="primary"
)
with gr.Accordion("Geometry Setting", open=False):
with gr.Row(equal_height=False):
choice_geometry_type = gr.Radio(
["Fundamental", "Homography"],
label="Reconstruct Geometry",
value=self.cfg["defaults"][
"setting_geometry"
],
)
# collect inputs
state_cache = gr.State({})
inputs = [
input_image0,
input_image1,
match_setting_threshold,
match_setting_max_keypoints,
detect_keypoints_threshold,
matcher_list,
ransac_method,
ransac_reproj_threshold,
ransac_confidence,
ransac_max_iter,
choice_geometry_type,
gr.State(self.matcher_zoo),
# state_cache,
]
# Add some examples
with gr.Row():
# Example inputs
with gr.Accordion(
"Open for More: Examples", open=True
):
gr.Examples(
examples=gen_examples(),
inputs=inputs,
outputs=[],
fn=run_matching,
cache_examples=False,
label=(
"Examples (click one of the images below to Run"
" Match). Thx: WxBS"
),
)
with gr.Accordion("Supported Algorithms", open=False):
# add a table of supported algorithms
self.display_supported_algorithms()
with gr.Column():
with gr.Accordion(
"Open for More: Keypoints", open=True
):
output_keypoints = gr.Image(
label="Keypoints", type="numpy"
)
with gr.Accordion(
"Open for More: Raw Matches", open=False
):
output_matches_raw = gr.Image(
label="Raw Matches",
type="numpy",
)
with gr.Accordion(
"Open for More: RANSAC Matches", open=True
):
output_matches_ransac = gr.Image(
label="Ransac Matches", type="numpy"
)
with gr.Accordion(
"Open for More: Matches Statistics", open=False
):
output_pred = gr.File(
label="Outputs", elem_id="download"
)
matches_result_info = gr.JSON(
label="Matches Statistics"
)
matcher_info = gr.JSON(label="Match info")
with gr.Accordion(
"Open for More: Warped Image", open=True
):
output_wrapped = gr.Image(
label="Wrapped Pair", type="numpy"
)
# send to input
button_rerun = gr.Button(
value="Send to Input Match Pair",
variant="primary",
)
with gr.Accordion(
"Open for More: Geometry info", open=False
):
geometry_result = gr.JSON(
label="Reconstructed Geometry"
)
# callbacks
match_image_src.change(
fn=self.ui_change_imagebox,
inputs=match_image_src,
outputs=input_image0,
)
match_image_src.change(
fn=self.ui_change_imagebox,
inputs=match_image_src,
outputs=input_image1,
)
# collect outputs
outputs = [
output_keypoints,
output_matches_raw,
output_matches_ransac,
matches_result_info,
matcher_info,
geometry_result,
output_wrapped,
state_cache,
output_pred,
]
# button callbacks
button_run.click(
fn=run_matching, inputs=inputs, outputs=outputs
)
# Reset images
reset_outputs = [
input_image0,
input_image1,
match_setting_threshold,
match_setting_max_keypoints,
detect_keypoints_threshold,
matcher_list,
input_image0,
input_image1,
match_image_src,
output_keypoints,
output_matches_raw,
output_matches_ransac,
matches_result_info,
matcher_info,
output_wrapped,
geometry_result,
ransac_method,
ransac_reproj_threshold,
ransac_confidence,
ransac_max_iter,
choice_geometry_type,
output_pred,
]
button_reset.click(
fn=self.ui_reset_state,
inputs=None,
outputs=reset_outputs,
)
# run ransac button action
button_ransac.click(
fn=run_ransac,
inputs=[
state_cache,
choice_geometry_type,
ransac_method,
ransac_reproj_threshold,
ransac_confidence,
ransac_max_iter,
],
outputs=[
output_matches_ransac,
matches_result_info,
output_wrapped,
output_pred,
],
)
# send warped image to match
button_rerun.click(
fn=send_to_match,
inputs=[state_cache],
outputs=[input_image0, input_image1],
)
# estimate geo
choice_geometry_type.change(
fn=generate_warp_images,
inputs=[
input_image0,
input_image1,
geometry_result,
choice_geometry_type,
],
outputs=[output_wrapped, geometry_result],
)
with gr.Tab("Structure from Motion(under-dev)"):
sfm_ui = AppSfmUI( # noqa: F841
{
**self.cfg,
"matcher_zoo": self.matcher_zoo,
"outputs": "experiments/sfm",
}
)
# sfm_ui.call()
def run(self):
self.app.queue().launch(
server_name=self.server_name,
server_port=self.server_port,
share=False,
)
def ui_change_imagebox(self, choice):
"""
Updates the image box with the given choice.
Args:
choice (list): The list of image sources to be displayed in the image box.
Returns:
dict: A dictionary containing the updated value, sources, and type for the image box.
"""
ret_dict = {
"value": None, # The updated value of the image box
"__type__": "update", # The type of update for the image box
}
if GRADIO_VERSION > "3":
return {
**ret_dict,
"sources": choice, # The list of image sources to be displayed
}
else:
return {
**ret_dict,
"source": choice, # The list of image sources to be displayed
}
def ui_reset_state(
self,
*args: Any,
) -> Tuple[
Optional[np.ndarray],
Optional[np.ndarray],
float,
int,
float,
str,
Dict[str, Any],
Dict[str, Any],
str,
Optional[np.ndarray],
Optional[np.ndarray],
Optional[np.ndarray],
Dict[str, Any],
Dict[str, Any],
Optional[np.ndarray],
Dict[str, Any],
str,
int,
float,
int,
]:
"""
Reset the state of the UI.
Returns:
tuple: A tuple containing the initial values for the UI state.
"""
key: str = list(self.matcher_zoo.keys())[
0
] # Get the first key from matcher_zoo
return (
None, # image0: Optional[np.ndarray]
None, # image1: Optional[np.ndarray]
self.cfg["defaults"][
"match_threshold"
], # matching_threshold: float
self.cfg["defaults"]["max_keypoints"], # max_keypoints: int
self.cfg["defaults"][
"keypoint_threshold"
], # keypoint_threshold: float
key, # matcher: str
self.ui_change_imagebox("upload"), # input image0: Dict[str, Any]
self.ui_change_imagebox("upload"), # input image1: Dict[str, Any]
"upload", # match_image_src: str
None, # keypoints: Optional[np.ndarray]
None, # raw matches: Optional[np.ndarray]
None, # ransac matches: Optional[np.ndarray]
{}, # matches result info: Dict[str, Any]
{}, # matcher config: Dict[str, Any]
None, # warped image: Optional[np.ndarray]
{}, # geometry result: Dict[str, Any]
self.cfg["defaults"]["ransac_method"], # ransac_method: str
self.cfg["defaults"][
"ransac_reproj_threshold"
], # ransac_reproj_threshold: float
self.cfg["defaults"][
"ransac_confidence"
], # ransac_confidence: float
self.cfg["defaults"]["ransac_max_iter"], # ransac_max_iter: int
self.cfg["defaults"]["setting_geometry"], # geometry: str
None, # predictions
)
def display_supported_algorithms(self, style="tab"):
def get_link(link, tag="Link"):
return "[{}]({})".format(tag, link) if link is not None else "None"
data = []
cfg = self.cfg["matcher_zoo"]
if style == "md":
markdown_table = "| Algo. | Conference | Code | Project | Paper |\n"
markdown_table += (
"| ----- | ---------- | ---- | ------- | ----- |\n"
)
for k, v in cfg.items():
if not v["info"]["display"]:
continue
github_link = get_link(v["info"]["github"])
project_link = get_link(v["info"]["project"])
paper_link = get_link(
v["info"]["paper"],
(
Path(v["info"]["paper"]).name[-10:]
if v["info"]["paper"] is not None
else "Link"
),
)
markdown_table += "{}|{}|{}|{}|{}\n".format(
v["info"]["name"], # display name
v["info"]["source"],
github_link,
project_link,
paper_link,
)
return gr.Markdown(markdown_table)
elif style == "tab":
for k, v in cfg.items():
if not v["info"].get("display", True):
continue
data.append(
[
v["info"]["name"],
v["info"]["source"],
v["info"]["github"],
v["info"]["paper"],
v["info"]["project"],
]
)
tab = gr.Dataframe(
headers=["Algo.", "Conference", "Code", "Paper", "Project"],
datatype=["str", "str", "str", "str", "str"],
col_count=(5, "fixed"),
value=data,
# wrap=True,
# min_width = 1000,
# height=1000,
)
return tab
class AppBaseUI:
def __init__(self, cfg: Dict[str, Any] = {}):
self.cfg = OmegaConf.create(cfg)
self.inputs = edict({})
def _init_ui(self):
NotImplemented
def call(self, **kwargs):
self._init_ui()
class AppSfmUI(AppBaseUI):
def __init__(self, cfg: Dict[str, Any] = None):
super().__init__(cfg)
assert "matcher_zoo" in self.cfg
self.matcher_zoo = self.cfg["matcher_zoo"]
self.sfm_engine = SfmEngine(cfg)
def init_retrieval_dropdown(self):
algos = []
for k, v in self.cfg["retrieval_zoo"].items():
if v.get("enable", True):
algos.append(k)
return algos
def _update_options(self, option):
if option == "sparse":
return gr.Textbox("sparse", visible=True)
elif option == "dense":
return gr.Textbox("dense", visible=True)
else:
return gr.Textbox("not set", visible=True)
def _on_select_custom_params(self, value: bool = False):
return gr.Textbox(
label="Camera Params",
value="0,0,0,0",
interactive=value,
visible=value,
)
def _init_ui(self):
with gr.Row():
# data settting and camera settings
with gr.Column():
self.inputs.input_images = gr.File(
label="SfM",
interactive=True,
file_count="multiple",
min_width=300,
)
# camera setting
with gr.Accordion("Camera Settings", open=True):
with gr.Column():
with gr.Row():
with gr.Column():
self.inputs.camera_model = gr.Dropdown(
choices=[
"PINHOLE",
"SIMPLE_RADIAL",
"OPENCV",
],
value="PINHOLE",
label="Camera Model",
interactive=True,
)
with gr.Column():
gr.Checkbox(
label="Shared Params",
value=True,
interactive=True,
)
camera_custom_params_cb = gr.Checkbox(
label="Custom Params",
value=False,
interactive=True,
)
with gr.Row():
self.inputs.camera_params = gr.Textbox(
label="Camera Params",
value="0,0,0,0",
interactive=False,
visible=False,
)
camera_custom_params_cb.select(
fn=self._on_select_custom_params,
inputs=camera_custom_params_cb,
outputs=self.inputs.camera_params,
)
with gr.Accordion("Matching Settings", open=True):
# feature extraction and matching setting
with gr.Row():
# matcher setting
self.inputs.matcher_key = gr.Dropdown(
choices=self.matcher_zoo.keys(),
value="disk+lightglue",
label="Matching Model",
interactive=True,
)
with gr.Row():
with gr.Accordion("Advanced Settings", open=False):
with gr.Column():
with gr.Row():
# matching setting
self.inputs.max_keypoints = gr.Slider(
label="Max Keypoints",
minimum=100,
maximum=10000,
value=1000,
interactive=True,
)
self.inputs.keypoint_threshold = gr.Slider(
label="Keypoint Threshold",
minimum=0,
maximum=1,
value=0.01,
)
with gr.Row():
self.inputs.match_threshold = gr.Slider(
label="Match Threshold",
minimum=0.01,
maximum=12.0,
value=0.2,
)
self.inputs.ransac_threshold = gr.Slider(
label="Ransac Threshold",
minimum=0.01,
maximum=12.0,
value=4.0,
step=0.01,
interactive=True,
)
with gr.Row():
self.inputs.ransac_confidence = gr.Slider(
label="Ransac Confidence",
minimum=0.01,
maximum=1.0,
value=0.9999,
step=0.0001,
interactive=True,
)
self.inputs.ransac_max_iter = gr.Slider(
label="Ransac Max Iter",
minimum=1,
maximum=100,
value=100,
step=1,
interactive=True,
)
with gr.Accordion("Scene Graph Settings", open=True):
# mapping setting
self.inputs.scene_graph = gr.Dropdown(
choices=["all", "swin", "oneref"],
value="all",
label="Scene Graph",
interactive=True,
)
# global feature setting
self.inputs.global_feature = gr.Dropdown(
choices=self.init_retrieval_dropdown(),
value="netvlad",
label="Global features",
interactive=True,
)
self.inputs.top_k = gr.Slider(
label="Number of Images per Image to Match",
minimum=1,
maximum=100,
value=10,
step=1,
)
# button_match = gr.Button("Run Matching", variant="primary")
# mapping setting
with gr.Column():
with gr.Accordion("Mapping Settings", open=True):
with gr.Row():
with gr.Accordion("Buddle Settings", open=True):
with gr.Row():
self.inputs.mapper_refine_focal_length = (
gr.Checkbox(
label="Refine Focal Length",
value=False,
interactive=True,
)
)
self.inputs.mapper_refine_principle_points = (
gr.Checkbox(
label="Refine Principle Points",
value=False,
interactive=True,
)
)
self.inputs.mapper_refine_extra_params = (
gr.Checkbox(
label="Refine Extra Params",
value=False,
interactive=True,
)
)
with gr.Accordion("Retriangluation Settings", open=True):
gr.Textbox(
label="Retriangluation Details",
)
button_sfm = gr.Button("Run SFM", variant="primary")
model_3d = gr.Model3D(
interactive=True,
)
output_image = gr.Image(
label="SFM Visualize",
type="numpy",
image_mode="RGB",
interactive=False,
)
button_sfm.click(
fn=self.sfm_engine.call,
inputs=[
self.inputs.matcher_key,
self.inputs.input_images, # images
self.inputs.camera_model,
self.inputs.camera_params,
self.inputs.max_keypoints,
self.inputs.keypoint_threshold,
self.inputs.match_threshold,
self.inputs.ransac_threshold,
self.inputs.ransac_confidence,
self.inputs.ransac_max_iter,
self.inputs.scene_graph,
self.inputs.global_feature,
self.inputs.top_k,
self.inputs.mapper_refine_focal_length,
self.inputs.mapper_refine_principle_points,
self.inputs.mapper_refine_extra_params,
],
outputs=[model_3d, output_image],
)
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