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import argparse
import numpy as np
import gradio as gr
from pathlib import Path
from typing import Dict, Any, Optional, Tuple, List, Union
from common.utils import (
ransac_zoo,
change_estimate_geom,
load_config,
get_matcher_zoo,
run_matching,
gen_examples,
GRADIO_VERSION,
)
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_interface(self):
with gr.Blocks() as self.app:
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=list(self.matcher_zoo.keys()),
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_features = 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 = 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"],
)
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
inputs = [
input_image0,
input_image1,
match_setting_threshold,
match_setting_max_features,
detect_keypoints_threshold,
matcher_list,
ransac_method,
ransac_reproj_threshold,
ransac_confidence,
ransac_max_iter,
choice_geometry_type,
gr.State(self.matcher_zoo),
]
# Add some examples
with gr.Row():
# Example inputs
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)"
),
)
with gr.Accordion("Supported Algorithms", open=False):
# add a table of supported algorithms
self.display_supported_algorithms()
with gr.Column():
output_keypoints = gr.Image(label="Keypoints", type="numpy")
output_matches_raw = gr.Image(
label="Raw Matches", type="numpy"
)
output_matches_ransac = gr.Image(
label="Ransac Matches", type="numpy"
)
with gr.Accordion(
"Open for More: Matches Statistics", open=False
):
matches_result_info = gr.JSON(
label="Matches Statistics"
)
matcher_info = gr.JSON(label="Match info")
with gr.Accordion(
"Open for More: Warped Image", open=False
):
output_wrapped = gr.Image(
label="Wrapped Pair", type="numpy"
)
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,
]
# 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_features,
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,
]
button_reset.click(
fn=self.ui_reset_state, inputs=None, outputs=reset_outputs
)
# estimate geo
choice_geometry_type.change(
fn=change_estimate_geom,
inputs=[
input_image0,
input_image1,
geometry_result,
choice_geometry_type,
],
outputs=[output_wrapped, geometry_result],
)
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_features: 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
)
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"]["display"]:
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
|