Spaces:
Running
Running
Realcat
commited on
Commit
β’
7acaad7
1
Parent(s):
9705edb
update: interface
Browse files- app.py +10 -382
- common/app_class.py +403 -0
- common/config.yaml +108 -0
- common/utils.py +67 -196
- common/viz.py +79 -0
app.py
CHANGED
@@ -1,385 +1,6 @@
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import argparse
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from pathlib import Path
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from typing import Dict, Any, Optional, Tuple, List, Union
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import gradio as gr
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from common.utils import (
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matcher_zoo,
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ransac_zoo,
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change_estimate_geom,
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run_matching,
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gen_examples,
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GRADIO_VERSION,
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DEFAULT_RANSAC_METHOD,
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DEFAULT_SETTING_GEOMETRY,
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DEFAULT_RANSAC_REPROJ_THRESHOLD,
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DEFAULT_RANSAC_CONFIDENCE,
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DEFAULT_RANSAC_MAX_ITER,
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DEFAULT_MATCHING_THRESHOLD,
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DEFAULT_SETTING_MAX_FEATURES,
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DEFAULT_DEFAULT_KEYPOINT_THRESHOLD,
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)
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DESCRIPTION = """
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# Image Matching WebUI
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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!
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<br/>
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π For more details about supported local features and matchers, please refer to https://github.com/Vincentqyw/image-matching-webui
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π 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.
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π Your feedback is valuable to me. Please do not hesitate to report any bugs [here](https://github.com/Vincentqyw/image-matching-webui/issues).
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"""
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def ui_change_imagebox(choice):
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"""
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Updates the image box with the given choice.
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Args:
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choice (list): The list of image sources to be displayed in the image box.
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Returns:
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dict: A dictionary containing the updated value, sources, and type for the image box.
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"""
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ret_dict = {
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"value": None, # The updated value of the image box
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"__type__": "update", # The type of update for the image box
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}
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if GRADIO_VERSION > "3":
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return {
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**ret_dict,
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"sources": choice, # The list of image sources to be displayed
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}
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else:
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return {
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**ret_dict,
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"source": choice, # The list of image sources to be displayed
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}
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def ui_reset_state(
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*args: Any,
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) -> Tuple[
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Optional[np.ndarray],
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Optional[np.ndarray],
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float,
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int,
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float,
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str,
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Dict[str, Any],
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Dict[str, Any],
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str,
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Optional[np.ndarray],
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Optional[np.ndarray],
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Optional[np.ndarray],
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Dict[str, Any],
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Dict[str, Any],
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Optional[np.ndarray],
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Dict[str, Any],
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str,
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int,
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float,
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int,
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]:
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"""
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Reset the state of the UI.
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Returns:
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tuple: A tuple containing the initial values for the UI state.
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"""
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key: str = list(matcher_zoo.keys())[0] # Get the first key from matcher_zoo
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return (
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None, # image0: Optional[np.ndarray]
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None, # image1: Optional[np.ndarray]
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DEFAULT_MATCHING_THRESHOLD, # matching_threshold: float
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DEFAULT_SETTING_MAX_FEATURES, # max_features: int
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DEFAULT_DEFAULT_KEYPOINT_THRESHOLD, # keypoint_threshold: float
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key, # matcher: str
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ui_change_imagebox("upload"), # input image0: Dict[str, Any]
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ui_change_imagebox("upload"), # input image1: Dict[str, Any]
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"upload", # match_image_src: str
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None, # keypoints: Optional[np.ndarray]
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None, # raw matches: Optional[np.ndarray]
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None, # ransac matches: Optional[np.ndarray]
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{}, # matches result info: Dict[str, Any]
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{}, # matcher config: Dict[str, Any]
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None, # warped image: Optional[np.ndarray]
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{}, # geometry result: Dict[str, Any]
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DEFAULT_RANSAC_METHOD, # ransac_method: str
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DEFAULT_RANSAC_REPROJ_THRESHOLD, # ransac_reproj_threshold: float
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DEFAULT_RANSAC_CONFIDENCE, # ransac_confidence: float
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DEFAULT_RANSAC_MAX_ITER, # ransac_max_iter: int
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DEFAULT_SETTING_GEOMETRY, # geometry: str
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)
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# "footer {visibility: hidden}"
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def run(server_name="0.0.0.0", server_port=7860):
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"""
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Runs the application.
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Args:
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config (dict): A dictionary containing configuration parameters for the application.
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Returns:
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None
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"""
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with gr.Blocks() as app:
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# gr.Markdown(DESCRIPTION)
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with gr.Row():
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with gr.Column(scale=1):
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gr.Image(
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str(Path(__file__).parent / "assets/logo.webp"),
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elem_id="logo-img",
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show_label=False,
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show_share_button=False,
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show_download_button=False,
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)
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with gr.Column(scale=3):
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gr.Markdown(DESCRIPTION)
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with gr.Row(equal_height=False):
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with gr.Column():
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with gr.Row():
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matcher_list = gr.Dropdown(
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choices=list(matcher_zoo.keys()),
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value="disk+lightglue",
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label="Matching Model",
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interactive=True,
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)
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match_image_src = gr.Radio(
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(
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["upload", "webcam", "clipboard"]
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if GRADIO_VERSION > "3"
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else ["upload", "webcam", "canvas"]
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),
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label="Image Source",
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value="upload",
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)
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with gr.Row():
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input_image0 = gr.Image(
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label="Image 0",
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type="numpy",
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image_mode="RGB",
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height=300 if GRADIO_VERSION > "3" else None,
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interactive=True,
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)
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input_image1 = gr.Image(
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label="Image 1",
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type="numpy",
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image_mode="RGB",
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height=300 if GRADIO_VERSION > "3" else None,
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interactive=True,
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)
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with gr.Row():
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button_reset = gr.Button(value="Reset")
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button_run = gr.Button(value="Run Match", variant="primary")
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with gr.Accordion("Advanced Setting", open=False):
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with gr.Accordion("Matching Setting", open=True):
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with gr.Row():
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match_setting_threshold = gr.Slider(
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minimum=0.0,
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maximum=1,
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step=0.001,
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label="Match thres.",
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value=0.1,
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)
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match_setting_max_features = gr.Slider(
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minimum=10,
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maximum=10000,
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step=10,
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label="Max features",
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value=1000,
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)
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# TODO: add line settings
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with gr.Row():
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detect_keypoints_threshold = gr.Slider(
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minimum=0,
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maximum=1,
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step=0.001,
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label="Keypoint thres.",
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value=0.015,
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)
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detect_line_threshold = gr.Slider(
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minimum=0.1,
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maximum=1,
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step=0.01,
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label="Line thres.",
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value=0.2,
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)
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# matcher_lists = gr.Radio(
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# ["NN-mutual", "Dual-Softmax"],
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# label="Matcher mode",
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# value="NN-mutual",
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# )
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with gr.Accordion("RANSAC Setting", open=True):
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with gr.Row(equal_height=False):
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ransac_method = gr.Dropdown(
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choices=ransac_zoo.keys(),
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value=DEFAULT_RANSAC_METHOD,
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label="RANSAC Method",
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interactive=True,
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)
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ransac_reproj_threshold = gr.Slider(
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minimum=0.0,
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maximum=12,
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step=0.01,
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label="Ransac Reproj threshold",
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value=8.0,
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)
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ransac_confidence = gr.Slider(
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minimum=0.0,
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maximum=1,
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step=0.00001,
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label="Ransac Confidence",
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value=DEFAULT_RANSAC_CONFIDENCE,
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)
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ransac_max_iter = gr.Slider(
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minimum=0.0,
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maximum=100000,
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step=100,
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label="Ransac Iterations",
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value=DEFAULT_RANSAC_MAX_ITER,
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)
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with gr.Accordion("Geometry Setting", open=False):
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with gr.Row(equal_height=False):
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choice_estimate_geom = gr.Radio(
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["Fundamental", "Homography"],
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label="Reconstruct Geometry",
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value=DEFAULT_SETTING_GEOMETRY,
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)
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# collect inputs
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inputs = [
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input_image0,
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input_image1,
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match_setting_threshold,
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match_setting_max_features,
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detect_keypoints_threshold,
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matcher_list,
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ransac_method,
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ransac_reproj_threshold,
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ransac_confidence,
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ransac_max_iter,
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choice_estimate_geom,
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]
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# Add some examples
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with gr.Row():
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# Example inputs
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gr.Examples(
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examples=gen_examples(),
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inputs=inputs,
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outputs=[],
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fn=run_matching,
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cache_examples=False,
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label=(
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"Examples (click one of the images below to Run"
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" Match)"
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),
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)
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with gr.Accordion("Open for More!", open=False):
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gr.Markdown(
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f"""
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<h3>Supported Algorithms</h3>
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{", ".join(matcher_zoo.keys())}
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"""
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)
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with gr.Column():
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output_keypoints = gr.Image(label="Keypoints", type="numpy")
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output_matches_raw = gr.Image(label="Raw Matches", type="numpy")
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output_matches_ransac = gr.Image(
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label="Ransac Matches", type="numpy"
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)
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with gr.Accordion(
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"Open for More: Matches Statistics", open=False
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):
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matches_result_info = gr.JSON(label="Matches Statistics")
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matcher_info = gr.JSON(label="Match info")
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with gr.Accordion("Open for More: Warped Image", open=False):
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output_wrapped = gr.Image(
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label="Wrapped Pair", type="numpy"
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)
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with gr.Accordion(
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"Open for More: Geometry info", open=False
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):
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geometry_result = gr.JSON(
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label="Reconstructed Geometry"
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)
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# callbacks
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match_image_src.change(
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fn=ui_change_imagebox,
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inputs=match_image_src,
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outputs=input_image0,
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)
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match_image_src.change(
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fn=ui_change_imagebox,
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inputs=match_image_src,
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outputs=input_image1,
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)
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# collect outputs
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outputs = [
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output_keypoints,
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output_matches_raw,
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output_matches_ransac,
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matches_result_info,
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matcher_info,
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geometry_result,
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output_wrapped,
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]
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# button callbacks
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button_run.click(fn=run_matching, inputs=inputs, outputs=outputs)
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# Reset images
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reset_outputs = [
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input_image0,
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input_image1,
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match_setting_threshold,
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match_setting_max_features,
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detect_keypoints_threshold,
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matcher_list,
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input_image0,
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input_image1,
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match_image_src,
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output_keypoints,
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output_matches_raw,
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output_matches_ransac,
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matches_result_info,
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matcher_info,
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output_wrapped,
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geometry_result,
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ransac_method,
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ransac_reproj_threshold,
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ransac_confidence,
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ransac_max_iter,
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choice_estimate_geom,
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]
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button_reset.click(
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fn=ui_reset_state, inputs=inputs, outputs=reset_outputs
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)
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# estimate geo
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choice_estimate_geom.change(
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fn=change_estimate_geom,
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inputs=[
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input_image0,
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input_image1,
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geometry_result,
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choice_estimate_geom,
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],
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outputs=[output_wrapped, geometry_result],
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)
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app.queue().launch(
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server_name=server_name, server_port=server_port, share=False
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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@@ -395,6 +16,13 @@ if __name__ == "__main__":
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default=7860,
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help="server port",
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)
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args = parser.parse_args()
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import argparse
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from pathlib import Path
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from common.app_class import ImageMatchingApp
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|
4 |
|
5 |
if __name__ == "__main__":
|
6 |
parser = argparse.ArgumentParser()
|
|
|
16 |
default=7860,
|
17 |
help="server port",
|
18 |
)
|
19 |
+
parser.add_argument(
|
20 |
+
"--config",
|
21 |
+
type=str,
|
22 |
+
default=Path(__file__).parent / "common/config.yaml",
|
23 |
+
help="config file",
|
24 |
+
)
|
25 |
args = parser.parse_args()
|
26 |
+
ImageMatchingApp(
|
27 |
+
args.server_name, args.server_port, config=args.config
|
28 |
+
).run()
|
common/app_class.py
ADDED
@@ -0,0 +1,403 @@
|
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|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import argparse
|
2 |
+
import numpy as np
|
3 |
+
import gradio as gr
|
4 |
+
from pathlib import Path
|
5 |
+
from typing import Dict, Any, Optional, Tuple, List, Union
|
6 |
+
from common.utils import (
|
7 |
+
ransac_zoo,
|
8 |
+
change_estimate_geom,
|
9 |
+
load_config,
|
10 |
+
get_matcher_zoo,
|
11 |
+
run_matching,
|
12 |
+
gen_examples,
|
13 |
+
GRADIO_VERSION,
|
14 |
+
)
|
15 |
+
|
16 |
+
DESCRIPTION = """
|
17 |
+
# Image Matching WebUI
|
18 |
+
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!
|
19 |
+
<br/>
|
20 |
+
π For more details about supported local features and matchers, please refer to https://github.com/Vincentqyw/image-matching-webui
|
21 |
+
|
22 |
+
π 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.
|
23 |
+
|
24 |
+
π Your feedback is valuable to me. Please do not hesitate to report any bugs [here](https://github.com/Vincentqyw/image-matching-webui/issues).
|
25 |
+
"""
|
26 |
+
|
27 |
+
|
28 |
+
class ImageMatchingApp:
|
29 |
+
def __init__(self, server_name="0.0.0.0", server_port=7860, **kwargs):
|
30 |
+
self.server_name = server_name
|
31 |
+
self.server_port = server_port
|
32 |
+
self.config_path = kwargs.get(
|
33 |
+
"config", Path(__file__).parent / "config.yaml"
|
34 |
+
)
|
35 |
+
self.cfg = load_config(self.config_path)
|
36 |
+
self.matcher_zoo = get_matcher_zoo(self.cfg["matcher_zoo"])
|
37 |
+
# self.ransac_zoo = get_ransac_zoo(self.cfg["ransac_zoo"])
|
38 |
+
self.app = None
|
39 |
+
self.init_interface()
|
40 |
+
# print all the keys
|
41 |
+
|
42 |
+
def init_interface(self):
|
43 |
+
with gr.Blocks() as self.app:
|
44 |
+
with gr.Row():
|
45 |
+
with gr.Column(scale=1):
|
46 |
+
gr.Image(
|
47 |
+
str(Path(__file__).parent.parent / "assets/logo.webp"),
|
48 |
+
elem_id="logo-img",
|
49 |
+
show_label=False,
|
50 |
+
show_share_button=False,
|
51 |
+
show_download_button=False,
|
52 |
+
)
|
53 |
+
with gr.Column(scale=3):
|
54 |
+
gr.Markdown(DESCRIPTION)
|
55 |
+
with gr.Row(equal_height=False):
|
56 |
+
with gr.Column():
|
57 |
+
with gr.Row():
|
58 |
+
matcher_list = gr.Dropdown(
|
59 |
+
choices=list(self.matcher_zoo.keys()),
|
60 |
+
value="disk+lightglue",
|
61 |
+
label="Matching Model",
|
62 |
+
interactive=True,
|
63 |
+
)
|
64 |
+
match_image_src = gr.Radio(
|
65 |
+
(
|
66 |
+
["upload", "webcam", "clipboard"]
|
67 |
+
if GRADIO_VERSION > "3"
|
68 |
+
else ["upload", "webcam", "canvas"]
|
69 |
+
),
|
70 |
+
label="Image Source",
|
71 |
+
value="upload",
|
72 |
+
)
|
73 |
+
with gr.Row():
|
74 |
+
input_image0 = gr.Image(
|
75 |
+
label="Image 0",
|
76 |
+
type="numpy",
|
77 |
+
image_mode="RGB",
|
78 |
+
height=300 if GRADIO_VERSION > "3" else None,
|
79 |
+
interactive=True,
|
80 |
+
)
|
81 |
+
input_image1 = gr.Image(
|
82 |
+
label="Image 1",
|
83 |
+
type="numpy",
|
84 |
+
image_mode="RGB",
|
85 |
+
height=300 if GRADIO_VERSION > "3" else None,
|
86 |
+
interactive=True,
|
87 |
+
)
|
88 |
+
|
89 |
+
with gr.Row():
|
90 |
+
button_reset = gr.Button(value="Reset")
|
91 |
+
button_run = gr.Button(
|
92 |
+
value="Run Match", variant="primary"
|
93 |
+
)
|
94 |
+
|
95 |
+
with gr.Accordion("Advanced Setting", open=False):
|
96 |
+
with gr.Accordion("Matching Setting", open=True):
|
97 |
+
with gr.Row():
|
98 |
+
match_setting_threshold = gr.Slider(
|
99 |
+
minimum=0.0,
|
100 |
+
maximum=1,
|
101 |
+
step=0.001,
|
102 |
+
label="Match thres.",
|
103 |
+
value=0.1,
|
104 |
+
)
|
105 |
+
match_setting_max_features = gr.Slider(
|
106 |
+
minimum=10,
|
107 |
+
maximum=10000,
|
108 |
+
step=10,
|
109 |
+
label="Max features",
|
110 |
+
value=1000,
|
111 |
+
)
|
112 |
+
# TODO: add line settings
|
113 |
+
with gr.Row():
|
114 |
+
detect_keypoints_threshold = gr.Slider(
|
115 |
+
minimum=0,
|
116 |
+
maximum=1,
|
117 |
+
step=0.001,
|
118 |
+
label="Keypoint thres.",
|
119 |
+
value=0.015,
|
120 |
+
)
|
121 |
+
detect_line_threshold = gr.Slider(
|
122 |
+
minimum=0.1,
|
123 |
+
maximum=1,
|
124 |
+
step=0.01,
|
125 |
+
label="Line thres.",
|
126 |
+
value=0.2,
|
127 |
+
)
|
128 |
+
# matcher_lists = gr.Radio(
|
129 |
+
# ["NN-mutual", "Dual-Softmax"],
|
130 |
+
# label="Matcher mode",
|
131 |
+
# value="NN-mutual",
|
132 |
+
# )
|
133 |
+
with gr.Accordion("RANSAC Setting", open=True):
|
134 |
+
with gr.Row(equal_height=False):
|
135 |
+
ransac_method = gr.Dropdown(
|
136 |
+
choices=ransac_zoo.keys(),
|
137 |
+
value=self.cfg["defaults"]["ransac_method"],
|
138 |
+
label="RANSAC Method",
|
139 |
+
interactive=True,
|
140 |
+
)
|
141 |
+
ransac_reproj_threshold = gr.Slider(
|
142 |
+
minimum=0.0,
|
143 |
+
maximum=12,
|
144 |
+
step=0.01,
|
145 |
+
label="Ransac Reproj threshold",
|
146 |
+
value=8.0,
|
147 |
+
)
|
148 |
+
ransac_confidence = gr.Slider(
|
149 |
+
minimum=0.0,
|
150 |
+
maximum=1,
|
151 |
+
step=0.00001,
|
152 |
+
label="Ransac Confidence",
|
153 |
+
value=self.cfg["defaults"]["ransac_confidence"],
|
154 |
+
)
|
155 |
+
ransac_max_iter = gr.Slider(
|
156 |
+
minimum=0.0,
|
157 |
+
maximum=100000,
|
158 |
+
step=100,
|
159 |
+
label="Ransac Iterations",
|
160 |
+
value=self.cfg["defaults"]["ransac_max_iter"],
|
161 |
+
)
|
162 |
+
|
163 |
+
with gr.Accordion("Geometry Setting", open=False):
|
164 |
+
with gr.Row(equal_height=False):
|
165 |
+
choice_estimate_geom = gr.Radio(
|
166 |
+
["Fundamental", "Homography"],
|
167 |
+
label="Reconstruct Geometry",
|
168 |
+
value=self.cfg["defaults"][
|
169 |
+
"setting_geometry"
|
170 |
+
],
|
171 |
+
)
|
172 |
+
|
173 |
+
# collect inputs
|
174 |
+
inputs = [
|
175 |
+
input_image0,
|
176 |
+
input_image1,
|
177 |
+
match_setting_threshold,
|
178 |
+
match_setting_max_features,
|
179 |
+
detect_keypoints_threshold,
|
180 |
+
matcher_list,
|
181 |
+
ransac_method,
|
182 |
+
ransac_reproj_threshold,
|
183 |
+
ransac_confidence,
|
184 |
+
ransac_max_iter,
|
185 |
+
choice_estimate_geom,
|
186 |
+
gr.State(self.matcher_zoo),
|
187 |
+
]
|
188 |
+
|
189 |
+
# Add some examples
|
190 |
+
with gr.Row():
|
191 |
+
# Example inputs
|
192 |
+
gr.Examples(
|
193 |
+
examples=gen_examples(),
|
194 |
+
inputs=inputs,
|
195 |
+
outputs=[],
|
196 |
+
fn=run_matching,
|
197 |
+
cache_examples=False,
|
198 |
+
label=(
|
199 |
+
"Examples (click one of the images below to Run"
|
200 |
+
" Match)"
|
201 |
+
),
|
202 |
+
)
|
203 |
+
with gr.Accordion("Open for More!", open=False):
|
204 |
+
gr.Markdown(
|
205 |
+
f"""
|
206 |
+
<h3>Supported Algorithms</h3>
|
207 |
+
{", ".join(self.matcher_zoo.keys())}
|
208 |
+
"""
|
209 |
+
)
|
210 |
+
|
211 |
+
with gr.Column():
|
212 |
+
output_keypoints = gr.Image(label="Keypoints", type="numpy")
|
213 |
+
output_matches_raw = gr.Image(
|
214 |
+
label="Raw Matches", type="numpy"
|
215 |
+
)
|
216 |
+
output_matches_ransac = gr.Image(
|
217 |
+
label="Ransac Matches", type="numpy"
|
218 |
+
)
|
219 |
+
with gr.Accordion(
|
220 |
+
"Open for More: Matches Statistics", open=False
|
221 |
+
):
|
222 |
+
matches_result_info = gr.JSON(
|
223 |
+
label="Matches Statistics"
|
224 |
+
)
|
225 |
+
matcher_info = gr.JSON(label="Match info")
|
226 |
+
|
227 |
+
with gr.Accordion(
|
228 |
+
"Open for More: Warped Image", open=False
|
229 |
+
):
|
230 |
+
output_wrapped = gr.Image(
|
231 |
+
label="Wrapped Pair", type="numpy"
|
232 |
+
)
|
233 |
+
with gr.Accordion(
|
234 |
+
"Open for More: Geometry info", open=False
|
235 |
+
):
|
236 |
+
geometry_result = gr.JSON(
|
237 |
+
label="Reconstructed Geometry"
|
238 |
+
)
|
239 |
+
|
240 |
+
# callbacks
|
241 |
+
match_image_src.change(
|
242 |
+
fn=self.ui_change_imagebox,
|
243 |
+
inputs=match_image_src,
|
244 |
+
outputs=input_image0,
|
245 |
+
)
|
246 |
+
match_image_src.change(
|
247 |
+
fn=self.ui_change_imagebox,
|
248 |
+
inputs=match_image_src,
|
249 |
+
outputs=input_image1,
|
250 |
+
)
|
251 |
+
|
252 |
+
# collect outputs
|
253 |
+
outputs = [
|
254 |
+
output_keypoints,
|
255 |
+
output_matches_raw,
|
256 |
+
output_matches_ransac,
|
257 |
+
matches_result_info,
|
258 |
+
matcher_info,
|
259 |
+
geometry_result,
|
260 |
+
output_wrapped,
|
261 |
+
]
|
262 |
+
# button callbacks
|
263 |
+
button_run.click(
|
264 |
+
fn=run_matching, inputs=inputs, outputs=outputs
|
265 |
+
)
|
266 |
+
|
267 |
+
# Reset images
|
268 |
+
reset_outputs = [
|
269 |
+
input_image0,
|
270 |
+
input_image1,
|
271 |
+
match_setting_threshold,
|
272 |
+
match_setting_max_features,
|
273 |
+
detect_keypoints_threshold,
|
274 |
+
matcher_list,
|
275 |
+
input_image0,
|
276 |
+
input_image1,
|
277 |
+
match_image_src,
|
278 |
+
output_keypoints,
|
279 |
+
output_matches_raw,
|
280 |
+
output_matches_ransac,
|
281 |
+
matches_result_info,
|
282 |
+
matcher_info,
|
283 |
+
output_wrapped,
|
284 |
+
geometry_result,
|
285 |
+
ransac_method,
|
286 |
+
ransac_reproj_threshold,
|
287 |
+
ransac_confidence,
|
288 |
+
ransac_max_iter,
|
289 |
+
choice_estimate_geom,
|
290 |
+
]
|
291 |
+
button_reset.click(
|
292 |
+
fn=self.ui_reset_state, inputs=inputs, outputs=reset_outputs
|
293 |
+
)
|
294 |
+
|
295 |
+
# estimate geo
|
296 |
+
choice_estimate_geom.change(
|
297 |
+
fn=change_estimate_geom,
|
298 |
+
inputs=[
|
299 |
+
input_image0,
|
300 |
+
input_image1,
|
301 |
+
geometry_result,
|
302 |
+
choice_estimate_geom,
|
303 |
+
],
|
304 |
+
outputs=[output_wrapped, geometry_result],
|
305 |
+
)
|
306 |
+
|
307 |
+
def run(self):
|
308 |
+
self.app.queue().launch(
|
309 |
+
server_name=self.server_name,
|
310 |
+
server_port=self.server_port,
|
311 |
+
share=False,
|
312 |
+
)
|
313 |
+
|
314 |
+
def ui_change_imagebox(self, choice):
|
315 |
+
"""
|
316 |
+
Updates the image box with the given choice.
|
317 |
+
|
318 |
+
Args:
|
319 |
+
choice (list): The list of image sources to be displayed in the image box.
|
320 |
+
|
321 |
+
Returns:
|
322 |
+
dict: A dictionary containing the updated value, sources, and type for the image box.
|
323 |
+
"""
|
324 |
+
ret_dict = {
|
325 |
+
"value": None, # The updated value of the image box
|
326 |
+
"__type__": "update", # The type of update for the image box
|
327 |
+
}
|
328 |
+
if GRADIO_VERSION > "3":
|
329 |
+
return {
|
330 |
+
**ret_dict,
|
331 |
+
"sources": choice, # The list of image sources to be displayed
|
332 |
+
}
|
333 |
+
else:
|
334 |
+
return {
|
335 |
+
**ret_dict,
|
336 |
+
"source": choice, # The list of image sources to be displayed
|
337 |
+
}
|
338 |
+
|
339 |
+
def ui_reset_state(
|
340 |
+
self,
|
341 |
+
*args: Any,
|
342 |
+
) -> Tuple[
|
343 |
+
Optional[np.ndarray],
|
344 |
+
Optional[np.ndarray],
|
345 |
+
float,
|
346 |
+
int,
|
347 |
+
float,
|
348 |
+
str,
|
349 |
+
Dict[str, Any],
|
350 |
+
Dict[str, Any],
|
351 |
+
str,
|
352 |
+
Optional[np.ndarray],
|
353 |
+
Optional[np.ndarray],
|
354 |
+
Optional[np.ndarray],
|
355 |
+
Dict[str, Any],
|
356 |
+
Dict[str, Any],
|
357 |
+
Optional[np.ndarray],
|
358 |
+
Dict[str, Any],
|
359 |
+
str,
|
360 |
+
int,
|
361 |
+
float,
|
362 |
+
int,
|
363 |
+
]:
|
364 |
+
"""
|
365 |
+
Reset the state of the UI.
|
366 |
+
|
367 |
+
Returns:
|
368 |
+
tuple: A tuple containing the initial values for the UI state.
|
369 |
+
"""
|
370 |
+
key: str = list(self.matcher_zoo.keys())[
|
371 |
+
0
|
372 |
+
] # Get the first key from matcher_zoo
|
373 |
+
return (
|
374 |
+
None, # image0: Optional[np.ndarray]
|
375 |
+
None, # image1: Optional[np.ndarray]
|
376 |
+
self.cfg["defaults"][
|
377 |
+
"match_threshold"
|
378 |
+
], # matching_threshold: float
|
379 |
+
self.cfg["defaults"]["max_keypoints"], # max_features: int
|
380 |
+
self.cfg["defaults"][
|
381 |
+
"keypoint_threshold"
|
382 |
+
], # keypoint_threshold: float
|
383 |
+
key, # matcher: str
|
384 |
+
self.ui_change_imagebox("upload"), # input image0: Dict[str, Any]
|
385 |
+
self.ui_change_imagebox("upload"), # input image1: Dict[str, Any]
|
386 |
+
"upload", # match_image_src: str
|
387 |
+
None, # keypoints: Optional[np.ndarray]
|
388 |
+
None, # raw matches: Optional[np.ndarray]
|
389 |
+
None, # ransac matches: Optional[np.ndarray]
|
390 |
+
{}, # matches result info: Dict[str, Any]
|
391 |
+
{}, # matcher config: Dict[str, Any]
|
392 |
+
None, # warped image: Optional[np.ndarray]
|
393 |
+
{}, # geometry result: Dict[str, Any]
|
394 |
+
self.cfg["defaults"]["ransac_method"], # ransac_method: str
|
395 |
+
self.cfg["defaults"][
|
396 |
+
"ransac_reproj_threshold"
|
397 |
+
], # ransac_reproj_threshold: float
|
398 |
+
self.cfg["defaults"][
|
399 |
+
"ransac_confidence"
|
400 |
+
], # ransac_confidence: float
|
401 |
+
self.cfg["defaults"]["ransac_max_iter"], # ransac_max_iter: int
|
402 |
+
self.cfg["defaults"]["setting_geometry"], # geometry: str
|
403 |
+
)
|
common/config.yaml
ADDED
@@ -0,0 +1,108 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
server:
|
2 |
+
name: "0.0.0.0"
|
3 |
+
port: 7860
|
4 |
+
|
5 |
+
defaults:
|
6 |
+
setting_threshold: 0.1
|
7 |
+
max_keypoints: 2000
|
8 |
+
keypoint_threshold: 0.05
|
9 |
+
enable_ransac: true
|
10 |
+
ransac_method: USAC_MAGSAC
|
11 |
+
ransac_reproj_threshold: 8
|
12 |
+
ransac_confidence: 0.999
|
13 |
+
ransac_max_iter: 10000
|
14 |
+
ransac_num_samples: 4
|
15 |
+
match_threshold: 0.2
|
16 |
+
setting_geometry: Homography
|
17 |
+
|
18 |
+
matcher_zoo:
|
19 |
+
roma:
|
20 |
+
matcher: roma
|
21 |
+
dense: true
|
22 |
+
loftr:
|
23 |
+
matcher: loftr
|
24 |
+
dense: true
|
25 |
+
topicfm:
|
26 |
+
matcher: topicfm
|
27 |
+
dense: true
|
28 |
+
aspanformer:
|
29 |
+
matcher: aspanformer
|
30 |
+
dense: true
|
31 |
+
dedode:
|
32 |
+
matcher: Dual-Softmax
|
33 |
+
feature: dedode
|
34 |
+
dense: false
|
35 |
+
superpoint+superglue:
|
36 |
+
matcher: superglue
|
37 |
+
feature: superpoint_max
|
38 |
+
dense: false
|
39 |
+
superpoint+lightglue:
|
40 |
+
matcher: superpoint-lightglue
|
41 |
+
feature: superpoint_max
|
42 |
+
dense: false
|
43 |
+
disk:
|
44 |
+
matcher: NN-mutual
|
45 |
+
feature: disk
|
46 |
+
dense: false
|
47 |
+
disk+dualsoftmax:
|
48 |
+
matcher: Dual-Softmax
|
49 |
+
feature: disk
|
50 |
+
dense: false
|
51 |
+
superpoint+dualsoftmax:
|
52 |
+
matcher: Dual-Softmax
|
53 |
+
feature: superpoint_max
|
54 |
+
dense: false
|
55 |
+
disk+lightglue:
|
56 |
+
matcher: disk-lightglue
|
57 |
+
feature: disk
|
58 |
+
dense: false
|
59 |
+
superpoint+mnn:
|
60 |
+
matcher: NN-mutual
|
61 |
+
feature: superpoint_max
|
62 |
+
dense: false
|
63 |
+
sift+sgmnet:
|
64 |
+
matcher: sgmnet
|
65 |
+
feature: sift
|
66 |
+
dense: false
|
67 |
+
sosnet:
|
68 |
+
matcher: NN-mutual
|
69 |
+
feature: sosnet
|
70 |
+
dense: false
|
71 |
+
hardnet:
|
72 |
+
matcher: NN-mutual
|
73 |
+
feature: hardnet
|
74 |
+
dense: false
|
75 |
+
d2net:
|
76 |
+
matcher: NN-mutual
|
77 |
+
feature: d2net-ss
|
78 |
+
dense: false
|
79 |
+
rord:
|
80 |
+
matcher: NN-mutual
|
81 |
+
feature: rord
|
82 |
+
dense: false
|
83 |
+
alike:
|
84 |
+
matcher: NN-mutual
|
85 |
+
feature: alike
|
86 |
+
dense: false
|
87 |
+
lanet:
|
88 |
+
matcher: NN-mutual
|
89 |
+
feature: lanet
|
90 |
+
dense: false
|
91 |
+
r2d2:
|
92 |
+
matcher: NN-mutual
|
93 |
+
feature: r2d2
|
94 |
+
dense: false
|
95 |
+
darkfeat:
|
96 |
+
matcher: NN-mutual
|
97 |
+
feature: darkfeat
|
98 |
+
dense: false
|
99 |
+
sift:
|
100 |
+
matcher: NN-mutual
|
101 |
+
feature: sift
|
102 |
+
dense: false
|
103 |
+
gluestick:
|
104 |
+
matcher: gluestick
|
105 |
+
dense: true
|
106 |
+
sold2:
|
107 |
+
matcher: sold2
|
108 |
+
dense: true
|
common/utils.py
CHANGED
@@ -1,20 +1,27 @@
|
|
1 |
import os
|
|
|
|
|
2 |
import random
|
3 |
import numpy as np
|
4 |
-
import torch
|
5 |
-
import cv2
|
6 |
import gradio as gr
|
7 |
from pathlib import Path
|
8 |
-
from typing import Dict, Any, Optional, Tuple, List, Union
|
9 |
from itertools import combinations
|
|
|
10 |
from hloc import matchers, extractors, logger
|
11 |
from hloc.utils.base_model import dynamic_load
|
12 |
from hloc import match_dense, match_features, extract_features
|
13 |
from hloc.utils.viz import add_text, plot_keypoints
|
14 |
-
from .viz import
|
|
|
|
|
|
|
|
|
|
|
|
|
15 |
|
16 |
device = "cuda" if torch.cuda.is_available() else "cpu"
|
17 |
|
|
|
18 |
DEFAULT_SETTING_THRESHOLD = 0.1
|
19 |
DEFAULT_SETTING_MAX_FEATURES = 2000
|
20 |
DEFAULT_DEFAULT_KEYPOINT_THRESHOLD = 0.01
|
@@ -27,6 +34,58 @@ DEFAULT_MIN_NUM_MATCHES = 4
|
|
27 |
DEFAULT_MATCHING_THRESHOLD = 0.2
|
28 |
DEFAULT_SETTING_GEOMETRY = "Homography"
|
29 |
GRADIO_VERSION = gr.__version__.split(".")[0]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
30 |
|
31 |
|
32 |
def get_model(match_conf: Dict[str, Any]):
|
@@ -83,7 +142,7 @@ def gen_examples():
|
|
83 |
return [pairs[i] for i in selected]
|
84 |
|
85 |
# image pair path
|
86 |
-
path =
|
87 |
pairs = gen_images_pairs(str(path), len(example_matchers))
|
88 |
match_setting_threshold = DEFAULT_SETTING_THRESHOLD
|
89 |
match_setting_max_features = DEFAULT_SETTING_MAX_FEATURES
|
@@ -343,85 +402,6 @@ def change_estimate_geom(
|
|
343 |
return None, None
|
344 |
|
345 |
|
346 |
-
def display_matches(
|
347 |
-
pred: Dict[str, np.ndarray], titles: List[str] = [], dpi: int = 300
|
348 |
-
) -> Tuple[np.ndarray, int]:
|
349 |
-
"""
|
350 |
-
Displays the matches between two images.
|
351 |
-
|
352 |
-
Args:
|
353 |
-
pred: Dictionary containing the original images and the matches.
|
354 |
-
titles: Optional titles for the plot.
|
355 |
-
dpi: Resolution of the plot.
|
356 |
-
|
357 |
-
Returns:
|
358 |
-
The resulting concatenated plot and the number of inliers.
|
359 |
-
"""
|
360 |
-
img0 = pred["image0_orig"]
|
361 |
-
img1 = pred["image1_orig"]
|
362 |
-
|
363 |
-
num_inliers = 0
|
364 |
-
if (
|
365 |
-
"keypoints0_orig" in pred
|
366 |
-
and "keypoints1_orig" in pred
|
367 |
-
and pred["keypoints0_orig"] is not None
|
368 |
-
and pred["keypoints1_orig"] is not None
|
369 |
-
):
|
370 |
-
mkpts0 = pred["keypoints0_orig"]
|
371 |
-
mkpts1 = pred["keypoints1_orig"]
|
372 |
-
num_inliers = len(mkpts0)
|
373 |
-
if "mconf" in pred:
|
374 |
-
mconf = pred["mconf"]
|
375 |
-
else:
|
376 |
-
mconf = np.ones(len(mkpts0))
|
377 |
-
fig_mkpts = draw_matches(
|
378 |
-
mkpts0,
|
379 |
-
mkpts1,
|
380 |
-
img0,
|
381 |
-
img1,
|
382 |
-
mconf,
|
383 |
-
dpi=dpi,
|
384 |
-
titles=titles,
|
385 |
-
)
|
386 |
-
fig = fig_mkpts
|
387 |
-
if (
|
388 |
-
"line0_orig" in pred
|
389 |
-
and "line1_orig" in pred
|
390 |
-
and pred["line0_orig"] is not None
|
391 |
-
and pred["line1_orig"] is not None
|
392 |
-
):
|
393 |
-
# lines
|
394 |
-
mtlines0 = pred["line0_orig"]
|
395 |
-
mtlines1 = pred["line1_orig"]
|
396 |
-
num_inliers = len(mtlines0)
|
397 |
-
fig_lines = plot_images(
|
398 |
-
[img0.squeeze(), img1.squeeze()],
|
399 |
-
["Image 0 - matched lines", "Image 1 - matched lines"],
|
400 |
-
dpi=300,
|
401 |
-
)
|
402 |
-
fig_lines = plot_color_line_matches([mtlines0, mtlines1], lw=2)
|
403 |
-
fig_lines = fig2im(fig_lines)
|
404 |
-
|
405 |
-
# keypoints
|
406 |
-
mkpts0 = pred.get("line_keypoints0_orig")
|
407 |
-
mkpts1 = pred.get("line_keypoints1_orig")
|
408 |
-
|
409 |
-
if mkpts0 is not None and mkpts1 is not None:
|
410 |
-
num_inliers = len(mkpts0)
|
411 |
-
if "mconf" in pred:
|
412 |
-
mconf = pred["mconf"]
|
413 |
-
else:
|
414 |
-
mconf = np.ones(len(mkpts0))
|
415 |
-
fig_mkpts = draw_matches(mkpts0, mkpts1, img0, img1, mconf, dpi=300)
|
416 |
-
fig_lines = cv2.resize(
|
417 |
-
fig_lines, (fig_mkpts.shape[1], fig_mkpts.shape[0])
|
418 |
-
)
|
419 |
-
fig = np.concatenate([fig_mkpts, fig_lines], axis=0)
|
420 |
-
else:
|
421 |
-
fig = fig_lines
|
422 |
-
return fig, num_inliers
|
423 |
-
|
424 |
-
|
425 |
def run_matching(
|
426 |
image0: np.ndarray,
|
427 |
image1: np.ndarray,
|
@@ -434,6 +414,7 @@ def run_matching(
|
|
434 |
ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE,
|
435 |
ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER,
|
436 |
choice_estimate_geom: str = DEFAULT_SETTING_GEOMETRY,
|
|
|
437 |
) -> Tuple[
|
438 |
np.ndarray,
|
439 |
np.ndarray,
|
@@ -477,7 +458,7 @@ def run_matching(
|
|
477 |
output_matches_ransac = None
|
478 |
|
479 |
model = matcher_zoo[key]
|
480 |
-
match_conf = model["
|
481 |
# update match config
|
482 |
match_conf["model"]["match_threshold"] = match_threshold
|
483 |
match_conf["model"]["max_keypoints"] = extract_max_keypoints
|
@@ -490,7 +471,7 @@ def run_matching(
|
|
490 |
del matcher
|
491 |
extract_conf = None
|
492 |
else:
|
493 |
-
extract_conf = model["
|
494 |
# update extract config
|
495 |
extract_conf["model"]["max_keypoints"] = extract_max_keypoints
|
496 |
extract_conf["model"]["keypoint_threshold"] = keypoint_threshold
|
@@ -587,113 +568,3 @@ ransac_zoo = {
|
|
587 |
"USAC_ACCURATE": cv2.USAC_ACCURATE,
|
588 |
"USAC_PARALLEL": cv2.USAC_PARALLEL,
|
589 |
}
|
590 |
-
|
591 |
-
# Matchers collections
|
592 |
-
matcher_zoo = {
|
593 |
-
# 'dedode-sparse': {
|
594 |
-
# 'config': match_dense.confs['dedode_sparse'],
|
595 |
-
# 'dense': True # dense mode, we need 2 images
|
596 |
-
# },
|
597 |
-
"roma": {"config": match_dense.confs["roma"], "dense": True},
|
598 |
-
"loftr": {"config": match_dense.confs["loftr"], "dense": True},
|
599 |
-
"topicfm": {"config": match_dense.confs["topicfm"], "dense": True},
|
600 |
-
"aspanformer": {"config": match_dense.confs["aspanformer"], "dense": True},
|
601 |
-
"dedode": {
|
602 |
-
"config": match_features.confs["Dual-Softmax"],
|
603 |
-
"config_feature": extract_features.confs["dedode"],
|
604 |
-
"dense": False,
|
605 |
-
},
|
606 |
-
"superpoint+superglue": {
|
607 |
-
"config": match_features.confs["superglue"],
|
608 |
-
"config_feature": extract_features.confs["superpoint_max"],
|
609 |
-
"dense": False,
|
610 |
-
},
|
611 |
-
"superpoint+lightglue": {
|
612 |
-
"config": match_features.confs["superpoint-lightglue"],
|
613 |
-
"config_feature": extract_features.confs["superpoint_max"],
|
614 |
-
"dense": False,
|
615 |
-
},
|
616 |
-
"disk": {
|
617 |
-
"config": match_features.confs["NN-mutual"],
|
618 |
-
"config_feature": extract_features.confs["disk"],
|
619 |
-
"dense": False,
|
620 |
-
},
|
621 |
-
"disk+dualsoftmax": {
|
622 |
-
"config": match_features.confs["Dual-Softmax"],
|
623 |
-
"config_feature": extract_features.confs["disk"],
|
624 |
-
"dense": False,
|
625 |
-
},
|
626 |
-
"superpoint+dualsoftmax": {
|
627 |
-
"config": match_features.confs["Dual-Softmax"],
|
628 |
-
"config_feature": extract_features.confs["superpoint_max"],
|
629 |
-
"dense": False,
|
630 |
-
},
|
631 |
-
"disk+lightglue": {
|
632 |
-
"config": match_features.confs["disk-lightglue"],
|
633 |
-
"config_feature": extract_features.confs["disk"],
|
634 |
-
"dense": False,
|
635 |
-
},
|
636 |
-
"superpoint+mnn": {
|
637 |
-
"config": match_features.confs["NN-mutual"],
|
638 |
-
"config_feature": extract_features.confs["superpoint_max"],
|
639 |
-
"dense": False,
|
640 |
-
},
|
641 |
-
"sift+sgmnet": {
|
642 |
-
"config": match_features.confs["sgmnet"],
|
643 |
-
"config_feature": extract_features.confs["sift"],
|
644 |
-
"dense": False,
|
645 |
-
},
|
646 |
-
"sosnet": {
|
647 |
-
"config": match_features.confs["NN-mutual"],
|
648 |
-
"config_feature": extract_features.confs["sosnet"],
|
649 |
-
"dense": False,
|
650 |
-
},
|
651 |
-
"hardnet": {
|
652 |
-
"config": match_features.confs["NN-mutual"],
|
653 |
-
"config_feature": extract_features.confs["hardnet"],
|
654 |
-
"dense": False,
|
655 |
-
},
|
656 |
-
"d2net": {
|
657 |
-
"config": match_features.confs["NN-mutual"],
|
658 |
-
"config_feature": extract_features.confs["d2net-ss"],
|
659 |
-
"dense": False,
|
660 |
-
},
|
661 |
-
"rord": {
|
662 |
-
"config": match_features.confs["NN-mutual"],
|
663 |
-
"config_feature": extract_features.confs["rord"],
|
664 |
-
"dense": False,
|
665 |
-
},
|
666 |
-
# "d2net-ms": {
|
667 |
-
# "config": match_features.confs["NN-mutual"],
|
668 |
-
# "config_feature": extract_features.confs["d2net-ms"],
|
669 |
-
# "dense": False,
|
670 |
-
# },
|
671 |
-
"alike": {
|
672 |
-
"config": match_features.confs["NN-mutual"],
|
673 |
-
"config_feature": extract_features.confs["alike"],
|
674 |
-
"dense": False,
|
675 |
-
},
|
676 |
-
"lanet": {
|
677 |
-
"config": match_features.confs["NN-mutual"],
|
678 |
-
"config_feature": extract_features.confs["lanet"],
|
679 |
-
"dense": False,
|
680 |
-
},
|
681 |
-
"r2d2": {
|
682 |
-
"config": match_features.confs["NN-mutual"],
|
683 |
-
"config_feature": extract_features.confs["r2d2"],
|
684 |
-
"dense": False,
|
685 |
-
},
|
686 |
-
"darkfeat": {
|
687 |
-
"config": match_features.confs["NN-mutual"],
|
688 |
-
"config_feature": extract_features.confs["darkfeat"],
|
689 |
-
"dense": False,
|
690 |
-
},
|
691 |
-
"sift": {
|
692 |
-
"config": match_features.confs["NN-mutual"],
|
693 |
-
"config_feature": extract_features.confs["sift"],
|
694 |
-
"dense": False,
|
695 |
-
},
|
696 |
-
"gluestick": {"config": match_dense.confs["gluestick"], "dense": True},
|
697 |
-
"sold2": {"config": match_dense.confs["sold2"], "dense": True},
|
698 |
-
# "DKMv3": {"config": match_dense.confs["dkm"], "dense": True},
|
699 |
-
}
|
|
|
1 |
import os
|
2 |
+
import cv2
|
3 |
+
import torch
|
4 |
import random
|
5 |
import numpy as np
|
|
|
|
|
6 |
import gradio as gr
|
7 |
from pathlib import Path
|
|
|
8 |
from itertools import combinations
|
9 |
+
from typing import Callable, Dict, Any, Optional, Tuple, List, Union
|
10 |
from hloc import matchers, extractors, logger
|
11 |
from hloc.utils.base_model import dynamic_load
|
12 |
from hloc import match_dense, match_features, extract_features
|
13 |
from hloc.utils.viz import add_text, plot_keypoints
|
14 |
+
from .viz import (
|
15 |
+
draw_matches,
|
16 |
+
fig2im,
|
17 |
+
plot_images,
|
18 |
+
display_matches,
|
19 |
+
plot_color_line_matches,
|
20 |
+
)
|
21 |
|
22 |
device = "cuda" if torch.cuda.is_available() else "cpu"
|
23 |
|
24 |
+
ROOT = Path(__file__).parent.parent
|
25 |
DEFAULT_SETTING_THRESHOLD = 0.1
|
26 |
DEFAULT_SETTING_MAX_FEATURES = 2000
|
27 |
DEFAULT_DEFAULT_KEYPOINT_THRESHOLD = 0.01
|
|
|
34 |
DEFAULT_MATCHING_THRESHOLD = 0.2
|
35 |
DEFAULT_SETTING_GEOMETRY = "Homography"
|
36 |
GRADIO_VERSION = gr.__version__.split(".")[0]
|
37 |
+
MATCHER_ZOO = None
|
38 |
+
|
39 |
+
|
40 |
+
def load_config(config_name: str) -> Dict[str, Any]:
|
41 |
+
"""
|
42 |
+
Load a YAML configuration file.
|
43 |
+
|
44 |
+
Args:
|
45 |
+
config_name: The path to the YAML configuration file.
|
46 |
+
|
47 |
+
Returns:
|
48 |
+
The configuration dictionary, with string keys and arbitrary values.
|
49 |
+
"""
|
50 |
+
import yaml
|
51 |
+
|
52 |
+
with open(config_name, "r") as stream:
|
53 |
+
try:
|
54 |
+
config: Dict[str, Any] = yaml.safe_load(stream)
|
55 |
+
except yaml.YAMLError as exc:
|
56 |
+
logger.error(exc)
|
57 |
+
return config
|
58 |
+
|
59 |
+
|
60 |
+
def get_matcher_zoo(
|
61 |
+
matcher_zoo: Dict[str, Dict[str, Union[str, bool]]]
|
62 |
+
) -> Dict[str, Dict[str, Union[Callable, bool]]]:
|
63 |
+
"""
|
64 |
+
Restore matcher configurations from a dictionary.
|
65 |
+
|
66 |
+
Args:
|
67 |
+
matcher_zoo: A dictionary with the matcher configurations,
|
68 |
+
where the configuration is a dictionary as loaded from a YAML file.
|
69 |
+
|
70 |
+
Returns:
|
71 |
+
A dictionary with the matcher configurations, where the configuration is
|
72 |
+
a function or a function instead of a string.
|
73 |
+
"""
|
74 |
+
matcher_zoo_restored = {}
|
75 |
+
for k, v in matcher_zoo.items():
|
76 |
+
dense = v["dense"]
|
77 |
+
if dense:
|
78 |
+
matcher_zoo_restored[k] = {
|
79 |
+
"matcher": match_dense.confs.get(v["matcher"]),
|
80 |
+
"dense": dense,
|
81 |
+
}
|
82 |
+
else:
|
83 |
+
matcher_zoo_restored[k] = {
|
84 |
+
"feature": extract_features.confs.get(v["feature"]),
|
85 |
+
"matcher": match_features.confs.get(v["matcher"]),
|
86 |
+
"dense": dense,
|
87 |
+
}
|
88 |
+
return matcher_zoo_restored
|
89 |
|
90 |
|
91 |
def get_model(match_conf: Dict[str, Any]):
|
|
|
142 |
return [pairs[i] for i in selected]
|
143 |
|
144 |
# image pair path
|
145 |
+
path = ROOT / "datasets/sacre_coeur/mapping"
|
146 |
pairs = gen_images_pairs(str(path), len(example_matchers))
|
147 |
match_setting_threshold = DEFAULT_SETTING_THRESHOLD
|
148 |
match_setting_max_features = DEFAULT_SETTING_MAX_FEATURES
|
|
|
402 |
return None, None
|
403 |
|
404 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
405 |
def run_matching(
|
406 |
image0: np.ndarray,
|
407 |
image1: np.ndarray,
|
|
|
414 |
ransac_confidence: float = DEFAULT_RANSAC_CONFIDENCE,
|
415 |
ransac_max_iter: int = DEFAULT_RANSAC_MAX_ITER,
|
416 |
choice_estimate_geom: str = DEFAULT_SETTING_GEOMETRY,
|
417 |
+
matcher_zoo: Dict[str, Any] = None,
|
418 |
) -> Tuple[
|
419 |
np.ndarray,
|
420 |
np.ndarray,
|
|
|
458 |
output_matches_ransac = None
|
459 |
|
460 |
model = matcher_zoo[key]
|
461 |
+
match_conf = model["matcher"]
|
462 |
# update match config
|
463 |
match_conf["model"]["match_threshold"] = match_threshold
|
464 |
match_conf["model"]["max_keypoints"] = extract_max_keypoints
|
|
|
471 |
del matcher
|
472 |
extract_conf = None
|
473 |
else:
|
474 |
+
extract_conf = model["feature"]
|
475 |
# update extract config
|
476 |
extract_conf["model"]["max_keypoints"] = extract_max_keypoints
|
477 |
extract_conf["model"]["keypoint_threshold"] = keypoint_threshold
|
|
|
568 |
"USAC_ACCURATE": cv2.USAC_ACCURATE,
|
569 |
"USAC_PARALLEL": cv2.USAC_PARALLEL,
|
570 |
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
common/viz.py
CHANGED
@@ -367,3 +367,82 @@ def draw_image_pairs(
|
|
367 |
plt.close()
|
368 |
else:
|
369 |
return fig2im(fig)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
367 |
plt.close()
|
368 |
else:
|
369 |
return fig2im(fig)
|
370 |
+
|
371 |
+
|
372 |
+
def display_matches(
|
373 |
+
pred: Dict[str, np.ndarray], titles: List[str] = [], dpi: int = 300
|
374 |
+
) -> Tuple[np.ndarray, int]:
|
375 |
+
"""
|
376 |
+
Displays the matches between two images.
|
377 |
+
|
378 |
+
Args:
|
379 |
+
pred: Dictionary containing the original images and the matches.
|
380 |
+
titles: Optional titles for the plot.
|
381 |
+
dpi: Resolution of the plot.
|
382 |
+
|
383 |
+
Returns:
|
384 |
+
The resulting concatenated plot and the number of inliers.
|
385 |
+
"""
|
386 |
+
img0 = pred["image0_orig"]
|
387 |
+
img1 = pred["image1_orig"]
|
388 |
+
|
389 |
+
num_inliers = 0
|
390 |
+
if (
|
391 |
+
"keypoints0_orig" in pred
|
392 |
+
and "keypoints1_orig" in pred
|
393 |
+
and pred["keypoints0_orig"] is not None
|
394 |
+
and pred["keypoints1_orig"] is not None
|
395 |
+
):
|
396 |
+
mkpts0 = pred["keypoints0_orig"]
|
397 |
+
mkpts1 = pred["keypoints1_orig"]
|
398 |
+
num_inliers = len(mkpts0)
|
399 |
+
if "mconf" in pred:
|
400 |
+
mconf = pred["mconf"]
|
401 |
+
else:
|
402 |
+
mconf = np.ones(len(mkpts0))
|
403 |
+
fig_mkpts = draw_matches(
|
404 |
+
mkpts0,
|
405 |
+
mkpts1,
|
406 |
+
img0,
|
407 |
+
img1,
|
408 |
+
mconf,
|
409 |
+
dpi=dpi,
|
410 |
+
titles=titles,
|
411 |
+
)
|
412 |
+
fig = fig_mkpts
|
413 |
+
if (
|
414 |
+
"line0_orig" in pred
|
415 |
+
and "line1_orig" in pred
|
416 |
+
and pred["line0_orig"] is not None
|
417 |
+
and pred["line1_orig"] is not None
|
418 |
+
):
|
419 |
+
# lines
|
420 |
+
mtlines0 = pred["line0_orig"]
|
421 |
+
mtlines1 = pred["line1_orig"]
|
422 |
+
num_inliers = len(mtlines0)
|
423 |
+
fig_lines = plot_images(
|
424 |
+
[img0.squeeze(), img1.squeeze()],
|
425 |
+
["Image 0 - matched lines", "Image 1 - matched lines"],
|
426 |
+
dpi=300,
|
427 |
+
)
|
428 |
+
fig_lines = plot_color_line_matches([mtlines0, mtlines1], lw=2)
|
429 |
+
fig_lines = fig2im(fig_lines)
|
430 |
+
|
431 |
+
# keypoints
|
432 |
+
mkpts0 = pred.get("line_keypoints0_orig")
|
433 |
+
mkpts1 = pred.get("line_keypoints1_orig")
|
434 |
+
|
435 |
+
if mkpts0 is not None and mkpts1 is not None:
|
436 |
+
num_inliers = len(mkpts0)
|
437 |
+
if "mconf" in pred:
|
438 |
+
mconf = pred["mconf"]
|
439 |
+
else:
|
440 |
+
mconf = np.ones(len(mkpts0))
|
441 |
+
fig_mkpts = draw_matches(mkpts0, mkpts1, img0, img1, mconf, dpi=300)
|
442 |
+
fig_lines = cv2.resize(
|
443 |
+
fig_lines, (fig_mkpts.shape[1], fig_mkpts.shape[0])
|
444 |
+
)
|
445 |
+
fig = np.concatenate([fig_mkpts, fig_lines], axis=0)
|
446 |
+
else:
|
447 |
+
fig = fig_lines
|
448 |
+
return fig, num_inliers
|