import argparse import gradio as gr from common.utils import ( matcher_zoo, ransac_zoo, change_estimate_geom, run_matching, gen_examples, DEFAULT_RANSAC_METHOD, DEFAULT_SETTING_GEOMETRY, DEFAULT_RANSAC_REPROJ_THRESHOLD, DEFAULT_RANSAC_CONFIDENCE, DEFAULT_RANSAC_MAX_ITER, DEFAULT_MATCHING_THRESHOLD, DEFAULT_SETTING_MAX_FEATURES, DEFAULT_DEFAULT_KEYPOINT_THRESHOLD, ) 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! 🔎 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 on HF, 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). """ def ui_change_imagebox(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. """ return { "value": None, # The updated value of the image box "source": choice, # The list of image sources to be displayed "__type__": "update", # The type of update for the image box } def ui_reset_state(*args): """ Reset the state of the UI. Returns: tuple: A tuple containing the initial values for the UI state. """ key = list(matcher_zoo.keys())[0] # Get the first key from matcher_zoo return ( None, # image0 None, # image1 DEFAULT_MATCHING_THRESHOLD, # matching_threshold DEFAULT_SETTING_MAX_FEATURES, # max_features DEFAULT_DEFAULT_KEYPOINT_THRESHOLD, # keypoint_threshold key, # matcher ui_change_imagebox("upload"), # input image0 ui_change_imagebox("upload"), # input image1 "upload", # match_image_src None, # keypoints None, # raw matches None, # ransac matches {}, # matches result info {}, # matcher config None, # warped image {}, # geometry result DEFAULT_RANSAC_METHOD, # ransac_method DEFAULT_RANSAC_REPROJ_THRESHOLD, # ransac_reproj_threshold DEFAULT_RANSAC_CONFIDENCE, # ransac_confidence DEFAULT_RANSAC_MAX_ITER, # ransac_max_iter DEFAULT_SETTING_GEOMETRY, # geometry ) # "footer {visibility: hidden}" def run(server_name="0.0.0.0", server_port=7860): """ Runs the application. Args: config (dict): A dictionary containing configuration parameters for the application. Returns: None """ with gr.Blocks(css="style.css") as app: gr.Markdown(DESCRIPTION) with gr.Row(equal_height=False): with gr.Column(): with gr.Row(): matcher_list = gr.Dropdown( choices=list(matcher_zoo.keys()), value="disk+lightglue", label="Matching Model", interactive=True, ) match_image_src = gr.Radio( ["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, interactive=True, ) input_image1 = gr.Image( label="Image 1", type="numpy", image_mode="RGB", height=300, 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): # enable_ransac = gr.Checkbox(label="Enable RANSAC") ransac_method = gr.Dropdown( choices=ransac_zoo.keys(), value=DEFAULT_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=0.99999, ) ransac_max_iter = gr.Slider( minimum=0.0, maximum=100000, step=100, label="Ransac Iterations", value=10000, ) with gr.Accordion("Geometry Setting", open=False): with gr.Row(equal_height=False): # show_geom = gr.Checkbox(label="Show Geometry") choice_estimate_geom = gr.Radio( ["Fundamental", "Homography"], label="Reconstruct Geometry", value=DEFAULT_SETTING_GEOMETRY, ) # with gr.Column(): # 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_estimate_geom, ] # 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("Open for More!", open=False): gr.Markdown( f"""

Supported Algorithms

{", ".join(matcher_zoo.keys())} """ ) 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=ui_change_imagebox, inputs=match_image_src, outputs=input_image0, ) match_image_src.change( fn=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_estimate_geom, ] button_reset.click( fn=ui_reset_state, inputs=inputs, outputs=reset_outputs ) # estimate geo choice_estimate_geom.change( fn=change_estimate_geom, inputs=[ input_image0, input_image1, geometry_result, choice_estimate_geom, ], outputs=[output_wrapped, geometry_result], ) app.queue().launch( server_name=server_name, server_port=server_port, share=False ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--server_name", type=str, default="0.0.0.0", help="server name", ) parser.add_argument( "--server_port", type=int, default=7860, help="server port", ) args = parser.parse_args() run(args.server_name, args.server_port)